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Environmental pollution, health expenditure and economic growth in the Sub-Saharan Africa countries: Panel ARDL approach
Accepted Manuscript Title: Environmental pollution, Health expenditure and Economic growth and in the Sub-Saharan Africa countries: Panel ARDL approach Authors: Zaidi Saida, Saidi Kais PII: DOI: Reference:
S2210-6707(17)30846-6 https://doi.org/10.1016/j.scs.2018.04.034 SCS 1074
To appear in: Received date: Revised date: Accepted date:
13-7-2017 2-4-2018 27-4-2018
Please cite this article as: Saida, Zaidi., & Kais, Saidi., Environmental pollution, Health expenditure and Economic growth and in the Sub-Saharan Africa countries: Panel ARDL approach.Sustainable Cities and Society https://doi.org/10.1016/j.scs.2018.04.034 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Environmental pollution, Health expenditure and Economic growth and in the Sub-Saharan Africa countries: Panel ARDL approach
Zaidi Saida*; Saidi Kais**
*Faculty of Economics and Management, University of Sfax, Sfax 3018, Tunisia. E-mail:[email protected]
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E-mail:[email protected]
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**Faculty of Economics and Management, University of Sfax, Sfax 3018, Tunisia.
Abstract
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This paper is interested in modeling the nexus between health expenditure (HE), Environmental pollution (CO2 emissions; Nitrous oxide emissions) and economic growth in the Sub-Saharan Africa countries using annual data over the period 1990-2015. We had applied the estimation method ARDL to model the long run and short run. In addition, we use the VECM Granger causality test for checking the direction of causality. Firstly, the results of ARDL test indicate that economic growth has positive impact on the HE while CO2 emissions and NOE have negatives impact on the HE in the long run. The results show that a 1% increase in per capita GDP will lead to a 0.332% increase in the health expenditure, but an increase in CO2 emissions and NOE of 1% will decrease the HE by 0.066% and 0.577%, respectively. On the other hand, the results of the VECM Granger causality show that there is a one-way relationship going from the HE to GDP per capita. On the contrary, a two-way causality relationship between CO2 emissions and GDP per capita and also between the HE and CO2 emissions is found.
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Keywords: Health expenditure, economic growth, Environmental pollution, Panel ARDL, VECM Granger causality
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1. Introduction
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The health-air quality-economic growth nexus has recently begun to be discussed. This literature can be classified into three categories of research. The first line related to the environment and economic growth, this relationship was the target of several researches since 1960. The results of studies of the relationship between environment and economic growth are mixed; there is a large consensus that economic growth contributes to environmental degradation (Lotfalipour et al. 2010; Marrero, 2009; Pao and Tsai, 2010; Wang, 2011; Arouri et al. 2012). The second line focuses on the relationship between environment and health expenditure (Wilson and Spengher, 1996; Burnett et al. 1998; Peters et al. 1999). The deterioration of the environment explained by contamination of the air by CO2, nitrogen oxides (NOX), sulfur dioxide (SO2) and methane (CH4). The topic of health and environment has newly started to be discussed. Research on the relationship between health and the environment becomes a necessity due to the spread of infectious diseases due to air pollution and high degrees of temperature (Durant, 2007; Béral-Guyonnet, 1996). The climate change affects the functioning of many ecosystems and their member species. It also impact on human health. Some of them will be beneficial. For example, milder winters help reduce winter mortality in temperate countries, and in warmer regions, rising temperatures could reduce the viability of mosquito disease vectors. However, scientists estimate that, overall, most of the climate change consequences will be harmful to health.
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African countries are considered the most affected by the effects of climate change. Indeed, Africa is the continent that has fewer resources to put in place preventive measures to mitigate the effects of climate change. Natural disasters and diseases related to climate and the environment are becoming more frequent, (Katsouyanni et al. 2003; Kuo et al. 2006; Schell et al. 2006; Wegmann et al. 2007; Kelly, 2003), the mortality average increases due to air pollutants (Brunekreef and Holgate, 2002). Malaria, cholera and other infectious diseases are also increasing. This is the result of climate change, deterioration of water, air quality, poverty and mismanagement of rubbish in large urban centers. The third part concerns the relationship between economic growth and health (Bloom and Canning, 2003; Currais and Rivera, 2003; Akram et al. 2008; Lucian et al. 2009).
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The purpose of this paper is to investigate the impact of economic growth and air quality on health. The contribution of the paper is summed up in two points; the first point is the econometric technique used PMG; this technique constrains the long term coefficient to be the same, but allows the short-run coefficient, the error variance and the adjustment rate to differ from one country to another. Then we use the ARDL model, the second point regards the choice of the sample and the variables studied. We choose CO2 and SO2 as environmental variables, the variables explaining health are malaria, cholera, sanitation and health expenditure and finally economic growth is expressed by GDP. The remainder of this article is structured as follows. Section 2 presents the literature review. Section 3 describes the econometric modeling approach and data used followed by section 4 presents the empirical findings. Section 5 concludes the study.
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2. Literature review
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The subject of the environment attracted the interest of researchers. The environment has undergone adverse effects in recent decades and has serious consequences especially on health. The environment-economic growth nexus was much debated in the literature. The issue of health and the environment had been raised since the 1950s following the severe episodes in London in 1952. The United States Department of Public Health Service in 1957 launched a research program of the effects of air pollution on health. Mehrara et al. (2014) examined the stationarity and co-integration between health expenditure and GDP based on panel cointegration analysis for a sample of 13 MEAN countries using data during the period 19952005. The results of the unit root tests indicate that health expenditure and GDP are nonstationary. Even though, the results indicate that health spending and GDP are integrated. We concluded that the share of health expenditure relative to GDP decreases with GDP. This implies that health care is not a luxury good in MEAN countries. Several studies have focused on the determinants of health; income was the most critical variable in previous studies.
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The relationship between income and health spending has been debated for several decades. Joseph Nowhouse (1977) argues that changes in income affect the variation in health care spending. Hitiris and Posnett (1992), and Hansen and King (1996) criticized Newhouse’s hypothesis; they denounced it and add other variables such as the mortality rate, the proportion of the population over the age of 65, and the share of public finances in health expenditure. The variation between countries in per capita health expenditure is explained by the variation in per capita GDP. The researchers believe that it is essential to compare the income elasticity of health expenditure with the unit. Some find the elasticity is greater than 1 (Hitiris, 1997; Clements et al. 2003), others find that the elasticity is about 1 (Santiago et al. 2013; Gbesemete and Gerdthan, 1992) and others believe that the elasticity is less than 1 (Baltagi and Moscone, 2010; Jaunky and Khadaroo, 2008). In addition, Devin and Hansen (2001) found no causality between health expenditure and economic growth. However, Hartwing (2010), Balagi (2011), Amiri and Venetelou (2012), Elmi and Sadeghi (2012) and Tang (2011) found mixed results. Other researchers paid attention to this research thread. A few micro studies; Grossman (1972), Murinnen (1982) and Wagstaff (1986) show correlation between health care use and income. They explain that most individuals are subsidized or they don’t have to pay the full price of using health care resources. Mizushima (2008); Lucian et al. (2010); Rivera and Currais, (2003); Bloom et al. (2003); Bloom et al. (2001); Akram et al. (2011) found positive relationship between health and economic growth.
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A broad literature review, already presented, advances the determinants of health spending, but the role of environmental quality is not studied. We try to fill this gap by looking at the role of carbon dioxide emissions and nitrous oxide emissions. Jerrett et al. (2003) finds that the increase in air pollution increases health expenditure. Similarly, Hansen and Selte (2000) study the case of Olso and use logit model. Their results assert that air pollution increases the number of sick days. In the same context Schartz and Dockery (1992a) find that pollution is strongly related to the causes of mortality; respiratory mortality (Pope et al. 1992, Schwartz, 1994a; Mead and Brajer, 2005), mortality from chronic obstructive pulmonary disease (Scwhartz and Dockery, 1992b) and cardiovascular mortality (Wordly et al. 1997). Janke et al. 3
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(2009) examine the relationship between air pollution and population mortality over the period 1998-2005. The authors found that carbon monoxide, nitrogen dioxide and PM (10) are closely correlated and higher levels of PM (10) and ozone are associated with higher mortality rates. Mehrara et al. (2011) aimed to examine the relationship between health spending and environmental quality in more than 114 developing countries between 1995 and 2007. They used panel cointegration tests, OLS and error correction models to study the long-run equilibrium and long-run and short-run elasticity. Their findings reported that income is the most determinant of health expenditure in different countries. Both in short-run and long-run there is a direct connection between environmental quality and health expenditure.
