4th Annual ECoFI Symposium 2017
28 October 2017, TH Hotel & Convention Centre, Alor Setar, Kedah, Malaysia
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Author 1*a, Author 2b, Author 3a
*a,
Corresponding author, Department of Economics and Agribusiness, School of Economics, Finance and Banking, Universiti Utara Malaysia
b
Department of Economics, Faculty of Business and Finance, Universiti Tuanku Abdul Rahman
Abstract
This is the paper template for authors submitting their work to be considered for the 4 th Annual ECoFI Symposium
2017. Authors are expected to insert their writings in the appropriate sections of this template. Kindly follow the
predetermined format of this template. The abstract should have a maximum of 200 words followed by keywords up
to 5. The abstract should briefly state what the paper is about (e.g. “This paper examines…”), the data and method
used, the findings and conclusion.
Keywords: ageing population; health care expenditure determinants; the elderly
4th Annual ECoFI Symposium 2017
28 October 2017, TH Hotel & Convention Centre, Alor Setar, Kedah, Malaysia
Place the title of your paper here
Abstract
This is the paper template for authors submitting their work to be considered for the 4 th Annual ECoFI Symposium
2017. Authors are expected to insert their writings in the appropriate sections of this template. Kindly follow the
predetermined format of this template. The abstract should have a maximum of 200 words followed by keywords up
to 5. The abstract should briefly state what the paper is about (e.g. “This paper examines…”), the data and method
used, the findings and conclusion.
Keywords: ageing population; health care expenditure determinants; the elderly
1. INTRODUCTION
The outline of a paper depends on its type. For an empirical paper, the expected outline is as follows: introduction,
literature review, methodology and data, empirical results and conclusion. For a conceptual or theoretical paper,
authors are asked to exercise discretion. Regardless of the paper type, headings should appear in uppercase letters, be
boldfaced, be preceded by Arabic numerals and be placed on the left margin. Unless otherwise mentioned, use a font
size of 10 and Times New Roman font style for all your text. The text should be justified both sides. Use single spacing.
There should be no spacing between paragraphs, as shown by the two following sample paragraphs. The first line of
the opening paragraph of a new section should have no indentation, as shown by the first line of this current paragraph.
If your work consists of any figures, use the format of the following Figure 1. Figures and tables should be numbered
consecutively. Do not insert any page number. The following paragraphs are extracted from the introductory section
of a sample paper.
The population is ageing globally. Likewise, it is empirically witnessed that many Asian countries, including
Malaysia, have experienced rapidly ageing populations over the last four decades. This shared attribute will retain to
be the region’s prominent demographic trend in the future due to the dual consequences of sharp decline in fertility
rates and rapid improvement in life expectancy, respectively (Lee, Mason & Park, 2011). Malaysia is projected to be
one of the ageing nations among the Southeast Asian (SEA) economies sometime between 2030 and 2035 depending
upon the reasonable combination of declining fertility rates and sustained advances in life expectancy.
Figure 1 depicts that Malaysia has undergone the gradual developments in both notable profiles of ageing
population and health care expenditure between 1970 and 2014. The country’s elderly population, by and large, has
been on the rise, with a sharp upward trend from 2000 onwards, sizably increased from 5.9 percent in 1970 to 9.8
percent of the total population in 2014 (World Bank, 2016). Equivalently, this constitutes as a 66 percent increase or
an increment of 3.9 percent through 2014. The rising trend is also empirically observed in the growth of health care
expenditure within the time frame, which covers for both public and private services.
Figure 1: Health care expenditure-to-GDP ratio and the ageing population in Malaysia.
Source: Rasiah, W. Abdullah and Tumin (2011); Ministry of Health (2016); World Bank (2016).
Notes: PuHCE-to-GDP, PrHCE-to-GDP and POP60 denote as public health care-to-GDP, private health care-to-GDP and ratio of elderly group
respectively.
2. LITERATURE REVIEW
This section contains a critical review of the relevant past studies. By critical review, we mean a critical discussion of
previous studies in the related area (rather than a mere reporting of them). Some authors prefer to do literature review
in the introductory section, which means that there is no separate section on literature review, and this is fine too.
Please take note of the citation and references format when reviewing previous studies. Please use author-year format
when citing any studies and studies with three authors or more should be cited as et al. The following paragraphs are
extracted from the literature-review section of a sample paper.
Historically, the study of health care expenditure modeling can be traced back to as early as 1960s with the
