The Link Between Health Care Expenditure and Life Expectancy: Turkey (1975-2015)

International Journal of Humanities and Social Science Invention
ISSN (Online): 2319 – 7722, ISSN (Print): 2319 – 7714
www.ijhssi.org ||Volume 6 Issue 1||January. 2017 || PP.59-64
www.ijhssi.org 59 | Page
The Link Between Health Care Expenditure and Life
Expectancy: Turkey (1975-2015)
Assoc. Prof. Dr. M. Akif Arvas1
Res. Asst. MustafaTorusdağ2
ABSTRACT: This paper aims to investigate the link between health care expenditure and life expectancy in
Turkey, causality and co-integration relationship between health care expenditure (HCE) and life expectancy
(LE) in Turkey for the period 1975-2015. In this paper we also used real GDP per capita and some
demographic variables such as dependency ratio of the old-aged populations, the number of practicing
physicians per hundred thousand persons that may explain the variations in HCE. According to the results of
analyze, drperpop, gdp, hce, le65, le70, le75 variables are stable at 1% significance in the first I(1) difference
value. The AGE valuable is stable 1% at the level value. There are two long-term cointegration relationships
between health spending and life expectant variables. AGE → DGDP, AGE → DLE40, AGE → DLE60,
DHCE → DDRPERPOP, DLE80 →DDRPERPOP, DHCE → DGDP and DLE60 → DLE80 shows the direction
of granger causality.
Keywords: Life expectancy, Health care expenditure, Johansen Cointegration, Granger Causality.
I. INTRODUCTİON
Life expectancy is an important indicator of how long someonelives on average. Health care
expenditure and life expectancy relations in the literature use this indicator commonly measurement of
population health and often used as a measure when comparing different populations. In the nineteenth century
Life expectancy has been on the rise mostlyin Western countries. In developing countries, life expectancy has
increased markedly in recent years, and per capita health spending has increased significantly. Question is
whether the medical expenditure has played an important role (Baal, Obulqasim, Brouwer, Nusselder and
Mackenbach, 2013). Health spending is an important indicator of health care quality and population health. In
many empirical studies, there appears to be a significant relationship between life expectancy and health
expenditure (i.e. Hermanowski et al. 2015).
In most developed countries, there is a direct correlation between health care spending and life
expectancy because healthcare is one of the major development challenges facing the continuously evolving
world today. (Deshpande, Kumar and Ramaswami, 2014). It is expected that the health expenditures will
increase as the population ages.(Bjorner, Arnberg, 2012).Health spending may change due to increases in
LE(Shiu and Chiu, 2008). Because of the economy is based on the distribution of scarce resources, the state is
also expected to allocate the necessary resources to health spending. For this reason, it is expected that the
increase in health expenditures, will increase life expectancy and a higher quality of health. It is important to
examine this relationship; Because government spending is more effective on health spending (Deshpande,
Kumar, Ramaswami, 2014).
In this paper we study the link between health care expenditure and life expectancy in Turkey as a
function of health care spending, available real per capital personal health care expenditures, real per capital
gross domestic product, dependancy ratio of the old aged population, life expectancy for population (LE65: 65-
69, LE70: 70-74 and LE75: 75-79) and number of practicing physicians per hundred thousand personsvariables.
The data were collected from the (Organization for Economic Cooperation and Development Statistics) OECD
STAT.In this study is investigated the link between life expectancy and healthcare expenditure for Turkey. The
study consist of chapter. The first part of study is an introduction to the subject. The second part deals
withpopulation ageing, life expectancy and health care expenditure. The third part includes a literature review.
The econometric methodology and the findings are examined in the fourth section and fifthsection of article
consist of conclusion and discuss.