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Sohaila and Zahra (2014) examined the impact of environmental quality and income in determining health expenditures over the period 1967 to 2010 in Iran. They have adopted a cointegration and ARDL approach to estimate the short and long-term impacts of environmental quality. The results show that carbon monoxide emissions, health expenditures, income, and sulfur oxide emissions are co-integrated. Carbon monoxide emissions, income and sulfur oxide emissions have a statistically significant positive effect on health expenditure of both short and long-term elasticity. Micheal et al. (2014) investigated the effect of CO2 emissions and economic growth on health expenditure in Ghana using FMOLS technique to estimate the long run relationship between variables over the period 1970-2008. Their finding revealed that GDP has a positive and significant effect on health. There is no effect of CO2 on health expenditure.
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3. Material and Methods
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3.1. Data collection
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The present paper uses annual data from 1990 to 2015 on real GDP per capita (Y) as proxy for economic growth (constant 2010 US$), CO2 emissions (CO2) (metric tons per capita) are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. Health expenditure (HE) is the sum of public and private health expenditure. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation. Nitrous oxide emissions (NOE) (thousand metric tons of CO2 equivalent) are emissions from agricultural biomass burning, industrial activities, and livestock management. Improved sanitation facilities (ISF) refer to the percentage of the population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. UP is urban population refers to people living in urban areas as defined by national statistical offices. The data are obtained from the World Development Indicator (2016). All variables are presented in logarithmic form. The descriptive statistics of the variables are presented in Table 1.
Table 1: Descriptive statistics HE
Y
NOE
4
ISF
CO2
UP
5.511176 5.171914 14.39019 1.423157 2.166697 1.195025 4.594471 232.5067 0.000000 3725.555 3168.837 676
HE Y NOE UP ISF CO2
HE 1.000000 -0.235173 -0.238725 0.020335 0.092758 -0.01476
6.252016 6.134928 10.12754 4.748713 0.793140 3.055687 14.55445 4812.396 0.000000 4226.363 424.6228 676
10429.81 3506.519 172723.3 41.77064 19731.68 3.770435 20.53613 10263.39 0.000000 7050551. 2.63E+11 676
23.24275 16.90000 81.90000 2.600000 16.46992 1.378861 4.247147 258.0182 0.000000 15712.10 183099.4 676
0.240727 0.108606 3.208218 0.011327 0.493095 4.228332 21.30921 11456.57 0.000000 162.7316 164.1215 676
Table 2: Correlation matrix of variables NOE
UP
1.000000 -0.033765 0.476601 0.520990 0.875246
1.000000 0.030856 -0.253684 -0.018551
1.000000 0.209460 0.448152
ISF
CO2
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Y
30.46354 28.37800 82.47400 5.416000 16.34264 1.149636 4.353354 200.4965 0.000000 20593.35 180280.3 676
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Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability Sum Sum Sq. Dev. Observations
1.000000
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1.000000 0.557094
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3.2. Modeling and Methodological Framework
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The objective of this paper is to investigate the relationship between health expenditure, environmental pollution (CO2 emissions and Nitrous oxide emissions) and economic growth using data from 1990 to 2015 for 26 Sub-Saharan Africa countries. Several study such as (Jerrett et al. 2003; Narayan and Narayan, 2008; Toplicianu and Toplicianu, 2014; Siti Khalijah, 2015; Chaabouni et al. 2016) have incorporated the environmental pollution such as CO2 emissions and Nitrous oxide emissions to affect the health expenditure. Moreover, economic growth affects the health expenditure (e.g. Samudram et al. 2009; Ke et al. 2011; Bedir, 2016). However, other studies have added the improved sanitation facilities variable to affect the health expenditure (e.g. Fink et al. 2011; Roushdy et al. 2012). For this, the empirical model is expressed as follows: =
+
2 +
+
+
+
+
(1)
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Where, the subscript = 1, … . , denotes the country and = 1, … . , denotes the time period. 2 are the CO2 emissions; is the health expenditure; is real GDP per capita; is the Nitrous oxide emissions; is the improved sanitation facilities; and is the urbanization. is residual term is assumed to be normally distributed.
☼ ARDL Approach 5
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In this context, we propose to use the Pooled Mean Group (PMG) method, the principle of which we explain beforehand, before explaining the actual implementation. The PMG estimator, developed by Pesaran, Shin and Smith (1999), belongs to the class of dynamic panel models in which it is assumed that the number of observations T is as large as that of individuals N. The PGM estimate constrains the long-run coefficients to be identical to the error-correction model but the long-run coefficients may differ from the error variances. Furthermore, this estimator is constructed under the assumption of heterogeneity of the short-term coefficients and homogeneity of the long-run slope coefficients (Pesaran et al., 1999). The initial conditions are treated as fixed or random and the long-run coefficients are a non-linear combination of the short-term coefficients. The model ARDL (Autoregressive Distributed Lag) is strongly inspired by the work of Bahmani-Oskooee (1996), Kumar et al. (2009, 2010), Bouteldja et al. (2013). The model can be written as follows: the basis of the Pooled-Mean Group is the estimation of the ARDL (autoregressive distributed lag) model (mi, ni, pi, qi, si, wi, zi). According to the model of Pesaran et al. (1999), the ARDL model, including the longterm relationship between the variables, is as follows:
θ ∆NOE ,
σ GDP ,
θ ∆NOE ,
ϑ GDP ,
β ∆CO2 , +∑
+ ϑ NOE ,
+∑
δ ∆ISF , + ϑ ISF ,
θ ∆NOE ,
+∑
+ π NOE ,
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π CO2 ,
β ∆GDP ,
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∑
,
+ σ HE ,
+ σ CO2 ,
+
+
(2a)
+∑
φ ∆HE ,
+∑
γ ∆GDP ,
ρ ∆UP ,
+ ϑ UP ,
+
+ ϑ CO2 ,
+ ϑ HE ,
+
+
,
∆GDP = α + ∑
ε
ρ ∆UP ,
+ σ UP ,
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∑
,
+ σ ISF ,
+
,
∆CO2 = α + ∑
ε
+∑
δ ∆ISF ,
γ ∆GDP ,
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,
+ σ NOE ,
+∑
φ ∆CO2 ,
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ε
+∑
+∑
U
∑
β ∆HE ,
N
∆HE = α + ∑
+∑
+∑
δ ∆ISF ,
+ π ISF ,
+∑
γ ∆CO2 ,
ρ ∆UP ,
+ π GDP ,
φ ∆HE ,
+ π UP ,
(2b) + + π HE ,
+
+
,
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∆NOE = α + ∑ ∑
θ ∆HE ,
τ CO2 ,
β ∆NOE , +∑
+ τ HE ,
+∑
δ ∆ISF , + τ ISF ,
+∑
φ ∆GDP , +∑
ρ ∆UP ,
+ τ UP ,
+ε
(2d)
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,
,
γ ∆CO2 ,
+ τ NOE ,
(2c) +
+ τ GDP ,
+
∆ISF = α + ∑ ∑
+∑
θ ∆HE ,
∂ CO2 ,
+∑
β ∆ISF ,
+ ∂ HE ,
+∑
φ ∆GDP , +∑
δ ∆NOE , + ∂ NOE ,
ρ ∆UP ,
+ ∂ UP ,
+ε
γ ∆CO2 ,
+
+ ∂ ISF ,
+ ∂ GDP ,
+
,
,
(2e)
∑
+∑
θ ∆HE ,
μ CO2 ,
+∑
β ∆UP ,
+ μ HE ,
φ ∆GDP ,
+∑
+∑
ρ ∆ISF ,
+ μ ISF ,
+ε
δ ∆NOE , + μ NOE ,
γ ∆CO2 ,
,
+ μ UP ,
+ + μ GDP ,
,
+
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∆UP = α + ∑
(2f)
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Where , 2 , , , , are the dependent variables, is the coefficient that captures the country specify, β , φ , γ , θ , δ et ρ represent the coefficients of the
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short-run dynamics relative to each country, is the error term of the model. Long-run coefficients are assumed to be identical to all countries. Thus, if is significantly negative, we can then conclude that there is a long-term relationship between the independent variable and the explanatory variables.