pioneering works of Mushkin (1962) and Newhouse (1977) (as cited in Tang (2010b)). Since then, the literature is
increasingly thrived in both developed and developing countries with rigorous studies investigating the relationship
between publicly funded health care expenditure and its determinants; income and a selection of non-income variables
such as ageing population, life expectancy and urbanization rate.
In early years, Newhouse (1977), who used a simple OLS regression model, examined the relationship between
per capita total health care expenditure and per capita income using a sample of 13 United Nation countries. He
discovered that the income elasticity for health care expenditure was greater than unity i.e. ranging from 1.13 to 1.31,
thus implying that health care is a luxury good by the technical definition.
In recent years, Tang (2010a) studied the determinants of health care expenditure in Malaysia over the 1967 –
2007 period. With the Johansen cointegration approach, he showed that the variables are collectively cointegrated and
further revealed that income, price level and ageing population do represent key drivers that influence the variations
in the country’s health care expenditure. Meanwhile, Nordin et al. (2015) analyzed the effect of ageing society on
health care expenditure in both China and India for the 1970 – 2011 period. Meanwhile, to defy the conventional
wisdom, empirical studies such as Barros (1998), Herwartz and Theilen (2003) unveiled that the potential effect of
ageing society on health care expenditure to be statistically negative, insignificant and technically termed as a “red
herring hypothesis.”
3. METHODOLOGY AND DATA
This section contains a description of the methodology and data used. The methodology sub-section consists of model
specification along with the definition of dependent and independent variables as well as the estimation method used.
The data sub-section consists of the description of the data used for all variables and the sample period and size. The
following paragraphs are extracted from the methodology-and-data section of a sample paper. Equations should be
numbered in squared brackets and should be spaced out to avoid cluttering the text. See sample equations in this
section. Equations should be inserted using MS Equation Editor. Subsections in each main section should be in italic,
as shown in the following three sample subsections.
Data
Secondary data are utilized to investigate the long run and short run relationships between per capita health care
expenditure and independent variables namely per capita economic growth or income, consumer price index (CPI),
ageing population, life expectancy at birth and urbanization rate in Malaysia from 1970 to 2014. Annual data on CPI,
life expectancy at birth and urbanization rate were collated from the World Bank’s website, and annual data on the
public health care expenditure and economic growth were obtained from the websites of Malaysia’s Ministry of Health
and Department of Statistics, accordingly. The annual data on ageing population were gathered from the United
Nations (2002)’s World Population Ageing 1950 – 2050 Report.
Model Specification
According to Newhouse (1977) and Anyanwu (1998) as cited in Imoughele and Ismaila (2013), the standard
econometric approach to a health care expenditure modeling consists of three main components and is determined by
Equation [1]:
HCE   h, d, e 
[1]
where HCE is health care expenditure, h is a vector of health care stock variables, d is a vector of demographic changes
and e is a vector of economic factors.
Therefore, following the works of Bilgel (2003) and Angko (2013) on the specific cases of determinants for the
public health care expenditure on per capita basis in Turkey and Ghana, respectively, the empirical model, which is
structured with considerable modifications, is given as per Equation [2]:
PCHCE  PCGDP, HCEPL, POP60, LIFEXP,URBAN 
[2]
where PCHCE is per capita health care expenditure (in million RM) is a function of PCGDP that is per capita income
(in million RM), HCEPL is health care expenditure price level (CPI, 2010 as the base year with 100), POP60 is
population aged 60 and above group (in percentage of total population), LIFEXP is life expectancy at birth (in total
number of years) and URBAN is natural urbanization rate (in percentage of total population).
Apart from ageing society factor, the motivation is to study the health care expenditure modeling in Malaysia that
captures its possible determinants. To achieve this objective, Equation [2] is then to be rewritten in the form of a linear
specification as shown in Equation [3]:
PCHCE t  0  1 PCGDPt  2 HCEPL t  3 POP 60 t  4 LIFEXPt  5URBAN t  t
[3]
Cointegration
Once the variables passed the individual unit root tests, a VAR framework is the feasible approach to be subsequently
employed in consideration of the multicollinearity problems to emerge in the time series analysis. With that, the long
run and short run relationships between LPCHCE and independent variables; LPCGDP, LHCEPL, LPOP60, LLIFEXP
and LURBAN can be potentially determined.
 LPCHCEt   1   1 1 L  ...  1 6 L    LPCHCEt   1,t 

