THE DETERMINANTS OF PUBLIC HEALTH CARE EXPENDITURE IN CHINA
Lang Hoch
B.A., Civil Engineering. Hunan University, China, 1990
THESIS
Submitted in partial satisfaction of
the requirement for the degree of
MASTER OF ARTS
in
ECONOMICS
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING 2010
THE DETERMINANTS OF PUBLIC HEALTH CARE EXPENDITURE IN CHINA
A Thesis
by
Lang Hoch
Approved by:
_____________________________________, Committee Chair
Craig A. Gallet, Ph.D.
_______________________________________, Second Reader
Jonathan D. Kaplan, Ph.D.
Date: ___________________________________
ii
Student: Lang Hoch
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit to be
awarded for the thesis.
______________________, Graduate Coordinator
Jonathan D. Kaplan, Ph.D.,
Department of Economics
iii
_____________
Date
Abstract
of
THE DETERMINANTS OF PUBLIC HEALTH CARE EXPENDITURE IN CHINA
By
Lang Hoch
This thesis addresses the relationship between public health care expenditure and
a variety of factors in China during the 1995-2007 period. Research of other countries
has found that health care expenditure grows roughly 2% per year, which has been most
commonly tied to changes in per capita income and age distribution. This thesis
differentiates itself from previous research by focusing on the impact of a variety of
factors, including income and age distribution, on province-level government health care
expenditure in China. Controlling for a variety of specification issues, as well as the
potential endogeneity of per capita income, results that mimic specifications of earlier
studies find the income elasticity of health care expenditure in China falls in the inelastic
range, which suggests health care is a necessity. However, we do find the results are
sensitive to how panel data issues are addressed, as well as the choice of regressors and
functional form of the model.
_________________________, Committee Chair
Craig A. Gallet, Ph.D.
iv
ACKNOWLEDGEMENTS
I would like to thank the following persons for their continued assistance and patience
during the research and completion of this study:
-Professor Craig A. Gallet for his clear guidance in this study and for always having an
open door and time for assistance, even in the late evening, weekend, holiday, and
furlough days.
- Professor Jonathan D. Kaplan for his sincerely suggestions and assistance, especially
with the econometrics and interpret.
- The entire Economics faculty at California State University, Sacramento, for their
inspiration and dedication in the success of students.
- My son Orion and my daughter Aria for the time and space you both sacrifice during
the long weeks and nights of the research process.
- Special thanks to my father-in-laws Professor George Hoch and Sally Free for their
special support and encouragement during my whole student career.
- Lastly, I am especially grateful to my husband to whom I’m deeply indebted for all the
emotion and economics support, sacrifice, and encouragement during my whole student
career and in particular during the completion of this thesis.
-Also I have to credit the vanilla latte in Tupelos for keeping my energy for this study.
v
TABLE OF CONTENTS
Page
Acknowledgements……………………………………………………….........................v
List of Tables……………………………………………………….................................vii
List of Figures…………………………………………………………………………...viii
Chapter
1.
INTRODUCTION…………………………………………………………………..1
2. LITERATURE REVIEW……………………………………………………………5
2.1
Development of the Literature ………………………………………………...5
2.2
Health Care and Health Outcomes in China…………………………….........18
3. EMPIRICAL MODEL AND DATA……………………………………………….22
3.1 Empirical Model……………………………………………………………….22
3.2 Data………………………………………………………………………........26
4. ESTIMATION RESULTS………………………………………………………….34
4.1 Estimation Issues………………………………………………………............34
4.2 Model 1 Estimation Results…………………………………………………...35
4.3 Model 2 Estimation Results…………………………………………...............39
5. CONCLUSION…………………………………………………………………….48
5.1 Summary of Findings………………………………………………………….48
5.2 Suggestions for Future Research………………………………………............49
References………………………………………………………………….…………….51
vi
LIST OF TABLES
Table
Page
1. 3.1. Variables Definitions……………………………………………………………..26
2. 3.2. Descriptive Statistics……………………………………………………………..27
3. 3.3. Descriptive Statistics in 1995, 2001, 2007……………………………………….28
4. 4.1. Model 1 Linear Results…………………………………………………………..36
5. 4.2. Model 1 Double-Log Results…………………………………………………….38
6. 4.3. Model 2 Linear Results…………………………………………………………..40
7. 4.4. Model 2 Double-Log Results…………………………………………………….42
8. 4.5. Province-Fixed Effects Coefficients from Linear Version of Models 1 and 2…..44
9. 4.6. Time-Fixed Effects Coefficients from Linear Version of Models 1 and ...……...45
10. 4.7. Estimated Income Elasticities…………………………………………………..46
vii
LIST OF FIGURES
Figure
Page
1. 3.1. The Trend of Public Health Expenditure in China in Chinese Yuan…………….29
2. 3.2. Real Per Capita Rural and Urban Health Expenditure in Chinese Yuan………...30
3. 3.3. Per Capita GPP in China from 1978 to 2007 in Chinese Yuan………………….31
4. 3.4. Mean of Real Per Capita GPP of 31 Provinces in Chinese Yuan…….……….....32
5. 3.5. Mean Province Value of Real Per Capita Rural Income in Chinese Yuan……….32
6. 3.6. Mean Province Value of Per Capita Waste Gas in Cubic Meter…………............33
viii
1
Chapter 1
INTRODUCTION
Initially quoted by Blomqvist and Carter (1997), according to the editors of the
March 27, 1993 edition of The Economist (see page 113), “As with luxury goods, health
spending tends to rise disproportionately as countries become richer.” With respect to
China, it has witnessed dramatic economic growth after Deng Xiao Ping launched his
“open economy” policy in 1978. Indeed, economic growth over the past decade has been
rapid, ranging between 9% and 13% per year. Is the growth of health expenditure in
China as rapid as that of income? Is health care also a luxury in China? Given that
China has a socialistic system of health care, which relies more heavily on government
funding, we seek to answer such questions utilizing a variety of data. Due to limitations
in the availability of data, though, this thesis focuses on the determinants of government
health care expenditure in China.
The impact of income and non-income factors on health care expenditure has
been the subject of many studies over the years. Such studies differ in many respects,
including the country of interest, data utilized, and empirical methodology. Early studies
(e.g., Newhouse (1977)) utilized cross-sectional data of several OECD countries and
found that per capita income (proxied by per capita GDP), as opposed to a variety of nonincome factors, plays the most prominent role in determining per capita health care
expenditure. Later studies, however, have either utilized data for specific countries (e.g.,
Mateo (2000)) or panel data from multiple countries (e.g., Hitiris and Posnett (1992)) and
2
found, in addition to income, that other non-income factors also affect health care
expenditure.
Perhaps due to the lack of data, research on the determinants of health care in
China has been quite scarce. Previous research (e.g., Chen et al. (2004)) that has dealt
with the health sector in China has tended to employ survey approaches with simple
descriptive statistics used to measure various health outcomes (e.g., infant mortality and
life expectancy). There is one panel study most recently, Chou (2007) employed paned
data of 1978-2004 to examine the health care expenditure relationship with income and
other factors such as dependency ratio of aged population, income, the share of public
funded health expenditure, and government budget deficit. Chou used the panel Lagrange
Multiplier (LM) unit root tests to study the stationarity properties of these variables that
allow for structural changes. Based on the estimated panel cointegrated regressions, Chou
found that government budget deficits have a significant long-run impact on China’s
health care expenditure. This also provides a strong evidence of on difference between
rich and poor areas in China’s health care financing policy, and the disparities in health
service coverage in China. Although data on direct measures of health outcomes across
provinces and time is not consistently available, the Chinese government does provide
public health care expenditure data at the province level across several years.
Accordingly, we can get a sense of the extent of health outcomes and government
financing policy on health care in China by studying province-level government
expenditure on public health care.
3
The main objective of this thesis, therefore, is to investigate the relationship
between per capita public health care expenditure, per capita income, and a variety of
non-income variables, using province-level panel data over the 1995-2007 period. This
thesis differs from previous research on China in four key respects. First, we use data
across 31 provinces over a 13 year period to econometrically examine relationships
within a panel data framework, which also we apply augmented Dickey-Fuller panel unit
root tests and Granger causality tests to find non- stationarity and cointegrated
relationship existed in both public health expenditure and per capita income. Second, we
focus on government health care expenditure to not only examine the traditional health
care expenditure model, in which per capita public health care expenditure is treated as a
function of a few demand-side factors (namely, per capita income and age
demographics), but to also examine a more rich model, which includes demand-side and
supply-side factors (e.g., number of physicians and number of hospital beds) as
determinants of health care expenditure. Third, given the escalation of pollution concerns
in China over the past several years, we also consider the role pollution plays as a
determinant of health care expenditure. Fourth, unlike most studies to date, we examine
the potential endogeneity of income by not only estimating our favored model with
ordinary least squares but with two stage least squares as well.
Briefly, similar to the existing literature, we find income and age distribution play
prominent roles in determining health care expenditure in China, with the estimated
income elasticity most often falling within the inelastic range, which is similar to most
studies to date. Yet the impact of other factors (e.g., pollution) on health care
4
expenditure is most often insignificantly different from zero. Furthermore, although we
favor a two-way fixed effects specification, results are somewhat sensitive to the
functional form (i.e., linear versus double-log), panel data treatments (i.e., one-way
versus two-way effects), and estimation method (i.e., ordinary least squares versus two
stage least squares).
