J Knowl Econ DOI 10.1007/s13132-016-0384-6
Examining the Relationship Between Corruption, Economic Growth, Environmental Degradation, and Energy Consumption: a Panel Analysis in MENA Region Hbib Sekrafi 1 & Asma Sghaier 2
Received: 26 January 2016 / Accepted: 12 May 2016 # Springer Science+Business Media New York 2016
Abstract The aim of this study is to investigate whether energy consumption, corruption, environmental quality, and political instability affect economic growth in 13 Middle East and North African (MENA) countries over the period 1984–2012 using both the static (POLS, FE, and RE) and dynamic (Diff-GMM and Sys-GMM) panel data approaches. The empirical results show that the increased corruption directly affects economic growth, environmental quality, and energy consumption. However, corruption has an indirect effect on economic growth through energy consumption and environmental quality, an indirect effect on environmental quality through economic growth and an indirect effect on energy consumption through CO2 emissions and GDP. Indeed, energy consumption and CO2 emissions affected the economic growth. Meanwhile, economic growth effected CO2 emissions and energy consumption, and finally, CO2 emissions affected economic growth. Our paper addresses these important issues using the general method of moments (GMM) in panel data. We also find that economic growth in MENA countries reacts negatively to the environmental degradation and political instability. These empirical insights are of particular interest to policymakers as they help to build sound economic policies to sustain economic development. Keywords Corruption . Economic growth . Environmental degradation . Energy consumption . Static and dynamic panel data . MENA countries JEL F43 . Q43 . Q56
* Asma Sghaier
[email protected]
1
Faculty of Economics and Management, University of Sousse, Sousse, Tunisia
2
LaREMFiQ, University of Sousse, Sousse, Tunisia
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Introduction Corruption is a complex phenomenon that raises a myriad of challenges for the economic and social sciences. The importance of corruption has been analyzed by economists in several areas of research, giving rise to various streams of literature. However, much of traditional economic analysis has tended to analyze the direct effect of corruption on macroeconomic variables. Several studies have analyzed the effect of corruption on economic growth (Svensson 2005; Mobolaji and Omoteso 2009; Maiyaki 2010; Dridi 2013). Other studies do not support empirical methods estimating a unique income–pollution relationship. So, corruption and other institutional inefficiencies are highlighted in the literature as crucially affecting the country’s total factor productivity as well as the government’s concerns and control for environmental quality. According to López and Mitra (2000), due to the non-optimal government decisions, actual emissions are above the socially optimal levels of any level of GDP per capital income. Indeed, the higher the degree of corruption, the greater will be the deviation from the social optimum. Damiana et al. (2003) concluded that the effect of trade liberalization on environmental policy is contingent upon the level of corruption: the greater the level of corruption, the larger the impact of trade liberalization on the environment will be. For some economists, corruption affects the environmental quality through these effects on natural resources. Indeed, the lobbying activities of special interest groups in many developing countries have played a significant role in influencing key government policies that determine land use decisions in these countries (Ascher W. 1999; Hafner 1998). Paunov (2016) has analyzed the impacts of corruption on smaller- and larger-sized firms that adopt quality certificates and patents. His conclusion is that corruption reduces the chance of these firms to obtain quality certificates. Corruption affects, particularly, smaller firms which become more sensitive to the adoption of quality certificates. Our objective, in this study, is to investigate the impact of corruption on CO2 emissions, energy consumption, and economic growth for a panel of the 13 Middle East and North African (MENA) countries during 1984–2012. For this reason, we have used simultaneous equations GMM-estimation for the empirical analysis. The following study analyzes the effect of corruption on different energy consumption to CO2 emissions and economic growth. Indeed, in sum, the majority of studies analyze the direct effect of corruption on pollution. The studies that have taken into account the indirect effects of corruption on pollution through growth and energy consumption channel are very limited (Welsch 2004a; Cole 2007). This paper attempts to shed a new light on the effects of corruption on economic growth, environmental degradation, and energy consumption, taking into account the indirect effects. Specifically, the analytical framework employed in this study integrates the approach used by Cole (2007). We have broken the effects of corruption on our variables into two effects: direct and indirect. Corruption directly affects the quality of the environment through regulation and taxation. However, corruption affects the environmental quality through its effect on economic growth. In fact, corruption negatively affects economic growth; as any increase in the level of corruption will be accompanied by a reduction in the level of GDP. This reduction in GDP levels will affect the environmental quality by
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referring to the environmental Kuznets curve theory. Through our research, we will try to investigate (1) the direct relationships between corruption and economic growth, environmental degradation, and energy consumption; and (2) the indirect relationships between corruption with economic growth, environmental degradation, and energy consumption through the mediating effects of corruption. The key research question (RQ), that we address in this study, is as follows: RQ: What are the relationships among corruption, economic growth, environmental degradation, and energy consumption in the MENA region? The rest of the paper is organized as follows. In the ensuing section, we present the literature review. This is followed by an empirical analysis for testing the hypotheses. We use the data from 13 countries of the MENA region. Then, we discuss the results and the implications for future research.
