Download Impacts of green ODA, economic growth, and corruption on climate

Impacts of green ODA, economic growth, and corruption on climate change 1
Presented at 2015 EAAERE Conference, Taiwan
Jeong Hwan Bae 2
Associate Professor, Department of Economics
Chonnam National University, South Korea
Taeho Park
Director & Professor, School of Global Innovation and Leadership
Lucas College and Graduate of Business
San Jose State University, U.S.A.
James S. Shortle
Professor, Department of Agricultural Economics and Rural Sociology
College of Agricultural Sciences
The Pennsylvania State University, USA
Abstract
This research empirically investigates the role of green official development assistance
(ODA) in mitigating CO2 emissions when economic growth and corruption are also
considered as determinants of the climate change. For green ODA recipient countries
between 2002 and 2010, we construct panel econometric models for per capita GDP at the
first stage to examine if corruption and green ODA affect economic growth. At the second
stage, predicted values of the GDP are used as a determinant of CO2 emissions where
corruption and green ODA are also accounted as determinants of CO2 emissions. The results
show that the direct effect of green ODA on CO2 emissions is larger than the indirect effect
of green ODA on CO2 emissions through economic growth, so higher levels of green ODA
mitigate CO2 emissions. Meanwhile, corruption affects indirectly CO2 emissions through its
1
This work was supported by the National Research Foundation of Korea Grant, funded by the Korea
n Government (NRF-2013-1822).
2
Corresponding author, Address: Yangsantaekjiro 20, Hyunjin Evervil 109-301, Bukgu, Gwangju, South
Korea, Email: [email protected].
1
impact on economic growth, so lower corruption increases CO2 emissions in total.
Key words: Corruption, green ODA, economic growth, climate change, joint estimation.
1. Introduction
Corruption is generally defined as “the abuse of public roles or resources for private
benefit” (Klitgaard, 1988) and is perceived as a harmful phenomenon in that it can affect the
environment as well as economic growth negatively3. The World Bank (2006) addressed that
corruption is not only the largest obstacle that intervenes in economic and social development,
but is also a major factor in causing environmental pollution. Thus, it is important to establish
a transparent government system to accomplish sustainable development. In this context,
corruption, climate change, and economic development are inter-related. Although corruption,
climate change, and economic growth can affect each other considerably, there are only a few
studies in the literature on the interactive effects among those factors.
L´opez and Mitra (2000) initiated study on the relationship between corruption, growth,
and environmental pollution, and showed that existence of corruption can distort the optimal
turning point of the inverted U-shaped environmental Kuznets curve (EKC) between
economic growth and environmental degradation for both of cooperative and non-cooperative
games between government and private firms. Since the paper of L´opez and Mitra (2000),
there have been several studies on the relationships between corruption and environmental
degradation, in combination of economic growth and other variables. For example,
Fredriksson and Millimet (2003) examined the role of corruption in explaining spatial
3
Corruption includes not only the actions of public officials, but also those of agents of non-governme
nt organizations and for-profit businesses. UNODC (2010, 2012) regards corruption as a set of behavior
s that erode economic, political, and institutional development such as bribery, nepotism, cronyism, emb
ezzlement, fraud, and the misappropriation of resources.
2
variation on environmental policy in the United States by theoretical and empirical
approaches. They argued that greater corruption raised the total number of bribes and thus
decreased the bribes given to each immoral bureaucrat, leading to more stringent
environmental policy while greater corruption allows lobbyists affecting a larger portion of
environmental bureaucrats, leading to less stringent policies.
Welsch (2004) investigated the direct and indirect impacts of corruption on environmental
pollution using cross section data for several air and water pollution indices across
122 countries. He claimed that corruption not only raises environmental pollution directly but
also affects pollution indirectly by deterring economic growth. Thus, the total effect of
corruption on pollution is indeterminate. In the same spirit as the study by Welsch (2004),
Cole (2007) analyzed how SO2 and CO2 emissions can be affected by government corruption.
Similar to Welsch’s approach, Cole studied the direct and indirect effects of corruption on
environmental pollution. Direct effects include the impact of corruption on environmental
pollution throughout mitigation of environmental regulation, while indirect effects reflect
impacts of corruption on environmental pollution through a reduction in economic growth.
Cole found that the two effects had opposite signs; as a result, the sum of two effects depends
on the relative size of the two effects. Like the approach used by Cole (2007), Leitão (2010)
considered endogeneity between corruption and per capita GDP and used the same
instrumental variables employed by Cole (2007). The result showed evidence that corruption
is positively related to the critical threshold level of income beyond which pollution declines,
which implies that higher corruption postpones more stringent environmental regulation.
Following Cole (2007) and Leitão (2010), our study sheds lights on direct and indirect
effects of green ODA as well as corruption on the emission of greenhouse gases (GHGs).
Green ODA has been devoted to funding climate finance in developing countries (Bierbaum
and Fay, 2010; OECD, 2011). As green ODA might dedicate to the reduction of CO2
3
emissions by investing in more efficient energy technology, more energy-saving facilities,
CO2 reservoirs, or the production of renewable energy, countries with higher green ODA
have higher chances of mitigating CO2 emissions. However, it is not yet proven whether
green ODA affects emission of CO2 emissions directly or indirectly.
Recent studies, such as Welsch (2004), Cole (2007), and Leitão (2010), following the
seminal paper by Lopez and Mitra (2000), have attempted to explain why some countries
perform better than others in mitigating CO2 emissions. Generally, they have focused on the
effects of corruption on economic growth and environmental regulation for developing as
well as industrialized countries in their research domain. Our study addresses the role of
green ODA in explaining why some developing countries reduce the emission of CO2 more
than others when the effects of corruption and economic growth on climate change are
controlled.
We begin with a literature review on relationship between corruption, economic growth,
and environmental pollution, including the climate change issue. Section three introduces
basic models, data, and methodology. Section four consists of estimation results for panel
fixed/random effect models, joint estimation results, robustness check, and calculation of
direct and indirect effects of green ODA, corruption, and openness on CO2 emissions. Section
five discusses major findings and climate policy implication for green ODA recipient
countries.
2. Literature review
Previous literature on the relationships among corruption, economic growth, and
environmental pollution can be summarized as follows. Lopez (1994) explained that the
relationship between income and environmental pollution levels can be determined by the
Frisch coefficient of preferences reflecting the marginal utility of income and substitution
4
elasticity between production factors and environmental pollution. He stressed that once
economy of a country grows sufficiently, the marginal utility of income diminishes, concerns
over environment increase, and the government begins to regulate the environmental
pollution more rigorously. However, Lopez and Mitra (2000) argued that governments in
developing countries are inclined to have more interests in not only social welfare
maximization, but also rent-seeking behavior. Bureaucrats in these developing countries are
more inefficient and more corrupt relative to those in developed countries. According to
Business International Corporation (1984), the corruption index in developing countries is 23 times higher than that in developed countries. Desai (1998) showed empirical findings that
the environment has been devastated due to the corruption of government organizations and
lobbying by interest groups.
