GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.128

Can the health insurance reforms stop an increase in medical
expenditures for middle- and old-aged persons in Japan?

Tamie Matsuura and Masaru Sasaki

April 2010; revised December 2011

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Can the health insurance reforms stop an increase in medical
expenditures for middle- and old-aged persons in Japan? ∗December 5, 2011
Tamie Matsuura
NLI Research Institute

Masaru Sasaki †Osaka University

Abstract: Using two-period panel data from the Nippon Life Insurance ResearchInstitute, this paper tests the hypothesis that an increase in the self-pay ratio of medical
expenditures associated with the Japanese health insurance reforms of April 2003
reduced household medical expenditures. We find that the increase in the self-pay ratio
had a positive but insignificant effect on the share of medical expenses in household
expenditure. However, when we employ the data as repeated cross-sectional
observations to increase the sample size, the increase in the self-pay ratio has asignificantly positive effect on the share of medical expenditures. This provides
corroborating evidence that middle- and old-aged persons were unable to reduce theirdemand for medical services with the increase in the self-pay ratio. An additional
finding is that medical services are a necessary good, particularly for those aged 61
years or older and those with medical expenditures accounting for a relatively highshare of medical expenditures in high household expenditure.
Keywords Health insurance, Medical expenditures, Engle curve, Middle- and old-aged
persons, JapanJEL Classification I11, I18∗

The authors are grateful of Mitsuya Ichien, Mari Kan, Shigeki Kano, Koichiro Matsushita, Shinpei

Sano, Koichiro Sawano, Takashi Unayama and the participants in the 2010 Japanese Economic
Association Meeting (Spring), Kobe University and Osaka Prefecture University. This research ispartially supported by the Japanese Society for the Promotion of Science, Grand-in-Aid, No.
20530196. The views expressed are those of the authors, and do not represent those of the NLI
Research Institute.
†

Corresponding to: ISER, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.

E-mail: sasaki@econ.osaka-u.ac.jp.

Introduction
It is well known that Japan is one of the world’s fastest aging societies and the medical
expenses associated with this process are rapidly increasing. According to the 2010 White
Paper on the Aging Society, early-stage elderly people (persons aged 65–74 years)
accounted for 17.3% of the Japanese population in 2000 and this had increased to 20.1%
in 2005 and further to 23.1% in 2010. 1 Concomitantly, the ratio of medical costs to GDP
was 7.1% in 1998 rising to 8.1% in 2006. 2 Preventing further escalation in medical costs,
particularly for older persons requiring additional medical care, is a necessary andpressing policy challenge. The alternatives are the financial collapse of the current
medical insurance system or the unprecedented burden of medical costs placed on thenext generation, who will have to pay through increases in direct and/or indirect taxation.
In response to this urgent policy concern, this paper estimates Engle curves for medical
services in Japan and addresses the containment effects of the health insurance reforms
that took effect in April 2003 on household medical demand and expenditures,
particularly for middle- and old-aged persons as they generally require and spend more on
additional medical care. We also consider heterogeneity in the effects of these reforms
across different groups by medical expenditure and additionally consider whether
medical services are a luxury or a necessary good from estimating Engle curves.
There are two types of universal health insurance schemes in Japan: an
employees’ health insurance program and a national health insurance program. Regular
employees are usually eligible to join an employees’ health insurance program (theprogram also covers their dependents), while others, including nonregular employees, theself-employed, the nonemployed, and the retired, have little choice but to join the national
health insurance program. In 2003, the Health Insurance Act was revised in such a waythat the self-pay ratio of medical expenditures for regular employees joining an
employees’ health insurance program was increased, while the ratio for those joining thenational health insurance program was unchanged (see Figure 1). To be more specific, theself-pay ratio of medical expenditures for both inpatient and outpatient treatment incurredby employees was raised from 20% to 30%. Members’ dependents incurred the increase1

The data are from the Japanese Cabinet Office: http://www8.cao.go.jp/kourei/whitepaper/index-w.html

2

OECD Health Data, 2002 and 2009.

2

only for inpatient treatment, as the rate for outpatient treatment was already at 30%. For
those joining the national health insurance program, the self-pay ratio of medical
expenditures for both forms of treatment was unchanged and remained at 30%.
Many studies in the literature have addressed price effects in the demand for
medical services. As early as 1971, the RAND Corporation and the US Department of
Health and Human Services commenced a joint project (the RAND Health Insurance
Experiment) that designed and administered natural and randomized experiments in thisarea in the US. This project artificially produced exogenous variations in health-related
policy by randomizing samples, thereby allowing health economists to examine thepolicy effects on the demand for medical services. These experiments suggested that theeffect of the provision of health insurance on health-related spending was minor(Manning et al. 1987; Newhouse et al. 1993). 3 Whereas the RAND Health Insurance
Experiment focused on partial-equilibrium analysis, Finkelstein (2007) estimated thegeneral equilibrium effect of the introduction of Medicare on health spending for theelderly, suggesting that its impact was much larger than that obtained from the RAND
experiment. 4 A number of other studies also support the positive impact of Medicare
eligibility on the use of health services (Card et al. 2008; Decker 2005; 5 Lichtenberg
2002).
Importantly, despite the lack of suitable data, health economists in Japan havealso undertaken research into effective policies designed to reduce medical expenditures.
For instance, Yoshida and Takagi (2002) estimated the demand for medical services
following the 1997 increase in the self-pay ratio of medical expenditures from 10% to
20% for household heads joining the employees’ health insurance program. Yoshida and
Takagi (2002) concluded that these reforms had no effect on the demand for medical
services. However, one limitation of their work was that the data were from only a single
3

Zweifel and Manning (2000) and Cutler and Zeckhauser (2000) comprehensively summarized a numberof these experiments.
4

Finkelstein and McKnight (2008) estimated that the introduction of Medicare induced the elderly to cut

copayments for health services by 40% in the top quartile of the copayment distribution, and therefore that
this had no impact on elderly mortality.
5

Decker (2005) focused on the effect of Medicare on the demand for health services for treating breast

cancer in women before and after 65 years of age.

3

company, implying that the sample was not random. To address this shortcoming, Kan
and Suzuki (2006) obtained data on 11 insurance associations from the Japanese Ministryof Health, Labour, and Welfare and reestimated the effect of the 1997 health insurance
reforms on the demand for medical services. In addition, they examined the policy effects
on outpatient and inpatient treatment separately.
In contrast to Yoshida and Takagi (2002), Kan and Suzuki (2006) found the priceelasticity of demand for outpatient treatment was significantly negative in a range
between –0.05 and –0.06. On this basis, the price elasticity for outpatient treatment
exceeded elasticities estimated by Bhattacharya et al. (1996), also using Japanese data.
However, the price elasticity of demand for inpatient treatment was insignificant. Later,
Yoshida and Ito (2000) calculated the demand for outpatient treatment from the numberof practice receipts and again considered the effect of the 1997 health insurance reforms.
They found that a rise in the self-pay ratio of medical expenditures did not affect demandfor household heads but did for their dependents. In these circumstances, the increased
burden of medical bills would fall on dependents rather than on household heads.
Sawano (2000) estimated Engle curves for medical services to investigate theimpact of the self-pay system on the demand for outpatient treatment for the elderly.
Sawano (2000) concluded that medical services were a necessary good and also that achange in the price of elderly care did not affect the demand for elderly care. Yoshida andKawamura (2009) also examined the elderly care system in a study of physician demandfor outpatient treatments. Their finding was that dependents more often visited aphysician because the copayment decreased for dependents under the elderly care system,
while it remained almost the same for household heads.
The first contribution of this paper is to examine the impact of the 2003 healthinsurance reforms on medical demand. The second is to focus attention on the demand for
medical care among middle- and old-aged persons, as they usually require more medicalcare than other age groups and now make up a growing share of the Japanese population.
We employ a two-period panel dataset on the consumption behavior of the middle-aged
and older population conducted by the Nippon Life Insurance (NLI) Research Institute.
The survey was administered first in December 2001 and again in December 2003, andthus spans the implementation date (April 2003) of the revised Health Insurance Act.
The main findings are as follows. To start with, we find that the increase in the4self-pay ratio of medical expenditures had a positive but insignificant effect on the shareof medical expenses in household expenditure. One possible reason for this result is thatthe standard error of the policy parameter is sufficiently large for small samples, thereby
reducing the significance of the policy parameter. As an alternative approach, we thenemploy the data not as a balanced panel, but rather as repeated cross-sectional
observations to increase the size of the sample. Here, we indeed find that the increase in
the self-pay ratio of medical expenditures had a significantly positive effect on the shareof medical expenditures, implying that the increase in copayments a patient has to pay
exceeds the decrease in medical expenditures through a decrease in the quantity
demanded for medical services. This supports the view that middle- and old-aged persons
were unable to reduce their demand for medical services with an increase in the self-pay
ratio. In addition, we find that medical services are a necessary good, particularly for
those aged 61 years or older and those with a relatively high share of medical
expenditures in household expenditure.
The remainder of the paper is organized as follows. We begin by providing theconceptual framework. In the subsequent sections, we detail the data and empirical
specifications, followed by the estimated results. The final section provides someconcluding remarks.