Literature Review The subject of the effect of corruption on economic growth, energy consumption, and CO2 emissions has been well-documented in the economic literature. Several empirical studies have focused on different countries and time periods, and have used different modeling. In the next paragraphs, we will review some of the previous studies related to the effect of corruption on economic growth, CO2 emissions, and energy consumption, also the effect of economic growth on energy consumption and environmental quality. This literature can be divided into subtitle to explain how each variable affects the other variables. Thus, our literature is presented under six subsections, e.g., (1) How corruption affects growth?, (2) How corruption affects environmental quality?, (3) How corruption affects energy?, (4) How economic growth affects energy?, (5) How economic growth affects environmental quality?, and (6) How energy affects environmental quality? The theoretical links between corruption, economic growth, energy consumption, and environmental degradation are presented in Fig. 1.
H3 energy H4
Corruption
H1
economic growth
H6 H5
Environmental degradaon H2
Fig. 1 The hypothesis of the research
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H1: How Corruption Affects Growth? The question whether corruption impedes economic growth and development has long been studied by many theoretical and empirical works. But, there is hardly any consensus among economists on the role of corruption. The World Bank (2009) reported that the annual average rate of economic growth of Asian countries was about 7 % from 1986 to 1996 while it was very poor 2.5 % in the rest of the world. However, these countries have also experienced a high level of corruption during this period. This finding seems to contradict the most of the earlier empirical studies in the previous period. Indeed, since the pioneering work of Mauro (1995), many researchers such as Ehrlich and Lui (1999), Glaeser and Saks (2006), and Mo (2001) verified that corruption hinders economic growth and development. The contradiction between past results and coexistence of strong economic growth and high levels of corruption in some countries leads us to verify the issues of the generality of these studies. Corruption positively affects economic growth and functions as an engine of economic growth when bureaucratic delays and strict regulations, imposed by the government, enable the private agents to take their ways out of politically imposed inefficiencies (Leff 1964). Thus, corruption is increasing the efficiency of the economy and leaves positive impacts on economic growth (Huntington 1968; Summers and Heston 1988; Acemoglu and Verdier 1998; Rock and Bonnett 2004). On the contrary, various studies expose that corruption is an important factor contributing to growth instability (Denizer et al. 2010). Weil (2008) found that corrupt officials may waste public funds by, for instance, putting the taxes collected directly into their own pockets, or by awarding contracts to private agents who pay the largest bribes, rather than to those who are the most efficient. Takuma et al. (2014) used data from 109 countries; they concluded that the interaction term of government corruption and financial openness has a significant and negative impact on economic growth. This implies that financial openness magnifies the negative effect of government corruption on economic growth. Dzhumashev (2014) concluded that corruption improves economic efficiency only when the actual government size is above the optimal level. It implies that economic growth maximizing the level of corruption is possible. Also, it finds that the rate of corruption declines with economic development. This is because with economic development, the wage rate rises and makes private rent seeking higher costs, thereby, discouraging corruption. H2: How Corruption Affects Environmental Quality ? In recent years, a burgeoning body of literature has shown the economic and environmental implications of corruption. However, these works seems to have mixed results. For some people, corruption negatively affects the environmental quality. Corruption affects the dysfunctional environmental governance systems that generally contribute to the extinction of species, the over-exploitation of natural resources, the pollution and degradation of ecosystems and wildlife habitats, the spread of diseases and invasive species, and the deprivation of local stakeholders related with wildlife and plants. In fact, Corruption induces socially sub-optimal environmental governance. Thus, corruption reduces environmental regulations and breaks the established rules (Aidt 2003; Dinda 2004). With high levels of corruption, the actors who pay the biggest bribes