II. POPULATİON AGEİNG, LİFE EXPECTANCY AND HEALTH CARE EXPENDİTURE
The aging of the population affects the aging population and the health care expenditures and aging of
the population. More than 80% of diseases, death and medical costs increase after age 65. Diseases are mostly
1
Yüzüncü Yıl Üniversitesi, İİBF., İktisat Bölümü, aarvas@yyu.edu.tr
2
Yüzüncü Yıl Üniversitesi, İİBF., İktisat Bölümü, mustafatorusdag@yyu.edu.tr

The Link Between Health Care Expenditure And Life Expectancy: Turkey (1975-2015)
chronic diseases brought about by aging. Older people spend more on health and use health care than people
under 65 years old. LE is a statistical measure of the average life span of a given population.In advanced age
groups, LE represents the expected time of death, and is also significantly affected by changes in morbidity and
health status(Shiu and Chiu, 2008).
“According to the “red-herring” hypothesis, the positive correlation between age and HCE is
exclusively due to the fact that mortality rises with age and a large share of HCE is caused by proximity to
death. As a consequence, rising longevity through falling mortality rates may even dampen the increase in HCE.
However, a weakness of previous empirical studies is that they use cross-sectional evidence to make inferences
on a development over time” (Breyer, 2011).
According to Fries (1980),“Red Herring hypothesis is in perfect agreement with the compression of
morbidity thesis which stated that the onset of disability is postponed and the time span of severe illness leading
to death shrinks when life expectancy increases. On this issue, while the study by Zweifel et al. (1999) is suffered
from the weakness of concentrating on patients in their last two years of life, subsequent studies by several
authors mainly confirm the red-herring hypothesis (Felder, 2013).
Figure 1. Three components of healthy ageing strategy
Source: Kenneth Howse (2012), “Healthy Ageing: The Role Of Health Care Services”, Perspectives in Public
Health, Vol. 132, No. 4, 171-177.
Although the definitions of healthy aging differ (by extending the quality of life as much as possible in
relation to health), the basic idea of a successful healthy aging strategy is “Life to years as well as years to life”.
A second component of a healthy aging strategy is possible by expanding biomedical technology and
technological innovation. The third component of a healthy aging strategy is the restructuring and regulation of
health delivery systems for people with age-related illness and disability (Howse, 2012).
III. LİTERATURE REVİEW
A group of literature in recent years has tried to examine the link between health expenditure and life
expectancy.
Deshpande, Kumar and Ramaswami (2014) seek to examine whether or not there is a relationship
between healthcare expenditure and national life expectancy in order to gain perspective on how to efficiently
increase the quality of health in a state. According to the results from estimation of single regression, it is found
that positive relationship between health expenditure and life expectancy so increase in spending would increase
life expectancy.
Cundiff (2010) studiesthe relationship among life expectancy, health care spending, medical resource
availability and lifestyle variables in the United States relative to other member countries in OECD. According
to analyze result United States performs very poorly relative to its peers. While United States spends more per
capita by far than any other country, because it has lower life expectancy and fewer medical resources than most
member countries is probably caused by the United States’ lack of health care resources relative to the peers
with high obesity rate.
Jaba, Balan, Robu (2014) aim to analyze the relationship between the dynamics of the inputs and the
output of the health care systems for the period 1995-2010. For the analyze to estimate life expectancy,panel
data method is employed. The obtained results show a significant relationship between health expenditures and
life expectancy. Country effects are significant and show the existence of important differences among the
countries.
Bjorner, Arnberg (2012), “Terminal costs, improved life expectancy and future public helath
expenditure”, in this paper presents emprical analysis of public health expenditures on individuals in Denmark.
The analysis separates out the individual effects of age and proximity to death (reflecting terminal costs of

dying) and employs unique micro data from the period 2000 to 2009, covering a random sample of 10% of the
Danish population. Health expenditure includes treatment in hospitals, subsidies to prescribed medication and
health care provided by general practitioners and specialists and covers about 80% of public health care
expenditure on individuals. According to analysis results when life expectancy increases, terminal costs are
postponed but the increases in health expenditure that follow from longer life expectancy are not as large as the
increase in the number of elderly persons would suggest healthy ageing is expected to reduce the impact of
increased life expectancy on real health expenditure by 50% compared to a situation without healthy ageing.