In addition, Cribb (1998) and Riggs and Stott (1998) argued that the enforcement of
environmental regulations in Indonesia and Thailand has been mitigated due to rent-seeking
behavior by bureaucrats. Moreover, Easterly (1997) proposed that the degree of improvement
in public goods can be considerably slower than that of economic growth, and corruption of
bureaucrats, which can be sustained until the economy can attain some higher income level.
Empirical research on the effect of corruption on environmental pollutions began with
Fredriksson and Millimet (2003), who explored the impacts of the U.S. state environmental
policies on FDI inflows, controlling state-level corruption. They assumed that corruption
affected FDI inflows through two channels: influence on the provision of public goods and
the impact on the stringency of environmental regulation. By estimating panel econometric
models, they showed that environmental policy stringency and corruption affected FDI
inflow in the United States. On the other hand, Barbier et al. (2005) examined theoretically as
well as empirically the impacts of corruption on resource conversion in low- and middleincome tropical countries. They found that improvement in the terms of trade reduced
5
deforestation but corruption could undermine the impact of the terms of trade on resource
conversion.
While Fredriksson and Millimet (2003) and Barbier et al. (2005) focused on the
relationships among corruption, environmental pollution, and trade variables for specific
countries, Welsch (2004) began to investigate relationships among economic growth,
corruption, and environmental pollution by decomposing the total effect of corruption on
environmental pollution into direct and indirect effects. Zellner’s technique of seemingly
unrelated regressions (SUR) was applied to estimating both direct and indirect effects of
corruption on emissions of SO2 and other pollutants through its influence on economic
growth. The SUR results showed that the positive direct effect of corruption on
environmental pollution was larger than the negative indirect effect, leading to a positive total
effect of corruption on environmental pollution. Thus, he argued that environmental pollution
was monotonically increased by corruption.
In examining the impacts of direct and indirect effects of corruption on environmental
pollution, Cole (2007) used joint estimation method. At first stage, he estimated GDP as a
function of corruption and other factors, and used the predicted GDP as a determinant of SO2
and CO2 emissions with other control variables at second stage. An instrumental method
was used to consider endogeneity between corruption and economic growth, as mentioned by
Hall and Jones (1999) and Mauro (1995). Corruption can affect the emission of SO2 and CO2
not only directly throughout mitigating environmental regulation or lax climate change
policies, but also indirectly throughout impacts on economic growth. He found that while the
direct effect of corruption on environmental degradation was positive, its indirect effect was
negative, but the absolute value of indirect effect was larger than that of direct effect. In the
same context, Leitão (2010) examined the direct and indirect effects of corruption on sulfur
emissions associated with economic growth. Instead of using a non-linear equation to
6
examine the inverted U-shaped relationship between sulfur emissions and per capita GDP, he
used a linearized equation on emissions and economic growth, proposed by Bradford et al.
(2005). Using this linearized equation, potential problems arising from regressions with nonlinear transformations of integrated variables can be avoided. By introducing the linear
equation of income turning point with regard to corruption, he examined whether corruption
can affect the income turning point, and found that corruption affected the income turning
point positively.
More recently, Biswas et al. (2012) examined whether a shadow economy, measured by
quantifying its magnitude based on the main drivers and indicators of informal activity in the
economy, contributed to environmental pollution. Using a panel fixed effect method with
data from over 100 countries between 1999 and 2005, they found that the shadow economy
increased local pollution (SO2) and global warming (CO2) significantly. However, they
showed that reduction in corruption would mitigate significantly the positive effect of the
shadow economy on environmental degradation. Contrary to the studies of Cole (2007) and
Leitão (2010), they did not consider endogeneity between economic growth and corruption,
but they used both corruption and per capita GDP as determinants of environmental pollution.
In the same context, Goel et al. (2013) investigated effects of corruption and the shadow
economy on environmental degradation for the Middle East and North Africa (MENA)
region over the years 2004-2007. By applying 2SLS to take potential endogeneity between
the shadow economy and corruption into account, they found that the shadow economy and
corruption contributed negatively to environmental pollution.
However, none of these previous studies in investigating direct and indirect effects of
corruption on environmental degradation accounted the potential impact of green ODA in
examining the direct and indirect effects of corruption on the emission of GHGs. We use the
joint estimation method that was proposed by Cole (2007) to measure direct and indirect
7
effects of corruption and green ODA on climate change given that economic growth is a
function of corruption and green ODA.
3. Methodology
3.1. Model specification
As a first step, we model economic growth as a function of corruption, green ODA, and
other determinant variables such as population growth rate, industry share of output, capital
stock per worker, human capital, inflation rate, and income inequality (GINI coefficient) as
presented in equation 1.
ln GDPit = α 0 + α1 IND / GDPit + α 2 POPGit + α 3 ln CKPCit + α 4 INFit + α 5CPI it
+ α 6 ln GODAit + α 7GINI it + ui + ε it
(1)
where IND/GDPit is the industry production ratio to total GDP, POPGit is the population
growth rate (annual percentage), CKPCit is the capital stock per worker, INFit is the inflation
rate evaluated at consumer prices (annual percentage), CPIit is the corruption perception
index, GODAit is the total amount of green ODA in the recipient country, GINIit is the gini
coefficient, and ui and εit are residual terms that follow a normal distribution with white
noise.
Capital stock per worker, population growth, inflation, and corruption were accounted as
determinants of GDP in studies of Cole (2007) and Leitão (2010) 4. Capital stock per worker
4
Human capital or openness of an economy are also major determinants of economic growth in the lit
erature. But data on human capital such as ratio of population with elementary, secondary, or tertiary e
8
and population growth rate are expected to affect economic growth positively, while inflation
and corruption would hinder economic growth. This study also considers share of industry
production in GDP as a determinant of GDP. According to several studies on the causes of
economic growth such as Kaldor (1977), Cornwall (1977), and Syrquin (1986), most of
technological innovation had been derived from manufacturing sector, and the technological
progress is generally known as an important driving force of economic growth. Recently,
Kniivilä (2007) also addressed the role of industrial sector growth in improving national
income levels of Asian countries.
For green ODA and GINI variables, higher green ODA would be expected to increase per
capita GDP while it is ambiguous whether income inequality would increase or decrease per
capita GDP. The effect of income inequality on economic growth has been hotly debated
during the last several decades. On the one hand, Kaldor (1956, 1961) proposed that the
higher income inequality might support economic growth by concentrating more income to
high-saving capitalists. Also, Forbes (2000), Barro (2000), and Banerjee and Duflo (2003)
argued that income inequality has a positive effect on economic growth. However, on the
other side, some recent studies have claimed that income inequality would affect economic
growth negatively through political economy channels or human capital development or
occupational choices (Galor and Zeira, 1993; Banerjee and Newman, 1993; Alesina and
Rodrik, 1994; Persson and Tabellini, 1994). In addition, some economic historians, such as
Engermann and Sokoloff (1997), Sokoloff and Engerman (2000), Engermann and Sokoloff
(2005), Khan and Sokoloff (2004), Sokoloff and Zolt (2005), and Easterly (2007), supported
this argument by assuming that high income inequality would cause bad (low quality)
institutions, low human capital investment, and underdevelopment.
ducation was inadequate for green ODA recipient countries. Also, we found negative impact of openne
ss on the per capita GDP, which was opposite to economic theory. So, these variables were excluded f
rom the estimation models on per capita GDP.