Conceptual framework
This section develops a simple demand model to examine the effects of an increase in the
self-pay ratio of medical expenditures on the demand for medical services. We employ
the same methodology as Sawano (2000). Let p and D(p) respectively denote the priceand demand for a medical service. An individual household pays τpD(τp) for the medical
service, where τ represents the average household self-pay ratio of medical expenditure.
An increase in τ then increases but simultaneously decreases the household expenditureof the medical service because of its decrease in the quantity demanded. Which particulareffect dominates depends on the price elasticity of the demand curve. Taking thedifferential of τpD(τp) with respect to τ yields:5∂ (τpD(τp))
D' (τp) = pD(τp) 1 + τp.
∂τD(τp) The last term indicates the price elasticity of medical service demand. If the demand
curve is elastic, an increase in τ decreases the household expenditure for this medical
service. On the other hand, if the demand curve is inelastic, household medical
expenditure increases. If the elasticity equals one, these opposing effects cancel eachother out, and the household medical expenditure remains unchanged, despite theincrease in the self-pay ratio of medical expenditures. Therefore, it is unclear whether anindividual household bears relatively more of the burden of medical bills with theincrease in the self-pay ratio of medical expenditures (for both employees and theirdependents) from 20% to 30%.
How does the increase in the self-pay ratio of medical expenditures affect the
total medical expenditure pD(τp), that is, the sum of out-of-pocket expenditures(τpD(τp)) and health care benefits ((1–τ)pD(τp)) incurred by the government? Without
doubt, an increase in τ decreases the total medical expenditure if the price remains
constant, and so how much the total medical expenditure decreases depends ultimately on
the price elasticity of demand for medical services. If the demand curve is elastic, the total
medical expenditure substantially decreases; in contrast, if the demand curve is inelastic,
the amount of any decrease is small.
We estimate Engle curves for medical services to test the effect of an increase in
the self-pay ratio of medical expenditures on the demand for medical services. Because
our two-period panel data straddle the implementation date of the Health Insurance Act,
we can employ a difference-in-difference (DID) method, allowing for comparison of atreatment group and a control group. Households that have at least one employee who
faces an increase in the self-pay ratio of medical expenditures belong to the treatmentgroup, while households in which a middle- or old-aged man and his spouse are either
self-employed, nonemployed, or retired belong to the control group. Following Leser(1963), Deaton and Muellbauer (1980), and Sawano (2000), the econometricspecification of the Engle curve follows a first-difference structure:∆MSi = β 0 + β1∆X i + β 2 ∆Di + β 3∆Ti Di + β 4 ∆ log( Ei ) + ∆ui ,where MSi is the ratio of household medical expenditures to total household expenditure,6

Ei is total expenditure per household member, Ti is a year dummy, Di is a dummy
indicating to which group the individual belongs (i.e. the treatment or control group), andTiDi is the cross term of Ti and Di. ∆TiDi indicates a policy variable and equal one if andonly if a household has at least one employee joining an employees’ insurance program
after the implementation date of the 2003 Health Insurance Act, irrespective of her or his
insurance status before the date. Finally, Xi represents a vector of individual
characteristics.
We should note that not all households belong to the same group during both
periods; that is, some employed individuals as of December 2001 had become
self-employed or retired by December 2003, and vice versa. The coefficient β2 therefore
captures the effect of changes in employment status of household members on household
medical expenditures. The coefficient β3 is the one for the cross term indicating the policy
effect of the increase in the self-pay ratio of medical expenditures on MS. Because we
assume total household expenditure (the denominator of MS) is exogenous according to
the static model of preference maximization, β3 also implies the effect of the increase in
the self-pay ratio of medical expenditures on the household expenditure for medical
services (the numerator of MS or τpD(τp)). 6 This policy measure allows us to distinguishthe effect of the policy change from any other factors (such as macroeconomic shocks)
that commonly affect every household. Finally, β4 indicates whether medical services area luxury or a necessary good. If the estimated coefficient β4 is positive, we can consider
medical services a luxury good, but if β4 is negative, medical services are a necessary
good.
There are two points to keep in mind when we use the DID method. First, it ispossible that the policy change is endogenous, in the sense that many employees and theirdependents faced with the increase in the self-pay ratio of medical expenditures wereencouraged to visit a hospital, even for nonemergency care, before the revised Health6

For simplicity, the problem of preference maximization is given by: max u(z1, z2) subject to τpz1+z2=I,

where z1 is the demand for (consumption of) medical services, z2 is the bundle of other consumption goods
with the price normalized to one, and I is exogenous total household expenditure. Therefore, the demand for
medical services is derived implicitly by z1=D(τp, I). We assume that both τ and I are exogenous andindependent of each other. For simplicity, I is omitted from the demand function for medical services.

7

Insurance Act took effect. This problem is not so serious because the survey was
conducted in December 2001, long before the implementation date of the Health
Insurance Act, even before this act was approved in the Diet in July 2002. We assume that
as of December 2001, people did not anticipate this legislative change. Second, we must
assume that both the treatment and the control groups are homogeneous with respect toany unobservable factors. Otherwise, it is difficult to determine whether any difference in
medical service demand between the two groups is attributable to the changing legislationor to the heterogeneity of the groups with respect to the other factors. To partly control for
this, various household characteristics are included in the estimations.
As we later explain, because we reconstitute balanced panel data to estimate
first-difference equations, the sample size unfortunately becomes very small. The
first-difference estimator is then less precise. To address this shortcoming, we would
prefer to treat the data as repeated cross-sectional observations instead of balanced panel
data in estimating the DID structure. This allows us to include unbalanced observations
and therefore increase the sample size. However, we should be aware of a shortcoming inusing repeated cross-section data. In sum, if the composition of the treatment and control
groups is systematically different before and after the implementation of the reforms, thepolicy parameter is potentially biased. This is because any difference in the demand for
medical services between the groups then results not only from the legislative change, butalso from exogenous differences between the two groups with respect to unobserved
factors.
In addition, we consider the heterogeneous effects of the revised Health
Insurance Act on the demand for medical services across different groups by medical
expenditure using quantile regression methods. We expect that the adverse effect of thischange is greater for those who spend relatively more of their household expenditure on
medical services. Note again that we do not treat the data as a panel in estimating thequantile regression, but rather as repeated cross-sectional observations. This is beneficial
for obtaining the sufficiently large size of samples, although there is the possibility thatthe treatment assignment is not random.

Data
8

The data used in this study are a Japanese micro-level dataset, the Survey for Living andLife Design (kurashi to seikatusekkei nikannsuru chousa) conducted by the NLI ResearchInstitute. 7 This survey was designed to reveal how middle- and old-aged persons (whose
share of the population in Japan is rapidly growing) transition through life. This survey
was conducted in December every second year from 1997 to 2003, and samples men born
between 1933 and 1947 as per the area sampling method. In 1997, therefore, therespondents were aged between 50 and 64 years. The survey included 1,502 respondents
in 1997, 1,034 in 1999, 910 in 2001, and 814 in 2003. As the purpose of the current study
is to address whether the Health Insurance Act that took effect in April 2003 is effective inconstraining medical expenses, we use the third (2001) and fourth (2003) surveys.
We now explain our means of differentiating between the treatment and thecontrol groups. To do this, it is necessary to identify the employment status of eachindividual respondent (men) and his spouse (women) because the increase in the self-pay
ratio of medical expenditures applies only to those joining an employees’ health insurance
program, as determined by employment status. Five types of employment status are
possible for each individual respondent:(i)regular employees—those who self-reported that they worked as a regular
employee;(ii)

nonregular employees (1)—those who self-reported that they worked as anonregular worker, and responded that they worked three-quarters of a regular
employee’s weekly hours of work (30 hours a week) or more; 8(iii)nonregular employees (2)—those who self-reported that they worked as anonregular worker, responded that they worked less than three-quarters of aregular employee’s weekly hours of work (30 hours a week), and earned an annual
income of JPY1.3 million or more if aged less than 60 years, or JPY1.8 million ormore if aged 60 years or older;7

A member of the Nippon Life Insurance Company (NISSAY) group.