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The PMG approach is essentially a version of the panel procedure of the ARDL model and consists in estimating the ARDL model by the maximum likelihood; it can be rewritten as an error correction model (ECM). The estimation of this model simultaneously evoked the intra and inter dimensions. Pesaran, Shin and Smith (1999) did not propose a cointegration test but they derived asymptotic properties for the estimation of the regressors of both stationary and non-stationary series. ☼ VECM Granger Causality
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The study looking at test the causal relationship between health expenditure, economic growth, carbon dioxide emissions, nitrous oxide emissions, improved sanitation facilities, and urban population in the Sub-Saharan Africa countries. We use the panel VECM to test the causality of Engle and Granger (1987). We adopt the two-step procedure, first estimating the long-term model in view to obtain the residues. =
+
+
+
2 +
+
+
+
(3)
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According to Engle and Granger (1987), the cointegrated variables may have an error correction representation where the error correction term (ECT) can be incorporated into the model. Consequently, a Vector Error Correction Model (VECM) is formulated to reintroduce the information lost in the differentiation process and establish a long-term equilibrium as well as the short-term dynamics. The delayed value of these residues is introduced as an error correction term VECM, the following error-correcting dynamic model is estimated:
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∑
∆
∆
+∑
2 = ∑
+∑
_
∆
∆
∆
+∑
∑
∆
+
∑
+∑ ∆
2
∆
+∑
∆
+∑
2 +
+∑
_
+∑
+∑
∆ _
+∑
+∑
∆
+
∆
+∑
+
∆
∆
+
+∑ ∆
+ +
+∑
∆
∆
∆ +
∆
+ +
+∑
∆
+∑
_
2
+
∆
+∑
+
∆
∆ +∑
∆
+
+
∆ +
+ +
+ +
M
∆
+∑
∆
+∑
= ∑
2
∆
∆
∑
=
+∑
∆
+∑
=
∆
2
+∑
_
+∑
2
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+∑
=
∆
∆
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+∑
_
+∑
∆
U
∆
+∑
∆
N
∑ ∆
+∑
=
A
∆
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Where Δ is the operator of differentiation, and k is the lag length. The long-run causality will be determined by the six equations. There are two sources of causality, that is, as ECT, if α ≠ 0, or as differentiated terms that indicate a short-term dynamic. The statistical significance of the coefficients associated with ECT gives the error correction mechanism that induces the return of variables to their long-term relationship. 4. Empirical analysis
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4.1. Panel Unit Root Tests
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Before starting econometric modeling, it is important to remember that an analysis of the stationarity of the proposed data sets is a prerequisite for any econometric analysis, especially when it comes to macroeconomic data or financial data. Then, it is necessary to determine the order of integration before using the co-integration techniques. This analysis is highly recommended because of the problems of fallacious regressions which may arise if the variables are not stationary. For this purpose; ADF Fisher Chi-square (ADF Fisher) and Levin, Lin & Chi (LLC) unit root tests are used in the paper. Panel unit root tests have been developed on the similar manner that underlie conventional ADF test. Table 3 shows the results of the panel unit root tests. 8
Table 3: Unit Root Tests SERIES
ADF test
LLC test
En différence
En niveau
En différence
Health expenditure (HE)
14.9675
469.426*
2.86290
-21.6738*
CO2 emissions (CO2)
32.2010
309.233*
1.38756
-15.8456*
Economic growth (Y)
19.5335
263.070*
4.81693
-7.04433*
Nitrous oxide emissions (NOE)
26.8867
427.127*
4.07184
-19.1227*
Improved sanitation facilities (ISF)
23.4979
64.1125*
1.16761
Urban population (UP)
41.7597
57.6142*
6.69736
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En niveau
-2.20551*
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-2.39827*
Notes: * denotes significance at the 1% level.
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It is obvious from the ADF and LLC results that, some of the data sets are integrated of (I (0)) or (I (1)). In level, the unit root test (ADF) results obtained indicate that all the series are not stationary. In addition, the results also indicate that, HE, Y, CO2, NOE, ISF and UP series are (I (1)). Moreover, the LLC statistics for variables are significant at the 1% level. This permit to reject the null hypothesis (H0), considering that all variables are stationary in the first difference.
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Therefore, we could apply the co-integration test to show that there is a long-term or non-relationship between health expenditure, economic growth, carbon dioxide emissions, nitrous oxide emissions, improved sanitation facilities, and urban population. 4.2. Panel co-integration tests
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Pedroni (1999) proposes several panel co-integration test statistics, which are approaches based on examining residues. If the variables are co-integrated, the residuals must be stationary. The null hypothesis proposes the absence of co-integration, in which the residues will be I (1), therefore it proposes that the non-stationary residues and contain a unit root.
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Test of Kao (1999) follows the same approach as the Pedroni test by proposing the null hypothesis of the absence of co-integration. Contrary to Pedroni's proposition, Kao assumes that the vectors of co-integration would be considered homogeneous according to the individuals. Considering the size of the sample among the various co-integration tests we retain that of Kao (1999) to test the co-integration relation of the variables. The Kao test is based on the null hypothesis of no co-integration. The results of the co-integration tests of Pedroni and Kao are shown in Table 4.
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Table 4: The results of the co-integration tests Alternative hypothesis: common AR coefs. (within-dimension) Weighted Prob.
Statistic
Prob.
Panel v-Statistics
-1.113962
0.8674
-3.135074
0.9991
Panel rho-Statistics
1.189588
0.8829
0.689238
Panel PP-Statistics
-3.670609*
0.0001
-5.489928*
Panel ADF-Statistics
-1.648439**
0.0496
-2.555738*
2.202380**
0.0362
-7.213509*
Group ADF-Statistics
-2.691034* Statistic
ADF
-0.779155*
0.0000 0.0036 Prob.
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Kao test
0.0053
N
Group PP-Statistics
0.0000
U
Prob.
0.7547
A
Group rho-Statistics
Statistic
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Alternative hypothesis: individual AR coefs. (between-dimension)
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Statistic
0.0021
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*, ** show that the variables are significant at the 1% and 5% levels.
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In the table above, Pedroni's co-integration test results indicate that except for panel v-statistics, panel rho-statistics and group rho-statistics, all other statistics are significant, so the null hypothesis of no co-integration is rejected. UP, ISF, CO2, NOE and GDP are co-integrated with health expenditure. Both panel PP-statistics and group PP-statistics have better properties; these two statistics are more reliable. The null hypothesis of no co-integration is rejected at 1% level by the panel PP-statistics and group PP-statistics. Once the co-integration presence is detected, the next objective is to estimate the long-term relationships between the variables.
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Thus, the results show that the probability associated with T-statistic is 0.0021; this makes it possible to reject the null hypothesis of absence of co-integration. We can therefore say that there exists a co-integration relation between the variables. 4.3. Panel ARDL results The PMG method assumes the presence of co-integration. This requires the separate use of cointegration tests and unit root testing on panel data (Bergheim, 2008). The PMG method derives the coherent and asymptotic properties of the estimator for the two stationary I (0) and non10
stationary explanatory variables I (1) (Roudet et al., 2007, Asteriou and Hall, 2007). The PMG approach not only makes it possible to estimate the long-term relationship between the cointegrated variables, it also gives the error correction coefficient which confirms the existence of the long-term relationship.