9
Based on these controversial research findings on the relationship between income
inequality and economic growth, we do not predetermine the relationship between the income
inequality and economic growth.
At the second stage, we construct GHG emission equation (GHGit) as a function of CPI,
GDP, square of GDP, green ODA, and other control variables, including the ratio of
alternative energy to primary energy production, energy intensity, population density, and
ratio of foreign direct investment (FDI) to GDP (equation 2).
lnGHGit = β 0 + β1CPI it + β 2YGDPit + β 3 lnODAit + β 4 ln EFFit + β 5 lnOPEN it
+ β 6 ln ALTE it + β 7 ln POPDit + β 8 ln FDI it + β 9YGDPit2 + ui + ε it
(2)
where YGDPit is predicted per capita GDP from equation 5, EFFit is the energy intensity,
ALTEit is the alternative and nuclear energy ratio to total primary energy production, POPDit
is the population density, FDIit is the share of foreign direct investment in GDP, and YGDP2it
is squared YGDPit.
The predicted GDP estimated from the first stage is used as an explanatory variable at the
second stage according to Cole (2007)’s joint estimation approach to capture direct and
indirect effects of corruption on climate change. Linear and square terms of the predicted per
capita GDP are included to test the EKC hypothesis. Energy intensity, openness, share of
alternative energy production in total primary energy production, population density, and the
ratio of FDI to GDP are commonly used as determinants of CO2 emissions in studies on
environmental and energy economics fields.
10
We presumed that a country with a less corrupt government, higher levels in the green
ODA, lower energy intensity, more alternative energy production 5, and higher population
density will lead to lower GHG emissions. Moreover, more dependency on trading (OPENit)
and higher FDI ratio to GDP will lead to higher GHG emissions as a pollution haven
hypothesis assumes that GHG-intensive industry might be relocated to developing countries.
By plugging the predicted per capita GDP (YGDP) based on the YRE4 model into the GHG
emission equation, we obtained the estimates of determinants for GHG emissions. We also
test the EKC hypothesis by including the square term of per capita GDP.
The total effect of corruption and green ODA can be divided into direct and indirect effects,
as shown in equation (3) and (4). In the RHS of equation (3), the first term shows the direct
effect of corruption on the emission of GHGs, and second term represents indirect effect of
corruption on the emission of GHGs. Several studies (Lopez and Mitra, 2000; Damania et al.
2003; Welsch, 2004; Cole, 2007; Leitão, 2010) have identified direct effects of corruption on
environmental pollution, such that corruption can undermine the stringency of environmental
regulation as well as effectiveness when an environmental regulation is enforced. On the
other side, corruption is known to have negative effects on economic growth in general
(Mauro, 1995; Hall and Jones, 1999; Kaufmann et al. 1999), which might lead to lower
pollution for some income levels, while higher pollution for the other income levels. Thus,
the total effect of corruption on the emission of GHGs could be ambiguous depending on the
relative size of direct and indirect effects.
The first and second terms in the RHS of equation (4) shows direct and indirect effects of
green ODA on the emission of GHGs, respectively. It is not known empirically whether the
effects of green ODA on emissions of GHGs or economic growth are positive or negative.
5
It is plausible that the green ODA affects energy efficiency or alternative energy production by the d
efinition of the green ODA. However, we found only weak correlations among these three variables an
d multicollinearity issue among them was not found to be significant as well.
11
However, it is conjectured that countries with higher green ODA might have increases in per
capita GDP. Also, countries with higher green ODA would have lower emissions of GHGs,
ceteris paribus.
dGHGit PGHGit PGHGit PGDPit
=
+
PCPI it
PGDPit PCPI it
dCPI it
(3)
dGHGit PGHGit PGHGit PGDPit
+
=
dODAit
PGDPit PODAit
PODAit
(4)
(GHGit : CO2 emission level, GDPit : per capita GDP, GDP 2it : square term of per capita
GDP, CPI it : corruption perception index, GODAit : green ODA, X it : control variables for
GHGs, Yit : control variables for GDP, Z it : control variables for alternative energy
( ALTE it ))
The direct and indirect effects of corruption and green ODA on CO2 emissions can be
explained graphically as shown in figure 1. We expect that corruption will increase CO2
emissions directly, while it will influence CO2 emissions indirectly through its impact on
economic growth. Green ODA will not only mitigate CO2 emissions directly, but also affect
CO2 emissions indirectly through its impact on economic growth.
[Insert Figure 1]
3.2. Data and estimation
Corruption data from 1995 to 2013 was collected from Transparency International.
Transparency International announces the corruption perception index (CPI) annually, based
12
on different assessments and business opinion surveys carried out by independent and
reputable institutions. Until 2011, countries were scored as 0 (highly corrupt) to 10 (highly
transparent), but since 2012, countries have been scored as 0 (highly corrupt) to 100 (highly
transparent). According to the annual mean values of CPI for all countries, the overall
corruption level across countries seems to be getting worse because the average CPI
diminished, from 5.96 in 1995 to 4.26 in 2013 (figure 2). However, when we consider that
the number of countries has increased from 40 in 1995 to 175 in 2013, the overall corruption
level may not have worsened considerably.
We include corruption data from 2000 to 2010 in the estimation of GDP and CO2
emissions to avoid an incremental effect due to the inclusion of different countries in the CPI
data.
[Insert Figure 2]
Data on per capita GHGs between 1995 and 2010 was drawn from the World Development
Index (World Bank, 2013). The average per capita GHGs between 1995 and 2010 did not
change significantly, ranging between 4.6 and 5.1 ton of CO2 equivalent (figure 3).
[Insert Figure 3]
Data on green ODA for recipient countries between 2002 and 2012 were collected from the
OECD Rio Marker Creditor Reporting System (CRS), which classifies ODA as related to
climate change mitigation (OECD, 2013). Climate Markers are statistical codes used by
OECD Development Assistance Committee (DAC) to measure aid targeting climate change
mitigation and adaptation purposes. Green ODA targeting climate change mitigation was
13
launched officially in 2002 after some pilot periods (1998-2002), while an aid program
targeting climate change adaptation started in 2000. According to the official criteria of the
climate change mitigation marker, the activities should be dedicated to one of the following
goals: i) mitigation of climate change by limiting anthropogenic emissions of GHGs, ii)
protection and/or enhancement of GHG sinks and reservoirs, iii) integration of climate
change concerns with the recipient countries’ development objectives through institution
building, capacity development, strengthening the regulatory and policy framework, or
research, or iv) developing countries’ efforts to meet their obligations under the Rio
Convention.