   
  

... ...   LPCGDPt   2 ,t 
 LPCGDPt   2  ...
 LHCEPLt   3  ...
... ...   LHCEPLt   3 ,t 

 
 

... ...   LPOP60 t  ... 
 LPOP60 t   4  ...
 LLIFEXP    ...
... ...   LLIFEXPt  ... 
t

  5 
  

 LURBAN t   6   6 1 L  ...  6 6 L    LURBAN t   6 ,t 
[4]
Trace and Maximum Eigenvalue Tests, which are developed by Johansen (1988), are meant to estimate the total
number of long run cointegrating relationships that potentially exist between the variables. If the variables are found
cointegrated over the long run, the short run relationship between the variables can be pursued via the vector error
correction model (VECM) procedure.
4. EMPIRICAL RESULTS
This section contains a description and presentation of the empirical results. Usually, the results are presented in tables
and their main points are highlighted in the text. The following paragraphs are extracted from the empirical-results
section of a sample paper – note that text and numbers in the tables should have a font size of 8 and tables should only
have horizontal lines and no vertical lines, as shown in Table 1 and 2 below.
Descriptive Statistics
Based on Table 1, the mean values of all variables are positive for a total of 45 observations. Irrespective of
measurement units, PCGDP registers with the highest mean value of 12,885.0 whereas LIFEXP, HCEPL, URBAN,
PCHCE and POP60 possess the mean values of about 71.0, 64.0, 53.0, 34.0 and 7.0, respectively. In term of the
covered values, PCGDP records the interval value of over 39,000 versus POP60 that only posts with the interval value
of 4.2. While POP60 has the lowest standard deviation, PCGDP exhibits the highest standard deviation, thereby
undoubtedly implying that the dispersion from its mean is the largest of all variables.
Table 1: Statistical Description of Variables
Variable
PCGDP
LIFEXP
HCEPL
URBAN
PCHCE
POP60
Mean
12,885.120
70.641
64.400
53.377
34.326
6.703
Median
8,203.579
71.214
62.072
51.814
21.703
6.250
Maximum
41,088.470
74.718
110.483
74.010
148.286
9.793
Minimum
1,109.203
64.463
23.340
33.454
1.875
5.590
Standard Deviation
11,664.800
3.012
25.529
12.616
35.922
1.173
Cointegration and Long Run Relationship Estimation
By having the variables that are proven to be cointegrated at I(1), the presence of long run relationship between the
variables can be determined via the Johansen cointegration test. Correspondingly, Table 4.4 summarizes the results of
the Johansen cointegration test. At the five percent significance level, the Trace statistic proves that there exist three
cointegrating equations but the Max-Eigen statistic only indicates that there is a stable long run cointegrating
relationship i.e. one cointegrating equation in the model.
Table 2: Results of the Phillips-Perron Unit Root Test
Constant
Variable
LPCHCE
LPCGDP
LHCEPL
-1.574
(0.487)
-1.667
(0.441)
-1.596
(0.476)
Level
Constant
-2.663
(0.256)
-2.501
(0.326)
-2.385
(0.382)
Plus Trend
Constant
-4.803
(0.000)*
-6.279
(0.000)*
-4.348
(0.001)*
First Difference
Constant
-4.821
(0.002)*
-6.537
(0.000)*
-5.395
(0.000)*
Plus Trend
Conclusion
I(1)
I(1)
I(1)
LPOP60
1.736
-2.937
-3.062
-3.303
I(1)
(1.000)
(0.161)
(0.037)*
(0.079)**
LLIFEXP
-1.792
-1.934
-4.466
-5.283
I(1)
(0.379)
(0.620)
(0.001)*
(0.001)*
LURBAN
-1.440
-1.014
-3.374
-9.697
I(1)
(0.554)
(0.932)
(0.018)*
(0.000)*
Notes: Figures in the parentheses are p-values. * and ** indicate the null of non-stationary being rejected at the five percent and 10 percent
significance levels, respectively.
5. CONCLUSION
This section contains a summary of this paper with a special emphasis on the main findings. The following paragraphs
are extracted from the concluding section of a sample paper
This paper employed the cointegration and VECM methods to examine the long run and short run relationships
between health care expenditure and possible determinants; income, price level, ageing population, life expectancy
and urbanization in Malaysia for the 1970 – 2014 period. The study disclosed that price level, ageing population, life
expectancy and urbanization to be statistically significant at the five percent significance level and positively related
to per capita health care expenditure.
Both short and long run cycles, are in the firm stance to jointly suggest that ageing population is a reliable
indicator, besides urbanization, that will drive the persistently rise in the variations of health care expenditure over the
future years in Malaysia. Thus, the findings of this study on ageing population as a key determinant for health care
expenditure seem harmonious with aforementioned studies such as Getzen, (1992), Ogawa, Amonthep and Rikiya
(2009) and Nordin et al. (2015).
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