This thesis study is organized as follows. In Chapter 2, we discuss the historical
development of the literature, paying particular attention to differences in the data and
estimation methodologies utilized by studies over the years. This is followed by Chapter
3, which presents our two empirical specifications, which differ in terms of the number of
variables included as determinants of per capita health care expenditure. We also present
and discuss the data to be used in the estimation of our models. Chapter 4 reports the
results of the estimation of linear and double-log specifications of our two models.
Finally, the thesis concludes in Chapter 5 with a summary of our results and concluding
comments.
5
Chapter 2
LITERATURE REVIEW
This chapter reviews the historical development of the literature concerning health
care expenditure and health outcomes. As will be discussed, the literature is quite varied.
For example, it not only differs in terms of geographic focus (e.g., studies can be
categorized as having either an international, national, or regional focus), but it also
differs in terms of data utilized, be it cross-sectional, time-series, or panel, as well as
estimation procedures. Common to many of the studies mentioned, though, is the
regression of per capita health care expenditure on a variety of determinants, with per
capita income receiving the most attention in the literature.
2.1
Development of the Literature
Liviatan (1964) was the first to apply regression analysis to explain changes in
aggregate health care expenditure. In his survey of household consumption in Israel,
Liviatan studied the relationship between health care expenditure, household income, and
household size. He was the first to explicitly measure the income elasticity of health care
expenditure (defined as the ratio of the percentage change in health care expenditure to
the percentage change in income), estimating the income elasticity for Israel to be
roughly 1.30.1
1
Accordingly, Liviatan (as well as most other studies) found health care to be a normal
good. With an elasticity of less than one signifying a necessity good, and an elasticity
exceeding one signifying a luxury good, Liviatan was the first to find income elasticity
tends to increase with household size, such that for smaller families health care tends to
be a necessity whereas for larger families it tends to be a luxury.
6
After Liviatan’s study, subsequent studies sought to replicate his analysis using
more recent data or alternative empirical specifications. For instance, Pryor (1968) and
Kleiman (1974) took a demand function approach by specifying that per capita health
care expenditure in developed countries depended upon per capita income and a selection
of non-income variables, such as age and education on the demand side, and physicians
and hospital bed on the supply side. Similar to Liviatin, they found a positive relationship
between health care expenditure and income. Moreover, the estimated income elasticities
exceeded one, indicating that health care is a luxury.
It was Newhouse (1977) who, after examining the relationship between the
aggregate health care expenditure and national income, found that income played a very
strong role in determining health care expenditure. In particular, using cross-national
data from 13 developed countries, he regressed country-level per capita medical-care
expenditure on per capita GDP using both linear and double-log specifications. He found
over 90 percent of the variation in medical-care expenditure across countries could be
explained by per capita GDP. Furthermore, the income elasticity of medical-care
expenditure fell in the 1.15 - 1.31 range, which again is consistent with health care being
a luxury.2
Newhouse discussed other potential determinants of medical-care expenditure
without formally estimating regressions. For instance, Newhouse thought the price paid
2
Interestingly, Newhouse found his results were sensitive to whether or not data from
Greece was included in his regressions. In particular, given that per capita GDP in
Greece was far below the average for his sample, when Greece was excluded from the
regressions the income elasticity, as well as its significance, changed accordingly to the
lower income of Greece.
7
by consumers for medical-care probably would not affect medical-care expenditure even
though we might expect consumer price and medical-care expenditure to be correlated,
since in many countries consumer health care is subsidized by the state. Also, although
some studies suggest medical-care expenditure is lower in nations with centralized
control of health care (e.g., see Anderson (1972)), Newhouse argued such findings could
simply be due to the bleak economic outlook of such countries (i.e., lower GDP
correlates with lower medical-care expenditure).
Subsequent studies have further sought to examine nuances of health care
expenditure. For instance, similar to Newhouse, Pfaff (1990) analyzed multiple countries
by pooling (over the 1960-86 period) cross-sectional data from 12 OECD countries.
However, unlike Newhouse, Pfaff explicitly considered the impact on health care
expenditure of additional factors beyond per capita income (namely, demographics,
technological improvements, health care indicators, and differences in health care
systems across countries). Utilizing linear and double-log specifications, Pfaff's results
were similar to Newhouse in that GDP per capita remains the primary determinant of
health care expenditure, even after controlling for a variety of other determinants,
including amongst others the proportion of the population that is older, technological
improvements in health care, number of hospital beds per capita, and the number of
doctors per thousand in the population.
A criticism of Newhouse is that he failed to control for the influence on health
care expenditure of different health care systems across countries. Pfaff addressed this by
including the transfer rate (defined as the percentage of health care expenditure paid by
8
the state) and the coverage rate (defined as the percentage of the population protected by
a public scheme) as additional determinants of health care expenditure.3 In terms of these
variables, Pfaff found that countries with higher transfer rates correlated with lower
health care expenditure, and countries with higher coverage rates correlated with lower
health care expenditure. All else equal, therefore, countries with universal coverage
tended to be those with the lowest per capita health care expenditure. Accordingly, Pfaff
concluded that a greater degree of public penetration in health care offered a better
chance of controlling rising health care expenditure. The public transfer rate was found
as the key factor of improving the public health services, which reflects the importance of
examine the government spending on public health care in China and its relationship with
income in this thesis study.
Hitiris and Posnett (1992) re-examined the determinants of health care
expenditure, as well as the impact of health care expenditure on health outcomes,
utilizing a relatively larger set of panel data for 20 OECD countries over the 1960-87
period (560 observations). Similar to other studies, they estimated linear and double-log
regressions. Three key causal relationships were examined: (i) the impact of per capita
3
In general, there are three types of national health care systems in OECD countries.
The first, labeled the Beveridge model, has universal coverage for all citizens (and has
the highest share of health care expenditure borne by the state). The second, labeled the
Bismark model, has compulsory universal coverage for citizens within the framework of
"social security", and is financed by employer and employee contributions through nonprofit insurance funds (having the next highest share of health care expenditure borne by
the state). Third is the private insurance model, whereby individuals select their own
health care financing options (which has the lowest share of health care expenditures
borne by the state). Countries with the highest share of health care expenditure paid by
the state include Sweden and the United Kingdom, whereas the United States has a much
lower share of health care expenditure paid by the state.
9
GDP on per capita health care expenditure, (ii) the impact of the percent of the population
over the age of 65, the government's share of health care expenditure, and a series of
country-specific dummy variables on per capita health care expenditure, and (iii) the
impact of per capita GDP and per capita health care expenditure on the mortality rate.
Similar to earlier studies, Hitiris and Posnett estimated a strong positive impact of
per capita GDP on health care expenditure, with the income elasticity near unity.4
Regarding the non-income variables, Hitiris and Posnett found a positive impact of the
percent of the population over the age of 65 on health care expenditure, with a
corresponding elasticity equal to 0.55, and also a significant positive relationship between
health care spending and the public finance share. They then argued that the system of
health care finance and delivery could have an important influence on the demand for
health care, either directly through its effect on the efficiency of health care production
functions, or indirectly through its influence on relative prices across countries.
Accordingly, this underlies the importance of adding the government share of health care
expenditure as a determinant of overall health care spending. Furthermore, they found
the coefficients on the country dummy variables to be significantly different from zero,
which suggests there are important country-specific factors beyond the age demographic
and government share of health care expenditure that influence health care expenditure.
4
In light of using data across several countries, Hitiris and Posnett paid particular
attention to the method of converting monetary values into a common metric. For
instance, utilizing an exchange rate adjustment, the income elasticity of health care
expenditure was found to be 1.03, whereas utilizing a purchasing power parity adjustment
the income elasticity was found to be 1.16.
10
Different from earlier studies, Hitiris and Posnett also examined the influence of
factors on crude mortality rates across countries. They found crude mortality rates were
positively correlated with per capita GDP (but with a very low elasticity of 0.067 or
0.087), and negatively correlated with per capita health care expenditure. Nonetheless,
the most important determinant of mortality was the percent of the population over the
age of 65 (although the corresponding elasticity is relatively low, ranging from 0.06 to
0.09), which shows us the relationship between the percentage of age 65 and older with
the total health outcomes is not certainly clear as they expected. They suggested that such
results should be treated with caution since the results were sensitive to functional form.
However, the aging issue still might be the case to increase the health care expenditure
even though the medical technology improvement could decrease the mortality rate.
Based on the Hitiris and Posnett (1992) model, Hansen and King (1995) also used
a panel of 20 OECD country-level data over the 1960-87 period to examine further
aspects of health care expenditure. For instance, they regressed per capita health care
expenditure on per capita GDP, the percept of the population age 65 and over, the percent
of the population age 15 and younger, the share of publicly funded health care
expenditure, and a relative price index of health care spending (which was calculated as
the ratio of a health service price index to the GDP deflator). Estimating a double-log
specification with ordinary least squares (OLS), Hansen and King found results that
suggested prior results could be misleading or spurious. In particular, they suspected
potential endogeneity could exist in regressions involving health care expenditure. For
instance, it might be that health care expenditure and the share of health care expenditure
11
that is publicly funded are simultaneously determined, and so an instrumental variable
approach is warranted. Also, unlike earlier studies, Hansen and King examined
stationarity and cointegration issues by first applying augmented Dickey-Fuller (ADF)
unit root tests to test the trend of each country's health care expenditure data in the period
of 28 years, then used Engle-Granger (EG) tests to test for a stationary linear relationship
between the non-stationary variables, and estimated the model in an error-correction
mechanism (ECM). They found most of the variables to be non-stationary in levels, but
there was no evidence that cointegration between two series existed in any of those
countries. Thus, they suggested there is no long-run relationship between per capita
health care expenditure, per capita GDP, and the non-income factors, which casts doubt
on earlier findings, such as those of Hitiris and Posnett (1992).