Breyer, Felder (2006), “Life expectancy and health care expenditures: A new calculation for Germany
using the costs of dying”, in this article aims to analyze the impact of population ageing on future health care
expenditures will be quite moderate due to the high costs of dying. If not age per se but proximity to death
determines the bulk of expenditures, a shift in the mortality risk to higher ages will not affect lifetime health
care expenditures as death occurs only once in every life so to take this effect into account when is calculated
the demographic impact on health care expenditures in Germany.
Ogungbenle, Olawumi, Obasuyi (2013), “Life Expectancy, Public Health Spending and Economic
Growth in Nigeria: A Vector Autoregressive (VAR) Model”,the main focus of this article study is to empirically
analyze the relationship existing among life expectancy, public health spending and economic growth in Nigeria.
According to the results of the study revealed that there is no bi-directional causality between life expectancy
and public health spending in Nigeria and the study also revealed that there is no bi-directional causality
between life expectancy and economic growth in Nigeria over the years. However, the study define that there is
bi- directional causality between public health spending and economic growth in Nigeria.
Obrizan, Wehby (2012), “Health Expenditures and Life Expectancy Around the World: a Quantile
Regression Approach”, in this paper was evaluated the heterogeneity in country health expenditure effects
throughout the life expectancy distribution applying quantile regression to an assembled dataset of 177 countries.
In the analyze found significant heterogeneities in expenditures effects on life expectancy that are completely
masked by ordinary least squares (OLS), which underestimates (overestimates) the expenditure returns for
countries ranking at low (high) life-expectancy quantiles. The largest returns from increased spending are for
countries at the left margin of the life expectancy distribution (mainly at quantiles 0.25 and lower), for which a
$100 increase in per capita spending leads to 11.5 and 11 months of life for males and females, respectively.
The results suggest that increasing healthcare spending in these countries may have significant population-wide
life expectancy returns.
Memarian (2015), “The Relationship between Health Care Expenditure, Life Expectancy and
Economic Growth in Iran”, in this article is investigated with ARDL econometric technique for period from
1989 to 2011 considering the importance of healthcare for human workforce in modern society and increasing
trend of life expectancy in developed countries.The results of analyze indicate that life expectancy and
healthcare expenditures have a significant positive impact on Gdp both in the short-term and in the long-term.
An increase in life expectancy and health care expenditures causes an increase in economic growth.
Lubitz, Cai, Kramarow and Lentzner (2003), “Health, Life Expectancy, and Health Care Spending
among the Elderly”, in this article investigate calassified persons’health according to functional status and
whether or not they were institutionalized and according to self-reported health using multistate life-table
methods 1992-1998 Medicae Current Beneficiary Survey and microsimulation to estimate life expectancy for
persons in various states of health. We linked annual health care expenditures with transitions between health
states. As a result of analyze elderly persons in better health had a longer life expectancy than those in poorer
health but had similar cumulative health care expenditures until death. A person with no functional limitation at
70 years of age had a life expectancy of 14.3 years and expected cumulative health care expenditures of about
$136,000 (in 1998 dollars); a person with a limitation in at least one activity of daily living had a life expectancy
of 11.6 years and expected cumulative expenditures of about $145,000. Expenditures varied little according to
self-reported health at the age of 70. Persons who were institutionalized at the age of 70 had cumulative
expenditures that were much higher than those for persons who were not institutionalized.
