Determinant of Life Expectancy in Malaysia
Vignesh Nagarajah
1
, Shankaran Kanadasan
1
,Yogambigai Rajamoorthy
1
1
Department of Economics, Faculty of Accountancy and Management , Universiti Tunku Abdul Rahman,
Malaysia
Keywords: Life Expectancy, Urbanization, Ordinary Least Square, Health Expenditure, Infant Mortality Rate
Abstract: This paper studies the effect of urbanization on life expectancy in Malaysia from 1991 to 2016. To achieve
the research objectives, data collected annually from World Bank database and Ordinary least square (OLS)
method used to investigate the research variables. The results show that urbanization exposes Malaysian to
better health care and has better life expectancy, yet, health expenditure has negatively influence with life
expectancy in Malaysia. Generally, urbanization has positive health impact in Malaysia.
1 INTRODUCTION
World Health Organization (WHO) estimated 70%
of the global population will lives in town and cities
by 2050, has significant impact on living standard,
lifestyles, social behaviour and health (World Health
Organization, 2010). The study conducted by Black
(1999) showed that urbanization also refers to the
changes in the ways in which these people live,
earning livelihoods, the meals which they consume
and the wide range of environmental factors to
which they are exposed. Moreover, WHO identify
health challenges in urbanization such as increase in
non-communicable diseases (cardiovascular
diseases, cancer, diabetes, respiratory diseases),
unhealthy diets, less physical activities, alcohol
abuse and risk of disease outbreak (World Health
Organization, 2010). Henderson (1999) found that
the increase in the number people in urban areas will
cause a significant increase in the costs, social
disparities and negative impact towards the
environment. Yet, urban living also giving
opportunities to access to better health care system.
According to Copplestone (1991) the better
infrastructure available to the people in the urban
areas exposes the people to better medical treatment
which helps the people to have a better access to the
medical services in the urban areas compared to
those in the rural areas.
Furthermore, Ashton (1992) found that, the
concept of epidemiological transition, which shows
while life expectancy is greater in urban areas
compared to in rural areas the inhabitants are often
merely suffering from different forms of ill-health,
often chronic or degenerative, rather than infective.
Malaysia is now one of the most prominent
countries which contribute towards the urbanization
in East Asia region. Therefore, urban growth in
Malaysia is one of the most prominent issues which
the government is looking forward to capitalize due
to its high rising urbanization. Moreover, the issue
of ageing population among Malaysia increasing
past decades. The life expectancy at birth in
Malaysia continuously rose to reach 74.7 years in
2016 compared to 72.2 years in 2000 (Department of
statistics Malaysia,2016). The longevity of
Malaysian should concern on new policy
implementation, resources allocation for elderly and
productivity contribution by this group. Life
expectancy is the most widely used as indicator to
measure a population's health status (OECD 2013).
More specifically, life expectancy related to health
of labour force, productivity and capability to adapt
technological progress (Madsen, 2012). This study
aims to investigate the relationship between
urbanization and the life expectancy of Malaysian.
1.1 Literature Review
There are many factors that affect life expectancy
based on previous research. A research conducted by
Chetty and Stepner (2016) in the United States
between 2001 and 2014 shows that income was
positively correlated with greater life expectancy.
However, the study conducted by Messias (2003)
Nagarajah, V., Kanadasan, S. and Rajamoorthy, Y.
Determinant of Life Expectancy in Malaysia.
DOI: 10.5220/0008787900970103
In Proceedings of the 2nd Syiah Kuala International Conference on Medicine and Health Sciences (SKIC-MHS 2018), pages 97-103
ISBN: 978-989-758-438-1
Copyright
c
2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
97
shows that the income disparity and the illiteracy
rates are negative correlated with the life expectancy
of the population. A person with more income tends
to keep himself healthy by seeking regulator medical
assistance compared to ones with lower income and
therefore people with better income tend to be
always healthier and those with lower income tend
to be not healthy. It relates to the health expenditure
in which the human life expectancy is expected to be
greater when the income is higher. The relationship
between the sosio-economic status and the life
expectancy of people are moderate in less developed
countries compared to developed countries and they
are not explained by the traditional behavioural
factors (Nikkhil Sudharsanan,2017). Sosio economic
status of a person could be a result of the variables
which are being studied in our study such as the
employment rate. Whereas, traditional behavioural
factors may also refers to the behaviours of not
spending enough for the health to ensure healthiness.