8

When data on the weekly hours of work are missing, but those for the daily hours of work and monthly

working days are available, we calculate the average weekly hours of work as the monthly working days
divided by 4.3 times the daily hours of work.

9

(iv)

nonregular employees (3)—those who self-reported that they worked as anonregular worker, responded that they worked less than three-quarters of aregular employee’s weekly hours of work (30 hours a week), and earned an annual
income of less than JPY1.3 million if aged less than 60 years, or less than JPY1.8
million if aged 60 years or older;(v)

self-employed—those who self-reported that they worked as a self-employedworker, including professionals;(vi)nonemployed—those who did not report that they worked, including the retired.

We consider nonregular employees (1) as substantially regular employees, the difference
being that while regular employees are eligible to receive generous welfare benefits incompensation for compliance of instruction in the course of employment, nonregular
employees usually do not participate in a welfare program but can flexibly arrange
workplace conditions and working hours. We should be aware that a nonregular
employee (2) earns too much income to be a dependent of his spouse, even if she is aregular employee, and thus has to participate individually in the national health insurance
program. In contrast, a nonregular employee (3) is still eligible to be a dependent of his
spouse if she is a regular employee.
Because the dataset does not include comparable information on theemployment status of spouses (all female), we obtain this using their annual salary or
income. Annual income includes salary, pension benefits (public and private), anddividends. We assume that a spouse is a regular employee signing up to her own
employees’ health insurance program if her annual salary is over JPY2.74 million if she isaged 40–44 years, JPY2.81 million if aged 45–49 years, JPY2.90 million if aged 50–54
years, JPY2.89 million if aged 55–59 years, JPY2.33 million if aged 60–64 years, andJPY2.23 million if she is 65 years or older. These figures represent the average annual
salary for middle school graduates for each age range. 9 We employ them as lax thresholdpoints to distinguish between regular and nonregular employees. Alternatively, we
consider a spouse who earns an annual salary of less than the corresponding annual salary9

The data are from the 2002 Japanese Wage Census conducted by the Japanese Ministry of Health, Labor,and Welfare: http://www.mhlw.go.jp/toukei/itiran/roudou/chingin/kouzou/z02/index.html.

10as a nonregular employee in the national health insurance program. However, if aspouse’s annual income, inclusive of salary, pension benefits, and dividends, is less thanJPY1.3 million (less than JPY1.8 million for those aged 60 years or older), and if her
husband is a regular employee, she is eligible to be her husband’s dependent, and so ispartially covered by his employees’ health insurance program. In this case, the spouse’s
self-pay ratio of medical expenditures was 20% for inpatient treatment prior to the healthinsurance reforms. The drawback of this approach is that we cannot identify whether aspouse earning her own salary is employed or self-employed.
Table 1 displays the cross-sectional matrix of the types of health insurance foreach individual (male) respondent and his spouse (female). There are four groups basedon the combination of insurance programs. Group 1 consists of households where both
the respondent and his spouse are regular employees (or regular employees of substance),in which case both have incurred the increase in the self-pay ratio of medical expenditures
for outpatient and inpatient treatment from 20% to 30%. Group 2 includes regular
employees whose spouse is either nonemployed, a nonregular employee, or
self-employed (an employer) with an annual income of less than JPY1.3 million (or lessthan JPY1.8 million for spouses aged 60 years or older). They then benefit from coverageof the employees’ health insurance program of which their partner is a member. Although
the revision of the Health Insurance Act increased the self-pay ratio of medical
expenditures for regular employees from 20% to 30% for both outpatient and inpatient
treatment, it increased the self-pay ratio for their spouses from 20% to 30% only for
inpatient treatment; the self-pay ratio for outpatient treatment remained at 30%.
Group 3 includes regular employees whose spouse is either self-employed or anonregular employee with an annual income of JPY1.3 million or more (or JPY1.8
million or more for spouses aged 60 years or older). Neither this type of nonregular
employee nor the employer is eligible to benefit from coverage of the employees’ healthinsurance program of which their partner is a member, and so they participate in thenational health insurance program. Although the regular employee incurred increases inthe burden of medical expenditures for inpatient and outpatient treatment, the burden forthe spouse remained unchanged (at 30% for both treatments). Finally, Group 4 consists ofhouseholds where both the respondent and his spouse are nonemployed, self-employed,or nonregular employees. Because both are in the national health insurance program, the11health insurance reforms had no adverse impact on their burden of medical expenditures.
We contend that the revision of the Health Insurance Act increased the burden of medical
expenditure to the greatest extent in Group 1, followed by Groups 2 and 3, and did notaffect the burden of medical expenditure for Group 4.
We consider three cases to distinguish between the treatment and control groups:<Treatment A>, Groups 1 and 2 as the treatment group and Groups 3 and 4 as the control
group; <Treatment B>, Groups 1 to 3 as the treatment group and Group 4 as the control
group; and <Treatment C>, individual respondents (men) who joined an employees’
health insurance program and those signing up for national health insurance. 10 The first
two treatments take account of the employment status of (female) spouses in grouping thetreatment and control groups, while the last treatment omits the employment status of(female) spouses and defines the two groups only by the (male) individual’s insurance
program (that is, employment status).
We remove households from our sample where a respondent is aged 70 years orolder because their medical expenditures are determined under a different medical system
for the elderly. In principle, the self-pay ratio of medical expenditures for those aged 70
years or older remains at 10%, regardless of employment status. For example, a regular
employee aged 68 years in 2001 became 70 years of age in 2003 and so automatically has
a lower burden of medical expenditures in 2003 (10% according to Figure 1).
We specify the ratio of household medical expenses to total household
expenditure as the dependent variable. Using this variable, we remove outliers from thesample: we define outliers as values of the dependent variable greater than one or morethan three standard deviations from the mean. The ratio is then regressed on the logarithmof total household expenditure per household member to estimate the Engle curve. In
addition, we include individual characteristics, particularly health condition, in the vectorof explanatory variables. For instance, the survey subjectively asked the sampled men
whether they were healthy, whether their spouse was still living, and if so, whether she
was healthy. We also include the number of family members and a dummy variableindicating whether a respondent was working.

10

We do not consider the option of distinguishing between Group 1 and the remaining groups, as Group 1

includes few households.

12

Because our purpose is to compare medical expenditures before and after thedate of implementation of the health reforms, we first remove any cross sectional
observations with data missing in 2001 or 2003 to obtain a balanced panel. The sample
size is then 81 households per year when the employment status of spouses is included ingrouping the treatment and control groups (that is, <Treatment A> and <Treatment B>).
However, the sample size substantially increases to 215 per year when the employment
status of spouses does not categorize the two groups (that is, <Treatment C>). 11 Table 2
provides some summary statistics. As shown, the share of medical expenditures in total
household expenditure decreased from 2001 to 2003 in <Treatment A> and <Treatment
B>. However, the difference is minimal in <Treatment C>.
Table 3 details the distribution of employment status each year. In <Treatment
A> and <Treatment B>, where the employment status of spouses is taken into account ingrouping, the proportion of those who were nonemployed increased from 25.93% to27.16% over the period. In contrast, the proportion of regular employees decreased from14.81% to 13.58% over the same period. Nonregular employees (1), here considered as
substantially regular employees, accounted for 28.40% of the sample in 2001, and thisdecreased markedly to 20.99% in 2003. More than one-quarter of the sampled men wereself-employed in 2001, while about one-third were self-employed in 2003. As one would
expect, the proportion of employees decreased over the sample period, while theproportion of nonemployed increased. Similarly, spouses engaging in nonregular work
were more likely to reduce their working hours or to be nonemployed during both periods.
When we ignore the employment status of spouses in grouping (<Treatment C>), theproportions of regular and nonregular employees (1) decreased, while that ofnonemployed increased. We then again confirm that older men were less likely to work.
Table 4 displays a transition matrix of employment status from 2001 to 2003. In<Treatment A> and <Treatment B>, the majority of men maintained their employment
status in 2001 and 2003. For example, 75% of regular employees remained regularly
employed and only 8.33% became nonemployed, while 65.22% of nonregular employees(1) maintained their employment status from 2001 to 2003 and only 4.35% became

11

The sample size is smaller in <Treatment A> and <Treatment B> because many observations are missing

for the annual income of employed spouses (female) used for grouping by employment status.