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The coefficient of the delayed error correction term measures the rate at which the dependent variable adapts to changes in the interdependent variable before converging to its equilibrium level (Apostolidou et al., 2015). And we can speak of the existence of long-term relation if the sign of the coefficient of the error correction term is negatively significant. 4.3.1. PMG1 long-run estimate
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Table 5 shows that only ISF and economic growth have long-term effects on health expenditure. While the coefficient of CO2 emissions and UP are negative, but not significant on health expenditure. Note that the reciprocal paths of these long-term effects between health expenditure, economic growth and ISF are observed. However, the long-term effects of ISF and economic growth on health expenditure (and vice versa) are significant at the 1% level. Moreover, we note that all the variables affect positively and in a significant way on the economic growth at the 1% level. Table 5: PMG long run estimates Dependant variables CO2
Y
NOE
UP
0.319*
-0.066
0.332*
-0.577*
0.302*
(0.000)
(0.140)
(0.000)
(0.000)
(0.000)
0.064**
-0.064*
0.038*
-0.070*
-0.015
(0.025)
(0.000)
(0.000)
(0.000)
(0.815)
EP
CO2
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HE
ISF
ISF
M
HE
A
Independent variable
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Y
A
NOE
UP
-0.302
0.777*
0.699*
-0.316*
0.597*
(0.175)
(0.000)
(0.000)
(0.000)
(0.000)
-0.231*
0.134
0.086*
0.554*
0.301*
(0.002)
(0.000)*
(0.000)
(0.001)
(0.000)
0.057
-0.048
0.037
0.034*
-0.055*
(0.882)
(0.002)*
(0.107)
(0.000)
(0.066)
-0.015
0.119*
0.043*
-0.030*
0.247*
(0.586)
(0.000)
(0.000)
(0.000)
(0.000)
Notes: *, ** show that the variables are significant at the 1% and 5% levels.
1
Pooled Mean Group Estimator
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4.3.2 PMG short-run estimates
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The table below presents the short-term estimate and the co-integration relation established by the error correction model. At equilibrium ECT is zero, if it's not the case the pairs of relationships have deviated from the long-term equilibrium. According the table we notice that the error correction coefficient is negative and significant at 1% level which confirms the existence of a statistically significant long-term relationship between health expenditure, GDP per capita and nitrous oxide emissions.
Coefficient
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Table 6: PMG short-run estimate, ΔHE is the dependent variable t-Statistic
p-value
2.967605
0.0034
6.879380
D (HE (-1))
-0.119235
-1.538279
0.1255
-1.172648
-1.013916
0.3118
-2.575121
-1.226504
0.2214
-1.235148
-0.810846
0.4184
-0.563690
-0.039331
0.9687
D (CO2 (-1))
-4.940380
-0.592055
0.5545
D (CO2 (-2))
9.623507
0.879149
0.3804
D(GDP)
0.316316
0.169761
0.8654
D (GDP (-1))
-1.013527
-0.596152
0.5517
D (GDP (-2))
0.406816
0.191516
0.8483
D(NOE)
2.754476
1.937948
0.054*
D (NOE (-1))
-0.013158
-0.014619
0.9884
D(ISF) D (ISF (-1))
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D(CO2)
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D (ISF (-2))
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Constant
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D (NOE (-2))
-0.163386
-0.271709
0.7861
D(UP)
-326.8169
-0.901836
0.3682
D (UP (-1))
801.6053
1.500181
0.1351
D (UP (-2))
-398.2695
-1.596984
0.1118
ECT (-1)
-0.305380
-3.252554
0.0013*
Source: Calculated by the authors using Eviews. 9
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4.4. VECM Granger Causality
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The results of Granger causality based on vector error correction (VECM) are provided in Table 7. According to the results in this table, in Granger's sense, there is a one-way relationship from health expenditure to economic growth in short and long-runs. This result is confirmed to the results of (Neycheva, 2008; Egbetunde and Fasanya, 2013).There is a bi-directional relationship between economic growth and CO2 emissions in short and long-runs. These results are in line with the findings of the (Arouri et al. 2012; Lee and Brahmasrene, 2013).The results also show a bi-directional causality between CO2 emissions and health expenditure for the entire of countries in short and long-runs. Similarly, in the short term, a unidirectional relationship ranging from health expenditure to nitrous oxide emissions and improved sanitation facilities is found, but there is a two-way relationship between health expenditure and urban population. Overall, the results reported a two-way relationship between economic growth and ISF; between nitrous oxide emissions and ISF; and between CO2 emissions and ISF in the short term.
Table 7: Granger Causality test results Short-run Causality
Longrun Causalit y
∆
∆
∆
∆
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∆
∆
ECT
∆
0.071* (0.00 8)
0.039* (0.003)
0.013* (0.001 )
-0.025* (0.000)
0.010* (0.003)
0.073**
(0.042)
-0.209 (0.425)
0.039** (0.018)
0.071 (0.09*)
∆
0.163 (0.159)
-0.043** (0.034)
0.021* * (0.041 )
0.055* (0.00 1)
0.151 (0.106 )
0.038* (0.00 2)
-0.051* (0.031)
0.017*
-0.090** (0.013)
0.016** (0.01 5)
0.090** (0.01 5)
(0.009)
0.300 (0.385)
(0.003)
0.409* (0.009)
0.039** (0.04 3)
0.023 (0.112 )
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-0.262** (0.044)
-0.228 (0.327)
0.075* (0.002)
-0.023** (0.010) -0.040* (0.000)
-0.013* (0.003)
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∆
0.731* (0.001 )
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-0.057 (0.853)
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0.306*
-0.035* (0.004)
(0.001) ∆
-0.019*
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∆
0.105 (0.19 5)
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∆
Notes: *, ** show that the variables are significant at the 1% and 5% levels. The values of the probabilities are between the parentheses.
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In the long term, the error correction term is significant at 1% and 5%, that is to say the differences between the actual values and the long-term values will be corrected with the ECT coefficients in each period. So the causality test shows in the long term there is a bidirectional relationship between health expenditure and urban population and between economic growth and ISF and also between CO2 emissions and ISF. These results are the same as for (Rahman, 2011; Boussalem et al. 2014). A unidirectional relationship running from CO2 emissions to nitrous oxide emissions and UP is found. Finally, there is a unidirectional relationship running from nitrous oxide emissions to economic growth. 5. Conclusion
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The aim of this article is to study the relationship in the long-term and short-term causality between health expenditure, environmental pollution and economic growth in the Sub-Saharan Africa countries for the period 1990-2015. The results of the unit root tests of Levin, Lin and Chu (1992) and Augmented Dickey (ADF) show that the variables are stationary at the first difference. The results of the Pedroni (1999) test indicate that there is a long-term relationship between variables. The ARDL model is employed in order to examine the long-term and shortterm impact of environmental pollution and economic growth on health expenditure, and VECM Granger causality test for checking the direction of causality. The results of ARDL test 14
indicate that economic growth has positive impact on health expenditure while economic growth has negative impact on the health expenditure in the long run.
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The Granger causality test in VECM indicates that both in the long term and in the short term, there is a two-way relationship between CO2 emissions and health expenditure. The results show that CO2 emissions have a negative and significant effect health expenditure in short-term and the last column shows the same relationship between these variables in longterm. Therefore, it is important that these countries should adopt measures and policies related to the quality of the environment to reduce health illnesses because the quality of the environment has been shown to be a contributing factor to the increase in health expenditure. Indeed, in terms of health policy, the sub-Saharan Africa countries should prioritize healthy eating through effective implementation of environmental management and control policies to reduce pressure on health spending. Moreover, in future research, we have considered other environmental approaches with other methods.
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In addition, the results also indicate that there is a positive bi-directional causality link between CO2 emissions and economic growth in short and long-runs. The CO2 emissions increase with economic growth, so, the countries have studied and examined investment requirements to promote environmental protection and increase technology transfer to reduce environmental pollution. In the short and long run, the empirical results have shown that there is a unidirectional relationship running from health expenditure to economic growth. The most important results of our study are that health spending has a positive impact on economic growth, it can be concluded that the positive impact of health spending on economic growth can be increased by increasing health spending, Means more productivity. In addition, the positive and significant impact of health spending on per capita GDP shows that the need for government intervention to implement policies to encourage health spending to build a more healthy and productive economy to support economic growth and development in these countries.