Even when the person has a good sosio economic
status, it is not necessarily that he is going to have
greater life expectancy due to traditional behavioural
factors.
Halfon (2009) found that from a Life Course
Perspective, health is a developmental process
occurring throughout the lifespan. The life course
approach conceptualizing health care needs
and services evolved from research. It finds the
important role played by early life events in shaping
an individual’s health trajectory. The life course
perspective is actually a community-focused theory
since social, economic, and environmental patterns
that affect one’s health are closely linked to
community and neighborhood settings. I believe
one’s health is one of a factor that is involved in a
human’s life expectancy and even the infant
mortality rate.
Reidpath and Allotey (2002) investigated that
the infant mortality rate(IMR) has been criticised as
a measure of population health because it is
narrowly based and likely to focus the attention of
health policy on a small part of the population to the
exclusion of the rest. More comprehensive measures
such as disability adjusted life expectancy (DALE)
have come into favour as alternatives. These more
comprehensive measures of population health,
however, are more complex, and for resource poor
countries, this added burden could mean diverting
funds from much needed programmes.
Unfortunately, the conjecture, that DALE is a better
measure of population health than IMR, has not been
empirically tested. There is a strong (generally)
linear association between DALE and IMR (r=0.91).
Countries with low DALE tend to have a high IMR.
The countries with the lowest IMRs had DALEs
above that predicted by the regression line. There is
little evidence that the use of IMR as a measure of
population health has a negative impact on older
groups in the population. IMR remains an important
indicator of health for whole populations, reflecting
the intuition that structural factors affecting the
health of entire populations have an impact on the
mortality rate of infants. For countries with limited
resources that require an easily calculated, pithy
measure of population health, IMR may remain a
suitable choice.
Cervellatia and Sunde (2005) found that the past
two centuries were characterized by widespread and
profound changes in human living conditions. For
aeons, a more or less stable and unchanged
environment prevailed, with a strong preponderance
of agriculture and trade of basic goods, rigid social
structures with usually a small ruling class, and
comparably poor medical conditions. But suddenly
within just more than two hundred years, that is just
a few generations, the economic environment
mutated utterly: the structure of the economy
changed completely with industrialization breaking
its way, reducing the importance of agricultural
activities in favor of the industrial and the service
sector. Personal life changed in every dimension to
an extent not seen before or after. The traditional
social environment ceased to exist, as the vast
majority of the population became educated, and
acquired knowledge beyond the working knowledge
of performing a few manual tasks inherited by
previous generations. Literacy, which used to be the
privilege of a little elite, became widespread among
the population. The process of human capital
accumulation accelerated as more and more people
acquired the ability to innovate, and to use
innovations. On the other hand, the spread of new
technologies in turn made it more profitable to
acquire knowledge. Also the biological environment
sharply changed. Lifetime duration, which had been
virtually the same for thousands of years, increased
sharply within just a few generations. Mortality fell
significantly and fertility behavior changed
profoundly, hygienic conditions improved as
sanitation became more important and widespread.
According to Acemoglu and Johnson (2007),
improving health around the world today is an
important social objective, which has obvious direct
payoffs in terms of longer and better lives for
millions. There is also a growing consensus that
improving health can have equally large indirect
payoffs through accelerating economic growth.
SKIC-MHS 2018 - The 2nd Syiah Kuala International Conference on Medicine and Health Sciences
98
Healthcare expenditure is deemed to have
significant influence on life expectancy since it
directly helps reduce mortality and morbidity as
well. A cross-provinces study shows that lower
health care expenditure is related with a statistically
significant increase in infant mortality and a
decrease in life expectancy in Canada. However, the
relationship was found to be independent of various
economic, sosio-demographic, nutritional and
lifestyle factors as well as provincial specificity of
time trend (Crémieuxe, 1999).
Apart from that, there is higher chances of
correlation between per capita income and health
expenditure, since higher per capita income may
lead to higher per capita health expenditure.