Murthy and Ukpolo (1994, 1995) applied the Johansen maximum likelihood
techniques to investigate the case of the United States over the period of 1960-87 in an
ECM model, because they discovered that all of the variables were integrated of order
one and that a single cointegrated vector existed. Specifically, in addition to the usual
suspects (i.e., per capita income, the age structure of the population, a relative price index
of health care, and the government's share of health care expenditure), Murthy and
Ukpolo added the number of practicing physicians as a determinant of per capita U.S.
health care expenditure. Utilizing a double-log specification, they found a large share of
public financing was associated with lower per capita health care expenditure. Also, per
capita health care expenditure was found to be cointegrated with per capita income, the
age structure of the population, and the relative price of health care, in which our thesis
12
study will address those tests to see if the data set of 13 years period is long enough to
have stationarity and cointegrated issue. Interestingly, they also found that the number of
physicians raise caused a lower per capita health care expenditure (which perhaps
suggests greater physician numbers improve public health, thus reducing health care
expenditure).5
Blomqvist and Carter (1997) re-examined whether health care was a luxury or a
necessity by studying annual time series data for 24 OECD countries over the 1960-91
period. By testing for unit roots, they found both per capita health care expenditure and
per capita income exhibit non-stationarity, in other words, the data series’ mean, variance
and covariances with lagged values of itself changed over time. Thus, applying EngleGranger causality tests, they found the presence of a cointegrated relationship between
health care expenditure and income in each country. Blomqvist and Carter found the
health care expenditure-income relationship is similar across countries, with the growing
rate at roughly 2% per year, as such later we could compare with Chinese health care
expenditure growth.
Published in the same year, Hitiris (1997) used panel data for ten European Union
countries over a 34 year period and found per capita income and the dependency rate
(defined as the percentage of the population of age 65 and above or age 15 and below
divided by the population of age 15 to 64) are the primary determinants of total
5
King and Hansen (1995) examined the sensitivity of the Murthy and Ukpolo results to
different specifications of their regression model. They found the role of physician
supply depended upon model specification, which led them to conclude that a more
proper undertaking of the determinants of health care expenditure should more formally
model demand and supply in the health care market.
13
expenditure on health, although other exogenous factors (i.e., the government share of
health care expenditure and the rate of health care inflation (proxied by the GDP
deflator)) also affect health care expenditure. However, re-examining the data used by
Hitiris (1997), Roberts (1997) found the Hitiris results to be questionable in such a long
period of 34 years.6
In a follow-up study, Roberts (1999) argued there are several serious
shortcomings with much of the literature on the determinants of health care spending,
including a lack of attention to dynamics, a failure to deal with heterogeneity, and a
neglect of sensitivity testing. Using longer annual data from 20 OECD countries over the
1960-93 period, Roberts estimated a series of double-log specifications, including
amongst other factors per capita income, the percent of the population 65 years and older,
and the relative price of health care as determinants of health care expenditure.
Addressing several time-series issues, Roberts found the long-run income elasticity is
greater than one, but the results were sensitive to whether or not trends in the data were
addressed (i.e. the results are more robust when the time trend is included). Furthermore,
she found the age demographic and the government share of health care spending fail to
significantly influence short-run health care spending (although the share of government
6
Specifically, Roberts argued (i) there were errors in the dependency rate variable used
by Hitiris, (ii) the use of the GDP deflator to measure health care price inflation is
suspect, and (iii) Hitiris failed to control for unit root and cointegration issues associated
with the data (such that, in particular, the significance of the dependency rate and the rate
of inflation in the Hitiris model was simply due to them following a common stochastic
trend in 34 year period). Nonetheless, addressing these weaknesses, Roberts did confirm
the overriding importance of income as a determinant of health care spending, suggesting
that the short-run income elasticity was less than one.
14
health care spending does have a significantly positive influence on health care
expenditure in the long-run).7
With the exception of Murthy and Ukpolo's (1994, 1995) focus on the U.S.,
earlier studies of health care expenditure relied on data across countries. However, a
criticism of using such cross-sectional country-level data is that it is difficult to fully
account for country nuances when estimating health care expenditure regressions. In
response to this criticism, Matteo (2000) studied the public-private mix of Canadian
health care expenditure throughout the 1975-96 period by estimating double-log
specifications of various health care expenditure categories as dependent upon income,
percent of the population age 65 and over, federal health transfers, a dummy variable for
the Canadian Health Act, and measures of income inequality. He found the publicprivate split in health care expenditure to be particularly sensitive to per capita income,
federal health transfers, and the income share of the wealthiest quintile of the population.8
Returning to the conventional cross-country approach, Or (2000) turned the issue
around by addressing the impact of health care spending on health outcomes.
Specifically, she utilized a pooled sample of 21 OECD countries over the period of 1970-
7
Other studies (e.g., Kao and Chiang (2000); Freeman (2003)) have further explored the
time-series properties of health care expenditure by establishing the panel unit root tests.
After the dynamic OLS cointegrating regressions was used to estimate the pooled state
time series, Kao and Chiang (2000) find the income elasticity of health care expenditure
falls in the 0.82-0.84 range, thus suggesting health care is a necessity.
8
For example, he found increasing income to cause private health care spending to
increase at a faster rate relative to public health care spending. Also, public health care
spending increases with federal transfer payments, and increases in the share of income
held by the wealthiest 20% of the population leads to a reduction in public health care
expenditure.
15
92 to address medical and non-medical determinants of premature mortality (measured as
gender-specific potential years of life lost). Amongst other factors, the determinants of
premature mortality included total health care expenditure, public financing of health
care, per capita GDP, the share of white-color workers in the workforce, pollution
(proxied by per capita NOx emissions), and various lifestyle indicators (i.e., alcohol,
tobacco, fat, and sugar per capita consumption). Using a fixed effects procedure, Or
found a significantly positive relationship between health care expenditure and health,
particularly for women. Also, she not only found higher public financing of health care
improves health outcomes, but environmental factors are a more important determinant of
health outcomes than medical determinants.9
Karatzas (2000) expanded on Murthy and Ukpolo's (1994, 1995) studies by
utilizing U.S. time-series data over a 30 year period to treat per capita health care
expenditure as a function of a health care price index, income distribution, age
distribution, the number of hospital beds, the number of physicians and nurses, and city
size. Karatzas found the health care price index has a significantly negative impact on
health care expenditure, income distribution has positive impact on health care
expenditure, and the number of practicing physicians also has a positive impact on health
care expenditure. Furthermore, the number of hospital beds played a significantly
negative role in overall health care expenditure. Interestingly, quite different from other
9
Of importance to this thesis, a limitation of Or's (1999) study is that it may be that other
measures of pollution, such as SO2 and particulate matter, also affect health outcomes
(and consequently health care expenditure). See Crémieux et al. (1999, 2005) for other
studies that measure the impact of health care spending on health outcomes.
16
studies, she also found both age distribution and city size play insignificant roles in
determining health care expenditure.10
Bilgel (2003) used time-series data for Turkey throughout the 1927-96 period to
study the impact of income, age distribution (i.e., percent of the population 65 years and
older and percent of the population 15 years and younger), and education (i.e., percent of
the population with a high school degree and percent of the population with a college
degree) on health care expenditure. Results indicated an income elasticity of health care
expenditure of approximately 0.75. Most interesting, though, Bilgel (2003) mentioned a
few limitations of the study, as well as others, that are relevant to this thesis. First, due to
data limitations, many supply-side determinants of health care expenditure (e.g.,
physician supply, nurse supply, and number of hospital beds) are often ignored. Second,
although the literature is largely silent on this, there may be potential endogeneity in
models of health care expenditure. For instance, as will be argued in the next chapter, it
may be that income is endogenous in the model (i.e., health care spending affects
income), which needs to be more fully addressed in the literature. Third, the stationarity
10
A number of other studies have focused on the role of age distribution in determining
health care spending. For example, Zweifel et al. (1999) applied a demand function
approach to health care expenditure utilizing quarterly longitudinal data from surveys
conducted over the 1983-94 period. The demand for health care was treated as a function
of age, age-squared, gender, insurance mechanisms, and time dummy variables. Rather
than merely dependent upon age distribution, Zweifel et al. (1999) found that health care
expenditure escalates as individuals approach the time of their death. However, there
continues to be debate in the literature on the impact of age distribution on health care
expenditures, as Seshamani et al. (2004) are critical of many of the modeling procedures
utilized by Zweifel et al. (1999).
17
of these variables should to be addressed when utilizing time-series data such as health
care expenditure.
Matteo (2003) sought to examine the sensitivity of the health care expenditure
regression results to different data constructs by comparing results across three data sets:
(i) U.S. state-level data for the 1980-97 period, (ii) Canadian province-level data for the
1965-2000 period, and (iii) national-level data for 16 OECD countries over the 1960-97
period. He found results were sensitive to whether parametric or nonparametric methods
were used. Also, because of the relatively short time periods studied, the power of unit
root tests for stationarity is rather weak. Consequently, he argued that until time-series
data of significant length is available, researchers should be less concerned with potential
unit roots in their data. Overall, he found that for the United States, Canada, and the
OECD countries health care spending is relatively income elastic at lower levels of
income but more inelastic at higher-levels of income.
