tiểu luận kinh tế lượng FACTORS AFFECTING HOUSEHOLDS’ HEALTH CARE EXPENDITURE IN COUNTRIES IN 2016

FOREIGN TRADE UNIVERSITY
FACULTY OF INTERNATIONAL ECONOMICS
---------***--------

ECONOMETRICS REPORT
FACTORS AFFECTING HOUSEHOLDS’ HEALTH CARE
EXPENDITURE IN COUNTRIES IN 2016
Group: 18
Class: K57 - VJCC
Lecturer: Dr. Tu Thuy Anh
Dr. Chu Thi Mai Phuong

GROUP MEMBERS
1. Nguyễn Thị Hương Mai – 1815520201
2. Phan Đức Long - 1815520199
3. Đỗ Thị Thu Phương - 1815520215


Table of Contents
ABSTRACT................................................................................................................1
I, INTRODUCTION:.................................................................................................2
1, Basic concept:......................................................................................................2
2, Why we choose this topic?..................................................................................2
II, LITERATURE REVIEW......................................................................................2
III, THEORETICAL BACKGROUND....................................................................4
1, Overview of the study of factors affecting households’ health care
expenditure in countries in 2016............................................................................4
2, Research model & research hypothesis.............................................................5
2.1 Research model..............................................................................................5
2.2 Research hypothesis......................................................................................7
IV. DESCRIPTIVE STATISTIC OF DATA:............................................................8

1.

Source of data...................................................................................................8

2. Statistical description..........................................................................................8
3. Correlation matrix between variables.............................................................10
V. ECONOMETRIC MODEL.................................................................................11
VI. ROBUSTNESS CHECK....................................................................................12
1. Multicollinearity................................................................................................12
2. Normality...........................................................................................................12
3. Heteroscedasticity.............................................................................................14
4. Testing an individual regression coefficient....................................................16
5. Testing the overall significance.........................................................................17
VII. FINDINGS & DISCUSSION...........................................................................18
VIII. CONCLUSION...............................................................................................19
REFERENCES.........................................................................................................20


ABSTRACT
The purpose of this report is to understand more about Econometrics by running a
regression model and discussing its result. The topic of our research team is health
care expenditure, one of the issues that are close to our lives today. We choose the
health expenditure as a dependent variable and life expectancy, final consumption
expenditure, out of pocket expenditure, GDP per capita as independent variables.
After collecting data from 158 countries in the world, we run the model and come up
with the result as follows.
The result indicates that apart from life expectancy, other independent variables all
have linear relationships with the dependent variable. Their regression coefficients are
statistically significant in the model.
However, the regression coefficient of life expectancy variable is not statistically

significant in our model. Therefore, the relationship between health expenditure and
life expectancy is not inferred.
Overall, we can conclude that our model is statistically significant at 5% level of
significance.
From the above results, we make some recommendations in order to give readers a
closer look about this model in practice.

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I.
INTRODUCTION:
1. Basic concept:
A healthy nation they say is a wealthy nation. Healthcare is important to the
society because people get ill, accidents and emergencies do arise and the hospitals are
needed to diagnose, treat and manage different types of ailments and diseases. Many
of people’s aspirations and desires cannot be met without longer, healthier, happy
lives. The healthcare industry is divided into several areas in order to meet the health
needs of individuals and the population at large. All over the world, the healthcare
industry would continue to thrive and grow as long as man exists hence forming an
enormous part of any country’s economy.
Expenditure on health is growing faster than the rest of the global economy,
accounting for 10% of global gross domestic product (GDP). World Health Organization
(WHO) reveals a swift upward trajectory of global health spending, which is particularly
noticeable in low- and middle-income countries where health spending is growing on
average 6% annually compared with 4% in high-income countries.

2. Why we choose this topic?
Since this subject has become more and more noteworthy, as economics students,
we decided to review the topic: “Factors Affecting Households' Health Care

Expenditure in Countries in 2016”.
In the report, we used econometrics tool “GRETL” to analyze the data we have
researched on World Bank. This essay aims at evaluating the impact of GDP per
capita, life expectancy at birth, final consumption expenditure and out of pocket
expenditure on health care expenditure of 158 random nations all over the world. In
the end, we are bound to achieve an objective look into the issue as well as apply
appropriate measures to make progress in practicing health care tasks.

