Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
The Impact of Environmental Quality and Pollution on
Health Expenditures: A Case Study of Petroleum Exporting
Countries
Ahmad Assadzadeh1, Faranak Bastan2, Amir Shahverdi3
A few studies have been conducted on assessing the determinants of
expenditures and environmental quality from a macroeconomic point of
view. In this article, we take the carbon dioxide emissions as a proxy
variable for environmental quality, and develop panel data models for oil
exporting countries. We examine the role of environmental pollution in
determining per capita health expenditures. We take a panel data
approach in order to explore the possibility of estimating environmental
quality. Our empirical analysis is based on 8 oil exporting countries, for
the period 2000–2010. The results the short-run elasticities revealed that
income and carbon diooxide emissions exerted a statistically significant
positive effect on health expenditures but life expectancy had negative
effect.
Key words: Health care expenditure; Pollution; environmental quality
JEL codes: I19, C23, Q50
1. Introduction
A great deal of economic and environment literature examined the determinants of
health expenditures (Hansen and King, 1996; Di Matteo and Di Matteo, 1998;
McCoskey and Selden, 1998; Gerdtham and Lothgren, 2000; Murthy and Okunade,
2000; Freeman, 2003; Jerrett et al., 2003; Di Matteo, 2005; Narayan and Narayan,
2008; Wang, 2009). This literature has examined several determinants of health
expenditures, including income, population aging, number of practicing physicians,
female labour force participation rate, the proportion of health care publicly funded,
amount of foreign aid, urbanisation rate, among other non-economic factors. What
has not been studied in this literature is the role that environmental quality plays in
the determination of health care expenditure. In this paper, we attempt to fill this
research gap through examining the role of environmental quality, proxied by carbon
monoxide emissions, on health care expenditures in 8 selected oil countries covering
2000-2010.
The environmental pollution is detrimental to human health is well recognized and
documented (Pearce and Turner, 1991; Schwartz and Dockery, 1992; Wordly et al.,
1997; Hansen and Selte, 2000; Jerrett et al., 2003; Neidell, 2004; Mead and Brajer,
2005; He, 2008). The impacts of environment degradation on human health affect
society not only in terms of loss of quality of life, but also in terms of expenditure on
health care (OECD Environmental Outlook, 2001). Health care expenditures due to
environmental degradation are substantial.
Reducing pollution to increase the environmental quality turns away resources from
investment and therefore drags down growth. There are several options to offset this
1
Associate Professor in Economics, University of Tabriz, Tabriz, Iran, Email:assadzadeh@gmail.com
PhD student of Economics, University of Tabriz, Tabriz, Iran, Email: faranak.bastan@gmail.com
3
PhD student of Economics, University of Tabriz, Tabriz, Iran, Email: shaverdi.a88@gmail.com
2
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
crowding-out effect: incorporating the external influences of the environment on
productivity, taking into account some policy-induced adjustments (see Ricci, 2007,
p.694), and assuming elastic labor supply (see Hettich, 1998) or constant returns to
scale in the pollution abatement sector (see Michel and Rotillon, 1995).
The costs of environmental contamination are undebatable, and put increased strain
on government budgets, potentially demanding increased health care expenditures
(see Pearce and Turner, 1991). Jerrett et al. (2003) argue that relations among
environmental conditions, government policies to protect public and ecosystem
health, and health care costs connect to a large debate on cost containment in the
health care system. The main source of environmental cost is air pollution. While air
pollution leads to environmental damage which has to be borne by the society, air
pollution that negatively affects human health has negative repercussions on labour
productivity. This affects industrial output and indeed national output, thus affecting
growth of firms and the economy. One strand of the literature, drawing on
survey/crosssectional data, has confirmed the negative association between air
pollution and human health; see, for instance, Hansen and Selte (2000), who explore
the relationship between air pollution on sick leave and labour productivity; and
Hausmann et al. (1984), Ostro (1987), Zuidema and Nentjes (1997), and Ostro and
Rothchild (1989) who examine the nexus between total suspended particles and/or
fine particles and work loss days.
The impacts of environment degradation on human health affect society not only in
terms of loss of quality of life, but also in terms of expenditure on health care (OECD
Environmental Outlook, 2001). Health care expenditures due to environmental
degradation are substantial. Evidence shows that environmental-related health costs
can add up to as much as US $130 billion per year for OECD countries, equivalent to
0.5% of GDP (OECD Environmental Outlook, 2001).