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benefit at the expense of socially optimal results (Fredriksson et al. 2004; Olken and Pande 2012). However, lower corruption levels are translated into stricter and more effectively enforced environmental policies (Pellegrini and Gerlagh 2004; Rehman et al. 2012; Zugravu et al. 2008). Also, corruption affects the environment through the informal sector (Biswas et al. 2011). Indeed, as highlighted by (Baksi and Bose 2010), stringent environmental regulations may encourage firms to migrate more towards the informal economy to maximize their profit. By allowing polluting companies to evade environmental regulations, production in the informal economy is likely to increase pollution levels and induce environmental degradation (Blackman 2000; Blackman and Bannister 1998). The positive effect of corruption on environmental quality is justified by Cole (2007). He finds, in a particular case, that corruption contributes to improving the environmental quality through its negative effect on economic growth. Reducing the economic growth reduces the amounts of pollutant emitted. The indirect effect of corruption on environmental quality was first addressed in literature by Welsch (2004b). Indeed, corruption has been found to reduce the income of PIB per capital and economic growth. There is an implied indirect effect of corruption on the environmental quality based on corruption’s effect on income that must be considered. H3: How Corruption Affects Energy? The energy sector, with its complex mixture of public and private actors and often enshrined centers of monopoly power, is prone to corruption. By referring to the corruption perceptions index (CPI), we noticed that of the 32 leading mining countries where extraction of coal, oil, natural gas, and uranium takes place, only nine have a score above 5.0 and the remaining 23 have scores of 4.8 or below (Transparency International (2015)). This finding is consistent with a research affected by Karl (2004). The author concludes that countries dependent on oil are often characterized by corruption and exceptionally poor governance. Fredriksson et al. (2004) according to corruption can affect the energy policy through three axes. First, greater corruptibility reduces the stringency of energy policy. Second, increasing costs of coordinating bribery leads to a more stringent energy policy Olson (1965). Third, the distribution of the worker and capital owner lobbies’ political pressures depends on how energy policy affects the lobby group members’ income. Fredriksson et al. (2004) use dynamic panel data on sector energy intensity (energy use per unit of value added) in OECD countries for the years 1982–1996. The empirical findings support several predictions. First, higher corruptibility strongly correlates with lower energy efficiency in OECD countries. Second, an increase in coordination costs appears to reduce the influence of the capital owner lobby. Third, the effects of coordination costs on the capital and worker lobby groups’ policy success are indeed related. Focusing exclusively on energy-intensive sectors, they have find that coordination costs have opposite effects on the policy influence of the two lobbies. Indeed, the relationship between the capital owner lobby’s coordination costs and its policy success is u-shaped. However, for the worker lobby the same relationship exhibits an inverted-u shape. Rabah and Markus 2011 have examined the relationship between oil rents, corruption and state stability. They concluded that an increase in oil rents significantly increases corruption (Al-Mulali U, Weng-Wai C, Sheau-Ting L, Mohammed AH).