Simultaneously, a nation’s capacity to purchase the
necessary goods and service that promote health can
be increased with per capita income. Poverty among
the societies is causally related to poor health of the
societies (Subramanian, 2002). In order to achieve
overall good health status, a certain level of health
care expenditure may be required (Starfield and Shi,
2002). Costa Rica has attained the highest life
expectancy among the developing world,74 years in
1985 and 78 years in 2002, the level comparable to a
developed country. From my opinion, this is
possible due to positive political and social
circumstance as well as right public health policy,
However, the main factors of this breakthrough were
health interventions, notably a primary health care
program (Rosero-Bixby, 1991). Expect a positive
relationship between health care expenditure and
health status if increasing resources gives an
improvement in the level and quality of health
services supplied to the population. There may also
be diminishing returns to scale above some level of
expenditure. Moreover, Hitiris and Posnet (1992)
found a small negative relationship between health
expenditure and mortality rates. Other than that,
Grubaugh and Santerre (1994) found a positive
impact of certain health inputs like number of
doctors and hospital beds on health measured by
infant mortality rates. Hadley (1982) shows a
positive relationship between health expenditure and
health), by using mortality data on the United States.
Evidences have also been found on positive
relationship between health care input and health
outcomes in the context of Europe (Collins and
Klein, 1980; Forbes and Mcgregor, 1984; and Elola,
1995).
Urban population which is part of urbanization
plays a crucial role in determining life expectancy.
Urban inhabitants of the developing countries
basically enjoy improved medical care and means of
life, better education, and other improved socio-
economic facilities, and this will impact positively
on the health outcomes. Kalediene and Petrauskiene
(2000) found that there was a positive correlation
between level of urbanization and life expectancy
while investigating the patterns of regional life
expectancy in Lithuania. However, state of
urbanization and residential conditions are critically
related to health status and health outcomes of
population of a country. In a study on Rio de
Janeiro, Szwarcwald et al (2000) found the worst
health situation in the cluster are composed of the
harbor area and northern vicinity, precisely in the
sector where the highest concentration of slum
residents were present. Besides,the remainder of the
city have shown a seven years higher life expectancy
compared to the the sector of the city as mentioned
above. However, Rogers and Wofford (1989) found
the opposite result when examining life expectancy
for 95 developing countries because they revealed
that urbanization was less significant in explaining
life expectancy than anticipated due to the unhealthy
condition in cities of the developing countries.
Rapid population growth in a certain country
itself is a result of the diffusion of scientific-medical
knowledge, and is the underlying cause of the
growth of urban population(Darin-Dabkin,1977).
Moreover, the concentration of population in urban
areas is also affected by economic growth, which is
reducing the percentage of the population employed
in agricultural and rural areas. Needless to say,
structural changes in employment, have led to
increased number of employment in the city centre
due to most of the service firms need to be centrally
located so they can obtain benefit from close
interaction with each other (Runde ,2015) .Apart
from that industrial activities also continues to
concentrate in the metropolitan area, which has a
pool of skilled man-power, access to consumer
markets, and a variety of auxiliary commercial
services. These structural changes have also had an
impact on the distribution of population in the urban
areas. The increasing role of the city centre for
commercial purposes forces population shift to
outlying districts. Plus, by developing new
transportation systems, it has allowed a degree of
dispersal within the metropolitan region and
facilitate outward spreading of urban areas.
2 METHOD
This study model adapted from Acemoglu and John
(2007). In order to estimate urbanization effect on
Determinant of Life Expectancy in Malaysia
99
life expectancy, we employed a closed-economy
neoclassical growth model with the assumption the
consumption good is produced with a constant return
to scale aggregate production function can be
express in equation (1):

, 0 <γ <1, (1)
Where Y is output,
is factor of production and
A is capture as level of technology, yet it
incorporates with variety of factor, t is time trend.
The aggregate amount of effective labour is
supplied, therefore can be written as equation (2):
(2)
Where,
denotes the individual level of human
capital and
is size of population. Generally,
improvements in health or life expectancy will
influence total production by affecting technology
can see in equation (3), human capital and size of
population (equation 4). The role of life expectancy
for total factor productivity and human capital
simply expressed as in Acemoglu and John (2007).:
(3)
With and
(4)
With α > 0. Reduction in mortality affects
population size directly, where more people survive
at each points in time indirectly affects the
likelihood of surviving until childbearing increase.