II.

LITERATURE REVIEW

Regarding to household's health care expenditure, in the past there are a number
of research and articles which indicated that expenditure on health is growing rapidly.
A study of “Determinants of Health Care Expenditures and the Contribution of
Associated Factors” in Korea during 2003-2010 showed that health care
expenditures have been drastically increasing every year. Medical expenses covered
by health insurance, which were about 13 trillion won in 2001, had jumped 2.6-fold
by 2010, reaching around 34 trillion Korean won. This was an average
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increase of over 11% annually in the first decade of the 21st century. Such a trend
raises concerns over the sustainability of health insurance finance following the
increase in health care expenditures. Medical costs can be explained by determinant
factors that are produced by multiplying the volume of health services by the unit cost
per service. According to the report “Determinants of Healthcare Expenditure in
Economic Cooperation Organization (ECO) Countries: Evidence from Panel
Cointegration Tests” from The International Journal of Health Policy and
Management, there are two completely contradictory views about the relationship

between healthcare spending and production levels. First, healthy workers are more
efficient than others. They have more time for working and their time is not wasted
for treatment. Secondly, health expenditures are considered as “costs”. These
expenditures cause resources transfer from other sectors of economy to the health
sector and are the reason why the level of production has diminished in countries.
Therefore, health economists pay more attention to health expenditures and study the
determinants of health expenditures. A research in the United States has shown that
the share of GDP devoted to healthcare expenditures grew from 9% in 1980 to 16% in
2008. Meanwhile, in Iran, the health expenditures per capita increased from $80 in
1995 to $247 in 2005 in average exchange rates. Long-term prediction also indicates
that health expenditures continue to increase. The findings of the study revealed a
positive long-term relationship between the percentage of urbanisation and the health
expenditures. In another happenings, Baltagi and Moscone (Badi H. Baltagi &
Moscone 2010) present a negative long-term relationship was found between the
health expenditures and ageing groups. In case the proportion of the individuals
below 15 and over 65 years old is more in a country, the country is considered healthy
and, as a result, people consume less expensive healthcare compared to a country with
unhealthy people. Banins found that health expenditures increased when a country
reached higher life expectancy and started to decrease after achieving its peak. A
detailed study from World Health Organization named “The determinants of health
expenditure: A Country-level Panel Data Analysis” gave some key finding as well.
First factor affecting on household health expenditure is income. In global literature,
Musgrove, Zeramdini and Carrin used cross section data from 191 countries in 1997
and found that income elasticity of health expenditure was between 1.133 and 1.275
depending on the data included.

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III.

THEORETICAL BACKGROUND

1. Overview of the study of factors affecting households’ health care
expenditure in countries in 2016
Health care expenditure is the amount spent by individuals, groups, nations, or
private or public organizations for medical care, prevention, promotion, rehabilitation,
community health activities, health administration and regulation and capital
formation with the predominant objective of improving health.
In general, health care is only valued to the extent that it improves health, so
health is primitive in the description of consumers‟ preference. Changes in consumer
attitudes toward health care can also change demand. For example, television, movies,
magazines, and advertising may be responsible for changes in people's preferences for
cosmetic surgery. Moreover, medical science has improved so much that we believe
there must be a cure for most ailments. As a result, consumers are willing to buy larger
quantities of medical services to prolong their life expectancy. Life expectancy has
been improving for many decades, and there is evidence that health among the elderly
is also improving. The aging process changes both the body and the mind. Many aging
changes are physiological in nature, as the body begins to degenerate and break down.
Declining health is a common issue with aging, with many illnesses and diseases
plaguing the elderly population. For this reason, the consumption for healthcare is
important to the elderly. Hence, when people are getting older, their spendings for
healthcare are increasing.