In his most recent study of this issue, Matteo (2004) followed his 2003 study with
a deeper panel data analysis of U.S. state-level (for the 1980-98 period) and Canadian
province-level (for the 1975-2000 period) per capita health care expenditure. Variables
included as determinants of per capita health care expenditure were, amongst others, per
capita income, age distribution, and a series of time effects (which he argued proxy for
technological advances). Matteo (2004) found results similar to prior studies. For
example, per capita health care expenditure is positively correlated with per capita
income, the percent of the population that is old (the population of age 65-74), and time.
However, results are sensitive to the model specification. For example, in simple models
18
with income and age distribution as the only determinants of health care expenditure, the
role of age plays a very prominent role. Yet when other variables are included in the
model (e.g., time) the impact of age on health care expenditure diminishes.
2.2
Health Care and Health Outcomes in China
Since the focus of this thesis is on health care expenditure in China, a brief review
of some of the literature on health in China is in order. In China, although most research
on health care and health outcomes has used national level data, there are a few papers
that have used province-level panel data to look at the impact of fiscal decentralization of
health policy, as well as to examine the fiscal relationship between central and provincial
governments in China (e.g., Kanbur and Zhang, 2003; Jin et al., 2005; Uchimura and
Jutting, 2007). For instance, Uchimura and Jutting (2007) used province-level panel data
to analyze the effect of fiscal decentralization on health outcomes in China during the
period of 1995 to 2001. They found that more decentralized provinces perform better
with respect of health outcomes if two conditions are met: (i) a functioning transfer
system exists between the provinces and the national government and (ii) the central
government has its own fiscal capacity. Even though it is indirectly related to this thesis
study of public health expenditure, what importance is that we know government
spending on public health is the determinant of health outcomes, through increasing the
supply of the health and medical services.
Although analyzing the determinants of health outcomes is not directly
undertaken in this thesis, there are two important factors resulting from such studies of
China that are appropriate to this thesis. First, these studies suggest there are severe
19
province-level data limitations in regards to China. Second, Uchimura and Jutting (2007)
argue that it is necessary for China to improve the delivery of health care to its citizens as
a self-chosen means of securing a harmonious society. Indeed, this is particularly
noticeable in poor and remote areas that continue to have limited access to basic social
services, for instance, Henan and Guizhou provinces and also Tibet and Xingjiang
autonomous regions.
Not only has China’s economy rapidly developed in the past three decades, but so
has pollution, with growing concern that such pollution will worsen health outcomes. In
a recent study, Chen et al. (2004) used time-series data for a 10 year period to study the
relationship between ambient air quality and various health outcome measures (i.e.,
mortality rates, morbidity rates, and lung cancer rates) for a number of cities in China.
They found that ambient air pollution had acute and chronic effects on mortality,
morbidity, hospital admissions, and lung function. Accordingly, a central feature of the
model to be discussed in the next chapter will be to evaluate the impact of pollution on
health care expenditure.
Most recently, two studies (i.e., Yu and Chu, 2007; Shiu and Chiu, 2008)
examined health care expenditure in Taiwan using annual time-series data from the last
40-plus years. Yu and Chu (2007) applied both a demand and supply approach to
modeling health care expenditure and found the income elasticity of health care
expenditure in Taiwan is less than one. However, Shiu and Chiu (2008) added variables
(i.e., age distribution, life expectancy (used as a proxy for proximity to death), and
number of physicians) as further determinants of health care expenditure. While they
20
found age distribution has little impact on health care expenditure, the coefficients of the
other variables are consistent with previous research.
Lastly, one study from mainland China (Chou, 2007) investigated the regional
health care expenditure relationship with income, age distribution and other factors such
as government budget deficit. Chou (2007) used panel data in logarithmic form cross 28
provinces during the period of 1978 to 2004, and also applied the panel Lagrange
Multiplier unit root tests that allow for structural changes, to estimate panel cointegrated
regressions on regional health expenditures and its determinants. To perform the LM unit
root tests, they employed finite-sample critical values derived through the bootstrap
method instead of relying on the critical values from asymptotic normal distribution.
Interestingly, they found that health expenditure per capita, income (measured by
provincial GDP per capita), the dependency ratio of old aged population, the share of
public funded health expenditure, and the regional government budget deficit are nonstationary even though the time period is over 26 years. The only stationarity existed in
the variable of the proportion of population of age 65 and older. They found that factors
of income, and old aged dependency ratio, have the positive impact on health expenditure
consistent with previous researches. Without considering the correlation of income, most
interestingly, regional government budget deficits were founded to have significant longrun impact on regional health care expenditures.
Review of all those previous papers, this thesis study is focus on Chinese
government expenditure on public health for its own consistency and representatively
reflect to the characteristic of Chinese socialistic structure. Based on the same theory of
21
most of previous studies, which is the theory of production function of health care from
both demand and supply side, this study is to examine the relationship between the
government expenditure on public health with its determinants which considered as per
capita income, age distribution, education, the number of physicians, the number of
hospital beds, the indicator of air pollution and the population density as well. Not only
we will test the unit root and cointegrated issue of both per capita income and public
health expenditure, but also we will exam the endogeneity issue between income and
public health care expenditure in the following chapters.
22
Chapter 3
EMPIRICAL MODEL AND DATA
This chapter discusses the empirical model and data used for its estimation. As
mentioned in Chapter 1, because of the limited availability of data from China at the
province level, there are a limited number of determinants of health care expenditure that
we are able to include in the empirical specification.
3.1
Empirical Model
There are two approaches typically taken in the literature when studying the
determinants of health care expenditure. The first focuses on a few demand-side
determinants (e.g., per capita income and age demographics), while the second introduces
additional factors, such as supply-side determinants (e.g., number of physicians and
number of hospital beds), into the model as well. As mentioned in the last chapter,
studies also typically estimate linear and/or double-log specifications. Accordingly, in
this thesis we estimate a variety of specifications of the determinants of per capita health
care expenditure, utilizing linear and double-log functional forms.
The first specification (labeled Model 1) is our baseline model which mimics
earlier studies of health care expenditure by focusing on a few demand-side determinants.
Specifically, this specification in its linear form is given as:
(1)
HCEit = β0 + β1GPPit + β2P65it + β3P15it + φi + δt + uit,
23
where HCEit is per capita government expenditure on public health care in province i for
year t.11 GPPit is per capita gross province product in province i for year t, P65it is the
percent of the population 65 years or older in province i for year t, and P15it is the percent
of the population 15 years or younger in province i for year t. Given that we will
estimate equations with panel data, we account for the possibility of province and/or time
effects in the model by including the vectors φi and δt, respectively.12 Finally, uit is a
normally distributed error term with zero mean and variance σ2u.
The second specification (labeled Model 2) is a more rich version of the baseline
model in that a few additional factors from both demand-side and supply-side are also
included as determinants of per capita government expenditure on public health care.
The model 2 specification in its linear form is given as:
(2) HCEit = β0 + β1GPPit + β2P65it + β3P15it + β4EDUit + β5DOCit + β6BEDit + β7DENit
+β8AIRit + φi + δt + µit,
where added to the baseline model of equation (1) are the percent of the population in
province i for time t that are college graduates (EDUit), the number of practicing
physicians per thousand population in province i for year t (DOCit), the number of
hospital beds per thousand population in province i for year t (BEDit), the population
11
Unfortunately, data on total health care spending (i.e., the sum of public and private
health care spending) is not consistently available at the province level for the years of
study. Also the regional difference in health care system devaluated the private health
data. Hence, we use per capita government expenditure on public health care as the
dependent variable throughout this thesis.
12
The province effects control for additional factors that vary across provinces but not
across time (e.g., various demographic measures); whereas the time effects control for
additional factors that vary across time but not across provinces (e.g., policies of the
national government).
24
density (DENit) as measured by the number of persons per square kilometer in province i
for year t, and a measure of air pollution (i.e., per capita industrial waste gas in cubic
meter, AIRit) in province i for year t.13
Concerning the coefficients in equations (1) and (2), we expect income and
education to have positive impacts on government expenditure on public health care (i.e.,
β1 and β4 greater than zero). Fuchs (1993) defined, “economics is an intellectual
middleman between nature and technology on the supply side and the preferences and
desires of individuals and society on the demand side. Health, in various forms, enters on
both sides of the equation.” (Fuchs, 1993, p19) the higher income and education demand
greater care of health, therefore, increasing the health care expenditure. As for
population demographics, we not only expect higher health care costs to be associated
with a higher percent of the population that is age 65 and older (i.e., β2 > 0), which is
intuitive since health problems tend to occur late in life (see Bilgel, 2003; Mateo, 2004),
but we also expect higher health care cost to be associated with a higher percent of the
population that is 15 years and younger (i.e., β3 > 0).14 We hold no expectation
13
It might be more appropriate to include more narrow measures of air pollution, such as
particulate matter less than 10 microns in diameter (PM10), which Peng et al. (2002)
suggest has a particularly adverse effect on morbidity and mortality, but data on such
measures are not consistently available across provinces and time. In keeping with other
studies (i.e., Crémieux et al., 1999; Crémieux et al., 2005) that find population density
affects health outcomes (namely infant mortality and life expectancy), which could then
affect health care expenditure, population density is included as an additional determinant
of health care spending. Unfortunately, though, because of limited availability of data
some additional factors, such as a price index of health care, could not be included in the
model.
