This dissertation has been motivated by the question of how countries should
optimally structure health care. Especially, there are two important economic and
policy questions asked that extend beyond the area of health economics. The
Örst is how the expansion of health insurance coverage a§ects the utilization and
health of its beneÖciaries (extensive margin); the second is how generous should
health insurance be (intensive margin) to balance the provision of care and Önancial
protection against risk while containing medical expenditures. The three chapters
in this dissertation aim to make empirical contributions to these ongoing research
questions.
First Chapter, ìThe E§ect of Patient Cost-Sharing on Utilization, Health and
Risk Protection: Evidence from Japanî addresses the second question. It investigates how cost-sharing, requiring patients to pay a share of the cost of care,
a§ects the demand for care, health itself, and risk protection among the elderly,
the largest consumers of health service. Previous studies of cost-sharing have had
di¢ culty separating the e§ect of cost-sharing on patients from the ináuence of medical providers and insurers. This paper overcomes that limitation by examining a sharp reduction in cost-sharing at age 70 in Japan in a regression discontinuity
design. I Önd that price elasticities of demand for both inpatient admissions and
outpatient visits among the elderly are comparable to prior estimates for the nonelderly. I also Önd that the welfare gain from risk protection is relatively small
compared to the deadweight loss of program Önancing, suggesting that the social
cost of lower cost-sharing may outweigh social beneÖt. Taken together, this study
shows that an increase in cost-sharing may be achieved without decreasing total
welfare.
Third Chapter, ìE§ects of Universal Health Insurance on Health Care Utilization, Supply-Side Responses and Mortality Rates: Evidence from Japanî (with
Ayako Kondo) address the Örst question. Even though most developed countries
have implemented some form of universal public health insurance, most studies on
the impact of the health insurance coverage have been limited to speciÖc subpopulations, such as infants and children, the elderly or the poor. We investigate the
e§ects of a massive expansion in health insurance coverage on utilization and health
by examining the introduction of universal health insurance in Japan in 1961. We
Önd that health care utilization increases more than would be expected from previous estimates of the elasticities of individual-level changes in health insurance
status such as RAND Health Insurance Experiment in the US.
The two chapters addressed above focus on consumersí incentives. Second
chapter, ìSupply-Induced Demand in Newborn Treatment: Evidence from Japanî
(with Kiyohide Fushimi) examines the incentives faced by medical providers. Since medical providers exert a strong ináuence over the quantity and types of medical
care demanded, measuring the size of supply-induced demand (SID) has been a
long-standing controversy in health economics. However, past studies may underestimate the size of SID since it is empirically di¢ cult to isolate SID from other
confounding hospital behaviors, such as changes in the selection of patients. We
overcome these empirical challenges by focusing on a speciÖc population: at-risk
newborns, and we measure the degree of SID by exploiting changes in reimbursement caused by the introduction of the partial prospective payment system (PPS)
in Japan, which makes some procedures relatively more proÖtable than other procedures. We Önd that hospitals respond to PPS adoption by increasing utilization
and increasing their manipulation of infantís reported birth weight, which determines infants reimbursement and maximum length of stay. We also Önd that this
induced demand substantially increases hospital reimbursements without improving infant health, implying that the additional money spent has no commensurate
health gains.
Data and IdentiÖcation
I use one of the most comprehensive sources of health-related datasets ever
assembled on Japan. Here I summarize the most important datasets in the study;
further details can be found in the Appendix A.3. My main outcomes are health
care utilization on the cost-side, and health outcomes, and out-of-pocket expenditures on the beneÖt-side.
1.3.1. Data
The dataset for health care utilization is the Patient Survey, a nationally representative repeated cross-section that collects administrative data from both hospitals
and clinics.20 Since the survey is conducted every three years, I have individual
patient level data for nine rounds of surveys between 1984 and 2008. One of the
biggest advantages of this survey relative to usual hospital discharge data is that
the Patient Survey includes information for outpatient visits as well. In contrast,
most existing datasets capture either outpatient visits or inpatient admissions. In
20See Bhattacharya et al. (1996) for an example of a study that uses the Patient Survey.
fact, the Agency for Healthcare Research and Quality (AHRQ) has recognized the
need to develop a methodology for studying preventive care in an outpatient setting by using inpatient data to identify admissions that should not occur in the
presence of su¢ cient preventive care (AHRQ, 2011).21 In my case, I can look at
changes in the number of patients for beneÖcial and preventive care in the outpatient setting.22 The disadvantage of this data is that, as in the case for most of the
discharge data, it only includes limited individual demographics such as gender,
and place of living (no education or income).