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H4: How Economic Growth Affects Energy? Since the pioneering work of Kraft and Kraft (1978), the relationship between energy consumption and economic growth has become a hot topic in energy economics science. A large volume of empirical research confirms the existence of a strong correlation between these two variables. Nothing that most empirical results indicate that economic growth can cause increases in energy consumption (Glasure 2002; Ghali and El-Sakka 2004; Akinlo 2008; Apergis and Payne 2009). The empirical results of the causal relationships are classified into three categories. The first category of studies found evidences supporting unidirectional or bidirectional causality. In this perspective, Ghosh and Kanjilal (2014) concluded an unidirectional causality running from energy consumption to economic activity. Acaravci and Ozturk (2010) found a one-way causal relationship to exist between economic growth and energy consumption. Narayan and Popp (2012) revealed a negative causal relationship to exist between energy consumption and economic growth in the G6 countries. Al-mulali et al. (2013) found that majority of Latin American and Caribbean countries maintained a positive bidirectional long-run relationship between energy consumption, CO2 emissions, and economic growth. The second category of research has found no causality existing between the variables (Soytas et al. 2009; Soytas and Sari 2003; Apergis et al. 2010; Hossain 2011). The third category is based on the classification of studies according to the methods or time periods studied, which are supposed to have an effect on the direction of causality (Heidari et al. 2015; Begum et al. 2015; Baranzini et al. 2013; Yalta 2011). H5: How Economic Growth Affects Environmental Quality? With the notable deterioration in air quality, a vast literature has sought the relationship between economic growth and environmental quality. This literature is based on environmental Kuznets curve (EKC). According to this theory, the increase in growth causes an increase in carbon dioxide emissions, CO2 emissions begin to decline, when some levels of output were reached. There is a growing literature interested in studying the environmental Kuznets curve (EKC). This hypothesis can be divided in two sets. The first set is related to cross-sectional studies (Ben Jebli and Ben Youssef 2015; Jalil and Mahmud 2009, Jayanthakumaran et al. 2012; Ozturk and Acaravci 2010; Shahbaz et al. 2013). The second set is related to panel studies (Acaravci and Ozturk 2010; Jaunky 2011; Arouri et al. 2012; Ozcan 2013; Al-Mulali and Ozturk 2015 and Apergis and Ozturk 2015). Ozcan (2013) used 12 MENA countries to analyze the empirical nexus between carbon emissions, energy consumption, and economic growth. He concludes that the inverted U-shaped EKC hypothesis is verified by three countries which are Egypt, Lebanon, and UAE. H6: How Energy Affects Environmental Quality? For many decades, the demand of fossil fuel energy has become increasingly growing and has reached an exponential growth rate. This remarkable increase caused disasters and catastrophic damages on the environment. In fact, the
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consumption of non-renewable energy increases the economic growth but increases also carbon dioxide (CO2) emissions. Several studies in literature review showed the negative effects of energy consumption on the environmental quality (Yan and Crookes 2010; Zhang et al. 2011; Chang et al. 2013). Al-Mulali and Ozturk (2015) used a database of countries in the MENA region to analyze the relationship between energy consumption, urbanization, trade openness, industrial output, and the political stability on the environmental degradation. The results of fully modified ordinary least square (FMOLS) concluded that energy consumption, urbanization, trade openness, and industrial development increases environmental damage in the MENA countries. In addition, they concluded bidirectional causality between energy consumption and environmental quality in short and long run. Omri et al. (2015) confirmed the existence of relationship between energy consumption and environmental quality, by referring to data from 14 countries in the MENA region. These results support the unidirectional causality from energy consumption to CO2 emissions without any feedback effects.
Analytical Framework Data and Methodology Analytical Framework In this paper, we have used dynamic simultaneous equation modeling to study the linkages among economic growth, environmental quality corruption, and energy consumption for a panel consisting of 13 MENA countries (Algeria, Bahrain, Egypt, Iran, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, Turkey, United Arab Emirates, and Yemen). However, to the best of our knowledge, none of the empirical studies have focused on investigating the effect of corruption on economic growth, environmental quality, and energy consumption by using simultaneous-equation modeling. Specifically, this study uses a three-equation structural model that allows simultaneous examination of the impact of (i) corruption, energy consumption, and CO2 emissions on economic growth; (ii) the economic growth and corruption on environmental quality; and (iii) corruption, economic growth, and CO2 emissions on energy consumption. Our study uses the methodology used by Cole (2007). To identify the different theoretical effects of corruption on the environment; we have used the following functional forms: Tcran ¼ f ðEC; CO2 ; CC; lkh; ideÞ