14
There are different arguments for why the percent of the population 15 years and
younger might have a positive impact on health care expenditure. For instance, since this
25
concerning the sign of the coefficient of population density, for it could be negative or
positive (e.g., it could be that higher population density correlates with greater demand
for health care, thus increasing government expenditure on public health care; or it could
be public health care is more efficient in provinces with greater population density, thus
decreasing per capita government expenditure on public health care). We do expect,
though, that the demand for health care will be greater in provinces with higher air
pollution levels, thus increasing government expenditure on public health care (i.e., β8 >
0). Lastly, increasing the number of physicians and hospital beds will increase the supply
of health care, which could increase or decrease per capita government expenditure on
public health care depending on the price elasticity of health care demand (increase with
inelastic of demand and decrease with elastic price of demand).
In both models 1 and 2, however, there is the potential for per capita income to be
endogenous. It could be, for example, that the more the government spends on health
care the greater is the productivity of labor, which could then affect per capita income.15
To address this issue, we not only estimate models 1 and 2 using ordinary least squares
(OLS), but we also estimate them using two stage least squares (2SLS). For the 2SLS
regressions, we use as instrumental variables per capita turnover of highway freight
(HWFRE) and per capita constructed floor space (FLSP). Although we will evaluate the
age range covers the very young (i.e., age 0 to 5), who have higher rates of morbidity
compared to mid-life age groups, this could correlate with higher health care expenditure
for the percent of the population 15 years and younger.
15
Interestingly, a recent study by Suhrcke and Urban (2009) finds that higher rates of
cardiovascular disease reduce GDP growth. In a similar light, lower health care spending
could reduce health outcomes (e.g., higher rates of morbidity and mortality), and thus
reduce per capita province income.
26
quality of these instruments in the next chapter, we expect them to be correlated with per
capita income (GPP) but not the error term, since Chinese economy and its GDP is based
on the exports and consumption pulled from the real estate expansion, which can be
measured or reflected by the highway freight turnover and yearly completed floor space
accordingly. We could have used some indicator or instrumental variable of investment if
we can find data on investment.
3.2
Data
Annual data are used to estimate equations (1) and (2) which cover all 31
provinces over the 1995-2007 period. Since there are a few missing observations,
however, we are working with an unbalanced panel data set consisting of 400
observations. All of the data come from the China Statistical Yearbook (various years).
See Table 3.1 for the variable names and definitions.
Table 3.1 Variable Definitions
Variables Definition
Dependent Variable:
Per capita government health care expenditure (CPI deflated)
HCE
Explanatory Variables:
GPP
Per capita gross province product (CPI deflated)
P65
Percent of population aged 65 and older
P15
Percent of population aged 15 and younger
EDU
Percent of population with college degree
BED
Number of hospital beds per thousand population
DOC
Number of physicians per thousand population
DEN
Number of persons per square kilometer
AIR
Per capita waste industrial gas in cubic meter
Instrumental Variables:
HWFRE
FLSP
Per capita regional turnover of highway freight (10,000 tons/km)
Per capita completed floor space by region (square meters)
27
Table 3.2 provides the means, standard deviations, as well as minimum and
maximum values, of the different variables defined in Table 3.1. Also Table 3.3 provides
the same descriptive statistic information but with more informative in terms of the great
changes between the data span over the years (shows comparable year of 1995, 2001, and
2007). In terms of the dependent variable, for example, there is much disparity in its
values, as the minimum per capita health care expenditure is 3.77 (corresponding to Inner
Mongolia in 1996) while the maximum per capita health care expenditure is 642.92
(corresponding to Beijing in 2007).
Table 3.2 Descriptive Statisticsa
Standard Deviation
Variable
Mean
76.39
HCE
79.47
8565.46
GPP
9778.01
1.98
P65
7.69
5.41
P15
22.42
0.15
EDU
0.15
2.19
BED
2.62
1.4
DOC
3.92
473.5
DEN
368.91
131.07
AIR
168.29
0.03
HWFRE
0.04
0.37
FLSP
0.31
a
Minimum
3.77
1587.51
3.57
7.97
0.02
1.09
1.7
1.97
4.03
0
0.01
Maximum
642.92
58063.87
16.37
35.39
0.87
42.63
9.6
2996.77
768.25
0.23
2.45
All monetary values are in Yuan.
As shows in Table 3.3, it is surprise to see that year 2001 was the lower point of
public health expenditure to compare with year 1995 and 2007, which we might have to
credit to the post impact of “1997 Asian Financial Crisis” or other factors such as
political policy reform, which caused the lowest government spending on public health
per capita in the year 2001 at 54.54 Yuan while 116.19 Yuan in the year of 1996. But
28
what interesting is that the mean of per capita income in 2001 is 8538.71 compare to
4690.67 Yuan in 1995, which is not matching or supporting to the lowest point of public
health expenditure in 2001.
29
As further information concerning per capita health care expenditure, Figure 3.1
plots the mean province values of HCE for each year of the sample. As the figure
illustrates, per capita health care expenditure again fell from 1995 to 1997 (coinciding
again with the 1997 Asian financial crisis), but has steadily increased since 1998.
Figure 3.1 The Trend of Public Health Expenditure in China in Chinese Yuan
200
160
120
80
40
0
1996
1998
2000
2002
2004
2006
To gauge differences in health care expenditure across China (additionally we
used unbalanced data set rural health expenditure with few missed data in some regions
such as Tibet, Chongqin, or Xingjiang), Figure 3.2 plots mean province values of per
capita rural and urban household expenditure on health and medical services over time.
As the figure illustrates, although health care spending has steadily increased for urban
30
and rural households, urban health care expenditure is higher and increasing at a faster
rate compared to rural health care expenditure.
Figure 3.2 Real Per Capita Rural and Urban Health Expenditure in Chinese Yuan
600
500
400
300
200
100
0
1996
1998
2000
2002
2004
2006
Mean of Real Rural HCE
Mean of Real Urban HCE
Turning to per capita income, while the mean level of GPP is 9778.01, there is
also great disparity across provinces in China, with the lowest value being 1587.51
(corresponding to Guizhou in 1998) and the highest value being 58,063.87
(corresponding to Shanghai in 2007).16 In Figure 3.3, per capita income for China over
the 1978-2007 is plotted, illustrating a rapid (i.e., nearly 50-fold) increase in the past 30
16
The ratio of the mean of HCE to the mean of GPP is less than 0.01, indicating that
the government spends less than 1% of province income on average for health care. As
Schieber et al. (1991) found, OECD countries on average spend about 8% of their GDP
on health. This suggests China is below other nations in terms of its contribution to
public health.
31
years.17 However, as illustrated in Figure 3.4, over the period of this study (1995-2007),
the increase in per capita income is roughly 4-fold.
Figure 3.3 Per Capita GPP in China from 1978 to 2007 in Chinese Yuan
20,000
16,000
12,000
8,000
4,000
0
1980
1985
1990
1995
2000
2005
As evidence of disparities in income across China, Figure 3.5 plots rural per capita
income for each year. Compared to Figure 3.4, while rural income has steadily increased
over time, it is roughly one-fifth that of the per capita income for all groups in China.
17
The dramatic economic growth over the past 30 years is due to a number of economic
policies adopted since 1978. In particular, the "open economy" policy launched by Deng
Xiaoping in 1978 promoted growth by allowing Chinese enterprises to trade with western
countries. In 1990, the ability of the Chinese population to lease property was expanded
(i.e., residential property can now be leased for 70 years and commercial property can
now be leased for 90 years). Lastly, the Chinese government has opened a stock market
allowing foreign investment into the economy. (Starr, 2001, p73.)
32
Figure 3.4 Mean of Real Per Capita GPP of 31 Provinces in Chinese Yuan
16,000
12,000
8,000
4,000
0
1996
1998
2000
2002
2004
2006
Figure 3.5 Mean Province Vaule of Real Per Capita Rural Income in Chinese Yuan
5,000
4,000
3,000
2,000
1,000
0
1996
1998
2000
2002
2004
2006
33
Figure 3.6 Mean Province Value of Per Capita Waste Gas in Cubic Meter
350
300
250
200
150
100
50
0
1996
1998
2000
2002
2004
2006
Lastly, since China’s economy has developed rapidly in recent decades, while the
economic growth linked to the the increasing of energy consumption for the industry and
as well as consumption emission of urbanization, therefore, an important element of this
thesis is to address the impact of pollution on public health care expenditure in China, in
Figure 3.6 we plot the mean of per capita province industrial waste gas for each year of
the sample. As the figure shows, similar to the growth of income, industrial waste gas
has generally increased over the period of analysis.
34
Chapter 4
ESTIMATION RESULTS
This chapter presents results from the estimation of the linear and double-log
specifications of models 1 and 2 presented in the last chapter. Before presenting the
results, a few more details should suffice.
4.1
Estimation Issues
As mentioned in the last chapter, the first specification (labeled Model 1) is our
baseline model which is similar to early studies that regressed health care expenditure
solely on a few demand-side determinants. Model 2, though, is a more rich version of the
baseline model in that it introduces a few more determinants of health care expenditure.
For each model, since it is being estimated with panel data (i.e., 13 years of data across
31 provinces), we estimate different fixed effects versions. Specifically, we consider four
separate specifications, one that simply pools the data without incorporating fixed effects
(labeled pooled in the tables), one that only includes province fixed effects (labeled
province-fixed in the tables), one that only includes time fixed effects (labeled time-fixed
in the tables), and one that includes both province and time fixed effects (labeled twoway fixed in the tables). Accordingly, we compare results across these specifications to
see how sensitive the results are to panel estimation issues.