The number of countries that received the green ODA increased, from 41 in 2002 to 114 in
2012 (figure 4). Until 2007, annual average amount of green ODA among the green ODA
recipient countries remained less than USD 50 million, but since 2008, the average size of
green ODA has increased to over USD 100 million. Also, annual total amount of the green
ODA was less than USD 5 billion in 2007, while it went up to USD 9 billion in 2009 and
over USD 18 billion in 2012 (figure 5). Among the 142 countries that received the green
ODA during 2002-2012, India, Indonesia, China, and Vietnam were the largest green ODA
recipient countries, followed by Brazil, Egypt, and Turkey. The proportion of green ODA for
the Asian countries to the total green ODA was about 37.5% and the ratio of green ODA for
Brazil, Egypt, and Turkey was 14.37%. Thus, Asian countries were major green ODA
recipient countries.
[Insert Figure 4]
[Insert Figure 5]
14
The ratio of alternative energy to total primary energy production between 2000 and 2010
was from the World Development Index (World Bank, 2013). Alternative energy includes
renewable energy as well as nuclear energy productions. The ratio of alternative energy
declined slightly, from 10.63% in 2000 to 10.53% in 2010 (figure 6).
[Insert Figure 6]
More details on the other control variables, such as population growth rate, per capita GDP,
and capital stock per worker, are provided in the Appendix. Data except green ODA for the
period 2000-2010 were transformed into five periods (three 3-year averages for 2000-2008,
and two 2-year averages for 2009-2010) to remove business cycle effects that are generally
inherent in annual data (Saha and Gounder, 2013). Data for green ODA were collected
between 2002 and 2012. As the green ODA is combined with the other data, the number of
total observation falls considerably, from about 130 to 50 countries, depending on different
models.
We transformed per capital GDP, capital stock per worker, and green ODA to a natural log
form at the first stage, and did all regressors except CPIit into natural logarithm forms at the
second stage to remove scale effects. By this transformation, we excluded vacant data or zero
data to obtain clean data for the econometric analysis.
The estimation was performed in two stages, as explained in Section 3.1, so per capita
GDP equation is estimated first, and the predicted value of per capita GDP is applied to the
GHG emission equation in the second stage to derive indirect effects of corruption as well as
green ODA on the climate change. At the first stage, per capita GDP was estimated by a
panel fixed effect method, because a Hausman test for the fixed and random effect estimates
showed that we should reject the null hypothesis that there is no endogeneity between
15
explanatory variables and residuals. At the second stage, the GHG equation was estimated by
the panel fixed effect or random effect method, depending on different model specifications.
We also considered an instrumental variable (IV) approach to consider potential
endogeneity between per capita GDP and corruption. Several reports (Hall, and Jones, 1999;
Cole, 2007; Leitão, 2010) controlled the endogeneity problem between economic growth and
corruption using Western European influences, political democracy, or economic freedom
indices as instrumental variables. The Western European influences include distance from the
equator and the extent to which Western languages are spoken as the mother tongue or
English is spoken as the first language in a country. However, we found that estimates from
the IV method did not provide better outcomes than those from the panel fixed effect
methods, because coefficient determinants (overall R2) were smaller than that of the YFE4
model in table 2, and some explanatory variables such as inflation and GINI were statistically
insignificant (see Appendix, Table A2).
In contrast to Cole (2007) and Leitão (2010), our model of per capita GDP was not
estimated based on the instrumental variable approach, but fixed effect model. This can be
explained by the inclusion of green ODA recipient countries, most of which are
underdeveloped or developing countries with high corruption. Also, we transformed the
annual data for all variables into 3-year average data to remove possible business cycle,
which might affect endogeneity between corruption and per capita GDP. Third, as shown in
Table 1, the correlation between per capita GDP and corruption was about 0.2, and this
relatively low correlation, compared to other correlations between per capita CO2 and
corruption or between per capita CO2 and per capita GDP, also reduces the likelihood of
endogeneity between the variables.
[Insert table 1]
16
Therefore, we estimated determinants of per capita GDP based on the fixed effect model at
the first stage. Biswas et al. (2012) indicated that a major problem in using those instrumental
variables such as Western European influences to remove the endogeneity problem between
corruption and economic growth is that they are constant over time for an individual country,
so cannot be used in a panel data context.
4. Results
4.1. Estimates of per capita GDP models
We consider four different model specifications such as YFE1, YFE2, YFE3, and YFE4 to
estimate determinants of per capita GDP as shown in table 2.
All Models were estimated by the panel fixed effect method because the Hausman test
statistics rejected the null hypothesis that there was no engodeneity between regressors and
residual. As the CPI and GINI variables are included in the estimation, the overall R2
increased significantly. Industry output share, capital stock per worker, inflation rate, CPI,
and GINI variables were statistically significant for most model specifications. The
estimation results show that as a country has higher industry output share in GDP, more
capital stock per worker, more transparent government, and higher income inequality, per
capita GDP increases. Furthermore, higher green ODA contributes to higher economic
growth. For all models, coefficients of constant terms are significant and positive, which
implies that technological progress contributed economic growth.
On the other hand, population growth was not included in the full model YFE4 as it was
insignificant over model YFE1 through YFE3. Instead, income inequality index (GINI) was
17
included and it increased the overall R2 substantially relative to the model YFE3. Finally,
model YFE4 was chosen as a full model to represent equation (1). The predicted per capita
GDP based on the model YFE4 will be used in estimating the per capita CO2 models.
[Insert table 2]
4.2. Estimates of emission of GHGs
Before we apply the joint estimation method to examine determinants of CO2 emissions,
single estimation models are investigated first to compare the estimation results between two
estimation approaches. There are four models CFE1, CFE2, CFE3, and CFE4 to examine the
impacts of corruption, economic growth, green ODA, and other control variables on CO2
emissions as shown in table 3. All models are estimated by the panel fixed effect method as
the Hausman test statistics are significant.
As a basic model, we included CPI, per capita GDP, and green ODA as determinants of
per capita CO2 in Model CFE1. All coefficients were significant and had the correct signs
except the coefficient of CPI. The CFE1 model shows that as a country is found to be less
corrupt, higher emissions of GHGs are expected, which was the opposite to our expectations.
Throughout the extended models CFE2 to CFE4, CPI has positive signs; again it is opposite
to our expectations. Therefore, we use predicted per capita GDP that was estimated in YRE4
to consider indirect effects of corruption and green ODA on the CO2 emissions as discussed
in the next section.
The extended models CFE2 to CFE4 include additional control variables, such as energy
intensity (lnEFF), share of export and import in per capita GDP (lnOPEN), share of
alternative energy in total primary energy production (lnALTE), population density
18
(lnPOPD), foreign direct investment (lnFDI), and square of GDP (lnPGDP2). The openness
of the economy as well as FDI were found to increase the GHG emissions, which supports
the ‘polluter’s haven hypothesis’ while increases in alternative energy and population density
devote to mitigating CO2 emissions. Energy intensity (LnEFF) affects the GHG emissions
negatively in the CFE3 model, which is contrary to our expectation, but it increases the CO2
emissions in the CFE2 models. As a variance inflation factor (VIF) is about 3 on average for
all models, there is no multicollinearity issue regarding the insignificance and opposite sign
of the energy intensity. As we suspect measurement error in collecting energy intensity data,
the CFE4 model excludes the energy intensity variable.