Substituting (2), (3) and (4) into (1): we obtain:




(5)
Basically, (5) relates life expectancy has
influence in technology, human capital, population
and quantity of labour. Our estimation of model as:
lnLF
t-1
= β
0
+ β
1
lnHE
t-1
+ β
3
lnUBN
t-1
+ β
4
lnIMR
t-1
+ε
t
(6)
Where the data convert to ln form before to the first
difference. LF is life expectancy, HE is health
expenditure, UBN is urban population, and IMR is
growth of infant mortality rate. The subscript t
represents the t refer to number of years. List of
variable used and source of data as in Table 1. The
data estimation period covers from 1991 to 2016
yearly, which has total 26 observations. Eview
software was used to analyse the data.
Table 1: List of variables.
Variables
Actual
data
measured
Convert
data
measured
Data
source
Dependent variable
Life
expectanc
y at birth
Total
(years)
lnLF
World
Bank
database
Independent variables
Health
Expenditu
re
RM’000
lnHE
Depart
ment of
statistic
Malaysi
a
Urban
population
Percentage
of total
population
lnUBN
World
Bank
database
Infant
mortality
rate
Percentage
lnIMR
Departm
ent of
statistic
Malaysi
a
3 RESULT AND DISCUSSION
The estimated result for life expectancy model
obtain as follows:
lnLF
t-1
= 0.001 - 0.003lnHE
t-1
+ 0.084lnUBN
t-1
+
0.04lnIMR
t-1
+0.0004 ε
t
(7)
S.E: (0.000) (0.002) (0.021)
(0.004)
t-statistic = [4.852***] [-1.789*] [3.923***]
[0.948
ns
]
R
2
= 0.470; Adjusted R
2
= 0.398; d = 0.503
The results show that explanatory variables
accounted for about 47 percent of the variation in the
life expectancy model (Equation 7). Estimations
reveal that the explanatory variables, namely health
expenditure and urban population, were the most
important explanatory variables with statistically
significance at the 0.10 level and 0.01 respectively.
Infant Mortality Rate was not significant
explanatory variable. The equation (7) shows that 1
percent increase in health expenditure will lead to
SKIC-MHS 2018 - The 2nd Syiah Kuala International Conference on Medicine and Health Sciences
100
0.3% decrease in life expectancy. However,1 percent
increase in urban population and infant mortality
rate will lead to 8.4% and 4% increase in life
expectancy respectively.
The life expectancy model in our shows that an
increase in health expenditure will lead decrease in
life expectancy. This finding different from the
research conducted by Kim and Lane (2013). Their
investigation covers 17 OECD countries between
1973 and 2000 shows that one percent increase in
public health expenditure increase the life
expectancy by 2.6 percent, however, the infant
mortality rate has negative relationship with life
expectancy. Moreover, investigation on 192
countries shows that health expenditure increase,
healthy life expectancy also increases (Lubitz et al.,
2003). In this study the negative relationship found
due to other two research focus on panel data
(combine more countries), however this study use
time series data to conduct the test. The negative
correlation between health expenditure and the life
expectancy is theoretically not consistent compared
to previous study.
Apart from that, most of the low resource
committee are also exposed to chronic diseases
which reduce their life expectancy in the long term
process even though they spend much in medical
expenditures. Without the proper governance and
enforcement to reduce the corruption in both public
and private medical hospitals, the increase in health
expenditure may shows negative health outcome and
thus reduce life expectancy. However, most of the
previous studies contradicts with our findings
because according to the study done by (Jaba,2014)
in examining the relationship between healthcare
expenditures (input) and life expectancy (as a proxy
for health outcomes) for 175 countries from 1995 to
2010 and their findings revealed a positive
association between life expectancy and healthcare
expenditures.
Aìsa et al (2014) found that an increase in
healthcare expenditures leads to improved life
expectancy. In line with these studies, Akinci (2014)
investigated the relationship between healthcare
expenditures and health outcomes for Middle
Eastern and Northern African countries using yearly
data from 1990 to 2010 and they found that
increases in total health-care expenditures reduce
infant, under-five, and maternal mortality rates.