IV. DATA AND METHODOLOGY
Emprically Results
Table 1. Variables and Definitions of Regression
Variable Definitions
HCE Real per capital personal health care expenditures
GDP Real per capital grossdomestic product
AGE Dependency ratio of the old-aged population
LE65 Life expectancy for population aged 65-69
DRPERPOP Number of practicing physicians per hundred thousand persons

𝑀𝑜𝑑𝑒𝑙: 𝐻𝐶𝐸𝑡 = 𝛽1 + 𝛽2 𝐺𝐷𝑃𝑡 + 𝛽3 𝐴𝐺𝐸 + 𝛽4 𝐿𝐸65 + 𝛽4 𝐿𝐸70 + 𝛽5 𝐿𝐸75 + 𝛽6 𝐷𝑅𝑃𝐸𝑅𝑃𝑂𝑃 + 𝜇𝑡
Table 2. Augmented Dickey-Fuller (ADF Test)
LevelValue 1stDifference Value
Değişkenler ADF-Test
İstatistiği
Gecikme
Uzunluğu
%1 Kritik
Değer
Değişkenler ADF-Test
İstatistiği
Gecikme
Uzunluğu
%1 Kritik
Değer
AGE -3.314 4 -2.945 (%5) ΔAGE _ _ _
drperpop -0.945 4 -3.626 ∆drperpop -3.896 3 -3.626
gdp 0.999 0 -3.605 ∆gdp -6.133 0 -3.610
hce 1.343 1 -3.610 ∆hce -3.685 0 -3.610
Le65 1.628 0 -3.605 ∆le40 -6.504 0 -3.610
Le70 0.674 0 -3.605 ∆le60 -6.570 0 -3.610
Le75 0.677 0 -3.605 ∆le80 -6.600 0 -3.610
In the table 2, drperpop, gdp, hce, le65, le70, le75 variables are stable at 1% significance in the first
I(1) difference value.The AGE valuable is stable at the level value. After determining that the variables are
stationary with the applied unit root test, Johansen Juselius cointegration analysis and Maximum Eigen value
and Trace (trace) statistics are used to determine whether there are long-term relationships between the variables.
The Johansen cointegration test was undertaken to determine the number of delays that play an important role in
the established VAR when investigating the long-run relationship between variables.
Table 3. Lag Length Criteria
Lag Log L LR FPE AIC SC HQ
0 -496.098 NA 3244.998 27.94991 28.25782 28.05738
1 -305.383 296.6674 1.302794 20.07687 22.54012 20.93661
2 -251.1262 63.30033 1.346402 19.78479 24.40338 21.39680
3 -155.9828 74.00038 0.286583 17.22127 23.99521 19.58555
4 9.791752* 64.46788* 0.008150* 10.73379* 19.66308* 13.85035*
In the table 3, the information criteria are examined as AIC, SIC and HQC, lag lenght is determined p=4.
Table 4. Johansen Juselius Cointegration Tests (UnrestrictedCointegration Rank Test-Trace)
Hypothesized Eigenvalue Trace Statistic %5 critical value
None* (r=0) 0.831006 215.429 139.275
At most 1* (r=1) 0.784355 149.647 107.346
At most 2* (r=2) 0.636893 92.884 79.341
At most 3* (r=3) 0.516039 55.401 55.245
At most 4 (r=4) 0.380184 28.548 35.010
At most 5 (r=5) 0.253340 10.850 18.397
At most 6 (r=6) 0.001107 0.0409 3.8414
* Trace test indicates 4 cointegrating eqn(s) at the 0.05 level
* Denotes rejection of the hypothesis at the 0.05 level
In the table 4, according to based on the maximum likelihood method, the Johansen cointegration
analysis is sensitive to the lag length. The delay length is determined as 1 according to the Akaike Information
Criteria, Schwarz Information Criteria, and Hannan-Quinn Information Criteria, which provide the stability
condition of the model within the VAR model framework. In Table 4, there is no cointegrated vector between
the variables if the trace statistic is greater than the critical value at the 5% significance level according to
Johansen cointegration results, and the null hypothesis (r = 0, r=1, r=2, r=3) is rejected. In this case, there is
three cointegrated vector among the variables (r = 4) hypothesis is accepted. According to this, life expectancy
and health expenditures have a long-term coexistence relationship.
Table 5. Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Eigenvalue Max-Eigen Statistic %5 critical value
None* (r=0) 0.831006 65.782 49.586
At most 1* (r=1) 0.784355 56.762 43.419
At most 2* (r=2) 0.636893 37.483 37.163
At most 3 (r=3) 0.516039 26.852 30.815
At most 4 (r=4) 0.380184 17.698 24.252
At most 5 (r=5) 0.253340 10.809 17.147
At most 6 (r=6) 0.001107 0.0409 3.8414
* Max-eigenvalue test indicates 3 cointegrating eqn(s) at the 0.05 level
* Denotes rejection of the hypothesis at the 0.05 level

In the table 5, according to the Johansen cointegration test results, there is a cointegrated vector
between the variables when the maximum eigenvalue statistic is greater than the critical value at the 5%
significance level. The hypotheses r = 0, r = 1 and r = 2 are rejected. In this case, there is two cointegrated
vector among the variables (r = 3) hypothesis is accepted. According to this, life expectancy and health
expenditures have a long-term coexistence relationship.Generally, there is no contradiction in trace statistic and
maximum eigenvalue test, but the maximum eigenvalue statistic will be preferred as it gives more precise results
when encountered (Tarı, 2014). That is, there are two long-term cointegration relationships between health
spending and life expectant variables.