According to the CFE4 model, the EKC relationship between GDP and CO2 emissions was
not detected as coefficients of linear and square terms of GDP were insignificant. As a
country has more concentrated population, receives larger green ODA, and invests more on
development of alternative energy technology, the CO2 emissions decline more according to
the estimation results. The constant term has a significant and negative estimate for its
parameter which means that technological progress reduces CO2 emissions.
[Insert table 3]
4.3. Joint estimation of per capita GDP and per capita CO2
The per capita CO2 equation was re-estimated by plugging an estimate of per capita GDP
(YGDP) into JRE1 and JRE2 models. These two models were estimated by the panel random
effect method (table 4) as Hausman test statistics were insignificant. Results from Models
JRE1 and JRE2 were compared with those from Model CFE4 where predicted value of GDP
was not used in the estimation of CO2 emissions. The most substantial change in the
19
estimation results from Model JFE1 and JFE2 compared with Model CFE4 is the sign of the
CPI, which is now negative for the per capita CO2 emissions. However, the parameter of the
CPI was insignificant so that there was a weak direct effect of the CPI on the emission of the
per capita CO2. We will examine again statistical significance of the direct effect of the
corruption on climate change by using an alternative CPI measure in the following section.
Regarding the insignificance of the corruption variable on per capita CO2, Biswas et al.
(2012) also identified that the CPI variable had an insignificant direct effect on per capita
CO2. This means that the indirect effects of CPI on per capita CO2 throughout its impact on
economic growth are predominant for the green ODA recipient countries.
Variables such as green ODA, openness, ratio of alternative energy use, population density,
and FDI in JRE1 and JRE2 models have the same sign as those in CFE4, but the magnitudes
of the coefficients in the JRE1 and JRE2 models are larger than those in CFE4. This result
implies that relative importance of those determinants in explaining CO2 emissions is underevaluated for the single estimation model.
As the linear as well as square terms of the predicted per capita GDP (YGDP and YGDP2)
in Model JRE2 were found to be statistically significant, the inverted U-shaped pattern
between economic growth and climate change was observed in the green ODA recipient
countries. So, further economic growth than an income turning point in the green ODA
recipient countries will reduce CO2 emissions.
According to the discussion of the estimation results for the Models in table 4, JRE2 was
adopted as the final model for examining the direct and indirect effects of corruption, green
ODA, and economic growth on per capita CO2 emissions.
[Insert table 4]
20
4.4. Total effects of corruption and green ODA on CO2 emissions
Based on the JRE2 model in Table 4, we calculate total effects of green ODA and
corruption on the climate change.
First, the total effect (TE) of green ODA on per capita CO2 is the sum of the direct effect
(DE) and the indirect effect (IE), as shown in equation (7). The indirect effect of green ODA
on per capita CO2 throughout its impact on per capita GDP is positive, but a negative direct
effect of green ODA on the per capita CO2 offsets the positive indirect effect, indicating that
an increase in green ODA will reduce per capita CO2 emissions. According to the total effect
of green ODA on the CO2 emissions, a 1% increase in the green ODA will reduce CO2
emissions by 0.015%.
d ln PCO2 ∂ ln PCO2 ∂ ln PGDP
+
= β 3 + ( β 2 + 2 β 9 YGDP )(α 6 )
d ln ODA ∂ ln PGDP ∂ ln ODA
= −0.203 + (3.365 − 2 * 0.058 * 24.71969) × (0.377) = −0.01544
T .E = D.E + I .E =
(7) 6
The total effect of CPI on per capita CO2 is equal to the indirect effect of CPI on per capita
CO2 as shown in equation (8). As one unit of CPI increases or if the bureaucrat of a country
becomes more transparent by one unit), CO2 emissions will increase by 0.1562%. Although
the direct effect of CPI on per capita CO2 is included in the calculation of total effect of CPI
on CO2 emissions, the sign of the total effect is still positive because the magnitude of the
direct negative effect (-0.073) in table 4 is far less than that of the indirect positive effect
(0.15622). As the direct negative effect of corruption on per capita CO2 emissions was
insignificant, we only calculated the indirect effect of corruption on per capita CO2 through
6
Mean of per capita GDP in logarithm form to calculated the total effect of the green ODA on CO2
emissions was 24.71969.
21
its impact on per capita GDP. So, as a country has a more transparent government, its per
capita GDP will increase accordingly, which will lead to a rise of per capita CO2 emissions.
∂ ln PCO2 ∂ ln PGDP
= ( β 2 + 2 β 9 YGDP )(α 5 )
∂ ln PGDP ∂CPI
= (3.365 − 2 * 0.058 * 24.71969) × 0.314 = 0.15622
T .E = I .E =
(8)
4.5. An alternative measure of corruption
We examined whether an alternative measure of corruption can change significantly the
overall outcomes. Instead of CPI abstracted from Transparency International, we used
freedom from corruption, which is basically derived from the CPI of Transparency
International, but for countries that are not covered by the CPI, the freedom from corruption
is derived by applying qualitative information from internationally recognized and reliable
sources, such as the Economist Intelligence Unit, the US Department of Commerce, Office of
the US Trade Representative, and official government publications of each country7. The
freedom from corruption index is based on a 100-point scale, where a score of 100 means
very little corruption or very transparent, and a score of 0 implies a very corrupt government.
Per capita GDP was estimated for the alternative corruption index with other explanatory
variables by using the panel random effect method (table A3). The predicted GDP (YGDP)
based on the YRE7 model in table A3 was applied to the model JFE3 and JFE4 in table 5.
According to Table 5, all parameter estimates were close to those in Table 4 so that the use of
an alternative corruption index did not change the estimation results, confirming the
robustness of estimation results on the effects of corruption and green ODA on CO2
7
For more details on the sources of freedom from corruption data, refer to index of economic freedo
m at heritage website (http://www.heritage.org/index/freedom-from-corruption).
22
emissions. Estimates of alternative CPI for model JFE3 and JFE4 based on the panel fixed
effect method are negative and insignificant, but magnitudes of the coefficients are much
smaller than those in model JRE2. Model FE5 as well as JFE4 show that the EKC hypothesis
is supported for the green ODA recipient countries.
[Insert table 5]
Equation (10) shows the total effect of green ODA on climate change; as green ODA
increases by 1%, per capita CO2 can be mitigated by 0.093%, which is slightly higher
elasticity relative to that in equation (7).
T . E = D. E + I . E =
d ln PCO2 ∂ ln PCO2 ∂ ln PGDP
+
= −0.225 + (0.5165) × (0.256) = −0.093
d ln ODA ∂ ln PGDP ∂ ln ODA
(10)
According to the model JFE4, the total effect of corruption on per capita CO2 emissions
becomes indirect effect as shown in equation (11). Therefore, one unit increase of CPI will
increase CO2 emission by 0.005%. So, the indirect effect of the alternative CPI on CO2
emissions is much smaller than that of the original CPI on CO2 emissions.