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S2210-6707(17)30846-6 https://doi.org/10.1016/j.scs.2018.04.034 SCS 1074
To appear in: Received date: Revised date: Accepted date:
13-7-2017 2-4-2018 27-4-2018
Please cite this article as: Saida, Zaidi., & Kais, Saidi., Environmental pollution, Health expenditure and Economic growth and in the Sub-Saharan Africa countries: Panel ARDL approach.Sustainable Cities and Society https://doi.org/10.1016/j.scs.2018.04.034 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Environmental pollution, Health expenditure and Economic growth and in the Sub-Saharan Africa countries: Panel ARDL approach
Zaidi Saida*; Saidi Kais**
*Faculty of Economics and Management, University of Sfax, Sfax 3018, Tunisia. E-mail:[email protected]
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E-mail:[email protected]
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**Faculty of Economics and Management, University of Sfax, Sfax 3018, Tunisia.
Abstract
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This paper is interested in modeling the nexus between health expenditure (HE), Environmental pollution (CO2 emissions; Nitrous oxide emissions) and economic growth in the Sub-Saharan Africa countries using annual data over the period 1990-2015. We had applied the estimation method ARDL to model the long run and short run. In addition, we use the VECM Granger causality test for checking the direction of causality. Firstly, the results of ARDL test indicate that economic growth has positive impact on the HE while CO2 emissions and NOE have negatives impact on the HE in the long run. The results show that a 1% increase in per capita GDP will lead to a 0.332% increase in the health expenditure, but an increase in CO2 emissions and NOE of 1% will decrease the HE by 0.066% and 0.577%, respectively. On the other hand, the results of the VECM Granger causality show that there is a one-way relationship going from the HE to GDP per capita. On the contrary, a two-way causality relationship between CO2 emissions and GDP per capita and also between the HE and CO2 emissions is found.
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Keywords: Health expenditure, economic growth, Environmental pollution, Panel ARDL, VECM Granger causality
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1. Introduction
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The health-air quality-economic growth nexus has recently begun to be discussed. This literature can be classified into three categories of research. The first line related to the environment and economic growth, this relationship was the target of several researches since 1960. The results of studies of the relationship between environment and economic growth are mixed; there is a large consensus that economic growth contributes to environmental degradation (Lotfalipour et al. 2010; Marrero, 2009; Pao and Tsai, 2010; Wang, 2011; Arouri et al. 2012). The second line focuses on the relationship between environment and health expenditure (Wilson and Spengher, 1996; Burnett et al. 1998; Peters et al. 1999). The deterioration of the environment explained by contamination of the air by CO2, nitrogen oxides (NOX), sulfur dioxide (SO2) and methane (CH4). The topic of health and environment has newly started to be discussed. Research on the relationship between health and the environment becomes a necessity due to the spread of infectious diseases due to air pollution and high degrees of temperature (Durant, 2007; Béral-Guyonnet, 1996). The climate change affects the functioning of many ecosystems and their member species. It also impact on human health. Some of them will be beneficial. For example, milder winters help reduce winter mortality in temperate countries, and in warmer regions, rising temperatures could reduce the viability of mosquito disease vectors. However, scientists estimate that, overall, most of the climate change consequences will be harmful to health.
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African countries are considered the most affected by the effects of climate change. Indeed, Africa is the continent that has fewer resources to put in place preventive measures to mitigate the effects of climate change. Natural disasters and diseases related to climate and the environment are becoming more frequent, (Katsouyanni et al. 2003; Kuo et al. 2006; Schell et al. 2006; Wegmann et al. 2007; Kelly, 2003), the mortality average increases due to air pollutants (Brunekreef and Holgate, 2002). Malaria, cholera and other infectious diseases are also increasing. This is the result of climate change, deterioration of water, air quality, poverty and mismanagement of rubbish in large urban centers. The third part concerns the relationship between economic growth and health (Bloom and Canning, 2003; Currais and Rivera, 2003; Akram et al. 2008; Lucian et al. 2009).
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The purpose of this paper is to investigate the impact of economic growth and air quality on health. The contribution of the paper is summed up in two points; the first point is the econometric technique used PMG; this technique constrains the long term coefficient to be the same, but allows the short-run coefficient, the error variance and the adjustment rate to differ from one country to another. Then we use the ARDL model, the second point regards the choice of the sample and the variables studied. We choose CO2 and SO2 as environmental variables, the variables explaining health are malaria, cholera, sanitation and health expenditure and finally economic growth is expressed by GDP. The remainder of this article is structured as follows. Section 2 presents the literature review. Section 3 describes the econometric modeling approach and data used followed by section 4 presents the empirical findings. Section 5 concludes the study.
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2. Literature review
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The subject of the environment attracted the interest of researchers. The environment has undergone adverse effects in recent decades and has serious consequences especially on health. The environment-economic growth nexus was much debated in the literature. The issue of health and the environment had been raised since the 1950s following the severe episodes in London in 1952. The United States Department of Public Health Service in 1957 launched a research program of the effects of air pollution on health. Mehrara et al. (2014) examined the stationarity and co-integration between health expenditure and GDP based on panel cointegration analysis for a sample of 13 MEAN countries using data during the period 19952005. The results of the unit root tests indicate that health expenditure and GDP are nonstationary. Even though, the results indicate that health spending and GDP are integrated. We concluded that the share of health expenditure relative to GDP decreases with GDP. This implies that health care is not a luxury good in MEAN countries. Several studies have focused on the determinants of health; income was the most critical variable in previous studies.
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The relationship between income and health spending has been debated for several decades. Joseph Nowhouse (1977) argues that changes in income affect the variation in health care spending. Hitiris and Posnett (1992), and Hansen and King (1996) criticized Newhouse’s hypothesis; they denounced it and add other variables such as the mortality rate, the proportion of the population over the age of 65, and the share of public finances in health expenditure. The variation between countries in per capita health expenditure is explained by the variation in per capita GDP. The researchers believe that it is essential to compare the income elasticity of health expenditure with the unit. Some find the elasticity is greater than 1 (Hitiris, 1997; Clements et al. 2003), others find that the elasticity is about 1 (Santiago et al. 2013; Gbesemete and Gerdthan, 1992) and others believe that the elasticity is less than 1 (Baltagi and Moscone, 2010; Jaunky and Khadaroo, 2008). In addition, Devin and Hansen (2001) found no causality between health expenditure and economic growth. However, Hartwing (2010), Balagi (2011), Amiri and Venetelou (2012), Elmi and Sadeghi (2012) and Tang (2011) found mixed results. Other researchers paid attention to this research thread. A few micro studies; Grossman (1972), Murinnen (1982) and Wagstaff (1986) show correlation between health care use and income. They explain that most individuals are subsidized or they don’t have to pay the full price of using health care resources. Mizushima (2008); Lucian et al. (2010); Rivera and Currais, (2003); Bloom et al. (2003); Bloom et al. (2001); Akram et al. (2011) found positive relationship between health and economic growth.
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A broad literature review, already presented, advances the determinants of health spending, but the role of environmental quality is not studied. We try to fill this gap by looking at the role of carbon dioxide emissions and nitrous oxide emissions. Jerrett et al. (2003) finds that the increase in air pollution increases health expenditure. Similarly, Hansen and Selte (2000) study the case of Olso and use logit model. Their results assert that air pollution increases the number of sick days. In the same context Schartz and Dockery (1992a) find that pollution is strongly related to the causes of mortality; respiratory mortality (Pope et al. 1992, Schwartz, 1994a; Mead and Brajer, 2005), mortality from chronic obstructive pulmonary disease (Scwhartz and Dockery, 1992b) and cardiovascular mortality (Wordly et al. 1997). Janke et al. 3
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(2009) examine the relationship between air pollution and population mortality over the period 1998-2005. The authors found that carbon monoxide, nitrogen dioxide and PM (10) are closely correlated and higher levels of PM (10) and ozone are associated with higher mortality rates. Mehrara et al. (2011) aimed to examine the relationship between health spending and environmental quality in more than 114 developing countries between 1995 and 2007. They used panel cointegration tests, OLS and error correction models to study the long-run equilibrium and long-run and short-run elasticity. Their findings reported that income is the most determinant of health expenditure in different countries. Both in short-run and long-run there is a direct connection between environmental quality and health expenditure.