The relationship between Life Expectancy and Healthcare Expenditure
4


Research has also found a relationship between health care spending and Gross
domestic products. GDP is a monetary measure of the market value of all the final

goods and services (including health care service) produced in a specific time period,
often annually. The growth of a country's GDP represents not only the economic
development but also the improvement of other aspects of that country such as
infrastructure, education, medical, etc. Furthermore, the hypothesis outcome of a
research team, whose members are Sojib Bin Zaman, Naznin Hossain, Varshil Mehta,
Shuchita Sharmin and Shakeel Ahmed Ibne Mahmood, suggests that there is a positive
relationship between GDP and Healthcare Expenditure. Countries with high GDP are
likely to spend more money on healthcare than countries with lower GDP.
In the report of WHO, the households with high Out of Pocket Expenditure
have the higher spending on healthcare than the lower ones. Out-of-pocket payments
(OOPs) are defined as direct payments made by individuals to health care providers at
the time of service use. This excludes any prepayment for health services, for example
in the form of taxes or specific insurance premiums or contributions and, where
possible, net of any reimbursements to the individual who made the payments. OOPs
are part of the health financing landscape in all countries relying on user fees and co
payments to mobilize revenue, rationalize the use of health services, contain health
system costs or improve health system efficiency and service quality.
According to WHO, Health expenditure share, or the percentage of the household
expenditure spent on health care, is a necessary spending for members of the
households. In the national accounts expenditure on goods and services that are used
for the direct satisfaction of individual needs (individual consumption) or collective
needs of members of the community (collective consumption) is recorded in the use of
income account under the transaction final consumption expenditure (FCE). The most
important part of final consumption expenditure is household final consumption
expenditure (including healthcare expense).
2. Research model & research hypothesis
2.1. Research model
2.1.1. Methodology
• Method of collecting data
The collected data is in the form of secondary information and cross - section

data, showing the factors which affect households‟ health care expenditure based on

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158 observations in 2016 in 158 countries. The data was taken from the highly
accurate source which is World Bank.
• Method used to analyze the data and derive the model
The team used multiple linear regression model in combination with OLS
(Ordinary Least Square) estimation method to analyze the relationship between health
expenditure and other factors including GDP per capita, life expectancy at birth, final
consumption expenditure and out of pocket expenditure.
During the course of the project, the team used the knowledge of econometrics
with the main support of GRETL software, Microsoft Excel, Microsoft Word for
synthesis and completion of this project.
2.1.2. Theoretical model specification
• Determine the model type
From the reference of previous researches, the team decided to use population
linear regression function to carry out the project. The population regression function
consists of 1 dependent variable and 4 independent variables.
HE = ₀ + ₁.LIFE + ₂.GDP +
+
Where:

₃.FCON +

₄.OOP

₀: intercept term
: partial regression coefficients

u: disturbance

•

Explain the variables
Table 1: Explain the variables and expected sign
Variables Meaning
Unit
Expected sign of
regression coefficient
HE

Health care expenditure

Current US$

LIFE

Life expectancy

Years

+

GDP

Gross domestic product
per capita

Current US$

+

6


FCON

Final consumption

Current US$

+

Current US$

+

expenditure
OOP

Out of pocket expenditure

Source: The research group self-synthesis

Theoretically, all independent variables have a positive relationship with
dependent variable.
- That life expectancy is higher leads to the increase in the elderly population.
It is well understood that ageing population require more health services which could
result in higher health expenditure.

- GDP is the most effective factor in determining the health expenditures.
Countries with good economic infrastructure have more knowledge about the benefits
of healthcare and, consequently, they use healthcare more than other countries.
- As the concern of good health among people is rising, they demand for more
health goods and services. Therefore, higher consumption expenditure may consist of
higher health expenditure.
- The expenses that the patient or the family pays directly to the health care
provider, without a third-party (insurer, or State) is known as “Out of Pocket
Expenditure‟. Higher out of pocket expenses will lead to higher health expenditure.
2.2. Research hypothesis
After studying related theories and referring to domestic and foreign studies, our
research team searched and synthesized following hypotheses to study the factors
affecting the households ‟healthcare expenditure” of countries in the world.
Table 2: Hypotheses of the factors affecting the households
Variables
Hypothesis
Expected sign of
regression
coefficient
LE

GDP

Life expectancy has a positive effect on
healthcare expenditure. The higher life
expectancy is, the more spending people spend on
their healthcare

+

GDP has a positive effect on healthcare
expenditure. The higher GDP is, the more
spending people spend on their healthcare

+

7


FCON

Final Consumption Expenditure has a positive
effect on healthcare
expenditure. The higher Final Consumption
Expenditure is, the more spending people spend
on their healthcare