CO2 ¼ f Tcran; Tcran2 ; CC; inv; ide
EC ¼ f ðT cran; CO2 ; CC; inv; ideÞ
ð1Þ
ð2Þ
ð3Þ
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Since our study is a panel data study, Eqs. (1, 2, and 3) can be written in panel data form as follows: Tcranit ¼ α0 þ α1 ECit þ α2 CO2it þ α3 CCit þ α4 lkhit þ α5 ideit þ εit
ð4Þ
CO2it ¼ β 0 þ β1 Tcranit þ β2 Tcran2 it þ β 3 CC it þ β 4 invit þ β 5 ideit þ φit
ð5Þ
EC it ¼ δ0 þ δ1 Tcranit þ δ2 CO2it þ δ3 CC it þ δ4 invit þ δ5 ideit þ ηit
ð6Þ
where i and t are the index countries and time period (i = 1,…, I and t = 1,…, T), respectively, Tcran is the annual growth rate of GDP per capital, CO2 is emissions of carbon dioxide per capital, CC is the level of the control corruption, INV is investment ratio to GDP, EC is the energy use per capital, and lkh is the tertiary enrollment rate. Data and Methodology In this paper, we have used annual data covering the period (1984, 2012). These data are collected from the World Development Indicators (WDI 2013) for (Tcran), energy consumption (EC), CO2 emissions (CO2), lkh, and investment (INV). The level of the control corruption (CC) variable is extracted from BThe World Wide Governance Indicators^ (WGI). To investigate the total effect of corruption on environmental quality, economic growth, and energy consumption, we have used the generalized method of moments (GMM) to estimate the Eqs (1, 2, and 3). The GMM method is the estimation method most commonly used in models with panel data and in the multiple-way linkages between certain variables. GMM-based estimation is a technique for instrumental variable estimation and has several advantages over conventional estimators (2SLS). GMM makes use of the orthogonality conditions to allow for efficient estimation in the presence of heteroscedasticity of unknown form (Hansen and Østerhus (2000) and Hayashi (2000)). We have used a two-step Arellano-Bond estimator. In the present study, we used a dynamic panel specification where lagged levels of the annual growth rate of GDP, emissions of carbon dioxide, and energy consumption are taken respectively into account by using the Arellano and Bond (1991) GMM estimator. Our proposed models are as follows: Tcranit ¼ α0 Tcranit−1 þ α1 ECit þ α2 CO2it þ α3 CCit þ α4 lkhit þ α5 ideit þ εit ð7Þ CO2it ¼ β 0 CO2it−1 þ β 1 Tcranit þ β 2 Tcran2 it þ β 3 CCit þ β 4 invit þ β 5 ideit þ φit ð8Þ
ECit ¼ δ0 ECit−1 þ δ1 Tcranit þ δ2 CO2it þ δ3 CCit þ δ4 invit þ δ5 ideit þ ηit
ð9Þ
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Where Tcranit − 1 is a lagged level of the annual growth rate of GDP, CO2it − 1 is the lagged levels of emissions of carbon dioxide, and ECit − 1 is the lagged levels of energy consumption.
Analyses and Results Table 1 illustrates an overview of our data. On average, the growth rate is 4.51 % on the period 1984–2012 for the MENA countries. The indicator of CC is generally negative in most countries in the MENA region. Indeed, the level of control of corruption equals −0.26 on average. In fact, various factors have been identified as creating favorable conditions for corruption to flourish in the region, including unique political and institutional dynamics, limited civil society activism, regional insecurity, and extreme oil wealth. MENA countries can be loosely grouped into three categories. At the top are such countries as Jordan and the Gulf sheikdoms, which in relative terms are doing fairly well on this agenda. They rank in the 30s and the 40s on the TI index. A second tier of countries is in the middle somewhere in the 70s to the 90s range including Morocco, Lebanon, and Algeria. A final set of countries falls towards the bottom and are perceived to struggle significantly with these issues, including Yemen, Syria, and Iran. Iraq comes at the 178th position, just above Myanmar and Somalia. The correlation between the dependent and independent variables is presented in Table 2. This table shows that economic growth correlates positively with the CO2 emissions and the control of corruption. The control of corruption also correlates positively with the CO2 emissions and energy consumption. Then, CO2 emissions positively correlate with the GDP and energy consumption. Before proceeding with the analysis of the results, we have to check two tests: the autocorrelation and instrument validity. AR(2) is the Arellano and Bond (1991) tests of second-order autocorrelation in the first differenced errors. When the regression errors are independent and identically distributed, the first differenced errors are, by construction, auto-correlated. Autocorrelation in the first differenced errors at orders is higher than the one that suggests that the GMM moment conditions may not be valid. The Sargan test (Arellano and Bond 1991) is a test of over identifying restrictions. A
Table 1 Descriptive analysis Variable
Obs
Mean
Std. Dev.