2. An Overview of the Literature
Jerrett et al. (2003) examined the relationship between environmental quality
(proxied by total pollution emissions and government expenditures devoted towards
defending environmental quality) and health care expenditures. They used crosssectional data from 49 counties of Ontario, Canada. They found that countries with
higher pollution have higher per capita health expenditures, and countries that spend
more on defending environmental quality have lower expenditures on health care.
Neidell (2004), estimated the effect of air pollution on child hospitalizations for
asthma. He found that carbon monoxide had a significant positive effect on asthma.
Hansen and Selte (2000), examined the relationship between air pollution and
human health effects. Their main focus was on investigating the impact of
deteriorating health due to air pollution, which leads to more sick leaves, on labor
productivity. They used data from Oslo and employed a logit model. They found that
an increase in small particulate matter increases number of sick leaves, which
negatively impacts trade and industry in Oslo.
Amore comprehensive analysis was undertaken by Karatzas (2000) who examined
the relationship between per capita health expenditure and economic factors,
demographic factors, and health stock, for the USA over the period 1962 to 1989.
His main findings were that per capita income, income distribution, number of
physicians, number of nurses, and per capita expenditure on health administration
had a statistically significant positive effect on per capita health expenditures, while
the heath price index, number of hospital beds, and the US cities with population of
over one hundred thousand inhabitants had a statistically significant negative effect
on per capita health expenditures.
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Narayan and Narayan (2007) examined the role of environmental quality in
determining per capita health expenditures in eight OECD countries for the period
1980–1999. They take a panel cointegration approach in order to explore the
possibility of estimating both short-run and long-run impacts of environmental quality.
They found that per capita health expenditure, per capita income, carbon monoxide
emissions, sulphur oxide emissions and nitrogen oxide emissions are panel
cointegrated. While short-run elasticitie reveal that income and carbon monoxide
emissions exert a statistically significant positive effect on health expenditures, in the
long-run in addition to income and carbon monoxide, we find that sulphur oxide
emissions have a statistically significant positive impact on health expenditures.
Zheng and et.al (2010), in their study, answer this question that Does pollution drive
up total health care expenditure? They used a panel dataset consisting of 31
Chinese provinces covering the period 1997-2003. They found that there is no
matter in the long run or the short run, public health expenditure is not only positively
affected by the province’s economy, but also by environmental quality.
3. Model, Data, Methodology and Results
3.1 Health Expenditure Model
In this research the relationships between health expenditure, income, environmental
quality and life expectancy by panel data approach has been investigated. We
estimate the main empirical implications of the model using panel data for the period
2000-2010.
Following Nanrayan and Nanrayan (2007), we specify the health expenditure model
by incorporating three different environmental quality indexes for province i at time t
as follows:
(1)
Here, HE is the per capita health expenditures PPP (constant 2005 international $),
GDP is the per capita income PPP (constant 2005 international $), CO2 is carbon
monoxide emissions (metric ton) and LIF is life expectancy. The term, εt, is the error
term bounded with the classical statistical properties. All variables are converted in
natural logarithmic form to allow us to interpret them as elasticities. The panel
version of Eq. (1) can be written by incorporating a subscript i, representing country,
as follows:
(2)
Where i is a country index and t indicates the number of the cross section regression
of the panel.
We expect an increase in income and environmental quality through more emissions
and a deterioration in life expectancy, have been had positively impact on health
expenditures. As countries grow they have more to spend on health care is well
known, and empirical studies support this relationship (see, inter alia, Gerdtham et
al., 1992; Hansen and King, 1996; Murthy and Ukpolo, 1994,1995). On the other
hand, when environmental quality increases, it negatively impacts peoples' health. A
deterioration in health demands more expenditures on health. In the medical science
literature, work has been done to suggest that there is a positive relationship
between air pollution and all causes of mortality (see, inter alia, Schwartz and
Dockery, 1992a; Dockery et al., 1992), cardiovascular mortality (Wordly et al., 1997),
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
respiratory mortality (Pope et al., 1992; Schwartz, 1994a; Shogren, 2001; Mead and
Brajer, 2005), and pneumonia and chronic obstructive pulmonary disease mortality
(Schwartz, 1994b; Schwartz and Dockery, 1992b).
The data set for the empirical analysis consists of a panel of 8 oil countries covering
the period 2000-2010. Statistical sources for all variables are used in this study
published by the World Bank (WDI).
The basic framework for this discussion is a regression model of the form:
yit = Xitβ + Ziα +eit
There are K regressors in Xit , not including a constant term. The heterogeneity, or
individual effect is Zi′α where Zi constant term and a set of individual or group
specific variables. Thus if we are interested in differences across group, we can test
the hypothesis that the constant terms are all equal with an F test. Under the null
hypothesis of equality, the efficient estimator is pooled least squares. The F ratio
used for this test is:
Where LSDV indicates the dummy variable model and Pooled indicates the pooled
or restricted model with only a single overall constant term. If the null hypothesis was
rejected, we have made the distinction between fixed end random effects models.