13nonemployed. However, we can also see that employees gradually reduced their workburden from regular employment to nonregular employment or retirement (ornonemployment). In a similar manner, spouses engaging in regular work also reduced
their working hours or retired as they became older while some nonregular spouses
increased their work burden. We observe similar results with <Treatment C>.
Table 5 provides the transition matrices of those incurring an increase in the
self-pay ratio of medical expenditures across the three treatments. As shown in<Treatment A>, 71.60% of households are not subject to the increase in the self-pay ratio
from 20% to 30%. As most are middle- or old-aged, they are less likely to have regular
employment, and are therefore exempt from the health insurance reforms. When wegroup individual men according to <Treatment B>, 54.32% of households did not incur
any additional burden from the increase in the self-pay ratio of medical expenditures. In<Treatment C>, 38.60% of men joined an employees’ insurance program in 2003 andtherefore had to pay the additional payments of medical services after the health reforms
took effect.

Results
Table 6 presents the estimates of the Engle curve for the policy effect on medical
expenditures from estimating the first-difference model with the balanced panel data.
Columns [1] and [4] provide the results when samples are grouped according to<Treatment A>; columns [2] and [5] provide the results for <Treatment B>, and columns[3] and [6] for <Treatment C>. The key outcome is that the coefficient on the policy
parameter (insurance type × year) is positive but statistically insignificant in all columns,
implying that the increase in the self-pay ratio of medical expenditures increased theshare of household expenditure spent on medical services, but that this is not statisticallysupported. Recall that while the increase in the self-pay ratio of medical expenditures
increased the price of medical services, it simultaneously lowered the quantity demandedof medical services. Our estimated results suggest that the former effect was dominant
over the latter one, but it should be aware that the net effect is statistically insignificant. In
addition, this provides corroborating evidence that if anything, the increase in the self-pay
ratio of medical expenditures would mildly decrease total medical expenditures (that is,14the sum of the expenditure incurred by an individual household and the health carebenefits incurred by the Japanese government).
One possible reason why this policy effect is statistically insignificant is that thesurvey date of 2003 was so close to the implementation of the reforms that the effect of
the reforms had not yet been felt. It is then necessary to attempt to capture the lasting
effect of the reforms. Another possible reason is that the sample size is relatively small inthe balanced panel data, which leads to a large standard error for the policy parameter, andthereby lowers the significance of the policy parameter. We should keep in mind that theincrease in the self-pay ratio of medical expenditures had the potential to increase theshare of household expenditure spent on medical services, although its effect was
statistically insignificant in our estimations when using small samples.
Considering the other factors determining the share of medical expenditures, thecoefficient for total expenditure per household member is negative at the 1% level ofsignificance in all columns. This confirms, as in Sawano (2000), that medical services area necessary good. The number of family members has a negative effect on medical
expenditures at the 5% level of significance in columns [4] to [6]. The share of medical
expenditures lowers with the number of family members, holding the total expenditure
per household member fixed. These results show the way of allocating a family budget to
medical services and other goods and services as the number of family members increases,
given that the total expenditure per household member is fixed. This could be because
while an individual man and his spouse are usually both middle- or old-aged, we expectthat other family members are younger and healthier, and so would rather direct
expenditure to consumption than to health care. As family size increases, therefore, theshare of medical expenditures in total household expenditure declines. 12 Finally,
own-health negatively affects the ratio of medical expenditures at the 5% level of
12

Note that the coefficient on the number of family members captures the partial effect of an increase in the

self-pay ratio of medical expenditures on the share of medical expenditures in household expenditure,holding the total expenditure per household member fixed. The complete effect can be obtained if we
regress the share of medical expenditures on family size and the logarithm of total household expenditure.
The complete effect would be expected positive; if one more member joins in a family while the total
household expenditure is fixed, the share of medical expenditures in household expenditure is expected toincrease because the new member also spends the money for medical services.

15significance according to column [6]; contrary to our predictions, however, theown-health of the spouse increases the share of medical expenditures, but only at the 10%
level of significance in column [4].
To obtain more precise estimators, we employ an alternative approach using thedata as repeated cross-sectional observations. Because the data include unbalanced
observations, this allows us to increase the sample size. Table 7 presents the estimatedresults. In contrast to the results shown in Table 6, the coefficient on the policy variable
turns out to be positive at the 5–10% level of significance in columns [2], [3], [5], and [6].
In effect, the increase in the self-pay ratio of medical expenditures has increased the shareof household expenditure spent on medical services. This implies that the price elasticity
of demand for medical services was less than one in absolute terms; that is, the quantity
demanded of medical services did not decrease sufficiently to counter the increased
burden of medical expenditures caused by the increase in the self-pay ratio of medical
expenditures.
We should take care when interpreting these results. As mentioned earlier, if thecomposition of the treatment and control groups systematically changed after thelegislative change, the policy parameter is potentially biased because any difference inthe demand for medical services between the two groups could be attributable tounobservable differences between the two groups with respect to individual
characteristics as well as to the legislative change.
Table 8 provides the first-difference estimators for the subsample of respondents
aged 61 years or older in 2001. We anticipate two opposing effects of an increase in the
self-pay ratio of medical expenditures. The first is that that the increase in the self-pay
ratio of medical expenditures seriously increases the burden of medical expenditures for
those aged 61 years or older because they typically require more medical care. The
second is that the health insurance reforms do not affect household medical expenditures
for those aged 61 years or older as they have already retired because many Japanesecompanies implement an age-based retirement system and set the retirement age at 60
years.
The results are similar to those in Table 6. That is, the health insurance reforms
positively but insignificantly affected the burden of household expenditures for medical
services for men aged 61 years or older. However, the coefficient on the policy variable is16smaller in magnitude in Table 8 than in Table 6. This suggests that the health insurance
reforms had only a minor effect on medical expenditures for those aged 61 years or older.
These results instead support the second hypothesis that the increase in the self-pay ratioof medical expenditures did not affect the burden of medical expenses for those aged 61
years or older. To obtain more precise estimators, Table 9 shows the estimated results
using the repeated cross-section data that include any unbalanced observations. The
coefficient on the policy variable turns out to be positive but only at the 10% level ofsignificance in columns [3] and [6] for <Treatment C>. This implies that the increase in
the self-pay ratio of medical expenditures increased the share of household expenses
spent on medical services, although the impact was only statistically marginal.
In both Tables 8 and 9, the coefficient on total expenditure per household
member is still negative, again implying that medical services are a necessary good. We
also find that the coefficient is larger in magnitude for those aged 61 years or older thanfor the unrestricted sample shown in Tables 6 and 7. Consequently, medical services are
more strongly a necessary good for those aged 61 years or older.
To see whether the effect of the legislative change on medical expenditures varyby the budget share of medical expenditures, Table 10 presents the estimates of quantile
regressions for the five quantile values {0.1, 0.25, 0.5, 0.75, 0.9}. Column [1] shows theresult when samples are grouped according to <Treatment A>, followed by column [2]for <Treatment B>, and column [3] for <Treatment C>. In columns [1] to [3], thecoefficient on the policy variable (insurance type × year) remains insignificant for all ofthe quantile values.
In column [1], while the sign of the coefficients on the policy variable is negative
for the lower quantile value, their sign turns out to be positive for the higher quantile
values. While an increase in the self-pay ratio of medical expenditures increased the priceof medical services, it simultaneously decreased the quantity demanded, thereby
lowering the household expenditure of medical services. For higher quantile values, theformer effect dominates the latter, although the net effect is statistically insignificant.
This implies that this particular health insurance reforms did not cut demand for medical
services more sharply for households with a higher burden of medical expenditures intotal household expenditure. The sign of the coefficient on the policy variable remains
positive, regardless of the quantile value in column [2]. The observed policy impact on17

the demand for medical services then substantially varies according to the criteria used todefine the treatment and control groups.
The coefficient on total expenditure per household member remains
significantly negative for any quantile value, regardless of the type of treatment. This isconsistent with the estimates in Tables 6 and 7. The magnitude of the estimatedcoefficient also increases almost systematically with the quantile value. This again
implies that medical services are more strongly a necessary good for households with ahigher share of medical expenditures in total household expenditure.
The coefficients for the number of family members, own-health, and spouse’s
own-health are significantly negative for many quantile values in all columns. As
discussed, family members other than the sample man and his spouse are usually younger
and healthier, so the increase in the family size would encourage the family to allocate its
budget to other goods and services rather than medical services, which lowers the ratio of
medical expenditures to household expenditure. Needless to say, a healthy man and his
spouse do not spend much on medical services. The coefficient for work is significantly
positive for many quantile values. That is, if a middle- or old-aged man works too much,
he requires more medical care to maintain his health condition.
Table 11 presents the estimates of the quantile regressions when the sample data
are limited to male household heads aged 61 years or older. As shown, the coefficient on
the policy variable is insignificant for any quantile value. The coefficient on total
expenditure per household remains significantly negative for each quantile value in allcolumns. This confirms again that medical services are a necessary good for men aged 61
years or older.