Table 6. Granger Causality Test
Direction of Causality Obs. F-Statistic Prob.
1- 𝐴𝐺𝐸 → 𝐷𝐺𝐷𝑃 36 2.81341 0.0451**
2- 𝐴𝐺𝐸 → 𝐷𝐿𝐸40 36 2.85657 0.0428**
3- 𝐴𝐺𝐸 → 𝐷𝐿𝐸60 36 2.95077 0.0382**
4- 𝐷𝐻𝐶𝐸 → 𝐷𝐷𝑅𝑃𝐸𝑅𝑃𝑂𝑃 36 3.49991 0.0199**
5-𝐷𝐻𝐶𝐸 → 𝐷𝐺𝐷𝑃 36 3.49881 0.0200**
6-𝐷𝐿𝐸60 → 𝐷𝐿𝐸80 36 4.29151 0.0081*
7-𝐷𝐿𝐸80 → 𝐷𝐷𝑅𝑃𝐸𝑅𝑃𝑂𝑃 36 4.72319 0.0051*
* 1%, the level of significance
** 5%, the level of significance
According to table 6, analysis results show that at a level of significance of 5%, we can reject null
hypotesiz that is we can not accept null hypotesiz so AGE does cause DGDP (AGE → DGDP), AGE → DLE40,
AGE → DLE60, DHCE → DDRPERPOP,DHCE → DGDP,and level of significance of 1%, we can reject null
hypotesiz that is we can not accept null hypotesiz so DLE80 →DDRPERPOP and DLE60 → DLE80 shows the
uni direction of causality.
V. CONCLUSİON AND DİSCUSS
In a developed world have experienced a significant increase in life expectancy in last years and per
capita health care expenditure has increased dramatically. There is a consensus amongst researchers that health
expenditures, reflecting the quality of healthcare, may have a positive impact on the population health status and
that this impact should be evaluated.A positive and significant relationship between life expectancy and
healthcare expenditure has been found in many empirical studies (i.e. Hermanowski et al. 2015).There is a direct
relationship between health expenditures and average life expectancy.
Ageing of the population is a crucial driver of health care expenditure (HCE) and effect population
ageing on health and increasing health care cost. Population ageing has become or is becoming a medical and
social problem in most developed countries.Ageing of the population is driver of health care expenditure (HCE)
and effect population ageing and increasing health care cost. “Red-herring” hypothesis is that positive
correlation between age and HCE is exclusively due to the fact that mortality rises with age and a large share of
HCE is caused by proximity to death, but does not apply to the results of our work. According to the results of
analyze, drperpop, gdp, hce, le65, le70, le75 variables are stable at 1% significance in the first I(1) difference
value. The AGE valuable is stable 1% at the level value. There are two long-term cointegration relationships
between health spending and life expectant variables. There is a uni direcitonal granger causality, AGE →
DGDP, AGE → DLE40, AGE → DLE60, DHCE → DDRPERPOP, DLE80 →DDRPERPOP, DHCE → DGDP
and DLE60 → DLE80.
REFERENCES
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[2]. Erfan Memarian (2015), “The Relationship between Health Care Expenditure, Life Expectancy and Economic Growth in Iran”,
Journal of Applied Environmental and Biological Sciences, 5(10S), 284-290.
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Expectancy”, Working Paper, 1-16.
[4]. Friedrich Breyer, Stefan Felder (2006), “Life expectancy and health care expenditures: A new calculation for Germany using the
costs of dying”, Health Policy, 75, 178-186.
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