For our preferred fixed effects specification, we also estimate a two stage least
squares version (labeled 2SLS in the tables) to account for the possibility that per capita
province income (GPP) is endogenous. As mentioned previously, per capita highway
traffic turnover (HWFRE) and completed floor space (FLSP) are used as instrumental
35
variables.18 We expect each of these variables to be positively correlated with GPP but
uncorrelated with the error term. Nonetheless, in the sections that follow we discuss a
number of tests performed to assess the validity of the 2SLS procedure.
Due to the modest time series length of our data, we do not expect to find nonstationarity concerns. Nonetheless, in keeping with the existing literature, we did test for
the presence of a unit root in the per capita health care expenditure variable using the
Levin, Lin and Chu (2002), Im, Pesaran and Shin (2003) procedures.
4.2 Model 1 Estimation Results
Tables 4.1 and 4.2 present the linear and double-log results of the estimation of
model 1.
18
In the 2SLS procedure, the first stage regression involves regressing GPP on HWFRE
and FLSP, as well as the exogenous variables included in models 1 and 2. In the second
stage regression, instead of GPP, predicted GPP (constructed from the first stage
regression) is included as a regressor in models 1 and 2. And the reason for using per
capita highway traffic turnover and completed floor space as instrumental variables is
that they are able to reflect the main factors of GDP growth in China, which is export
industry, real estate as the leader to pull up the consumption of Chinese in whole, of
course reflect the government spending on the infrastructures in China. However, it is
still the data limitation to find valid instruments.
36
Table 4.1 Model 1 Linear Results
ProvinceVariable
Pooled
fixed
GPP
0.009*** 0.006***
-0.001
-0.001
p65
- 7.429*** 2.572
-2.808
-3.535
p15
2.983*** 3.123*
-0.787
-1.68
Adjusted R2
0.492
0.694
Overall fit (F)
41.72
13.89
Timefixed
0.008***
-0.001
-7.571***
-2.536
1.662**
-0.692
0.611
17.51
TW
Fixed
0.003***
-0.001
-3.935
-3.919
0.932
-1.596
0.853
37.8
Redundant FE χ2
33.862
24.13
22.022
Hausman RE χ
2
11.539
Hausman Endogeneity (χ )
2
2SLS
0.004***
-0.001
-3.669
-3.833
1.129
-1.43
0.852
0.318
83.62
Stage 1 fit (F)
2.04
*** significance at the 1% level; ** significance at the 5% level; * significance at the
10% level. Heteroskedasticity and autocorrelation (HAC) robust standard errors are
shown in parentheses below the estimated coefficients
Considering the ordinary least squares (OLS) results for the linear version (i.e.,
columns 1 - 4 of Table 4.1), as indicated at the bottom of Table 4.1 the adjusted Rsquared value is modest for the regression without any fixed effects (see column 1), but
increases as additional fixed effects are incorporated into the specification. Across the
OLS regressions, the F-statistic rejects the null for each specification (i.e., the overall fit
is significant). Due to problems in constructing the variance-covariance matrix in the
random effects regression, however, we are only able to evaluate random versus fixed
effects from the specification that only includes province fixed effects. As indicated, the
Hausman RE test does favor the fixed effects specification over the random effects
specification. Moreover, across all three fixed effects regressions, likelihood ratio tests
37
of the fixed effects coefficients (i.e., redundant FE) supports them being jointly
significant, and so across the OLS results we favor the two-way fixed effects results in
column 4 (since the province and time effects are jointly significant). As for the 2SLS
results, which are the counterpart to the preferred two-way fixed effects results of column
4, while the adjusted R-squared value is higher compared to the OLS results, the
Hausman endogeneity test fails to reject the null, thus suggesting that GPP is exogenous
in the model.19
Turning to the individual coefficients of the linear model, across all of the
regressions the results are consistent with other studies in that we find per capita income
has a significantly positive impact on health care expenditure. Unexpectedly, though, for
the pooled and time-fixed regressions, the coefficient of P65 is significantly negative,
which is counter to much of the evidence to date. Lastly, across three of the five
regressions, P15 has a significantly positive effect on health care expenditure.
19
Furthermore, although we find the instruments are highly correlated with GPP (as
evidenced by the high F-statistic associated with the stage 1 regression), the Sargan test
of over-identifying restrictions does reject the null, which does not favor exogeneity of
the instruments. Unfortunately, though, we were unable to find data on a more
appropriate set of instrumental variables.
38
Table 4.2 Model 1 Double-Log Results
ProvinceVariable
Pooled
fixed
Log of GPP
0.910*** 0.699***
-0.08
-0.097
Log of P65
-0.847*** 0.409
-0.182
-0.282
Log of P15
0.172
0.767***
-0.216
-0.278
Adjusted R2
0.371
0.626
Overall fit (F)
73.67
26.83
59.172
Redundant FE (χ2)
9.301
Hausman RE (χ2)
Timefixed
0.952***
-0.063
-0.879***
-0.133
0.121
-0.159
0.659
58.73
156.143
TW
Fixed
0.757***
-0.202
0.596***
-0.173
0.283**
-0.117
0.938
379.85
88.213
2SLS
2.17**
-0.922
0.717***
-0.186
0.587**
-0.258
0.918
Hausman Endogeneity (χ2)
Stage 1 fit (F)
Sargan (χ2)
3.605
4.726
2.102
*** significance at the 1% level; ** significance at the 5% level; * significance at the
10% level. Heteroskedasticity and autocorrelation (HAC) robust standard errors are
shown in parentheses below the estimated coefficients
The results shown in Table 4.2 for the double-log specification of model 1 are
similar to the linear version in a number of ways. First, adjusted R-squared increases as
additional fixed effects are added to the model and are consistent with linear results of
model 1. Second, the overall specification, as well as the fixed effects, are jointly
significant. Third, the Hausman RE tests favors fixed over random effects.20 Fourth,
similar to the linear case, increases in per capita income and the percent of the population
15 years and younger increase per capita health care expenditure. Indeed, given the
20
However, unlike the linear results, the Hausman endogeneity test favors GPP being
endogenous (although at the 10% level of significance). Moreover, the stage 1 regression
of 2SLS is jointly significant (i.e., the instruments are correlated with GPP) and the
Sargan test fails to reject the null of instrument exogeneity in this case.
39
coefficient of the log of GPP is the income elasticity, which is defined as the percentage
change in health expenditures in response to a give percentage change in income, with
the exception of the 2SLS results the income elasticity falls in the inelastic range. Lastly,
we do find the results are sensitive to model specification, as (i) the sign and significance
of the coefficient of the log of P65 is significantly negative (positive) in the pooled and
time-fixed (two-way fixed and 2SLS) regressions and (ii) the estimated income elasticity
in the 2SLS regression is over twice as large as its counterparts in the OLS regressions.
4.3
Model 2 Estimation Results
To further address the potential issue of omitted variable bias in the baseline
regressions, in model 2 we add additional variables controlling for education, pollution,
population density, and supply-side factors (i.e., hospital beds and physicians). The
linear results are presented in Table 4.3, while the double-log results are presented in
Table 4.4.
40
Table 4.3 Model 2 Linear Results
ProvinceVariable
Pooled
fixed
GPP
0.006***
0.005**
-0.001
-0.002
P65
-2.81
-8.318**
-2.256
-3.654
P15
5.827***
4.221***
-0.906
-1.594
EDU
198.730*** 257.743***
-37.749
-52.666
AIR
-0.052
0.002
-0.038
-0.052
DEN
-0.004
-0.230*
-0.014
-0.138
BED
-0.628
-0.483
-0.805
-0.479
DOC
16.195*** -3.027
-2.816
-8.558
Adjusted R2
0.603
0.759
Overall fit (F)
31.44
18.1
Redundant FE(χ2 )
14.362
Hausman RE(χ2)
26.438
Hausman Endogeneity (χ2)
Stage 1 fit (F)
Timefixed
0.006***
-0.001
-4.703
-2.555
4.488
-0.879
15.378
-62.215
-0.13
-0.047
0.015
-0.014
-0.992
-0.333
25.911
-3.115
0.729
24.83
28.615
TW
Fixed
0.006***
-0.002
-5.161
-3.71
1.198
-1.552
-55.417
-69.026
-0.096**
-0.048
-0.209*
-0.108
-0.334
-0.318
-12.762
-8.481
0.879
45.78
18.905
2SLS
0.007***
-0.002
-4.777*
-2.848
1.345
-1.051
-85.685
-52.434
-0.102***
-0.026
-0.256***
-0.071
-0.364
-0.706
-12.799**
-5.138
0.974
0.886
34.369
Sargan (χ2)
2.68
*** significance at the 1% level; ** significance at the 5% level; * significance at the
10% level. Heteroskedasticity and autocorrelation (HAC) robust standard errors are
shown in parentheses below the estimated coefficients
Comparing the results of Table 4.1 to Table 4.3, adding a few more variables as
determinants of per capita health care expenditure does increase adjusted R-squared
somewhat across the various regressions. Furthermore, we continue to favor fixed effects
over random effects, the Hausman endogeneity test continues to favor treating per capita
41
province income as exogenous in the model, and the fixed effects are consistently
significant across the OLS regressions (and so the two-way fixed effects is our preferred
specification).21 As for the estimated coefficients, while the income and age-related
coefficients are similar to those provided in Table 4.1 (e.g., income continues to have a
positive and significant impact on health care expenditure), the coefficients of the other
variables are mixed. For instance, across three of the four sets of OLS results, as
expected education has a positive impact on health care expenditure (although only
significantly so in the first two regressions), whereas for the coefficients of the remaining
variables they are largely insignificant.22 Accordingly, this does not lend support to those
studies (e.g., Newhouse, 1977; Hitiris and Posnett, 1992) that argued income and age
demographics play the greatest role in determining health care expenditure.