N
U
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Sohaila and Zahra (2014) examined the impact of environmental quality and income in determining health expenditures over the period 1967 to 2010 in Iran. They have adopted a cointegration and ARDL approach to estimate the short and long-term impacts of environmental quality. The results show that carbon monoxide emissions, health expenditures, income, and sulfur oxide emissions are co-integrated. Carbon monoxide emissions, income and sulfur oxide emissions have a statistically significant positive effect on health expenditure of both short and long-term elasticity. Micheal et al. (2014) investigated the effect of CO2 emissions and economic growth on health expenditure in Ghana using FMOLS technique to estimate the long run relationship between variables over the period 1970-2008. Their finding revealed that GDP has a positive and significant effect on health. There is no effect of CO2 on health expenditure.
A
3. Material and Methods
M
3.1. Data collection
A
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EP
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The present paper uses annual data from 1990 to 2015 on real GDP per capita (Y) as proxy for economic growth (constant 2010 US$), CO2 emissions (CO2) (metric tons per capita) are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. Health expenditure (HE) is the sum of public and private health expenditure. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation. Nitrous oxide emissions (NOE) (thousand metric tons of CO2 equivalent) are emissions from agricultural biomass burning, industrial activities, and livestock management. Improved sanitation facilities (ISF) refer to the percentage of the population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. UP is urban population refers to people living in urban areas as defined by national statistical offices. The data are obtained from the World Development Indicator (2016). All variables are presented in logarithmic form. The descriptive statistics of the variables are presented in Table 1.
Table 1: Descriptive statistics HE
Y
NOE
4
ISF
CO2
UP
5.511176 5.171914 14.39019 1.423157 2.166697 1.195025 4.594471 232.5067 0.000000 3725.555 3168.837 676
HE Y NOE UP ISF CO2
HE 1.000000 -0.235173 -0.238725 0.020335 0.092758 -0.01476
6.252016 6.134928 10.12754 4.748713 0.793140 3.055687 14.55445 4812.396 0.000000 4226.363 424.6228 676
10429.81 3506.519 172723.3 41.77064 19731.68 3.770435 20.53613 10263.39 0.000000 7050551. 2.63E+11 676
23.24275 16.90000 81.90000 2.600000 16.46992 1.378861 4.247147 258.0182 0.000000 15712.10 183099.4 676
0.240727 0.108606 3.208218 0.011327 0.493095 4.228332 21.30921 11456.57 0.000000 162.7316 164.1215 676
Table 2: Correlation matrix of variables NOE
UP
1.000000 -0.033765 0.476601 0.520990 0.875246
1.000000 0.030856 -0.253684 -0.018551
1.000000 0.209460 0.448152
ISF
CO2
SC R
Y
30.46354 28.37800 82.47400 5.416000 16.34264 1.149636 4.353354 200.4965 0.000000 20593.35 180280.3 676
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Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability Sum Sum Sq. Dev. Observations
1.000000
U
1.000000 0.557094
N
3.2. Modeling and Methodological Framework
EP
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M
A
The objective of this paper is to investigate the relationship between health expenditure, environmental pollution (CO2 emissions and Nitrous oxide emissions) and economic growth using data from 1990 to 2015 for 26 Sub-Saharan Africa countries. Several study such as (Jerrett et al. 2003; Narayan and Narayan, 2008; Toplicianu and Toplicianu, 2014; Siti Khalijah, 2015; Chaabouni et al. 2016) have incorporated the environmental pollution such as CO2 emissions and Nitrous oxide emissions to affect the health expenditure. Moreover, economic growth affects the health expenditure (e.g. Samudram et al. 2009; Ke et al. 2011; Bedir, 2016). However, other studies have added the improved sanitation facilities variable to affect the health expenditure (e.g. Fink et al. 2011; Roushdy et al. 2012). For this, the empirical model is expressed as follows: =
+
2 +
+
+
+
+
(1)
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Where, the subscript = 1, … . , denotes the country and = 1, … . , denotes the time period. 2 are the CO2 emissions; is the health expenditure; is real GDP per capita; is the Nitrous oxide emissions; is the improved sanitation facilities; and is the urbanization. is residual term is assumed to be normally distributed.
☼ ARDL Approach 5
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In this context, we propose to use the Pooled Mean Group (PMG) method, the principle of which we explain beforehand, before explaining the actual implementation. The PMG estimator, developed by Pesaran, Shin and Smith (1999), belongs to the class of dynamic panel models in which it is assumed that the number of observations T is as large as that of individuals N. The PGM estimate constrains the long-run coefficients to be identical to the error-correction model but the long-run coefficients may differ from the error variances. Furthermore, this estimator is constructed under the assumption of heterogeneity of the short-term coefficients and homogeneity of the long-run slope coefficients (Pesaran et al., 1999). The initial conditions are treated as fixed or random and the long-run coefficients are a non-linear combination of the short-term coefficients. The model ARDL (Autoregressive Distributed Lag) is strongly inspired by the work of Bahmani-Oskooee (1996), Kumar et al. (2009, 2010), Bouteldja et al. (2013). The model can be written as follows: the basis of the Pooled-Mean Group is the estimation of the ARDL (autoregressive distributed lag) model (mi, ni, pi, qi, si, wi, zi). According to the model of Pesaran et al. (1999), the ARDL model, including the longterm relationship between the variables, is as follows:
θ ∆NOE ,
σ GDP ,
θ ∆NOE ,
ϑ GDP ,
β ∆CO2 , +∑
+ ϑ NOE ,
+∑
δ ∆ISF , + ϑ ISF ,
θ ∆NOE ,
+∑
+ π NOE ,
CC
π CO2 ,
β ∆GDP ,
EP
∑
,
+ σ HE ,
+ σ CO2 ,
+
+
(2a)
+∑
φ ∆HE ,
+∑
γ ∆GDP ,
ρ ∆UP ,
+ ϑ UP ,
+
+ ϑ CO2 ,
+ ϑ HE ,
+
+
,
∆GDP = α + ∑
ε
ρ ∆UP ,
+ σ UP ,
TE D
∑
,
+ σ ISF ,
+
,
∆CO2 = α + ∑
ε
+∑
δ ∆ISF ,
γ ∆GDP ,
A
,
+ σ NOE ,
+∑
φ ∆CO2 ,
M
ε
+∑
+∑
U
∑
β ∆HE ,
N
∆HE = α + ∑
+∑
+∑
δ ∆ISF ,
+ π ISF ,
+∑
γ ∆CO2 ,
ρ ∆UP ,
+ π GDP ,
φ ∆HE ,
+ π UP ,
(2b) + + π HE ,
+
+
,
A
∆NOE = α + ∑ ∑
θ ∆HE ,
τ CO2 ,
β ∆NOE , +∑
+ τ HE ,
+∑
δ ∆ISF , + τ ISF ,
+∑
φ ∆GDP , +∑
ρ ∆UP ,
+ τ UP ,
+ε
(2d)
6
,
,
γ ∆CO2 ,
+ τ NOE ,
(2c) +
+ τ GDP ,
+
∆ISF = α + ∑ ∑
+∑
θ ∆HE ,
∂ CO2 ,
+∑
β ∆ISF ,
+ ∂ HE ,
+∑
φ ∆GDP , +∑
δ ∆NOE , + ∂ NOE ,
ρ ∆UP ,
+ ∂ UP ,
+ε
γ ∆CO2 ,
+
+ ∂ ISF ,
+ ∂ GDP ,
+
,
,
(2e)
∑
+∑
θ ∆HE ,
μ CO2 ,
+∑
β ∆UP ,
+ μ HE ,
φ ∆GDP ,
+∑
+∑
ρ ∆ISF ,
+ μ ISF ,
+ε
δ ∆NOE , + μ NOE ,
γ ∆CO2 ,
,
+ μ UP ,
+ + μ GDP ,
,
+
IP T
∆UP = α + ∑
(2f)
SC R
Where , 2 , , , , are the dependent variables, is the coefficient that captures the country specify, β , φ , γ , θ , δ et ρ represent the coefficients of the
N
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short-run dynamics relative to each country, is the error term of the model. Long-run coefficients are assumed to be identical to all countries. Thus, if is significantly negative, we can then conclude that there is a long-term relationship between the independent variable and the explanatory variables.