+

OOP

Out of Pocket Expenditure has a positive effect
on healthcare expenditure. The higher Out of
Pocket Expenditure is, the more spending people
spend on their healthcare

+

Source: The research group self-synthesis

IV. DESCRIPTIVE STATISTIC OF DATA:
1. Source of data
Source of data used for each variable is in this below table:
Table 3: Source of data used
Variable
Type of variable
Short - form Year

Source of data

Health expenditure

Dependent

HE

2016

World bank

Life expectancy

Independent

LIFE

2016

World bank

Gross
Domestic
Product

Independent

GDP

2016

World bank

Final
consumption
expenditure

Independent

FCON

2016

World bank

Out of pocket
expenditure

Independent

OOP

2016

World bank

Source: />
2. Statistical description
The typical data representing the variables are listed in the table below:

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Variable

Table 4: The typical data representing the variables
Number of observation
Mean
Std.Dev
Max

Min

LIFE

158

69.78425

9.151229

82.84268

45.1

GDP

158

13164.41

18743.24

104965.3

234.2356

FCON

158

79.45112

19.26681

154.0412

12.17321

OOP

158

204.6979

304.3439

2332.798

1.773446

HE

158

1006.304

1733.355

8021.81

12.85765

Source: The research group self-synthesis

The standard deviation of variable LIFE is 9.151229. It can be seen that the data
are relatively high standard deviation, high level of dispersion, showing the relatively
high difference among countries. Developed countries like Japan, Switzerland, Italy,
etc usually have a high average life expectancy meanwhile that in Africa or some part
in Asia are of the low average longevity. As population in rich countries is now
becoming older, the spending on healthy expenditure will increase.

● The standard deviation of variable GDP is 18743.24. We can realize the high
standard deviation result in large gap in the average income between countries. This is
understandable due to the variance in development of each region. This featured
marked a lot because income and spending have positive relation, the increasing of the
former will lead to the rise of the latter, including the spending on health expenditure.
● The mean value of FCON is relatively high, about $79.45112 US and standard
deviation is 19.26681. Most private sector healthcare expenditure was from household
final consumption expenditure, for example, the expenses for medicine, medical
device as well as the cost for treatment. The standard deviation is high because the
need and consumption for health expenditure in each region changes depending on
situation, because of the variation in price.
● The standard deviation of variable OOP is 304.3439, reach the top with 2332.798
in Switzerland, followed by developed countries such as Norway, Australia, etc and get
the lowest value of 1.773446 in Mozambique, followed by mostly African countries like
Malawi, Congo, etc.This resulted from the fact that This private cost borne by citizens is
also referred to as the out-of-pocket health expenditure. Out of pocket payments are those
which are made at the point of service. In most of the developing
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countries around the world, total spending on health care is dominated by huge
amount of private out-of-pocket health expenditures. The tremendous significance of
health care imparts huge importance to this issue due to which healthcare reforms are
being initiated in many countries all over the world.
2. Correlation matrix between variables
Before running the regression model, we consider the correlation between
variables. Thus, we can establish some hypothesis about the relationship between
dependent variable and independent variables. The correlation between variables are
as follows:
Table 5: Correlation matrix between variables

HE
GDP
LIFE
FCON
OOP
HE

1000

GDP

0.919

1.0000

LIFE

0.5711

0.6108

1.0000

FCON

-0.1831

-0.3551

-0.2810

1.0000

OOP

0.9006

0.8394

0.5899

-0.1880

1.0000

Source: The research group self-synthesis

From the above table, we could see that the correlation between GDP, LIFE, OOP
and HE is extremely strong.
The correlation coefficient between the HE and GDP is 0.9190 (a strong positive
correlation), showing a positive relationship.
The correlation coefficient between the HE and LIFE is 0.5711 (a relatively strong
positive correlation), showing a positive relationship.
The correlation coefficient between the HE and FCON is -0.1831 (a weak negative
correlation), indicating a inverse relationship.
The correlation coefficient between HE and OOP is 0.9006 (a strong positive
correlation), indicating a positive relationship.
The correlations between independent variables are relatively high. Therefore, there
could be multi-collinear in our model. Multi-collinear can cause wrong sign in the

10


correlation coefficient. We will run the model to clarify the relationship between
variables in the following section.
V. Econometric model
The regression result is given as below:

PRF: HE = ₀ + ₁.LIFE + ₂.GDP + ₃.FCON + ₄.OOP + u
SRF: HE = -737,284 - 4,24430LIFE + 0,058GDP + 10,046FCON + 2,289OOP + e.
Meaning of Regressors:
•

₀ = -737,284 means when LIFE, GDP, FCON, OOP equal to 0, HE value
equal to -737,284

•

₁ = -4,24430 means when GDP, FCON, OOP are constant, if LIFE increases
by 1, HE decreases by -4,24430

•

₂ = 0,058 means when LIFE, FCON, OOP are constant, if GDP increases by 1
HE increases by 0,058

•

₃ = 10,046 means when LIFE, GDP, OOP are constant, if FCON increases by
1, HE increases by 10,046

•

₄ = 2,289 means when LIFE, GDP, FCON are constant, if OOP increases by
1, HE increases by 2,289

•

The expected sign of ₁ is different from the result ((+) in Table 2 and (-) in the
result). The expected signs of the rest of regressors is the same as the result (+).

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VI. ROBUSTNESS CHECK
1. Multicollinearity
In statistics, multicollinearity (also collinearity) is a phenomenon in which one
predictor variable in a multiple regression model can be linearly predicted from the
others with a substantial degree of accuracy. In this situation the coefficient estimates
of the multiple regression may change erratically in response to small changes in the
model or the data. Multicollinearity does not reduce the predictive power or reliability
of the model as a whole, at least within the sample data set; it only affects calculations
regarding individual predictors. That is, a multivariate regression model with collinear
predictors can indicate how well the entire bundle of predictors predicts the outcome
variable, but it may not give valid results about any individual predictor, or about
which predictors are redundant with respect to others.
Detection:
VIF stands for Variance Inflation Factor. During regression analysis, VIF
assesses whether factors are correlated to each other (multicollinearity), which could
affect p-values and the model isn’t going to be as reliable. If a VIF is greater than 10,

you have high multicollinearity and the variation will seem larger and the factor will
appear to be more influential than it is. If VIF is closer to 1, then the model is much
stronger, as the factors are not impacted by correlation with other factors.
Our multicollinearity check is given as below:

Our model has VIF < 10, so multicollinearity is not our problem.
2. Normality
In statistics, sometimes It is needed to assess the normality of a given set of data. For
many statistical processes, it is prerequisite to make the assessment of the normality of
the data, since it is an important assumption in parametric testing. There are various
normality tests are available for the determination of normality of a data. In statistics,
the normality tests are used in order to determine whether a given set of data is well-

12


defined by a normal distribution. They are also used to measure how likely a set of
data to be normally distributed for a random variable.
Detection:
The Jarque-Bera test:
JB= N6.(S2+K24)
Where S and K are the sample Skewness and Kurtosis statistics.
The JB test has an asymptotic chi-square distribution with two degrees of freedom.

* Hypotheses:
H0: The error term is normal distributed
H1: The error term is not normal distributed
* Significance level:
α = 0.05

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p-value = 0,00000 < 0,05
=> At 5% level of significant, we have enough evidence to reject H0
=> The data is not normally distributed
However, according to Central Limit Theorem, in some situations, when independent
random variables are added, their properly normalized sum tends toward a normal
distribution (informally a "bell curve") even if the original variables themselves are not
normally distributed. Therefore, in our situation (n = 158, k=5), because of the large
number of observations, it is still possible to conduct a significant test as usual.

3. Heteroscedasticity
In statistics, a collection of random variables is heteroscedastic, if there are sub
populations that have different variabilities from others. Here “variability” could be
quantified by the variance or any other measure of statistical dispersion. Thus
heteroscedasticity is the absence of homoscedasticity.
The existence of heteroscedasticity is a major concern in the application of
regression analysis, including the analysis of variance, as it can invalidate statistical
tests of significance that assume that the modelling errors are uncorrelated and
uniform—hence that their variances do not vary with the effects being modeled. For
instance, while the ordinary least squares estimator is still unbiased in the presence of
heteroscedasticity, it is inefficient because the true variance and covariance are
underestimated. Similarly, in testing for differences between sub-populations using a
location test, some standard tests assume that variances within groups are equal.
14