The double-log results presented in Table 4.4 are quite similar to those obtained in
the baseline model. For instance, with the exception of the province-fixed results, the
income elasticity is positive, although compared to the results in Table 4.2 it is somewhat
lower than those obtained in the baseline model. Also, consistent with Table 4.2, we find
the percent of the population 15 years and younger has a significantly positive (across all
regressions) impact on health care expenditure, whereas the impact of the population 65
and over is mixed.
21
The Sargan test results in Table 4.3 fail to reject the null of instrument irrelevant.
A greater share of the coefficients are significantly different from zero for the 2SLS
counterpart to the two-way fixed effects specification. However, as mentioned, the
Hausman test favors OLS over 2SLS. Moreover, some of the results are inconsistent
with our expectations (e.g., in the 2SLS regression air pollution has a negative impact on
health care expenditure).
22
42
Table 4.4 Model 2 Double-Log Results
ProvinceTwo-way
Variable
Pooled
fixed
Time-fixed
Fixed
2SLS
Log of GPP
0.599*** -0.272*
0.764***
0.732***
2.385*
-0.084
-0.161
-0.057
-0.205
-1.29
Log of P65
0.229
-0.858*** 0.376***
0.524***
0.618***
-0.225
-0.275
-0.146
-0.159
-0.221
Log of P15
0.842*** 0.869***
0.436***
0.216**
0.643*
-0.227
-0.259
-0.176
-0.11
-0.338
Log of EDU
0.299*** 0.976***
0.029
0.139**
0.013
-0.074
-0.129
-0.078
-0.065
-0.102
Log of AIR
-0.13*** 0.169
-0.14***
0.051
0.042
-0.043
-0.143
-0.037
-0.067
-0.059
Log of DEN
-0.14*** -0.222
-0.171***
-0.133
-0.338
-0.032
-0.692
-0.029
-0.142
-0.241
Log of BED
0.405
0.143
0.146
-0.013
-0.179
-0.28
-0.176
-0.169
-0.028
-0.136
Log of DOC
0.39
-0.652**
0.586***
-0.163
-0.19
-0.249
-0.321
-0.144
-0.148
-0.177
Adjusted Rsquared
0.536
0.751
0.791
0.946
0.913
Overall fit (F)
547.47
40.09
76.88
329.03
Redundant FE (χ2)
33.777
105.922
63.686
Hausman RE (χ2)
45.422
Hausman Endogeneity (χ2)
5.283
Stage 1 fit (F)
5.286
Sargan (χ2)
1.544
*** significance at the 1% level; ** significance at the 5% level; * significance at the
10% level. Heteroskedasticity and autocorrelation (HAC) robust standard errors are
shown in parentheses below the estimated coefficients
Quite different from Table 4.3, a greater share of the coefficients are significantly
different from zero in the double-log regressions. For instance, as expected education
continues to have a positive impact on health care expenditure (now significantly so in
three of the five regressions). Unexpectedly, though, waste gas emissions continue to
have a negative impact on health care expenditure on pooled and time-fixed effects
43
specifications, numbers of hospital beds fail to significantly affect public health care
spending, and the coefficients of physician counts is sensitive to specification. Lastly,
similar to the double-log results of model 1, the Hausman test supports province income
being endogenous, and (i) the instruments are relevant correlated with per capita income
and (ii) the Sargan test favors these instruments being exogeneous.23
As further indication of variation in health care expenditure in the data, in Table
4.5 we report the estimated coefficients of the province dummy variables for the linear
versions of models 1 and 2, and in Table 4.6 we report the estimated coefficients of the
year dummy variables for the linear version of model 1 and 2. As Table 4.5 shows, for
similar values of the regressors, based on the coefficients of the province dummy
variables in model 1, Beijing has the highest per capita public health expenditure,
followed by Tibet and Shanghai; whereas Henan province spends the least per capita on
public health. For model 2, while Beijing continues to have the highest per capita health
care expenditure, the results are somewhat different from model 1 in that Tianjin has the
next highest per capita health care spending and Gansu has the lowest per capita health
care spending. As indicated in table 4.6, for similar values of the regressors, in both
models 1 and 2 per capita health care spending has trended upward since the late 1990s
(as the coefficients decrease in absolute value as time progresses, and eventually become
positive in 2007).
23
The elasticity of health care spending with respect to P65 falls in the inelastic range,
which is similar to the findings of Hitiris and Posnett (1992). Also, the negative
coefficient estimates of DOC are consistent with Murthy and Ukpolo (1994, 1995) who
also found a negative impact of physician numbers on health care spending.
44
Table 4.5 Province- Fixed Effects Coefficients from Linear Versions of Models 1 and 2
Model 1
Model 2
Provinces
Coefficient
Standard Error
Coefficient
Standard Error
Beijing
Tianjin
-138.38***
(22.62)
-143.47***
(25.58)
Hebei
-200.72***
(27.08)
-348.77***
(76.52)
Shanxi
-185.59***
(29.76)
-337.75***
(77.91)
Inner Mongolia
-188.87***
(29.17)
-387.23***
(100.75)
Liaoning
-183.38***
(22.76)
-315.71***
(68.96)
Jilin
-180.12***
(27.33)
-356.99***
(87.83)
Heilongjiang
-191.22***
(28.40)
-398.09***
(100.52)
Shanghai
-62.74***
(36.59)
-273.79***
(189.11)
Jiangsu
-184.13***
(22.13)
-270.46***
(57.85)
Zhejiang
-169.41***
(21.09)
-313.22***
(72.27)
Anhui
-201.38***
(27.75)
-349.46***
(76.33)
Fujian
-192.91***
(26.86)
-384.07***
(89.58)
Jiangxi
-199.54***
(29.95)
-385.22***
(87.57)
Shandong
-202.00***
(24.29)
-312.83***
(64.71)
Henan
-207.78***
(28.69)
-328.17***
(68.49)
Hubei
-197.56***
(27.05)
-354.49***
(75.85)
Hunan
-201.52***
(25.87)
-370.20***
(81.37)
Guangdong
-184.89***
(30.44)
-346.34***
(77.74)
Guangxi
-193.61***
(28.44)
-388.24***
(91.70)
Hainan
-183.11***
(30.43)
-366.55***
(85.72)
chongqing
-187.69***
(24.72)
-346.37***
(78.66)
Sichuan
-190.21***
(25.83)
-387.61***
(92.78)
Guizhou
-190.61***
(33.54)
-386.31***
(93.97)
Yunnan
-162.49***
(30.41)
-381.02***
(102.30)
Tibet
-57.15***
(43.93)
-301.38***
(103.97)
Shaanxi
-192.39***
(28.66)
-376.70***
(86.73)
Gansu
-189.04***
(32.30)
-405.08***
(102.86)
Qinghai
-140.86***
(34.93)
-358.10***
(103.61)
Ningxia
-180.49***
(35.62)
-369.71***
(95.45)
Xinjiang
-160.34***
(33.59)
-368.85***
(102.03)
*** significance at the 1% level; ** significance at the 5% level; * significance at the
10% level. Heteroskedasticity and autocorrelation (HAC) robust standard errors are
shown in parentheses below the estimated coefficients. Beijing is the base province.
45
Table 4.6 Time-Fixed Effects Coefficients of Linear Versions in Models 1 & 2
Model 1
Model 2
Year
Coefficient Standard Error
Coefficient Standard Error
1996
-16.34
-8.51
-18.97**
-8.36
1997
-82.97***
-7.61
-85.51***
-7.89
1998
-73.31***
-8.26
-70.66***
-8.18
1999
-77.08***
-8.21
-80.70***
-8.80
2000
-78.66***
-7.93
-81.83***
-8.98
2001
-72.82***
-7.02
-76.34***
-7.64
2002
-63.85***
-10.37
-69.06***
-11.41
2003
-54.53***
-10.96
-54.66***
-10.60
2004
-53.09***
-12.60
-51.60***
-11.35
2005
-43.00***
-14.20
-40.06***
-13.19
2006
-25.96
-15.81
-17.69
-15.75
2007
25.98
-21.44
35.49
-25.49
*** significance at the 1% level; ** significance at the 5% level; * significance at the
10% level. Heteroskedasticity and autocorrelation (HAC) robust standard errors are
shown in parentheses next to the estimated coefficients. The base year is 1995.
Since much of the literature focuses on the income elasticity of health care
expenditure, based on the linear and double-log results, we present in Table 4.7 below the
estimated income elasticities across all of the specifications contained in Tables 4.1 - 4.4.