TE D
M
A
The PMG approach is essentially a version of the panel procedure of the ARDL model and consists in estimating the ARDL model by the maximum likelihood; it can be rewritten as an error correction model (ECM). The estimation of this model simultaneously evoked the intra and inter dimensions. Pesaran, Shin and Smith (1999) did not propose a cointegration test but they derived asymptotic properties for the estimation of the regressors of both stationary and non-stationary series. ☼ VECM Granger Causality
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The study looking at test the causal relationship between health expenditure, economic growth, carbon dioxide emissions, nitrous oxide emissions, improved sanitation facilities, and urban population in the Sub-Saharan Africa countries. We use the panel VECM to test the causality of Engle and Granger (1987). We adopt the two-step procedure, first estimating the long-term model in view to obtain the residues. =
+
+
+
2 +
+
+
+
(3)
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According to Engle and Granger (1987), the cointegrated variables may have an error correction representation where the error correction term (ECT) can be incorporated into the model. Consequently, a Vector Error Correction Model (VECM) is formulated to reintroduce the information lost in the differentiation process and establish a long-term equilibrium as well as the short-term dynamics. The delayed value of these residues is introduced as an error correction term VECM, the following error-correcting dynamic model is estimated:
7
∑
∆
∆
+∑
2 = ∑
+∑
_
∆
∆
∆
+∑
∑
∆
+
∑
+∑ ∆
2
∆
+∑
∆
+∑
2 +
+∑
_
+∑
+∑
∆ _
+∑
+∑
∆
+
∆
+∑
+
∆
∆
+
+∑ ∆
+ +
+∑
∆
∆
∆ +
∆
+ +
+∑
∆
+∑
_
2
+
∆
+∑
+
∆
∆ +∑
∆
+
+
∆ +
+ +
+ +
M
∆
+∑
∆
+∑
= ∑
2
∆
∆
∑
=
+∑
∆
+∑
=
∆
2
+∑
_
+∑
2
IP T
+∑
=
∆
∆
SC R
+∑
_
+∑
∆
U
∆
+∑
∆
N
∑ ∆
+∑
=
A
∆
EP
TE D
Where Δ is the operator of differentiation, and k is the lag length. The long-run causality will be determined by the six equations. There are two sources of causality, that is, as ECT, if α ≠ 0, or as differentiated terms that indicate a short-term dynamic. The statistical significance of the coefficients associated with ECT gives the error correction mechanism that induces the return of variables to their long-term relationship. 4. Empirical analysis
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4.1. Panel Unit Root Tests
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Before starting econometric modeling, it is important to remember that an analysis of the stationarity of the proposed data sets is a prerequisite for any econometric analysis, especially when it comes to macroeconomic data or financial data. Then, it is necessary to determine the order of integration before using the co-integration techniques. This analysis is highly recommended because of the problems of fallacious regressions which may arise if the variables are not stationary. For this purpose; ADF Fisher Chi-square (ADF Fisher) and Levin, Lin & Chi (LLC) unit root tests are used in the paper. Panel unit root tests have been developed on the similar manner that underlie conventional ADF test. Table 3 shows the results of the panel unit root tests. 8
Table 3: Unit Root Tests SERIES
ADF test
LLC test
En différence
En niveau
En différence
Health expenditure (HE)
14.9675
469.426*
2.86290
-21.6738*
CO2 emissions (CO2)
32.2010
309.233*
1.38756
-15.8456*
Economic growth (Y)
19.5335
263.070*
4.81693
-7.04433*
Nitrous oxide emissions (NOE)
26.8867
427.127*
4.07184
-19.1227*
Improved sanitation facilities (ISF)
23.4979
64.1125*
1.16761
Urban population (UP)
41.7597
57.6142*
6.69736
IP T
En niveau
-2.20551*
SC R
-2.39827*
Notes: * denotes significance at the 1% level.
A
N
U
It is obvious from the ADF and LLC results that, some of the data sets are integrated of (I (0)) or (I (1)). In level, the unit root test (ADF) results obtained indicate that all the series are not stationary. In addition, the results also indicate that, HE, Y, CO2, NOE, ISF and UP series are (I (1)). Moreover, the LLC statistics for variables are significant at the 1% level. This permit to reject the null hypothesis (H0), considering that all variables are stationary in the first difference.
TE D
M
Therefore, we could apply the co-integration test to show that there is a long-term or non-relationship between health expenditure, economic growth, carbon dioxide emissions, nitrous oxide emissions, improved sanitation facilities, and urban population. 4.2. Panel co-integration tests
EP
Pedroni (1999) proposes several panel co-integration test statistics, which are approaches based on examining residues. If the variables are co-integrated, the residuals must be stationary. The null hypothesis proposes the absence of co-integration, in which the residues will be I (1), therefore it proposes that the non-stationary residues and contain a unit root.
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Test of Kao (1999) follows the same approach as the Pedroni test by proposing the null hypothesis of the absence of co-integration. Contrary to Pedroni's proposition, Kao assumes that the vectors of co-integration would be considered homogeneous according to the individuals. Considering the size of the sample among the various co-integration tests we retain that of Kao (1999) to test the co-integration relation of the variables. The Kao test is based on the null hypothesis of no co-integration. The results of the co-integration tests of Pedroni and Kao are shown in Table 4.
9
Table 4: The results of the co-integration tests Alternative hypothesis: common AR coefs. (within-dimension) Weighted Prob.
Statistic
Prob.
Panel v-Statistics
-1.113962
0.8674
-3.135074
0.9991
Panel rho-Statistics
1.189588
0.8829
0.689238
Panel PP-Statistics
-3.670609*
0.0001
-5.489928*
Panel ADF-Statistics
-1.648439**
0.0496
-2.555738*
2.202380**
0.0362
-7.213509*
Group ADF-Statistics
-2.691034* Statistic
ADF
-0.779155*
0.0000 0.0036 Prob.
M
Kao test
0.0053
N
Group PP-Statistics
0.0000
U
Prob.
0.7547
A
Group rho-Statistics
Statistic
SC R
Alternative hypothesis: individual AR coefs. (between-dimension)
IP T
Statistic
0.0021
TE D
*, ** show that the variables are significant at the 1% and 5% levels.
CC
EP
In the table above, Pedroni's co-integration test results indicate that except for panel v-statistics, panel rho-statistics and group rho-statistics, all other statistics are significant, so the null hypothesis of no co-integration is rejected. UP, ISF, CO2, NOE and GDP are co-integrated with health expenditure. Both panel PP-statistics and group PP-statistics have better properties; these two statistics are more reliable. The null hypothesis of no co-integration is rejected at 1% level by the panel PP-statistics and group PP-statistics. Once the co-integration presence is detected, the next objective is to estimate the long-term relationships between the variables.
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Thus, the results show that the probability associated with T-statistic is 0.0021; this makes it possible to reject the null hypothesis of absence of co-integration. We can therefore say that there exists a co-integration relation between the variables. 4.3. Panel ARDL results The PMG method assumes the presence of co-integration. This requires the separate use of cointegration tests and unit root testing on panel data (Bergheim, 2008). The PMG method derives the coherent and asymptotic properties of the estimator for the two stationary I (0) and non10
stationary explanatory variables I (1) (Roudet et al., 2007, Asteriou and Hall, 2007). The PMG approach not only makes it possible to estimate the long-term relationship between the cointegrated variables, it also gives the error correction coefficient which confirms the existence of the long-term relationship.