T .E = I .E =
∂ ln PCO2 ∂ ln PGDP
= ( 4.966 − 2 * 0.09 * 24.71969) * 0.009 = 0.004648
∂ ln PGDP ∂CPI
(11)
5. Conclusions
23
In this study, we examined impacts of economic growth, corruption, and green ODA on
climate change for the green ODA recipient countries. We found that countries with more
green ODA generate less CO2 emissions, while green ODA indirectly increased CO2
emissions through its impact on per capita GDP. Hence, the sum of direct and indirect effects
of green ODA on CO2 emissions was found to be negative, which means that increase of
green ODA would reduce CO2 emissions. But the marginal effect of green ODA on climate
change was not considerable relative to other variables such as population density and use of
alternative energy. In this context, stringent evaluation and monitoring system for the green
ODA recipient countries should be reinforced to improve contribution of the green ODA on
CO2 emissions.
Next, corruption is found to be an obstacle to economic growth, but to directly delay
mitigation of climate change in developing countries that have received green ODA. The
direct effect of corruption on climate change was not statistically significant when CPI from
transparency international was used as the determinant of CO2 emissions, and also
insignificant when the alternative measure of corruption was used in the CO2 estimation
model. The indirect effect of corruption through its impact on per capita GDP was positive,
where per capita GDP of an average green ODA country increased the CO2 emissions. As the
magnitude of the indirect effect was larger than that of the direct effect, aggregation of the
direct and indirect effect of corruption on climate change resulted in the increase of CO2
emissions. But marginal effect of corruption on climate change was not remarkable relative to
other control variables such as openness, FDI, and per capita GDP.
The estimate result shows that the total effect of corruption on climate change is not
influential for the green ODA recipient countries. It might be because most green ODA
recipient countries do not have enough regulations on environment and climate change, so
24
improvement in corruption associated with environmental and climate change regulations
would not change CO2 emissions significantly.
Furthermore, our empirical analysis showed that increases in inflows of the FDI and
openness of an economy for green ODA recipient countries can hinder mitigation of CO2
emissions, while investment in alternative energy can mitigate CO2 emissions significantly.
Finally, relation between economic growth and CO2 emissions for green ODA recipient
countries shows the inverted U shape, which supports the EKC hypothesis.
Contrary to Cole (2007) and Leitão (2010), we found no endogeneity problem between
corruption and economic growth for green ODA recipient countries. This might be
attributable to the limited number of countries used in the analysis. Also, it might be because
we transformed the annual data into 3-year period data to avoid business cycles and
autocorrelation problems. Further future research should be carried out to assess causal
relationships among income inequality, corruption, economic growth, and climate change by
using more extensive countries.
Appendices
1. Data Sources of variables
[insert Table A1]
2. IV estimation results for per capita GDP equations
[insert Table A2]
[insert Table A3]
25
26
5. References
Alesina, A., and Rodrik, D. (1994), ‘Distributive politics and economic growth’, Quarterly
Journal of Economics 108: 465-490.
Banerjee, A. V., Newman, A. F. (1993), ‘Occupational choice and the process of
development’, Journal of Political Economy 101(2):274-298.
Banerjee, A.V., Duflo, E. (2003), ‘Inequality and growth: what can the data say?’, Journal
of Economic Growth 8(3): 267-299.
Barbier, E. B., Damamnia, R., and Leonard, D. (2005), ‘Corruption, Trade and Resource
Conversion’, Journal of Environmental Economics and Management 50: 276-299.
Barro, R.J., (2000), ‘Inequality and Growth in a Panel of Countries’, Journal of Economic
Growth 5: 5-32.
Bertinelli, L., Strobl, E. (2005), ‘The Environmental Kuznets Curve semi-parametrically
revisited’, Economics Letters 88(3): 350-357.
Bierbaum, R. and Fay, M. (2010), World Development Report 2010: Development and
Climate Change, Washington, DC: World Bank.
Biswas, K.A., Farzanegan, M.R., and Thum, M. (2012), ‘Pollution, shadow economy and
corruption: Theory and evidence’, Ecological Economics 75:114-125.
Bradford, D. F., Schlieckert, R., and Shore, S. H. (2000), ‘The environmental Kuznets
curve: exploring a fresh specification’, No. W8001, National Bureau of Economic Research.
Business International Corporation (1984), Introduction to the country assessment service,
New York: Business International Corporation.
27
Business International Corporation. (1984), Managing and Evaluating Country Risk
Assessment. BIC, New York.
Cole, M. A. (2007), ‘Corruption, Income, and the Environment: An Empirical Analysis’,
Ecological Economics 62:637-647.
Cribb, R. (1998), ‘Environmental Policy and Politics in Indonesia’, in Desai, U. (Ed),
Ecological Policy and Politics in Developing Countries: Growth, Democracy and
Environment. State University of New York Press, Albany.
Damania, R., Fredriksson, P.G., and List, J.A. (2003), ‘Trade liberalization, corruption, and
environmental policy formation: theory and evidence’, Journal of Environmental Economics
and Management 46(3):490-512.
Desai, U. (Ed.) (1998), Ecological Policy and Politics in Developing Countries: Growth,
Democracy and Environment, State University of New York Press, Albany.
Easterly, W. (2007), ‘Inequality does cause underdevelopment: Insights from a new
instrument’, Journal of Development Economics 84:755-776.
Engerman, S. L. and Sokoloff, K. L. (2005), ‘The evolution of suffrage institutions in the
Americas’, Journal of Economic History 65(4):891-921.
Engermann, S. L. and Sokoloff, K. L. (1997), ‘Factor endowments, institutions, and
differential paths of growth among new world economies: a view from economic historians
of the United States’, In: Haber, Stephen (Ed.), How Latin America Fell Behind, Stanford
University Press, Stanford CA.
Forbes, K. J. (2000), ‘A Reassessment of the relationship between inequality and growth’,
American Economic Review 90(4):869-887.
28
Fredriksson, P.G., List, J.A., Millimet, D.L. (2003), ‘Bureaucratic corruption,
environmental policy and inbound US FDI: theory and evidence’, Journal of Public
Economics 87:1407–1430.
Galor, O., and Zeira, J. (1993), ‘Income distribution and macroeconomics’, Review of
Economic Studies 60:35-52.
Goel, R. K., Herrala R., and Mazhar, U. (2013), ‘Institutional quality and environmental
pollution: MENA countries versus the rest of the world’, Economic Systems 37:509-521.
Grossman, G.M, and Kruger, A. B. (2002), ‘Economic Growth and the Environment’,
Quarterly Journal of Economics 110(2): 353-377.
Hall, R.E., and Jones, C.I.. (1999), ‘Why do some countries produce so much more output
per worker than others?’, Quarterly Journal of Economics 114: 83-116.
Heritage
(2014),
‘Index
of
economic
freedom’,
[Available
at]
http://www.heritage.org/index/freedom-from-corruption.
International Energy Agency (2013), ‘Renewable energy statistics’, [Available at]
http://www.iea.org/policiesandmeasures/renewableenergy/.