Min
Max
Tcran
493
EC
493
4.682586
6.523964
−42.45112
46.5
9.786469
1.069607
7.57758
Ide
493
12.27103
2.023846
3.407211
−5.288191
33.56602 26.61561
Inv
493
1.456008
5.00497
0.0002134
Lkh
493
1.183021
.3073372
−1.100672
1.783071
CC
493
−0.2609519
1.133995
−14.57475
2.602149
CO2
493
1.727455
1.16036
−0.3710637
4.227272
J Knowl Econ Table 2 Correlations between the various variables used in the regression models Tcran Tcran
EC
Ide
CC
CO2
Lkh
inv
1.0000
EC
−0.0640
1.0000
Ide
0.0789
−0.2192
CC
0.1011
−0.0806
0.0697
1.0000
co2
0.1192
0.1032
0.0674
0.3357
1.0000
Lkh
−0.0075
0.0321
0.2681
−0.0469
0.1589
1.0000
inv
0.0014
−0.2364
−0.0746
0.0054
0.1256
0.1274
1.0000
1.0000
rejection from this test indicates that the model or instruments may be miss-specified. The lower panel of Table 3 includes the estimated results. The AR(2) tests show no evidence of autocorrelation at conventional levels of significance. Sargan tests show no
Table 3 GMM estimation of equations Variable
Eq. 1
Eq. 2
Eq. 3
Economic growth
CO2 emissions
Energy consumption
Tcran
–
0.0282611 (8.59)*
0.039754 (12.30)*
tcran2
–
−0.0007169 (−9.62)*
–
L1.tcran
−0.4921838 (−8.11)*
–
–
CO2
−11.75461 (−2.51)*
–
0.5498225 (6.33)*
L1.co2
–
0.3756906 (6.38)*
–
Cc
0.2454108 (4.43)*
0.0197099 (14.04)*
Ec
5.513558 (5.92)*
–
L.ec
–
0.8610009 (16.47)*
Lkh
−3.572606 (-0.68)
Ide
0.0422978 (1.29)
Inv
–
Dependent variables
–0.0243991 (−8.37)*
– −0.0056163 (-4.56)*
0.0039707 (5.82)*
0.0527118
−0.071758
(12.27)*
(−9.90)*
Arellano-Bond test for AR(2)
−1.64 [0.101]
−0.41 [0.682]
−0.87 [0.385]
Sargan test
17.89 [0.162]
5.38 [0.865]
6.81 [0.814]
Hansen test
15.76 [0.262]
11.61 [0.312]
9.43 [0.582]
DWH test (p value)
4.009 (.000)
3.278 (.048)
3.666 (.025)
Values in (.) are the T-statistic, the values in [.] are p value. Hansen and Sargan test refers to the overidentification test for the restrictions in GMM estimation. The AR(2) test is the Arellano–Bond test for the existence of the second-order autocorrelation in first differences DWH test Durbin–Wu–Hausman endogeneity test *Indicate significance at the 1 % level, **indicate significance at the 5 % level, ***indicate significance at the 10 % level
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evidence of miss-specification at conventional significance levels. These results indicate that the dynamic panel models are a good specification. Based on simultaneous equations, GMM-estimation, the empirical results of Eqs. (1), (2), and (3) are presented respectively in columns 2, 3, and 4 of Table 3. Before running GMM estimation, some tests have been audited. Two important specification tests are used for simultaneous equation regression models: test of endogeneity/ exogeneity and test of overidentifying restrictions (Newey 1985; Smith and Blundell 1986). For testing the endogeneity, we have used the the Durbin–Wu–Hausman. The null hypothesis that an ordinary least squares (OLS) estimator of the same equation would yield consistent estimates, i.e., an endogeneity among the regressors would not have deleterious effects on OLS estimates. In our studies, the results of the DWH test indicate that endogenous regressors’ effects on the estimates are meaningful, and instrumental variables techniques are required. The overidentifying restrictions are verified to using the Hansen and Sargan test to provide some evidence of the instruments’ validity. In our studies, the null hypothesis of overidentifying restrictions cannot be rejected. That is, the null hypothesis that the instruments are appropriate cannot be rejected. Eq. (1) allows examining the impact of corruption, CO2 emissions, and energy consumption on economic growth. The value of Tcran(−1) is (−0.492) which implies that economic growth is corrected by (0.492) percent each year. The control of corruption has a positively significant effect of economic growth. Indeed, an increase by 1 % of the control of corruption increases economic growth by 0.245 %. This result is consistent with that of Ahmad et al. 2012, who find that a decrease in corruption raises the economic growth rate in an inverted U-shaped way. The relationship between our variables shows that the effect of the energy consumption on economic growth in MENA countries is positive and statistically significant. This implies that the higher energy consumption, the more important the economic growth is an increase of 1 % in the energy consumption can increase the GDP by 5.51 %. An increase in energy consumption leads to an increase in the GDP per capital, i.e., the level of energy consumption increases monotonically with GDP per capital. This confirms the results showed by Omri et al. 2015, Shahbaz and Lean 2012 and Sharma 2010. As expected, CO2 emission is negatively and significantly related to economic growth. The coefficient