The Patient Survey consists of two types of data: outpatient data and discharge
data. I use the former to examine outpatient visits and the latter for inpatient
admissions. The outpatient data is collected during one day in the middle of October of the survey year and provides information on all patients who had outpatient
visits to the surveyed hospitals and clinics during the survey day.23 This data includes patientsíexact date of birth and the survey date, which is equivalent to the
exact date of the visits. The discharge data contain the records of all patients who
were discharged from surveyed hospitals and clinics in September of the survey
year. The discharge data report the exact dates of birth, admission, surgery, and
discharge, which enable me to compute age at admission.24 Hospital and clinic
information are obtained from the Survey of Medical Institutions and merged with
Patient Survey.
As health outcomes, I examine both mortality and morbidity. I examine mortality since it is one of the few objective, well-measured health outcomes and is also
often easily available, and comparable across di§erent countries. I use the universe
of death records between 1987-1991, which report the exact dates of birth, death,
place of death, and cause of death using International ClassiÖcation of Diseases
(ICD) Ninth. The main advantage of the death records is that they cover all deaths
that occur in Japan, unlike hospital discharge records, which only report deaths
that occur in the hospital.25 I complement the mortality results by examining
23Since outpatient visits are collected on only one day, the survey is susceptible to external
factors such as weather. Therefore it is important to include the survey year Öxed e§ects in the
speciÖcation to account for this common shock within years. This short survey period is another
reason why I do not exploit the year-to-year variation in cost-sharing in this paper.
24I describe these dates in chorological order for simplicity, but each unit of data is per discharge.
25A rare exception is hospital discharge records in California used in Card et al. (2008, 2009) that
tracks mortality within one year of discharge. To my knowledge, data that tracks post-discharge
mortality does not exist in Japan.
These two issues are less relevant for outpatient visits, since I will show later
that there does not appear to be a catch-up e§ect, and reaching the stop-loss is very
unlikely since outpatient visits are not costly. The more relevant case is inpatient
admissions. I will show later that overall age trend does not seem to display
any catch-up e§ects, but close inspection of inpatient admissions with elective
surgery shows some drop-o§ just below age 70, and a sudden surge just over age
70. Though not far from perfect, to partially account for the catch-up e§ect, I run
a ìdonut-holeî RD by excluding a few observations around the threshold. This
approach was initially proposed by Barreca et al. (2011) to account for pronounced
heaping in the observations around the threshold in RD framework.42 The caveat
of this methodology is that there is no clear economic or statistical consensus
on the optimal size of the donut and excluding observations near the threshold
undermines the virtue of the RD design, that is, comparing outcomes just below
and above the threshold. Nonetheless, this donut-hole RD may show whether my
RD estimates are sensitive to the catch-up e§ects.
Accounting for non-linearity associated with stop-loss is much harder, since
to fully understand the size of the di§erence between true and nominal price, I
may need data on episodes of illness rather than monthly aggregated data (Keeler).
and Rolph, 1988).43 I argue that the e§ect of the stop-loss on over-utilization is
probably much smaller in my case rather than RAND HIE because the stop-loss is
set by monthly in Japan rather than annually like the RAND HIE and most health
insurances in the U.S. To the extent that illnesses are unpredictable, this shorter
interval may make it harder for people to time and overuse the medical services.
Keeler et al. (1977) and Ellis (1986) formally show that the more time left in the
accounting period, the more the e§ective price falls. Furthermore, even under an
annual stop-loss, Keeler and Rolph (1988) empirically shows that people in the
RAND HIE respond myopically to stop-loss, i.e., people do not appear to change
the timing of medical purchases to reduce costs. Nonetheless, to partially account
for this e§ect, I simply apply formula of (1 xt)Pt for those whose out-of-pocket
medical expenditures are more than median in each survey year t since this problem
is most relevant for consumers who are close to reaching the stop-loss. Since the
probability of reaching the stop-loss is not high even for the inpatient admissions
(14 percent for those admitted, and 2 percent for non-conditional population), the
nominal price (38.0 thousand Yen) for those just below age 70 is not so di§erent
from the ìtrueî price (35.3 thousand Yen). Therefore, the bias coming from the
non-linearity associated with stop-loss may be negligible in this case.
optimally structure health care. Especially, there are two important economic and
policy questions asked that extend beyond the area of health economics. The
Örst is how the expansion of health insurance coverage a§ects the utilization and
health of its beneÖciaries (extensive margin); the second is how generous should
health insurance be (intensive margin) to balance the provision of care and Önancial
protection against risk while containing medical expenditures. The three chapters
in this dissertation aim to make empirical contributions to these ongoing research
questions.