Detection: White test
H0: Var(ui) = σ2 for all i

H1: Var(ui) ≠ σ2 for all i

p-value = 0,000 < 0,05
=> At 5% level of significant, we have enough evidence to reject
H0 => Heteroscedasticity problem hasn’t been solved.
Fix with Robust standard errors
Result:

Heteros does not affect statistical inferrence.
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Thus, we have the following model after remedying defects:
HE = -737,294 - 4.24430*LIFE + 0.0587388*GDP + 10.0464*FCON +
2.28789*OOP + e
4. Testing an individual regression coefficient
Firstly, we established the hypothesis:

H0: βj = 0
H1: βj ≠ 0

Secondly, we use the way of comparing p-value of each estimated coefficients with
(level of significance) in order to decide whether to reject or not. If ((P>|t|) < ), we
reject Ho. With = 0.05, the results are clear:
Table 6: Comparing p-value of each estimated coefficients and decision
Variables
P>|t|
Decision
GDP

0.00

Reject H0

LIFE

0.471

Do not reject H0

FCON

0.00

Reject H0

OOP

0.00

Reject H0
Source: The research group self-synthesis

Because the p-value of coefficient of LIFE > 0.05 (level of significance), we can
conclude that β1 is not statistically significant at 5% level of significance.
Other variables all have p-value < 0.05 which means that GDP, FCON, OOP will
actually affect the value of HE. ₂, ₃, ₄ are statistically significant at 5% level of
significance.
CONCLUSION: To clarify how the independent variables impact the dependent
variable, we would like to show mechanism of each variable on the total health

expenditure.
₁ is not significant in the estimated model which means that LIFE actually do not
affect the value of HE. We tried to research and found that the relationship between LIFE
and HE is not linear. There may exist other trends that we have not considered in our
hypotheses. A research show that elderly persons in better health had a longer life
expectancy than those in poorer health but had similar health care expenditures until
death. The expected health expenditures for healthier elderly persons, despite their greater
longevity, were similar to those for less healthy persons. Health-promotion
16


efforts aimed at persons under 65 years of age may improve the health and longevity
of the elderly without increasing health expenditures. This meant that in spite of
spending money for medical service, people also have another choice to maintain
fitness such as exercising, taking part in activities like yoga, dancing, ... so the impact
of life expectancy on HE is somehow not exactly defined.
₂ is significant in the estimated model. Because ₂ = 0.0587388, the relationship
between GDP and HE is a positive relationship. This means that every change in value
of GDP will lead to a change in value of HE in the same direction. The result is
consistent with our prediction. It is clearly because when GDP per capita increases,
people will want to spend more to improve their living standard (house, food, ...
including health care). Besides, as gross domestic product increases, government will
invest more on public services, especially health services, improving access to new
health care technologies and treatments. Therefore, it is reasonable to expect that the
sign expectation of this variable is (+).
₃ is significant in the estimated model. Because ₃ = 10.04638, the relationship
between FCON and HE is a positive relationship. This means that every change in
value of FCON will lead to a change in value of HE in the same direction. The result
is consistent with our prediction. The total consumption comprises of many elements
and health expenditure is one of those. Therefore, it could be easily understood that

when total consumption increases, the expenditure for health also increase even by a
small value. Therefore, it is reasonable to expect that the sign expectation of this
variable is (+).
₄ is significant in the estimated model. Because ₄ = 2.287889, the relationship
between OOP and HE is a positive relationship. This means that every change in value of
OOP will lead to a change in value of HE in the same direction. The result is consistent
with our prediction. Out-of-pocket costs include deductibles, coinsurance, and copayments for covered services plus all costs for services that aren't covered. In poor
resource-settings in particular, where health care providers tend to be inadequately paid,
user fees constitute a major source of revenue for health workers. They primarily serve to
sustain the provision of health services, creating perverse financial incentives. Therefore,
it is reasonable to expect that the sign expectation of this variable is (+).
5. Testing the overall significance
Firstly, we established the hypothesis:
2