Kaldor, N. (1956), ‘Alternative theories of distribution’, Review of Economic Studies 23:
94-100.
Kaldor, N. (1961), ‘Capital accumulation and economic growth’, in Lutz, F.A., Hague,
D.C. (eds.), The Theory of Capital, St. Martin's Press,New York.
Kaufmann, D., Kraay, A., and Zoido-Lobaton, P. (1999), ‘Governance Matters’, World
Bank Policy Research Working Paper No. 2196.
29
Khan, B., Sokoloff, Z., and Kenneth L. (2004), ‘The innovation of patent systems in the
nineteenth century: a comparative perspective’, Working Paper, National Bureau of
Economic Research.
Klitgaard, R. (1988),
Controlling Corruption, University of California Press, Berkeley,
CA.
L´opez, R. and Mitra, S. (2000), ‘Corruption, Pollution, and the Kuznets Environment
Curve’, Journal of Environmental Economics and Management 40:137-150
Leitão, A., (2010), ‘Corruption and the environmental Kuznets curve: Empirical evidence
for sulfur’, Ecological Economics 69:2191–2201.
Lopez, R. (1994), ‘The environment as a factor of production: the effects of economic
growth and trade liberalization’, Journal of Environmental Economics and Management
27(2):163-185.
Mauro, P. (1995), ‘Corruption and growth’, The Quarterly Journal of Economics
110(3):681-712.
OECD (2011), First ever comprehensive data on aid for climate change adaptation,
OECD.
OECD (2013), User’s Guide to the Creditor Reporting System (CRS) Aid Activities
database, Aid statistics.
Persson, T., and Tabellini, G. (1994), ‘Is inequality harmful for growth?’, American
Economic Review 84:600-6.
30
Riggs, J. and Stott, P. (1998), ‘Forest Tales: Politics, Policy Making, and the Environment
in Thailand’, in Desai, U. (ed), Ecological Policy and Politics in Developing Countries:
Growth, Democracy and Environment, State University of New York Press, Albany
Roberts, J. T., and Grimes, P. E. (1997), ‘Carbon intensity and economic development
1962-1971: A brief exploration of the environmental Kuznets curve’, World Development
25:191-198.
Saha, S. and Gounder, R. (2013), ‘Corruption and economic development nexus:
Variations across income levels in a non-linear framework’, Economic Modelling 31:70-79.
Sokoloff, K. L., and Engerman, S. L. (2000), ‘Institutions, factor endowments, and paths of
development in the New World’, Journal of Economic Perspectives 14(3):217–232.
Sokoloff, K. L., Zolt, E. M. (2005), ‘Inequality and the evolution of institutions of taxation:
evidence from the economic history of the Americas’, Paper presented at NBER Workshop
on Political Economy.
Transparency International. (2012), ‘Corruption Perception Index’, [Available at]
http://www.transparency.org/country.
UNODC (2010), Methodologies, including evidence-based approaches, for assessing
areas of special vulnerability to corruption in the public and private sectors, United Nations
Office on Drugs and Crime.
UNODC (2012), Corruption, Environment and the United Nations Convention. In Impact
of Corruption on the Environment and the United Nations Convention against Corruption,
Marrakesh: UNODC, p. 54.
31
Welsch, H. (2004), ‘Corruption, growth and the environment: a cross-country analysis’,
Environment and Development Economics 9:663–693
World
Bank
(2013),
‘World
development
indicator’,
[Available
at]
[Available
at]
http://www.worldbank.org/wdi/.
World
Bank
(2006),
‘Governance
and
Anti-corruption’,
http://www.worldbank.org/wbi/governance/index.html.
Table 1. Correlation between per capita CO2, corruption perception index, per capita
GDP, and green ODA
CPI
Per capita GDP
Variables
Per capita CO2
Per capita CO2
1
CPI
0.3986
1
Per capita GDP
0.4997
0.2002
1
Green ODA
-0.0639
-0.0577
0.401
Green ODA
1
(Note: values of all variables are 3-year averages and are transformed to natural log)
Table 2. Effects of corruption and green ODA on per capita GDP
Variable
YFE1
YFE2
YFE3
YFE4
IND/GDP
0.010
0.028***
0.051***
0.050***
(0.007)
(0.007)
(0.008)
(0.009)
0.069
0.061
0.041
(0.067)
(0.062)
(0.097)
0.704***
0.326***
0.130
0.356***
(0.059)
(0.066)
(0.081)
(0.081)
0.023**
0.013
-0.026*
-0.033**
(0.011)
(0.011)
(0.015)
(0.016)
0.330***
0.328***
0.314***
(0.031)
(0.074)
(0.077)
0.285***
0.377***
(0.042)
(0.043)
POPG
lnCKPC
INF
CPI
lnODA
32
lnGINI
1.273***
(0.323)
Constant
Overall R
2
Hausman
test
statistics
27.816***
23.988***
21.785***
18.257***
(0.341)
0.1964
(0.488)
0.3010
(0.563)
0.2763
(1.229)
0.3667
12.83***
14.61***
6.43*
15.58***
(Standard errors are in parentheses, *** indicates significance level within 1%, ** indicates
significance level within 5%, * indicates significance level within 10%. The YFE1 through YFE4
models were estimated by the fixed effect method. The estimate of per capita GDP from the YFE4
model is used for the estimation of JFE1 and JFE2 models in table 4)
Table 3. Estimates of per capita CO2 emissions across different model specifications
and estimation methods
Variable
CPI
LnPGDP
LnODA
CFE1
CFE2
CFE3
CFE4
0.300***
0.209***
0.122***
0.141***
(0.037)
(0.037)
(0.038)
(0.036)
0.447***
0.406***
0.298***
0.691
(0.030)
(0.029)
(0.041)
(0.628)
-0.156***
-0.140***
-0.140***
-0.140***
(0.021)
(0.019)
(0.021)
(0.020)
0.106
-0.123
(0.090)
(0.088)
0.485***
0.346***
0.319***
(0.082)
(0.079)
(0.078)
-0.083***
-0.080***
(0.023)
(0.024)
-0.073**
-0.069*
(0.036)
(0.036)
0.154***
0.145***
(0.036)
(0.036)
LnEFF
LnOPEN
LnALTE
LnPOPD
LnFDI
2
LnPGDP
-0.008
(0.013)
Constant
-11.139***
-9.688***
-8.704***
-14.209*
(0.697)
(0.854)
(0.861)
(7.787)
0.4021
0.4244
0.4675
0.4658
Hausman test
16.43***(0.000)
9.33**(0.025)
11.83*(0.08)
6.56*(0.087)
N of Observation
525
478
450
450
Overall R
2
(Standard errors are in parentheses, *** indicates significance level within 1%, ** indicates
33
significance level within 5%, * indicates significance level within 10%. All models are estimated by
the fixed effect method).