According to the research by Anyanwu and
Erhijakpor (2009), they documented a positive
relationship between health expenditure and life
expectancy for Sub-Saharan countries and negative
relationship for Northern Africa. The difference is
due to the availability of physician in the country
and the undernourishment that affects African
continent.
The positive correlation between urban
population and the life expectancy is theoretically
consistent. This is because most of the urbanized
area have high per capita income and this can lead to
a higher standard living whereby people are exposed
to better job opportunity, facilities and
infrastructure. Moreover, they are exposed to many
health education, talks and seminar in their
workplace especially. Hence they are more health
conscious, follows healthy lifestyle with exercising
and proper dieting and this will enhance the quality
of life. Besides, they also can afford to spend more
in the medical expenses for their own betterment if it
is necessary. Eventually, this will lead to higher life
expectancy. This is in line with previous
investigation done by Lithuania and co-researchers,
where they found that there was a positive
correlation between level of urbanization and life
expectancy (Lithuania et la., 2000). However,
Rogers and Wofford (1989) found the opposite
result when examining the life expectancy for 95
developing countries whereby they revealed that
urbanization was less influential in explaining life
expectancy than anticipated, perhaps because of
unhealthy condition in cities of the developing
countries.
The positive correlation between infant mortality
rate and the life expectancy is however theoretically
inconsistent. When the infant mortality rate is
higher, the life expectancy was expected to move in
the opposite direction. This is because, the high
infant mortality rate shows that the infants are not
being produced in a healthy way which concludes
that the parents will also not have a good generic
which will cause the human life expectancy to
decrease when the infant mortality rate increases.
however, the study shows that the infant mortality
rate in insignificant variable in the model. A study
done by (Reidpath and Allotey (2002), supported the
insignificance, by saying that, as a measure of
population health because it is narrowly based and
likely to focus the attention of health policy on a
small part of the population to the exclusion of the
rest.
Determinant of Life Expectancy in Malaysia
101
Table 2: Summary of residual test.
Diagnostic
test
Hypothesis
Result
Normality
test (Jarque-
Bera)
H
0
: error term
is normally
distributed.
H
A
: error term
is not
normally
distributed
JB
statistic:
0.7713
Prob.
value:
0.6800
Heterosced
asticity test
(White)
H
0
: No
multicollinear
ity among the
variables.
H
A
: There is
multicollinear
ity among the
variables
Prob. F
(9,16):0.
1842
Prob.
Chi
Square
(2):
0.1866
Serial
Correlation
test (LM)
H
0
: There is
no
autocorrelatio
n among the
residuals.
H
A
: There is
autocorrelatio
n among the
residuals.
Prob. F
(10,12):
0.2676
Prob.
Chi-
Square
(2):0.16
29
Multicollin
earity test
(Variance
Inflation
Factor)
H
0
: The
variance is
homoscedasti
city
H
A
: The
variance is
heteroscedasti
city.
VIF =
1.887
4 CONCLUSIONS
In this study, we have investigated the effect of
infant mortality rate, health expenditure and urban
population on life expectancy. The result showed
that health expenditure and urban population are the
most important variables in determining the life
expectancy of Malaysian, compare infant mortality
rate appeared to be not significant in the model. The
possible explanations that can be draw from the
results is the causes of infant mortality rate are
strongly related to those structural factors like
economic development, general living conditions,
social well-being, and the quality of the
environment, that affect the health of entire
populations. In particular, both infant mortality rate
and age urban population demonstrate significant
positive relationship with the life expectancy,
whereas health expenditure shows significant
negative relationship with the life expectancy.
Employment can enhance welfareness and combat
poverty which can make the life of people to better
off. Moreover, life expectancy is one of the factors
in measuring the Human Development Index (HDI)
of each nation along with adult literacy, education,
and standard of living. The World Health
Organization has published statistics called Healthy
life expectancy (HALE) since 2001. Hence, in order
to enhance better quality life, all these factors of
medical expenses, migration of people to city center
and the infant mortality rate have to be analysed
more thoroughly to create a new policy for the
benefits of the social and economy of a certain
nation.
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