First Chapter, ìThe E§ect of Patient Cost-Sharing on Utilization, Health and
Risk Protection: Evidence from Japanî addresses the second question. It investigates how cost-sharing, requiring patients to pay a share of the cost of care,
a§ects the demand for care, health itself, and risk protection among the elderly,
the largest consumers of health service. Previous studies of cost-sharing have had
di¢ culty separating the e§ect of cost-sharing on patients from the ináuence of medical providers and insurers. This paper overcomes that limitation by examining a sharp reduction in cost-sharing at age 70 in Japan in a regression discontinuity
design. I Önd that price elasticities of demand for both inpatient admissions and
outpatient visits among the elderly are comparable to prior estimates for the nonelderly. I also Önd that the welfare gain from risk protection is relatively small
compared to the deadweight loss of program Önancing, suggesting that the social
cost of lower cost-sharing may outweigh social beneÖt. Taken together, this study
shows that an increase in cost-sharing may be achieved without decreasing total
welfare.
Third Chapter, ìE§ects of Universal Health Insurance on Health Care Utilization, Supply-Side Responses and Mortality Rates: Evidence from Japanî (with
Ayako Kondo) address the Örst question. Even though most developed countries
have implemented some form of universal public health insurance, most studies on
the impact of the health insurance coverage have been limited to speciÖc subpopulations, such as infants and children, the elderly or the poor. We investigate the
e§ects of a massive expansion in health insurance coverage on utilization and health
by examining the introduction of universal health insurance in Japan in 1961. We
Önd that health care utilization increases more than would be expected from previous estimates of the elasticities of individual-level changes in health insurance
status such as RAND Health Insurance Experiment in the US.
The two chapters addressed above focus on consumersí incentives. Second
chapter, ìSupply-Induced Demand in Newborn Treatment: Evidence from Japanî
(with Kiyohide Fushimi) examines the incentives faced by medical providers. Since medical providers exert a strong ináuence over the quantity and types of medical
care demanded, measuring the size of supply-induced demand (SID) has been a
long-standing controversy in health economics. However, past studies may underestimate the size of SID since it is empirically di¢ cult to isolate SID from other
confounding hospital behaviors, such as changes in the selection of patients. We
overcome these empirical challenges by focusing on a speciÖc population: at-risk
newborns, and we measure the degree of SID by exploiting changes in reimbursement caused by the introduction of the partial prospective payment system (PPS)
in Japan, which makes some procedures relatively more proÖtable than other procedures. We Önd that hospitals respond to PPS adoption by increasing utilization
and increasing their manipulation of infantís reported birth weight, which determines infants reimbursement and maximum length of stay. We also Önd that this
induced demand substantially increases hospital reimbursements without improving infant health, implying that the additional money spent has no commensurate
health gains.
Data and IdentiÖcation
I use one of the most comprehensive sources of health-related datasets ever
assembled on Japan. Here I summarize the most important datasets in the study;
further details can be found in the Appendix A.3. My main outcomes are health
care utilization on the cost-side, and health outcomes, and out-of-pocket expenditures on the beneÖt-side.
1.3.1. Data
The dataset for health care utilization is the Patient Survey, a nationally representative repeated cross-section that collects administrative data from both hospitals
and clinics.20 Since the survey is conducted every three years, I have individual
patient level data for nine rounds of surveys between 1984 and 2008. One of the
biggest advantages of this survey relative to usual hospital discharge data is that
the Patient Survey includes information for outpatient visits as well. In contrast,
most existing datasets capture either outpatient visits or inpatient admissions. In
20See Bhattacharya et al. (1996) for an example of a study that uses the Patient Survey.
fact, the Agency for Healthcare Research and Quality (AHRQ) has recognized the
need to develop a methodology for studying preventive care in an outpatient setting by using inpatient data to identify admissions that should not occur in the
presence of su¢ cient preventive care (AHRQ, 2011).21 In my case, I can look at
changes in the number of patients for beneÖcial and preventive care in the outpatient setting.22 The disadvantage of this data is that, as in the case for most of the
discharge data, it only includes limited individual demographics such as gender,
and place of living (no education or income).