of CO2 emissions is −11.75 implying that a 1 % increase in the CO2 emissions decreases economics by −11.75 for sample countries. This result is consistent with the literature that indicates that environmental quality proxied by CO2 emissions has a negative effect on economic growth (Jayanthakumaran et al. 2012; Omri et al. 2015). Eq. (2) reports the results of the relationship between environmental quality, corruption, and economic growth. The value of CO2(−1) (0.375) implies that environmental quality is corrected by (0.375) percent each year. The coefficient of the control of corruption is positive and statistically significant. Corruption negatively affects the environmental quality. We find that economic growth has negative and statistically significant effects at 1 % level on environmental quality. The coefficient of economic growth is −0.028 implying that a 1 % increase in the growth rate of the GDP per capital decreases environmental quality by 0.028 % for sample countries. The validity of the ECK hypothesis requires a significant coefficient of Tcran2. In our results, the coefficient of Tcran2 is positively significant and the coefficient of Tcran is negatively
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significant. We can conclude the existence of the ECK curve. The inverted U-shaped EKC hypothesis is verified by this panel of MENA countries with a turning point Tcran = 19.66. A development in the annual growth rate of GDP per capital leads to an increase in per capital CO2 emissions. This result is consistent with that of Arouri et al. 2012 who show that the EKC hypothesis is verified in 12 Middle East and North African (MENA) countries. Eq (3) analyses the impact of economic growth, CO2 emissions, and corruption on energy consumption. The results in column 3 shows that the growth rate of GDP per capital and the CO2 emissions have a positive and statistically significant effect on energy consumption in the MENA region. The 1 % increase in Tcran increases energy consumption by 0.0397 % for sample countries. The results here are consistent with these of a recent studies on this subject by Ang (2008), Bowden and Payne (2009), Belloumi (2009), Shahbaz and Lean (2012), and Omri et al. (2015). Similarly, 1 % increase in CO2 emissions increases energy consumption by 0.549 % for sample countries. The associated coefficient with the delayed endogenous variable is positively significant at 1 % level. Energy consumption is corrected by (0.861 %) percent each year. Furthermore, the variable of control corruption has a negative and significant impact on energy consumption at 1 % level. This implies 1 % increase in corruption (decrease of control corruption) increases energy consumption by 0.0243. After verifying our hypotheses, it is now possible to quantify the total impact of corruption on pollution, growth and energy consumption. Firstly, Table 4 provides the direct, indirect, and total effect of corruption on economic growth, pollution, and energy consumption respectively for each of the three equations presented in Table 3 (Eq. 1, Eq. 2, and Eq. 3). The indirect effect is calculated using the average of annual growth rate of GDP per capital. The results shown in Table 4 indicate that the control of corruption positively affects economic growth. However, the total effect is negative. This result is explained by dominance of indirect effects through energy consumption and CO2 emissions. Increasing income levels through control of corruption increases CO2 emissions. The negative effect of CO2 emissions on economic growth dominates the positive effect of the control of corruption. The economic structure of the MENA region is characterized by dominance of the extractive industry and polluted industry. Also, dominance of indirect effects is explained by the Bhavre pollution^ hypothesis. Most investors install their projects in the developing countries which are characterized by a more or less fragile fiscal system. This fiscal system allows investors to issue unrequited, additional amounts of pollutant. In addition, the indirect effect of CC on CO2 emission is positive. A 1 % increase in the level of control of corruption increases 0.005 % CO2 emissions via economic ∂CO2 growth. Indeed, as it has already been indicated since ∂Tcran (forms of the indirect effect), the indirect effects of corruption on emissions are contingent upon the level of per capital income. Corruption affects the environmental quality in MENA region, through its effect on GDP per capital most as the weakening of environmental regulations (Al-Mulali et al. 2015). This result is explained by the non-political stability; any increase in control and environmental regulation promote polluters to migrate more towards the informal economy.