Concluding remarks
Using a panel of two-period data gathered by the NLI Research Institute, this paper
explored the hypothesis that the increase in the self-pay ratio of medical expendituresassociated with health insurance reforms taking effect in Japan in April 2003 cut
household medical expenditure. We focused our attention on the consumption of medical
services for middle- and old-aged persons, whose share of the Japanese population has
been rapidly growing in recent years. We estimated Engle curves for medical services and18

employed the difference-in-difference method, dividing our sample into a treatmentgroup whose self-pay ratio of medical expenditures increased from 20% to 30%, and acontrol group whose self-pay ratio remained unchanged. In addition, we estimatedquantile regressions to consider any heterogeneity in the effect of this legislative change
across different groups by medical expenditure.
Our main finding using the balanced panel data is that the increase in the self-pay
ratio had a positive but insignificant effect on household medical expenses. This result
implies that a decrease in the quantity demanded of medical services through the pricerise is not enough to offset the increase in the medical expenditures incurred by ahousehold through the price rise, although this net effect is statistically insignificant. If
anything, the price elasticity of demand for medical services by middle- and old-aged
persons was rather inelastic. We obtained similar results when limiting our sample data topersons aged 61 years or older.
Our plans for this line of research involve capturing the long-term effect, if any,of the 2003 health insurance reforms. One possible reason why we found a somewhat
insignificant effect of the reforms is that the survey date in 2003 was so close to the dateof implementation. Accordingly, the reforms may not yet have had time to be effective.
Capturing any lasting effect of the reforms is then a crucial task.
Another possible reason is the small sample size; that is, because the sample size
is relatively small, we obtain a large standard error of the policy parameter, thus reducing
the significance of the policy parameter. To address this shortcoming, we estimated thepolicy effect using the data as repeated cross-sectional observations. This allowed us toinclude unbalanced observations and thereby increase the sample size. We then found thatthe increase in the self-pay ratio had a significantly positive effect on the share ofhousehold medical expenditures. This result implies that middle- and old-aged persons
were unable to cut sufficiently the quantity demanded of medical services in response to
the increase in the self-pay ratio of medical expenditures. However, we should be awarethat this policy effect could include potential bias if the composition of the treatment andcontrol groups systematically changed following implementation of the reforms.
We also found that per household expenditure had a significantly negative effecton medical expenditures, implying (as expected) that medical services are a necessary
good. After confining the sample data to persons aged 61 years or older, this coefficient19became larger in absolute terms. Accordingly, medical services are more strongly anecessary good for those requiring more medical care. We verify these results using theestimates of the quantile regressions, and confirm that the coefficient on household
expenditure per household member increases in absolute terms with the quantile value.
This suggests that medical services are more strongly a necessary good for individuals
from households where medical expenditures account for a larger share of total household
expenditure.

References
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medical services in Japan. Journal of Human Resources, 31, 450–476.

Card, D., Dobkin, C., and Maestas, N. (2008) “The Impact of Nearly Universal Insurance
Coverage on Health Care Utilization: Evidence from Medicare,” American Economic
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Cutler, D., & Zeckhauser, R. (2000). The anatomy of health insurance. In A. J. Culyer & J.
P. Newhouse (Eds.), Handbook of health economics (pp. 563–643). Amsterdam: Elsevier.

Deaton, A., & Muellbauer, J. (1980). Economics and consumer behavior. New York:
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Finkelstein, A. (2007) “The Aggregate Effects of Health Insurance: Evidence from theIntroduction of Medicare,” Quarterly Journal of Economics, Vol. 122(1), pp. 1–37.

Finkelstein, A., and McKnight, R. (2008) “What did Medicare do? The initial impact ofMedicare on mortality and out of pocket medical spending,” Journal of Public Economics,
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20

Kan, M. and Suzuki, W. (2006) “The demand for medical care in Japan: Initial findings
from a Japanese natural experiment,” Applied Economics Letters, Vol. 13, pp. 273–277.

Leser, C. E. (1963) “Form of Engle Functions,” Econometrica, Vol. 31(4), pp. 694–703.

Lichtenberg, F. (2002) “The Effects of Medicare on Health Care Utilization andOutcomes,” in Frontier in Health Policy Research, Vol. 5, A. Garber, ed., MIT Press,
Cambridge, MA.

Manning, W. G., and Newhouse, J., Duan, N., Keeler, E., Leibowitz, A., and Marquis, S.
(1987) “Health Insurance and the Demand for Health Care: Evidence from a randomized
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Newhouse, J., and the Insurance Experiment Group (1993) Free for All? Lessons from the
RAND Health Insurance Experiment, Harvard University Press, Cambridge, MA.

Sawano, K. (2000) “Co-payment, coinsurance rate and the elderly care in Japan,” Iryo
and Shakai, Vol. 10(2), pp. 115–138 (in Japanese).

Yoshida, A. and Ito, S. (2000) “The effects of the medical insurance system on theoutpatients’ demand for medical care,” Japanese Journal of Health Economics andPolicy, Vol. 7, pp. 101–121 (in Japanese).

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system for the elderly in Japan?” Japan and the World Economy, Vol. 21, pp. 256–269.

Yoshida, A. and Takagi, S. (2002) “Effect of the reform of the social medical insurance
system in Japan,” The Japanese Economic Review, Vol. 53, pp. 444–465.

Zweifel, P. and Manning, W. G. (2000) “Moral hazard and consumer incentives in healthcare,” in Handbook of Health Economics, A. J. Culyer and J. P. Newhouse, eds., Elsevier,
Amsterdam, pp. 409–459.
21

Table 1: Cross-sectional Matrix of Health Insurance Types
Employment statusover JPY X* million
wife
for annual salaryhusband(regular employee)
regular employee
husband employee's insurance
wife
employee's insurance
nonregular employee (1) husband employee's insurance
wife
employee's insurance
nonregular employee (2) husband national insurance
wife
employee's insurance
nonregular employee (3) husband employee's insurance (dependent)
wife
employee's insurance
self-employedhusband national insurance
wife
employee's insurance
nonemployedhusband employee's insurance (dependent)
wife
employee's insuranceGroup 1
Group 2
Group 3

JPY 1.3** （annual income)～X* million (annual salary)(nonregular employee)
employee's insurance
national insurance
employee's insurance
national insurance
national insurance
national insurance
national insurance
national insurance
national insurance
national insurance
national insurance
national insurance

less than JPY 1.3** million(annual inome)(nonregular employee+nonemployed)
employee's insurance
employee's insurance (dependent)
employee's insurance
employee's insurance (dependent)
national insurance
national insurance
national insurance
national insurance
national insurance
national insurance
national insurance
national insurance

bereaved
employee's insurace
employee's insurace
national insurance
national insurance
national insurance
national insurance

Both incurred increases in the self-pay ratio of medical expenditures for outpatient and hospital treatments from 20% to 30%.
the % of medical expenditures for the regular employee was raised from 20% to 30% for both outpatient and hospital treatments,
but the % for the spouse (non-regular employee or the non-employee) was raised from 20% to 30% only for hospital treatments.
The regular employee incurred increases in the burden of medical expenditures for both hospital and outpatient treatments,
while the burden born by the spouse (non-regular employee or the employer) had remained unchanged.
The revision of the health insurance act had no adverse impact on the burden of medical expenditures.

Group 4
Husbandregular employee
those who self-reported that they worked as a regular employee
nonregular employee (1) those who self-reported that they worked as a non-regular worker, and responded that they worked
three quarters of regular employees’ weekly hours of work, 30 hours a week or more.
nonregular employee (2)those who self-reported that they worked as a non-regular worker, responded that they worked less than threequarters of regular employees’ weekly hours of work (30 hours a week), and earned an annual income of over
JPY 1.3 million if aged under 60 years or over JPY1.8 million if aged over 60 years
nonregular employee (3) those who self-reported that they worked as a non-regular worker, responded that they worked less than threequarters of regular employees’ weekly hours of work (30 hours a week), and earned an annual income of less than
JPY 1.3 million if aged under 60 years or less than JPY1.8 million if aged over 60 years
self-employed
those who self-reported that they worked as a self-employed worker, including professionals
nonemployed
those who did not reported that they worked, including the retired* JPY2.74 million (40 - 44 yrs of age), JPY2.81 million (45 - 49 yrs of age), JPY2.90 million (50 - 54 yrs of age)
JPY2.89 million between (55 - 59 yrs of age), JPY2.33 million (60 - 64 yrs of age), and JPY2.23 million (over 65 yrs of age)** JPY1.8 million for spouses more than 60 years of age
22