H0 : R = 0
H1: R2≠ 0

17


Secondly, we could see in the above table p - value

= 0.00 < 0.05 (5% of level
F - test

significance). In this case, we have enough evidence to reject H 0
=> The estimated model is statistically significant at 5% level of significance.
VII. FINDINGS & DISCUSSION
It can be seen that health care expenditure is a concern these days. The study of the

factors that influence health expenditure is becoming increasingly important and
urgent. There are 2 factors in the model that need more concern for improvement so
the team would like to give some recommendations for these factors.
Firstly, GDP plays an important role on how much can be spent on health. For low
GDP countries where health expenditure is often lower than the minimum required to
provide very basic services, great effort is needed to make more resources available
for heath from both public and private sources. Countries with high health expenditure
may need to find ways to increase the value they are getting for their money.
Secondly, the rising out-of-pocket health expenditures all over the globe are a
cause of worry for all the policy makers and economists. Although there is no magic
bullet, available information illustrates that countries can succeed with well-designed
policies and strategies to reduce out of pocket expenditure and its negative impacts.
The main strategies that countries use include:
● Abolish user fees and charges in public health facilities;
● Target and exempt specific population groups such as the poor and vulnerable,
pregnant women and children from official payments;
● Target and exempt a range of health services such as maternal and child care
from official payments and deliver them free of charge.
These strategies need political support, decision-making and proper preparation.
User fee abolition and exemption can have a large impact on both demand and supply
of health services. They likely increase the demand for services which subsequently
affects the workload of health workers.

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VIII. CONCLUSION
This is completed under the contribution of members with knowledge gained
from the research and study of Econometrics. By doing this essay, we can better
understand the process of running the econometric model, analyzing, verifying the fit

of the model and the relationship between variables in the model. In addition, we can
apply the knowledge learned and through the econometric model analysis to draw
useful conclusions about socio-economic problems.
Within the scope of the essay, the team examined the effect of life expectancy at
birth, GDP per capita, final consumption expenditure and out of pocket expenditure on
health care expenditure of 158 countries in 2016. According to the model, GDP per
capita, final consumption expenditure and out of pocket expenditure are statistically
significant in the model.
We would like to thank Ms. Tu Thuy Anh and Ms. Chu Thi Mai Phuong for their
guidance and suggestions to help us understand the problem and analyze in the right
direction. In the process, due to our limited understanding and collecting data, it is
inevitable that there are some mistakes in the assignment. In addition, the selected
variables are not necessarily the best ones that affect health care expenditure. These
factors are correlated with some of the variables studied but they may not be
completely accurate. Therefore, we hope this essay will contribute as a review and an
analysis of some factors that affect health expenditure for everyone to consult and
learn more about the model as well as the issue.

19


REFERENCES
1. AnthonyJ. Culyer, JosephP. Newhouse, 2000, HandbookofHealthEconomics
Volume 1A
2. Business Theory and Practice, On the examination of out-of-pocket health
expenditures in India, Pakistan, Sri Lanka, Maldives, Bhutan, Bangladesh and
Nepal ( />3. Danuvas Sagarik, 2016, Determinants of Health Expenditures in ASEAN Region:
Theory and Evidence
4. EconomicsOnline, 2019, Healthcareasameritgood
( />5. Harvard University, 2017, The Economics of Health Care ( />6. Health Affairs, Out-Of-Pocket Medical Spending For Care Of Chronic Conditions

( />7. The Journal of Medical Research and Innovation, An Association of Total Health
Expenditure with GDP and Life Expectancy
( />8. The National Center for Biotechnology Information NCBI, Determinants of
Healthcare Expenditure in Economic Cooperation Organization (ECO) Countries:
Evidence from Panel Cointegration Tests
( />9. The National Center for Biotechnology Information NCBI, Determinants of Health
Care Expenditures and the Contribution of Associated Factors: 16 Cities and
Provinces in Korea, 2003-2010
( />10. The National Center for Biotechnology Information NCBI, Estimating health
expenditure shares from household surveys
( />11.The New England Journal of Medicine, Health, Life Expectancy, and Health Care
Spending among the Elderly
( />20


12. OECD/European Union 2018, Health at a Glance: Europe 2018
13. School of Business and Economics, Unversity Malaysia Sabah, What are the
determinants of health care expenditure? Empirical results from Asian countries
( />14. WHO, The determinants of health expenditure
( />15. WHO, Out-of-pocket payments, user fees and catastrophic expenditure
( />
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