(∂Tcran/∂CO2) * (∂CO2/∂cc) + (∂Tcran/∂EC)* (∂EC/∂cc) = −0.35549
(∂Tcran/∂cc) + (∂Tcran/∂CO2) * (∂CO2/∂cc) + ¶(Tcran¶/EC)* (∂EC/∂cc) = −0.11049
Total effect
(dCO2/dcc) = (∂CO2/∂cc) + (∂CO2/∂Tcran)* (∂Tcran/∂cc) = 0.0242
(∂CO2/∂Tcran) * (∂Tcran/∂cc) = 0.00523
∂CO2/∂cc = 0.019
∂Tcran/∂cc = 0.245
Indirect effect
Direct effect
Eq (2)
Eq (1)
Table 4 Decomposing the impact of corruption on pollution, growth, and energy
(∂EC/∂cc) + (∂EC/∂CO2) * (∂CO2/∂cc) + (∂EC/∂Tcran) * (∂Tcran/∂cc) = −0.0145
(∂EC/∂CO2)*(∂CO2/∂cc) + (∂EC/∂Tcran) (* (∂Tcran/∂cc)=0.01045
∂EC/∂cc = −0.024
Eq (3)
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Finally, the indirect effect of control corruption on energy consumption is negative. A 1 % increase in the control of corruption can decrease energy consumption by 0.0145 %. Taxation is a key tool by which governments can influence energy use to contain its environmental impacts. However, MENA countries’ energy taxes are not harness and are often taxed at very low—or zero—rates. Our results, therefore, confirm the common belief that energy consumption is associated with corruption. Fredrikssona et al. (2004) have investigated the effect of corruption and industry sector size on energy policy outcomes. They have concluded that greater corruptibility of policy makers reduces energy policy stringency.
Conclusions and Political Implications Obtaining a consistent estimation of the total effect that corruption has on economic growth, energy consumption and environmental quality is complicated by the endogenous response of our variables to corruption. Corruption directly affects economic growth, environmental quality and energy consumption. However, corruption has an indirect effect on economic growth through energy consumption and environmental quality, an indirect effect on environmental quality through economic growth and an indirect effect on energy consumption through CO2 emissions and GDP. Indeed, energy consumption and CO2 emissions affected the economic growth; economic growth affected CO2 emissions and energy consumption and finally CO2 emissions affected economic growth. Our paper addresses these important issues using the generalized method of moments (GMM) in panel data. While our results confirm the positively direct effect of control corruption on economic growth, the total effect indicates a negative effect. In addition to its direct effect, corruption affects growth through two channels; the environmental quality and energy consumption. The negative indirect effect through the two channels dominates the direct effect. Control of corruption was found to have a positive direct effect on per capital emissions of CO2. Control of corruption was also estimated to have an indirect impact on CO2 emissions which stems from the positive relationship between control of corruption possesses with economic growth. The positive indirect effect of control of corruption through economic growth reinforces the direct effect of control corruption on the environmental quality. From our results, we can draw some implications. The authorities are encouraged to deal with environmental regulations and improve energy efficiency to increase their level of growth. The countries of the MENA region are in their growing phase; any increase in the income levels increases the amount of energy used, and hence increases the level of pollution. The effect of CO2 emissions on economic growth is superior to the effect of corruption on incomes levels. The countries of the MENA regions can reduce corruption through compliance with international convention of Environmental Protection, the Kyoto Protocol for example. Control programs against corruption should give particular importance to improving the quality of public regulations. Anti-corruption policies pursued by individual countries must be coordinated with those of neighboring countries. The international cooperation under bilateral and multilateral forms is to be encouraged to reduce the informal sector size.
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