Table 2: Summary Statistics(1) The employment status of spouses is taken into account in grouping.
<Treatments A & B>
2001
VariableObs
Mean
Std. Dev. Min
Max
medical cost/expenditure
81
0.096
0.106
0.006
0.500
ln(per expenditure)
81
4.349
0.537
3.219
5.695
# of family
81
2.802
1.134
1
7
health (=1)
79
0.810
0.395
0
1
work (=1)
81
0.741
0.441
0
1
spouse (=1)
81
0.926
0.264
0
1
spouse's health (=1)
81
0.778
0.418
0
1
group 1
81
0.062
0.242
0
1
group 2
81
0.235
0.426
0
1
group 3
81
0.198
0.401
0
1
group 4
81
0.506
0.503
0
1
2003
VariableObs
Mean
Std. Dev. Min
Max
medical cost/expenditure
81
0.089
0.095
0.008
0.600
ln(per expenditure)
81
4.329
0.611
2.108
5.520
# of family
81
2.852
1.174
1
7
health (=1)
80
0.788
0.412
0
1
work (=1)
81
0.728
0.448
0
1
spouse (=1)
81
0.926
0.264
0
1
spouse's health (=1)
81
0.753
0.434
0
1
group 1
81
0.025
0.156
0
1
group 2
81
0.259
0.441
0
1
group 3
81
0.173
0.380
0
1
group 4
81
0.543
0.501
0
1
The employment status of spouses is categorized by their annual income or earnings.
(2) The employment status of spouses is not taken into account in grouping<Treatments C>
2001
VariableObs
Mean
Std. Dev. Min
Max
medical cost/expenditure
215
0.109
0.134
0.005
0.818
ln(per expenditure)
215
4.258
0.672
1.273
5.858
# of family
215
2.991
1.211
1
7
health (=1)
215
0.781
0.414
0
1
work (=1)
215
0.721
0.450
0
1
spouse (=1)
215
0.972
0.165
0
1
spouse's health (=1)
215
0.809
0.394
0
1
employee's insurance (=1)
215
0.447
0.498
0
1
2003
Variable
Obs
medical cost/expenditureln(per expenditure)# of family
health (=1)
work (=1)
spouse (=1)
spouse's health (=1)
employee's insurance (=1)

Mean
Std. Dev. Min
Max
215
0.110
0.126
0.006
0.667
215
4.292
0.596
2.108
5.520
215
2.981
1.260
1
7
215
0.772
0.420
0
1
215
0.674
0.470
0
1
215
0.967
0.178
0
1
215
0.800
0.401
0
1
215
0.386
0.488
0
1
23

Table 3: Distribution of Employment Status(1) The employment status of spouses is taken into account in grouping. <Treatments A & B>
Husband (male)
2001
2003
Freq.
Percent Cum.
Freq.
Percent Cum.
regular employee
12
14.81
14.81
11
13.58
13.58
nonregular employee (1)
23
28.40
43.21
17
20.99
34.57
nonregular employee (2)
1
1.23
44.44
1
1.23
35.80
nonregular employee (3)
1
1.23
45.67
3
3.70
39.50
self-employed
23
28.40
74.07
27
33.33
72.83
nonemployed
21
25.93
100.0022
27.16
100.00Total
81100.0081
100.00Spouse (female)

2001
Freq.

Percent Cum.
Freq.
regular employee
9
12.00
12.00
nonregular employee (over JPY1.3 million)
21
28.00
40.00
nonregular (less JPY1.3 million)+nonemployed45
60.00
100.00Total
75
100.00The employment status of spouses is categorized by their annual income or earnings.

2003
Percent Cum.
10
13.33
13.33
19
25.33
38.66
46
61.33
100.0075
100.00(2) The employment status of spouses is not taken into account in grouping. <Treatment C>
Male
2001
2003
Freq.
Percent Cum.
Percent Cum.
regular employee
35
16.28
16.28
26
12.09
nonregular employee (1)
61
28.37
44.65
57
26.51
nonregular employee (2)
2
0.93
45.58
1
0.47
nonregular employee (3)
4
1.86
47.44
8
3.72
self-employed
53
24.65
72.09
53
24.65
nonemployed60
27.91
100.00
70
32.56
Total
215
100.00215
100.00

24

12.09
38.60
39.07
42.79
67.44
100.00Table 4: Transitions of Employment Status(1) The employment status of spouses is taken into account in grouping. <Treatments A & B>
Husband 2003
nonregularnonregularnonregular
2001
regular(1)(2)(3)
self-employed nonemployed
regular
9
1
0
1
0
1
75.00
8.33
0.00
8.33
0.00
8.33
nonregular (1)
2
15
1
1
3
1
8.70
65.22
4.35
4.35
13.04
4.35
nonregular (2)
0
1
0
0
0
0
0.00
100.00
0.00
0.00
0.00
0.00
nonregular (3)
0
0
0
1
0
0
0.00
0.00
0.00
100.000.00
0.00
self-employed
0
0
0
0
23
0
0.00
0.00
0.00
0.00
100.000.00
nonemployed
0
0
0
0
1
20
0.00
0.00
0.00
0.00
4.76
95.24
Total
11
17
1
3
27
22
13.58
20.99
1.23
3.70
33.33
27.16
Spouse2001
regular2003
regularTotal
12100.0023
100.001
100.001
100.0023
100.0021
100.0081
100.00nonregular+
nonemployed Total

nonregular

7
1
77.78
11.11
11.11
nonregular (over JPY1.3 million)
0
7
14
0.00
33.33
66.67
nonregular (less JPY 1.3 million)
3
38
4+ nonemployed
6.67
84.44
8.89
Total
10
46
19
13.33
61.33
25.33
The employment status of spouses is categorized by their annual income or earnings.

9100.0021
100.0045
100.0075
100.00(2) The employment status of spouses is not taken into account in grouping. <Treatment C>
Male
2003
nonregularnonregularnonregular
2001
regular(1)(2)(3)
self-employed nonemployed
regular
25
7
0
1
1
1
71.43
20.00
0.00
2.86
2.86
2.86
nonregular (1)
0
43
1
4
4
9
0.00
70.49
1.64
6.56
6.56
14.75
nonregular (2)
0
2
0
0
0
0
0.00
100.00
0.00
0.00
0.00
0.00
nonregular (3)
0
3
0
1
0
0
0.00
75.00
0.00
25.00
0.00
0.00
self-employed
0
1
0
1
45
6
0.00
1.89
0.00
1.89
84.91
11.32
nonemployed
1
1
0
1
3
54
1.67
1.67
0.00
1.67
5.00
90.00
Total
26
57
1
8
53
70
12.09
26.51
0.47
3.72
24.65
32.56

25

Total
35
100.0061
100.002
100.004
100.0053
100.0060
100.00215
100.00Table 5: Transitions of Medical Insurance Programs<Treatment A>
2003
2001
0

0
1 Total
52
5
57
91.23
8.77
100.00
1
6
18
24
25.00
75.00
100.00
Total
58
23
81
71.60
28.40
100.00group 1 and 2 = 1, group 3 and 4 = 0
We assume that group 1 and 2 are adversely affected by the revision of
the health insurance act.
<Treatment B>
2003
2001
0

0
1 Total
39
2
41
95.12
4.88
100.00
1
5
35
40
12.50
87.50
100.00
Total
44
37
81
54.32
45.68
100.00group 1, 2 and 3 = 1, group 4 = 0
We assume that group 1, 2 and 3 are adversely affected by the revision of
the health insurance act.

<Treatment C>
2003
2001
0

0
111
93.28
1
21
21.88
Total
132
61.40
employee's insurance program = 1
national insurance program = 0

1 Total
8
119
6.72
100.0
75
96
78.13
100.0
83
215
38.60
100.0026

Table 6: Estimated Results (First Difference Estimation)
denpendent value: ∆(medical cost/expenditure)
independent
A
B
C
A
B
variables
[1]
[2]
[3]
[4]
[5]
-0.114 ***-0.119 ***-0.127 ***-0.130 ***-0.134 ***∆ln(per expenditure)(0.030)(0.031)(0.020)(0.033)(0.032)-0.033-0.0190.016-0.009-0.001∆insurance status(0.030)(0.025)(0.020)(0.029)(0.025)
0.045
0.039
0.012
0.037
0.033
∆insurance status×year(0.031)(0.024)(0.020)(0.034)(0.023)-0.040 **-0.041 **∆# of family(0.019)(0.018)-0.026-0.027∆own health(0.040)(0.039)0.025
0.018∆ work
(0.048)(0.041)
droppeddropped∆spouse∆spouse's healthconstant-0.022 *(0.011)-0.028 *(0.015)0.001(0.014)0.057 *
(0.032)-0.017(0.012)0.055(0.034)-0.021(0.015)C[6]-0.138 ***(0.019)0.035(0.021)0.007(0.019)-0.027 **(0.014)-0.053 **(0.027)-0.037(0.028)0.002(0.014)

Number of obs
81
81
215
78
78
215
F value
6.55
5.36
14.55
4.09
3.83
10.04
Prob > F
0.001
0.002
0.000
0.001
0.001
0.000
R2
0.374
0.373
0.292
0.424
0.426
0.328
Numbers in parentheses indicate standard errors. *** 1% significant, ** 5% significant, * 10% significant[1] and [4]: the insurance status =1 if a household belongs to either group 1or 2, but otherwise 0. <Treatment A>[2] and [5]: the insurance status =1 if a household belongs to either group 1, 2 or 3 but otherwise 0. <Treatment B>[3] and [6]: the insurance status =1 if a man belongs to the employee's insurance program, but otherwise 0. <Treatment C>
Note that there are a few husbands who lost their wife in the balanced panel, so the spouse dummy was dropped in [4] and [5].