The specification test devised by Hausman (1978) is used to test for orthogonality of
the random effects and the regressors. The test is based on the idea that under the
hypothesis oh no correlation, both OLS in the LSDV model and GLS are consistent,
but OLS is inefficient, whereas under the alternative, OLS is consistent, but GLS is
not. The chi-square test is based on the Wald criterion:
W = χ 2[K −1] =[b −βˆ]′ψ −1[b −βˆ]
ψ =Var[b −βˆ] =Var[b] −Var[βˆ]
b is the slope estimator in LSDV model (fixed effect) and β is the slope estimator in the random
effect model (Greene, 2004, pp 284-302).
3.2. Results
The result of these tests that were shown in the tables of estimation models indicate
null hypothesis of F test is rejected. In other hand the result of F test show LSDV
model is better model and pooled least square isn’t better model and individual
effects is not considered (table1). As previously attended, if the null hypothesis in F
test was rejected, we have made the distinction between fixed end random effects
models by Hausman test. The results of Hausman test show the null hypothesis isn’t
rejected and random effect is efficient and consistent (table 2).
Table 3 reports the results of our estimations using random effect method according
to Equation (2). The panel data estimations suggest coefficient of GDP and CO2
emission at the 1% level, while life expectancy coefficient at the 10% level of
significance.
Result show that per capita GDP and co2 emission remain two statistically significant
determinants of per capita health expenditure function, while co2 emission remains
the factor that affects public health spending the most.
Findings show that there is a positive relation between health expenditure and
income and CO2 emission and negative relation.
Per capita income and carbon monoxide emissions have a statistically significant
positive impact on per capita health expenditures. Our results from estimators reveal
robust elasticities.
Among the variables, co2 emission has the largest impact on health expenditure.
Specifically, 1% rise in CO2 emission increases per capita health care expenditure
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
by 0.79%. Also we find that a 1% increase in per capita income increases per capita
health expenditures by around 0.48% and 1% decrease in life expectancy increases
per capita health expenditures 0.67 %.
TABLE 1. panel data fixed effect estimates
coef
Std Err
t
0.48017
0.2039052
2.35
0 .4027819
3.299396
0.12
0.4986073
0.3287228
1.52
-8.7874
11.41139
-0.77
ln
lgdp
life
Lco2
cons
Sigma-u
F test that all
u-i=0
lg
llif
Lco2
0.575566
F(7,77)=13.04
Sigma-e
Prob>F=0.0000
p>t
0.021
0.90
0.13
0.444
0.347987
Table2. Result of hausman test
(b)
(B)
(b-B)
S.E
0.4801794
0.4810485
-0.0008691
0.1446538
0.4027819
-0.6742104
1.076992
2.817957
0.4986073
0.7949964
-0.2963891
0.2693056
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 2.72
Prob>chi2 = 0.437
ln
lgdp
life
Lco2
cons
TABLE 3. panel data random effect estimates
coef
Std Err
t
0.4810485
0.1437102
3.35
-0.6742104
1.716139
-0.39
0.7949964
0.1885024
4.22
-4.867476
6.493535
-0.75
Sigma-u
0.46814319
Sigma-e
p>t
0.001
0.694
0.00
0.454
0. 34798731
4. Conclusions
The aim of this paper was to contribute to the ecological and health economics
literature which has modelled the determinants of health expenditures. Furthermore,
we employ the panel data techniques to estimate the short-run elasticity. Our
empirical analysis was based on a panel of the OPEC countries, namely Iraq, Iran,
Libya, Kuwait, Qatar, Nigeria, Saudi Arabia and Venezuela for the period 2000–
2010. Our novelty was that we extended this work through introducing environmental
quality variables. In other words, we proposed a model that examined the
relationship between per capita health expenditures, per capita income, life
expectancy and carbon monoxide emissions.
In the short-run two variables (GDP percapita and co2 emission) are found to have
statistically significant positive effect and life expectancy negative effect public health
expenditure. Among the variables, CO2emission has the largest impact on per capita
public spending.
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Our results suggest that health policy should include environmental quality issues,
for failure to do so is likely to see an increase in health care expenditures. This
implies that if the proportion of health expenditure goes to caring for those affected
from deterioration in environmental quality, then there is less funds available to cater
for upgrading environmental quality and, if this process continues, it is likely to lead
to more pressures on government budgets.
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