The Patient Survey consists of two types of data: outpatient data and discharge
data. I use the former to examine outpatient visits and the latter for inpatient
admissions. The outpatient data is collected during one day in the middle of October of the survey year and provides information on all patients who had outpatient
visits to the surveyed hospitals and clinics during the survey day.23 This data includes patientsíexact date of birth and the survey date, which is equivalent to the
exact date of the visits. The discharge data contain the records of all patients who
were discharged from surveyed hospitals and clinics in September of the survey
year. The discharge data report the exact dates of birth, admission, surgery, and
discharge, which enable me to compute age at admission.24 Hospital and clinic
information are obtained from the Survey of Medical Institutions and merged with
Patient Survey.
As health outcomes, I examine both mortality and morbidity. I examine mortality since it is one of the few objective, well-measured health outcomes and is also
often easily available, and comparable across di§erent countries. I use the universe
of death records between 1987-1991, which report the exact dates of birth, death,
place of death, and cause of death using International ClassiÖcation of Diseases
(ICD) Ninth. The main advantage of the death records is that they cover all deaths
that occur in Japan, unlike hospital discharge records, which only report deaths
that occur in the hospital.25 I complement the mortality results by examining
23Since outpatient visits are collected on only one day, the survey is susceptible to external
factors such as weather. Therefore it is important to include the survey year Öxed e§ects in the
speciÖcation to account for this common shock within years. This short survey period is another
reason why I do not exploit the year-to-year variation in cost-sharing in this paper.
24I describe these dates in chorological order for simplicity, but each unit of data is per discharge.
25A rare exception is hospital discharge records in California used in Card et al. (2008, 2009) that
tracks mortality within one year of discharge. To my knowledge, data that tracks post-discharge
mortality does not exist in Japan.
These two issues are less relevant for outpatient visits, since I will show later
that there does not appear to be a catch-up e§ect, and reaching the stop-loss is very
unlikely since outpatient visits are not costly. The more relevant case is inpatient
admissions. I will show later that overall age trend does not seem to display
any catch-up e§ects, but close inspection of inpatient admissions with elective
surgery shows some drop-o§ just below age 70, and a sudden surge just over age
70. Though not far from perfect, to partially account for the catch-up e§ect, I run
a ìdonut-holeî RD by excluding a few observations around the threshold. This
approach was initially proposed by Barreca et al. (2011) to account for pronounced
heaping in the observations around the threshold in RD framework.42 The caveat
of this methodology is that there is no clear economic or statistical consensus
on the optimal size of the donut and excluding observations near the threshold
undermines the virtue of the RD design, that is, comparing outcomes just below
and above the threshold. Nonetheless, this donut-hole RD may show whether my
RD estimates are sensitive to the catch-up e§ects.
Accounting for non-linearity associated with stop-loss is much harder, since
to fully understand the size of the di§erence between true and nominal price, I
may need data on episodes of illness rather than monthly aggregated data (Keeler).
and Rolph, 1988).43 I argue that the e§ect of the stop-loss on over-utilization is
probably much smaller in my case rather than RAND HIE because the stop-loss is
set by monthly in Japan rather than annually like the RAND HIE and most health
insurances in the U.S. To the extent that illnesses are unpredictable, this shorter
interval may make it harder for people to time and overuse the medical services.
Keeler et al. (1977) and Ellis (1986) formally show that the more time left in the
accounting period, the more the e§ective price falls. Furthermore, even under an
annual stop-loss, Keeler and Rolph (1988) empirically shows that people in the
RAND HIE respond myopically to stop-loss, i.e., people do not appear to change
the timing of medical purchases to reduce costs. Nonetheless, to partially account
for this e§ect, I simply apply formula of (1 xt)Pt for those whose out-of-pocket
medical expenditures are more than median in each survey year t since this problem
is most relevant for consumers who are close to reaching the stop-loss. Since the
probability of reaching the stop-loss is not high even for the inpatient admissions
(14 percent for those admitted, and 2 percent for non-conditional population), the
nominal price (38.0 thousand Yen) for those just below age 70 is not so di§erent
from the ìtrueî price (35.3 thousand Yen). Therefore, the bias coming from the
non-linearity associated with stop-loss may be negligible in this case.