27Table 7: Estimated Results (Repeated Cross-Section)
dependent value: medical cost/expenditure
independentA
B
CA
variables
[1]
[2]
[3]
[4]
ln(per expenditure)-0.051 ***-0.052 ***-0.069 ***-0.066 ***(0.012)(0.012)(0.010)(0.014)
year (2003=1)
0.001-0.009-0.0110.007(0.011)(0.012)(0.011)(0.011)
insurance status-0.021 *-0.012-0.030 ***-0.020(0.012)(0.013)(0.011)(0.012)0.017
0.037 **
0.030 *
0.008
insurance status×year(0.019)(0.019)(0.016)(0.018)# of family-0.015 **(0.006)own health-0.032 *(0.016)work
0.036 ***
(0.013)
spouse0.038(0.024)
spouse's health-0.055 **(0.021)constant
0.311 ***
0.316 ***
0.409 ***
0.427 ***
(0.053)(0.054)(0.045)(0.070)B[5]-0.068 ***(0.013)-0.007(0.012)-0.014(0.014)0.038 **
(0.018)-0.015 ***(0.006)-0.031 *(0.016)
0.026 **
(0.012)0.039(0.024)-0.055 **(0.022)0.446 ***
(0.071)C[6]-0.094 ***(0.011)-0.010(0.011)-0.021 *(0.011)0.030 **
(0.015)-0.026 ***(0.004)-0.026 *(0.014)0.005(0.013)0.608 ***
(0.061)

Number of obs
392
392
733
385
385
721
F value
5.846
5.375
13.9496.375
6.665
14.294
Prob > F
0.000
0.000
0.000
0.000
0.000
0.000
R2
0.100
0.106
0.140
0.179
0.184
0.199
Numbers in parentheses indicate standard errors. *** 1% significant, ** 5% significant, * 10% significant[1] and [4]: the insurance status =1 if a household belongs to either group 1or 2, but otherwise 0. <Treatment A>[2] and [5]: the insurance status =1 if a household belongs to either group 1, 2 or 3 but otherwise 0. <Treatment B>[3] and [6]: the insurance status =1 if a man belongs to the employee's insurance program, but otherwise 0. <Treatment C>
The OLS model is estimated using the repeated cross-section data.
28

Table 8: Estimated Results (First Difference Estimation): 61 years of age or older
dependent value: ∆(medical cost/expenditure)
independent
A
B
C
A
B
variables
[1]
[2]
[3]
[4]
[5]
-0.141 ***-0.144 ***-0.143 ***-0.157 ***-0.158 ***∆ln(per expenditure)(0.032)(0.033)(0.024)(0.033)(0.033)-0.017
0.011
0.024
0.016
0.023
∆insurance status(0.035)(0.024)(0.020)(0.028)(0.025)
0.026
0.018
0.023
0.013
0.013
∆insurance status×year(0.035)(0.029)(0.026)(0.039)(0.028)-0.044 *-0.041 *∆# of family(0.026)(0.024)-0.020-0.020∆own health(0.063)(0.062)0.019
0.013∆ work
(0.043)(0.042)droppeddropped∆spouse∆spouse's healthconstant-0.028 *(0.015)-0.027(0.018)-0.008(0.015)0.025(0.048)-0.023(0.016)0.025(0.047)-0.023(0.019)C[6]-0.146 ***(0.026)0.027(0.020)0.020(0.027)-0.011(0.016)-0.036(0.039)-0.001(0.022)-0.006(0.015)

Number of obs
51
51
138
50
50
138
F value
8.34
8.74
12.28
4.99
6.16
7.03
Prob > F
0.000
0.000
0.000
0.000
0.000
0.000
R2
0.519
0.519
0.351
0.559
0.560
0.358
Numbers in parentheses indicate standard errors. *** 1% significant, ** 5% significant, * 10% significant[1] and [4]: the insurance status =1 if a household belongs to either group 1or 2, but otherwise 0. <Treatment A>[2] and [5]: the insurance status =1 if a household belongs to either group 1, 2 or 3 but otherwise 0. <Treatment B>[3] and [6]: the insurance status =1 if a man belongs to the employee's insurance program, but otherwise 0. <Treatment C>
Note that there are a few husbands who lost their wife in the balanced panel data, so the spouse dummy was dropped in [4] and [5].

29Table 9: Estimated Results (Repeated Cross-Section): 61 years of age or older
dependent value: medical cost/expenditure
independentA
B
CA
variables
[1]
[2]
[3]
[4]
ln(per expenditure)-0.059 ***-0.059 ***-0.072 ***-0.082 ***(0.015)(0.015)(0.012)(0.017)
year (2003=1)-0.007-0.013-0.017-0.002(0.012)(0.013)(0.012)(0.012)
insurance status-0.027-0.008-0.030 **-0.021(0.017)(0.019)(0.013)(0.017)0.022
0.031
0.034 *
0.015
insurance status×year(0.024)(0.023)(0.018)(0.023)# of family-0.021 ***(0.007)own health-0.039 **(0.018)work
0.030 **
(0.015)
spouse0.025(0.029)spouse's health-0.048 *(0.027)
constant
0.350 ***
0.347 ***
0.425 ***
0.533 ***
(0.068)(0.070)(0.055)(0.090)B[5]-0.085 ***(0.017)-0.008(0.013)0.004(0.018)0.030(0.021)-0.022 ***(0.006)-0.041 **(0.018)0.014(0.013)0.023(0.029)-0.050 *(0.027)0.554 ***
(0.090)C[6]-0.102 ***(0.014)-0.013(0.012)-0.023(0.014)0.031 *
(0.017)-0.029 ***(0.005)-0.027 *(0.015)0.004(0.014)0.651 ***
(0.074)

Number of obs
275
275
503
271
271
494
F value
4.994
3.923
9.759
5.140
5.248
10.328Prob > F
0.001
0.004
0.000
0.000
0.000
0.000
R2
0.133
0.136
0.154
0.244
0.254
0.220
Numbers in parentheses indicate standard errors. *** 1% significant, ** 5% significant, * 10% significant[1] and [4]: the insurance status =1 if a household belongs to either group 1or 2, but otherwise 0. <Treatment A>[2] and [5]: the insurance status =1 if a household belongs to either group 1, 2 or 3 but otherwise 0. <Treatment B>[3] and [6]: the insurance status =1 if a man belongs to the employee's insurance program, but otherwise 0. <Treatment C>
The OLS model is estimated using the repeated cross-section data.
30