IP T
The coefficient of the delayed error correction term measures the rate at which the dependent variable adapts to changes in the interdependent variable before converging to its equilibrium level (Apostolidou et al., 2015). And we can speak of the existence of long-term relation if the sign of the coefficient of the error correction term is negatively significant. 4.3.1. PMG1 long-run estimate
N
U
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Table 5 shows that only ISF and economic growth have long-term effects on health expenditure. While the coefficient of CO2 emissions and UP are negative, but not significant on health expenditure. Note that the reciprocal paths of these long-term effects between health expenditure, economic growth and ISF are observed. However, the long-term effects of ISF and economic growth on health expenditure (and vice versa) are significant at the 1% level. Moreover, we note that all the variables affect positively and in a significant way on the economic growth at the 1% level. Table 5: PMG long run estimates Dependant variables CO2
Y
NOE
UP
0.319*
-0.066
0.332*
-0.577*
0.302*
(0.000)
(0.140)
(0.000)
(0.000)
(0.000)
0.064**
-0.064*
0.038*
-0.070*
-0.015
(0.025)
(0.000)
(0.000)
(0.000)
(0.815)
EP
CO2
TE D
HE
ISF
ISF
M
HE
A
Independent variable
CC
Y
A
NOE
UP
-0.302
0.777*
0.699*
-0.316*
0.597*
(0.175)
(0.000)
(0.000)
(0.000)
(0.000)
-0.231*
0.134
0.086*
0.554*
0.301*
(0.002)
(0.000)*
(0.000)
(0.001)
(0.000)
0.057
-0.048
0.037
0.034*
-0.055*
(0.882)
(0.002)*
(0.107)
(0.000)
(0.066)
-0.015
0.119*
0.043*
-0.030*
0.247*
(0.586)
(0.000)
(0.000)
(0.000)
(0.000)
Notes: *, ** show that the variables are significant at the 1% and 5% levels.
1
Pooled Mean Group Estimator
11
4.3.2 PMG short-run estimates
A
N
U
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The table below presents the short-term estimate and the co-integration relation established by the error correction model. At equilibrium ECT is zero, if it's not the case the pairs of relationships have deviated from the long-term equilibrium. According the table we notice that the error correction coefficient is negative and significant at 1% level which confirms the existence of a statistically significant long-term relationship between health expenditure, GDP per capita and nitrous oxide emissions.
Coefficient
M
Table 6: PMG short-run estimate, ΔHE is the dependent variable t-Statistic
p-value
2.967605
0.0034
6.879380
D (HE (-1))
-0.119235
-1.538279
0.1255
-1.172648
-1.013916
0.3118
-2.575121
-1.226504
0.2214
-1.235148
-0.810846
0.4184
-0.563690
-0.039331
0.9687
D (CO2 (-1))
-4.940380
-0.592055
0.5545
D (CO2 (-2))
9.623507
0.879149
0.3804
D(GDP)
0.316316
0.169761
0.8654
D (GDP (-1))
-1.013527
-0.596152
0.5517
D (GDP (-2))
0.406816
0.191516
0.8483
D(NOE)
2.754476
1.937948
0.054*
D (NOE (-1))
-0.013158
-0.014619
0.9884
D(ISF) D (ISF (-1))
A
CC
D(CO2)
EP
D (ISF (-2))
TE D
Constant
12
D (NOE (-2))
-0.163386
-0.271709
0.7861
D(UP)
-326.8169
-0.901836
0.3682
D (UP (-1))
801.6053
1.500181
0.1351
D (UP (-2))
-398.2695
-1.596984
0.1118
ECT (-1)
-0.305380
-3.252554
0.0013*
Source: Calculated by the authors using Eviews. 9
IP T
4.4. VECM Granger Causality
A
CC
EP
TE D
M
A
N
U
SC R
The results of Granger causality based on vector error correction (VECM) are provided in Table 7. According to the results in this table, in Granger's sense, there is a one-way relationship from health expenditure to economic growth in short and long-runs. This result is confirmed to the results of (Neycheva, 2008; Egbetunde and Fasanya, 2013).There is a bi-directional relationship between economic growth and CO2 emissions in short and long-runs. These results are in line with the findings of the (Arouri et al. 2012; Lee and Brahmasrene, 2013).The results also show a bi-directional causality between CO2 emissions and health expenditure for the entire of countries in short and long-runs. Similarly, in the short term, a unidirectional relationship ranging from health expenditure to nitrous oxide emissions and improved sanitation facilities is found, but there is a two-way relationship between health expenditure and urban population. Overall, the results reported a two-way relationship between economic growth and ISF; between nitrous oxide emissions and ISF; and between CO2 emissions and ISF in the short term.
Table 7: Granger Causality test results Short-run Causality
Longrun Causalit y
∆
∆
∆
∆
13
∆
∆
ECT
∆
0.071* (0.00 8)
0.039* (0.003)
0.013* (0.001 )
-0.025* (0.000)
0.010* (0.003)
0.073**
(0.042)
-0.209 (0.425)
0.039** (0.018)
0.071 (0.09*)
∆
0.163 (0.159)
-0.043** (0.034)
0.021* * (0.041 )
0.055* (0.00 1)
0.151 (0.106 )
0.038* (0.00 2)
-0.051* (0.031)
0.017*
-0.090** (0.013)
0.016** (0.01 5)
0.090** (0.01 5)
(0.009)
0.300 (0.385)
(0.003)
0.409* (0.009)
0.039** (0.04 3)
0.023 (0.112 )
A
-0.262** (0.044)
-0.228 (0.327)
0.075* (0.002)
-0.023** (0.010) -0.040* (0.000)
-0.013* (0.003)
M
∆
0.731* (0.001 )
U
-0.057 (0.853)
N
0.306*
-0.035* (0.004)
(0.001) ∆
-0.019*
IP T
∆
0.105 (0.19 5)
SC R
∆
Notes: *, ** show that the variables are significant at the 1% and 5% levels. The values of the probabilities are between the parentheses.
CC
EP
TE D
In the long term, the error correction term is significant at 1% and 5%, that is to say the differences between the actual values and the long-term values will be corrected with the ECT coefficients in each period. So the causality test shows in the long term there is a bidirectional relationship between health expenditure and urban population and between economic growth and ISF and also between CO2 emissions and ISF. These results are the same as for (Rahman, 2011; Boussalem et al. 2014). A unidirectional relationship running from CO2 emissions to nitrous oxide emissions and UP is found. Finally, there is a unidirectional relationship running from nitrous oxide emissions to economic growth. 5. Conclusion
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The aim of this article is to study the relationship in the long-term and short-term causality between health expenditure, environmental pollution and economic growth in the Sub-Saharan Africa countries for the period 1990-2015. The results of the unit root tests of Levin, Lin and Chu (1992) and Augmented Dickey (ADF) show that the variables are stationary at the first difference. The results of the Pedroni (1999) test indicate that there is a long-term relationship between variables. The ARDL model is employed in order to examine the long-term and shortterm impact of environmental pollution and economic growth on health expenditure, and VECM Granger causality test for checking the direction of causality. The results of ARDL test 14
indicate that economic growth has positive impact on health expenditure while economic growth has negative impact on the health expenditure in the long run.
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The Granger causality test in VECM indicates that both in the long term and in the short term, there is a two-way relationship between CO2 emissions and health expenditure. The results show that CO2 emissions have a negative and significant effect health expenditure in short-term and the last column shows the same relationship between these variables in longterm. Therefore, it is important that these countries should adopt measures and policies related to the quality of the environment to reduce health illnesses because the quality of the environment has been shown to be a contributing factor to the increase in health expenditure. Indeed, in terms of health policy, the sub-Saharan Africa countries should prioritize healthy eating through effective implementation of environmental management and control policies to reduce pressure on health spending. Moreover, in future research, we have considered other environmental approaches with other methods.
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In addition, the results also indicate that there is a positive bi-directional causality link between CO2 emissions and economic growth in short and long-runs. The CO2 emissions increase with economic growth, so, the countries have studied and examined investment requirements to promote environmental protection and increase technology transfer to reduce environmental pollution. In the short and long run, the empirical results have shown that there is a unidirectional relationship running from health expenditure to economic growth. The most important results of our study are that health spending has a positive impact on economic growth, it can be concluded that the positive impact of health spending on economic growth can be increased by increasing health spending, Means more productivity. In addition, the positive and significant impact of health spending on per capita GDP shows that the need for government intervention to implement policies to encourage health spending to build a more healthy and productive economy to support economic growth and development in these countries.
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