As mentioned, for the double-log specifications the income elasticity is simply the
estimated coefficient of per capita province income. However, a criticism of the doublelog form is that it forces the income elasticity to remain constant across provinces and
time. On the contrary, the linear specification allows the income elasticity to vary across
all observations of our data. For the sake of comparison, though, for the linear models
Table 4.7 provides the income elasticities evaluated at the sample means. As the table
shows below, the income elasticity ranged from 0.369 to 2.385. Further, it seems that the
income elasticity is not only sensitive to whether a linear or double-log specification is
46
used, but also to whether model 1 or model 2 is estimated. Compared to previous studies
that only include a few determinants of health care spending, for example, the income
elasticity of the two-way fixed effects double-log specification is 0.757, which means
health care is a necessity, not a luxury, in China.24
Table 4.7 Estimated Income Elasticities
Model 1
Model 2
Linear Specification:
Pooled
1.107
0.738
Province-Fixed
0.738
0.615
Time-Fixed
0.984
0.738
Two-Way Fixed
0.369
0.738
2SLS
0.492
0.861
Double-Log Specification:
Pooled
0.910
0.599
Province-Fixed
0.699
-0.272
Time-Fixed
0.952
0.764
Two-Way Fixed
0.757
0.732
2SLS
2.247
2.385
a
For the linear form, the reported income elasticity is evaluated at the mean
Across all of the specifications estimated in this thesis, the two-way fixed effects
specification performs the best in the sense that (i) it has the highest adjusted R-squared
among the other OLS regressions, (ii) the fixed effects are jointly significant and the
omitted variable bias controlled, and (iii) the coefficients of income and the two age
demographic measures (particularly in the double-log specification) are most similar to
the literature to date. Furthermore, since for the linear specification we do not support
endogeneity of income, but do support endogeneity in the double-log specification,
24
Note also that many of the income elasticity values falling in the inelastic range are
similar to estimates obtained by Yu and Chu (2007) for Taiwan.
47
coupled with the results in the tables being sensitive to whether or not endogeneity is
addressed, we are skeptical that the approach taken in this thesis fully explores the
endogeneity issue. Rather, given the current lack of data on valid instruments, we view
our results as an indication that more work needs to be done on potential endogeneity in
the health care expenditure literature.
48
Chapter 5
CONCLUSION
5.1
Summary of Findings
This thesis has investigated the impact of a number of factors, including per capita
income, age distribution, and various other demand- and supply-side factors, on per
capita government health care expenditure in China. We also examined the
appropriateness of a number of estimation issues, such as a variety of means to control
for the panel nature of our data, as well as whether or not addressing potential
endogeneity of income affects the results and omitted variable bias controlled.
Additionally, we compared our results to the literature.
To accomplish these tasks, we used province level panel data (across 31 Chinese
provinces over a 13 year period) to estimate two different model specifications and
functional forms. Specifically, in the first model per capita health care expenditure is
treated as a function of per capita income and two age demographic variables, while in
the second model we added several additional regressors (i.e., education, air pollution,
population density, and numbers of doctors and hospital beds) to model 1. We estimated
each of these models using linear and double-log functional forms. Furthermore, not
only were these models estimated with ordinary least squares, but we also estimated our
preferred two-way fixed effects specification using two stage least squares.
Overall, the results are not consistent across model specifications due to the
omitted variables bias. For instance, although per capita income consistently has a
positive impact on per capita health care expenditure, with the associated income
49
elasticity generally falling in the inelastic range, which is similar to many other studies,
the sign and significance of the coefficients of the other variables are sensitive to the
specification and estimation technique. Across all of our specifications, though, we do
favor the two-way fixed effects version, since it has a relatively high adjusted R-squared,
the fixed effects are jointly significant, and the coefficients of income and the two age
demographics are most reasonable (especially in the double-log specification) with
controlled the omitted variable bias and fixed effects. Even there is no evidence to show
the stationarity existed in such a short period, but the estimation results of double-log
form somehow to show that might be stationarity existed but the tests could not provide
the evidence. Indeed, for the double-log specification, the income elasticity is similar in
magnitude to the existing literature, and both age measures (i.e., the percent of the
population age 65 and older and the percent of the population age 15 and younger) have
positive effects on public health care expenditure.
Furthermore, our results show significant differences in health care spending
across space and time. For instance, as Table 4.5 showed, there is substantial variation in
spending across provinces, with Beijing accounting for a larger share of health care
spending than other provinces. Also, as Table 4.6 showed, similar to other countries, per
capita health care spending has tended to increase over time.
5.2
Suggestions for Future Research
Due to data limitations pertaining to private health care expenditure, hopefully
future studies can not only examine patterns of government health care spending, but
patterns of other measures of health care spending (e.g., both public and private health
50
care spending, as well as the corresponding share of each of these with respect to total
health care spending), and a variety of health outcome measures. In addition, given that
our two stage least squares results were mixed, future research might also consider
alternative instrumental variables. Moreover, although current data limitations make this
difficult to address, additional endogeneity issues might be addressed in future research.
For instance, it might be that the number of physicians and the number of hospital beds
are endogenous as well, since health care expenditures could affect each of these
variables. Accordingly, we view this thesis as a first attempt to address an issue that will
likely become more pressing as the China economy continues to grow at a rapid pace.
Lastly, given that pollution will continue to impact China in the years to come, future
research can further explore the link between different measures of pollution, health care
expenditure, and health outcomes.
51
REFERENCES
Bilgel, F. (2003). The determinants of health care expenditure in Turkey, 1927-1996: An
econometric analysis. Department of business-economics. Istanbul University.
Blomqvist, A. G., & Cater, R.A.L. (1997). Is health care really a Luxury? Journal of
Health Economics, 16, 207-229.
Chen, B. Hong, C., & Kan, H. (2004). Exposures and health outcomes from outdoor air
pollutants in China. Toxicology, 198, 291-300.
Chou, W. (2007). Explaining China's regional health expenditures using lm-type unit root
tests. Journal of Health Economics, 26(4), 682-698.
Cremieux, P., Ouelette, P., & Pilon, C. (1999). Health care spending as determinants of
health outcome. Heath Economics, 8, 627-639.
Cremieux, P., Meilleur, M., Ouellette, P., Petit,P., Zelder, M., & Potvin, K. (2005).
Public and private pharmaceutical spending as determinants of health outcomes
in Canada. Health Economic, 14, 107-116. Retrieved June 20, 2006 from
SpringerLink database.
Devlin, N. & Hansen, P. (2001). Health care spending and economic output: Granger
causality. Applied Economics Letters, 8, 561-564.
Freeman D. G. (2003). Is health care a necessity or a luxury? Pooled estimates of income
elasticity from US state-level data. Applied Economics, 35, 495-502.
Fuchs, V. (1993). The Future of Health Policy, Harvard University Press.
Hansen, P., & King, A. (1996). The determinants of health care expenditure: a
cointergration approach. Journal of Health Economics, 15, 127-137.
Hitiris, T., & Posnett, J. (1992). The determinants and effects of health expenditures in
developed countries. Journal of Health Economics, 11, 173-181.
Jin, Hehui, Qian,Yingyi, & Weingast, B, R. (2005). Regional Decentralization and Fiscal
Incentives: Federalism, Chinese Style. Journal of Public Economics, 89, 17191742.
Kao, C., & Chiang. M. (2000). On the estimation and inference of a cointergrated
regression in panel data. Nonstationary Panels, Panel Cointergration and
Dynamic Panels, Vol 15, 179-222.
52
Karatzas, G. (2000). On the determination of the US aggregate health care expenditure.
Applied Economics, 32, 1085-1099.
King, A., & Hansen, P. (1996). Aggregate health care expenditure in the United States:
evidence from cointergration tests: a comment. Applied Economics Letters, 3, 3739.
Leu, R. E. (1986). The public-private mix and international health care costs, in A. J.
Culyer and B. Jonsson (eds). Public and Private Health Services. Basil Blackwell,
Oxford.
Liviatan, Nissan. (1964). Consumption patterns in Israel. Jerusalem: Falk Project for
Economics Research in Israel.
Matteo, L. D. (1999). The determinants of the public-private mix in Canadian health care
expenditure: 1975-1996. Health Policy, 52, 87-112.
Matteo, L.D. (2003). The income elasticity of health care spending. Europe Journal of
Health Economics, 4, 20-29.
Matteo, L.D. (2005). The macro determinants of health expenditure in the United States
and Canada: assessing the impact of income, age distribution and time. Health
Policy, 71, 23-42.
Murthy, N. R. V., & Ukpolo, V. (1995). Aggregate health care expenditure in the United
States: new results. Applied Economics Letters, 2, 419-421.
Pfaff, M. (1990). Difference in health care spending across countries: statistical
evidence. Journal of Health Politics and Law, 15, No.1.
Roberts, J. (1999). Sensitivity of elasticity estimates for OECD health care spending:
analysis of a dynamic heterogeneous data field. Health Economics, 8, 459-472.
Roberts, J. (2000). Spurious regression problem in the determinants of health care
expenditure: a comment on Hitiris (1997). Applied Economics Letters, 7, 279-283.
Schieber, George J., Poullier, Jean-Pierre, & Greenwald, Leslie M. (1991). Health Care
Systems in Twenty-Four Countries. Health Affairs, Fall 1991.
Seshamani, M., & Gary, A. (2004) Ageing and health-care expenditure: the red herring
argument revisited. Health Economics, 13, 303-314.
53
Shiu, Y. M., & Chiu M. C. (2008). Re-estimating the demographic impact on health care
expenditure: evidence from Taiwan. The Geneva Papers, 33, 728-743.
Starr, J. B. (2001). Understanding China: A guide to China’s economy, history, and
political structure. Hill and Wang, A division of Farrar, Straus and Giroux, New
York.
Uchimura, Hiroko, & Jutting, Johannes. (2007). Fiscal decentralization, Chinese style:
good for health outcomes? OECD Development Centre, Working Paper No. 264,
(DEV/DOC 2007/ 7).
Yu T. H., & Chu H.Y.(2007). Is health care really a luxury? A demand and supply
approach. Applied Economics, 39, 1127-1131.
Or, Zeynep, (2000). Determinants of health outcomes in industrialised countries: a
pooled, cross-country, time-series analysis. OECD Economics Studies, 30, 2000/I
Zweifel, P., Felder, S., & Meiers, M. (1999). Ageing of population and health care
expenditure: A red herring? Health Economics, 8, 485-496.