Table 10: Quantile Regression Results
[1] A
[2] B
[3] C
medical cost/expenditure
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
q10 ln(per expenditure)-0.020(0.002) ***-0.019(0.002) ***-0.023(0.001) ***year (2003=1)
0.005(0.003)0.000(0.004)0.002(0.002)
insurance status
0.000(0.004)-0.003(0.005)-0.002(0.003)-0.006(0.005)0.003(0.005)-0.001(0.003)insurance status×year# of family-0.006(0.001) ***-0.005(0.001) ***-0.007(0.001) ***own health (=1)-0.008(0.003) **-0.008(0.004) **-0.007(0.002) ***work (=1)
0.007(0.003) ***0.007(0.003) **0.006(0.002) ***spouse (=1)
0.003
(0.006)0.006(0.006)
spouse's health (=1)-0.003(0.004)-0.007(0.004)q25 ln(per expenditure)-0.029(0.003) ***-0.029(0.003) ***-0.036(0.002) ***year (2003=1)
0.006(0.004)0.004(0.004)0.002(0.003)insurance status-0.004(0.005)-0.004(0.005)-0.001(0.004)0.001(0.007)0.004(0.006)0.006(0.005)insurance status×year# of family-0.010(0.001) ***-0.010(0.001) ***-0.011(0.001) ***own health (=1)-0.013(0.004) ***-0.014(0.004) ***-0.014(0.003) **work (=1)
0.018(0.004) ***0.018(0.004) ***0.008(0.003)
spouse (=1)-0.003(0.008)-0.003(0.008)
spouse's health (=1)-0.006(0.005)-0.005(0.005)q50 ln(per expenditure)-0.042(0.004) ***-0.042(0.005) ***-0.057(0.004) ***year (2003=1)
0.009(0.005) **0.003(0.007)-0.001(0.006)
insurance status-0.008(0.006)-0.007(0.009)-0.008(0.007)-0.008(0.009)0.008(0.011)
0.012(0.009)insurance status×year# of family-0.011(0.002) ***-0.012(0.003) ***-0.018(0.002) ***own health (=1)-0.017(0.005) ***-0.014(0.007) **-0.024(0.006) ***work (=1)
0.026(0.005) ***0.020(0.007) ***0.004(0.006)
spouse (=1)
0.000
(0.010)0.010(0.013)
spouse's health (=1)-0.032(0.006) ***-0.035(0.009) ***q75 ln(per expenditure)-0.055(0.010) ***-0.061(0.011) ***-0.097(0.013) ***year (2003=1)
0.012(0.011)0.000(0.013)-0.002(0.015)insurance status-0.032(0.015) **-0.005(0.016)-0.019(0.019)0.023(0.020)0.025(0.019)0.007(0.024)insurance status×year# of family-0.010(0.005) **-0.011(0.005) **-0.027(0.006) ***own health (=1)-0.039(0.011) ***-0.045(0.012) ***-0.025(0.015) *work (=1)
0.033(0.012) ***0.021(0.013)0.022(0.016)
spouse (=1)
0.038
(0.022) *0.039(0.023) *spouse's health (=1)
-0.079(0.014) ***-0.087(0.015) ***q90 ln(per expenditure)-0.109(0.038) ***-0.114(0.035) ***-0.150(0.037) ***year (2003=1)
0.024(0.032)0.002(0.033)-0.038(0.040)insurance status-0.003(0.044)0.026(0.040)-0.070(0.048)-0.001(0.059)0.033(0.051)
0.067(0.061)
insurance status×year# of family-0.018(0.016)-0.015(0.015)-0.027(0.014) *own health (=1)
-0.092(0.035) ***-0.084(0.033) **-0.034(0.040)work (=1)
0.070(0.033) **0.040(0.033)0.022(0.042)
spouse (=1)
0.207
(0.064) ***0.176(0.058) ***spouse's health (=1)-0.233(0.044) ***-0.232(0.038) ***Number of obs
385
385
721
.10 Pseudo R2
0.076
0.073
0.077
.25 Pseudo R2
0.089
0.089
0.087
.50 Pseudo R2
0.113
0.109
0.101
.75 Pseudo R2
0.141
0.144
0.134
.90 Pseudo R2
0.193
0.200
0.161
Numbers in parentheses indicate standard errors. *** 1% significant, ** 5% significant, * 10% significant[1]: the insurance status =1 if a household belongs to either group 1or 2, but otherwise 0. <Treatment A>[2]: the insurance status =1 if a household belongs to either group 1, 2 or 3 but otherwise 0.<Treatment B>[3]: the insurance status =1 if a man belongs to the employee's insurance program, but otherwise 0.<Treatmen C>
The quantile regression model is estimated using the repeated cross-section data.
31

Table 11: Quantile Regression Results: 61 years of age or older
[1] A
[2] B
[3] C
medical cost/expenditure
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
q10 ln(per expenditure)-0.020(0.002) ***-0.022(0.002) ***-0.026(0.002) ***year (2003=1)
0.001(0.004)0.000(0.004)0.001(0.003)insurance status-0.005(0.006)-0.006(0.006)-0.005(0.005)0.002(0.007)0.009(0.007)0.002(0.006)
insurance status×year# of family-0.005(0.001) ***-0.006(0.002) ***-0.008(0.001) ***own health (=1)-0.009(0.004) ***-0.008(0.004) **-0.012(0.003) ***work (=1)
0.009(0.003) ***0.008(0.004) **0.008(0.003) **spouse (=1)
0.006
(0.008)0.007(0.009)spouse's health (=1)-0.010(0.005) **-0.009(0.005) *q25 ln(per expenditure)-0.033(0.003) ***-0.034(0.003) ***-0.039(0.002) ***year (2003=1)
0.006(0.004)0.006(0.004)0.000(0.004)insurance status-0.005(0.006)0.002(0.006)-0.001(0.006)0.002(0.008)0.001(0.007)0.008(0.007)insurance status×year# of family-0.011(0.002) ***-0.011(0.002) ***-0.011(0.001) ***own health (=1)-0.015(0.004) ***-0.013(0.004) ***-0.015(0.004) ***work (=1)
0.019(0.004) ***0.016(0.004) ***0.009(0.004) **spouse (=1)-0.004(0.008)-0.005(0.008)
spouse's health (=1)-0.005(0.005)-0.002(0.005)q50 ln(per expenditure)-0.049(0.004) ***-0.048(0.005) ***-0.059(0.005) ***year (2003=1)
0.007(0.005)0.004(0.007)-0.001(0.007)insurance status-0.006(0.008)-0.002(0.010)-0.004(0.010)-0.005(0.010)0.009(0.012)0.011(0.012)
insurance status×year# of family-0.015(0.002) ***-0.016(0.003) ***-0.018(0.003) ***own health (=1)-0.020(0.005) ***-0.020(0.007) ***-0.027(0.007) ***work (=1)
0.023(0.005) ***0.018(0.007) **0.001(0.007)
spouse (=1)
0.017
(0.009) **0.013(0.013)
spouse's health (=1)-0.026(0.006) ***-0.027(0.009) ***q75 ln(per expenditure)-0.069(0.015) ***-0.069(0.009) ***-0.103(0.011) ***year (2003=1)
0.007(0.015)0.002(0.010)-0.011(0.013)insurance status-0.028(0.025)0.007(0.015)-0.013(0.019)0.022(0.030)0.024(0.016)0.013(0.022)
insurance status×year# of family-0.018(0.008) **-0.014(0.005) ***-0.032(0.006) ***own health (=1)-0.048(0.016) ***-0.051(0.010) ***-0.027(0.013) **work (=1)
0.028(0.015) *0.008(0.010)0.008(0.013)
spouse (=1)
0.035
(0.029)0.030(0.017) *spouse's health (=1)
-0.071(0.019) ***-0.072(0.012) ***q90 ln(per expenditure)-0.149(0.039) ***-0.133(0.039) ***-0.149(0.046) ***year (2003=1)
0.008(0.032)0.010(0.033)-0.029(0.046)
insurance status-0.023(0.050)0.017(0.048)-0.055(0.067)0.003(0.062)0.011(0.056)0.072(0.079)insurance status×year# of family-0.018(0.014)-0.016(0.014)-0.035(0.016) **own health (=1)
-0.076(0.034) **-0.092(0.033) ***-0.012(0.046)
work (=1)
0.060(0.034) *0.039(0.033)0.022(0.048)
spouse (=1)
0.211
(0.063) ***0.184(0.054) ***spouse's health (=1)-0.271(0.042) ***-0.260(0.044) ***Number of obs
271
271
494
.10 Pseudo R2
0.095
0.095
0.082
.25 Pseudo R2
0.101
0.100
0.093
.50 Pseudo R2
0.123
0.119
0.111
.75 Pseudo R2
0.161
0.170
0.155
.90 Pseudo R2
0.257
0.261
0.185
Numbers in parentheses indicate standard errors. *** 1% significant, ** 5% significant, * 10% significant[1]: the insurance status =1 if a household belongs to either group 1or 2, but otherwise 0. <Treatment A>[2]: the insurance status =1 if a household belongs to either group 1, 2 or 3 but otherwise 0.<Treatment B>[3]: the insurance status =1 if a man belongs to the employee's insurance program, but otherwise 0.<Treatmen C>
The quantile regression model is estimated using the repeated cross-section data.
32

Figure 1: Overview of The 2003 Health Insurance Reform in Japan
Before the 2003 health insurance reform
Over 70 years of age
0 - 69 years of age
insured person
20%

10%dependent
outpatient30%
inpatient
20%

employees' health insurance

After the 2003 health insurance reform
Over 70 years of age
3 - 69 years of age

30%

10%
30%

⇒
0 - 2 years of age
20%
national healthinsurance

employees' health insurance

33

national healthinsurance