Table 4. Estimates of per capita CO2 emissions based on the YFE4 model
Variable
CFE4
JRE1
JRE2
CPI
0.141***
-0.065
-0.073
(0.036)
(0.049)
(0.049)
-0.202***
-0.203***
(0.020)
(0.035)
(0.035)
0.319***
0.391***
0.421***
(0.078)
(0.080)
(0.082)
-0.136***
-0.147***
(0.024)
(0.033)
(0.033)
-0.069*
-0.128***
-0.129***
(0.036)
(0.041)
(0.040)
0.145***
0.186***
0.191***
(0.036)
(0.033)
(0.033)
0.533***
3.365**
(0.090)
(1.714)
LNPGDP
0.691
(0.628)
LNODA
-0.140***
LNOPEN
LNALTENERGY
LNPOPD
LNFDI
2
LNPGDP
-0.080***
-0.008
(0.013)
YGDP
2
YGDP
-0.058*
(0.035)
Constant
-14.209*
-14.447***
-48.853**
0.4675
(2.004)
0.5368
(20.885)
0.5426
11.80***
4.94
5.60
0.008
0.1760
0.1312
(7.787)
Overall R2
Hausman test
P-value
(Standard errors are in parentheses, *** indicates significance level within 1%, ** indicates
significance level within 5%, * indicates significance level within 10%. JRE1 and JRE2 were
estimated by the random effect model, and they use estimates for per capita GDP based on YRE4 as
the determinant of per capita CO2 emissions).
Table 5. Estimates of per capita CO2 emissions for the alternative corruption index
Variable
YFE5
JFE3
JFE4
CPI†
0.008*
-0.004
-0.004
(0.005)
(0.008)
(0.008)
34
LNPGDP
2.198***
(0.713)
LNODA
LNOPEN
LNALTENERGY
LNPOPD
LNFDI
LNPGDP2
-0.146***
-0.204***
-0.225***
(0.024)
(0.045)
(0.046)
0.174*
0.475***
0.508***
(0.102)
(0.132)
(0.132)
-0.121***
-0.324***
-0.348***
(0.031)
(0.061)
(0.061)
-0.037
-0.060
-0.043
(0.044)
(0.057)
(0.057)
0.187***
0.263***
0.269***
(0.042)0.009
(0.042)
(0.042)
0.574***
4.966**
(0.113)
(2.195)
-0.038***
(0.014)
YGDP
YGDP2
-0.090**
(0.045)
Constant
-34.004***
-70.365***
-70.604***
(8.901)
(26.748)
(27.180)
Overall R2
0.4562
0.4715
0.4794
Hausman test
15.60**
8.94**
10.51**
P-value
(0.0014)
0.0301
0.0147
(Standard errors are in parentheses, *** indicates significance level within 1%, ** indicates
significance level within 5%, * indicates significance level within 10%. All models were estimated by
the fixed effect method. JFE3 and JFE4 models but FE5 use estimates for per capita GDP based on
YRE7 as a determinant of per capita CO2 emission in table a of Appendix. †: Alternative corruption
perception index).
35
Table A1. Definition and source of variables
Variable
Definition
Source
ln ODA
Natural log of green ODA
OECD (2013)
ln OPEN
Natural log of openness which is ratio of sum of export World Bank (2012)
and import in GDP
lnALTE
Natural log of fraction of alternative energy in total World Bank (2012)
primary energy production
lnPOPD
Natural log of population density
World Bank (2012)
lnFDI
Natural log of foreign direct investment
World Bank (2012)
CPI
Corruption perception index
Transparency International
(2012)
lnPGDP
Natural log of per capita GDP
World Bank (2012)
IND/GDP
Ratio of industrial output in GDP
World Bank (2012)
POPG
Population growth rate
World Bank (2012)
lnCKPC
Natural log of capital stock per worker
Penn World Table 8.0 data
INF
Inflation rate
World Bank (2012)
lnGINI
Natural log of GINI
World Bank (2012)
Table A2. Comparison of estimates for per capita GDP models when corruption is
instrumented
Variable
av_cpi
av_ind_gdp
av_popg
av_lnckpc
av_inf
YIV1
YIV2
YIV3
0.166***
0.033
1.622**
(0.042)
(0.118)
(0.812)
0.018**
0.050***
0.056***
(0.008)
(0.009)
(0.013)
0.081
-0.001
0.542*
(0.063)
(0.101)
(0.313)
0.545***
0.089
0.335**
(0.074)
(0.080)
(0.130)
0.016
-0.022
-0.035
(0.011)
(0.014)
(0.023)
0.240***
0.366***
(0.038)
(0.068)
av_lnoda
av_lngini
-0.833
(1.354)
_cons
26.074***
22.703***
36
20.838***
R-square
Sargan-Hansen test
(p-value)
(0.593)
(0.670)
(2.546)
0.2799
0.2422
0.1926
4.0030
(0.1351)
2.0070
(0.1566)
0.6850
(0.7101)
(Notes: YIV1 model uses countries that use Western European languages as a first language as
instrumental variables, countries that use English as a mother tongue, and economic freedom as
instrumental variables. YIV2 model uses economic freedom and democracy index, which is the
average of political rights and civil liberties (1-7: 1 represents the highest degree of freedom and 7 the
lowest). YIV3 uses Western European languages as a first language, countries that use English as a
mother tongue, and latitudes of countries. For all models, the Sargan-Hansen test on overidentification restrictions showed that the excluded instruments were valid instruments, i.e.,
uncorrelated with the error term and correctly excluded from the estimated equation).
Table A3. Estimates of per capita GDP with an alternative corruption index
Variable
YFE5
YRE6
YRE7
IND/GDP
0.020**
0.045***
0.058***
(0.008)
(0.011)
(0.012)
-0.068
-0.048
(0.071)
(0.129)
0.159*
-0.111
0.105
(0.082)
(0.113)
(0.103)
0.014
-0.018
-0.035*
(0.012)
(0.017)
(0.018)
0.035***
0.015
0.009
(0.003)
(0.012)
(0.012)
0.215***
0.256***
(0.051)
(0.052)
POPG
lnCKPC
INF
CPI
†
lnODA
lnGINI
2.210***
(0.385)
Constant
23.434***
21.448***
14.137***
(0.573)
(0.748)
(1.542)
Overall R
0.2601
0.1994
0.3485
Hausman test
20.5***
1.39
0.69
(0.0001)
(0.7075)
0.8751
2
P-value
(Standard errors are in parentheses, *** indicates significance level within 1%, ** indicates
significance level within 5%, * indicates significance level within 10%. †: alternative corruption index.
YRE6 and YRE7 models were estimated by a random effect method. Estimate of per capita GDP
37
from YRE7 model was used in the estimation of GHGs models JFE3 and JFE4)
Figure 1. Direct and indirect effects of corruption and green ODA on climate change
Figure 2. Annual mean, maximum, and minimum of corruption perception index
3
4
CPI
5
6
7
between 1995 and 2013
1995
2000
2005
year
CPI
2010
lb/ub
38
2015
Figure 3. Annual mean, maximum, and minimum of per capita CO2 emissions
3.5
CO2 emissions (metric tons per capita)
4
4.5
5
5.5
6
between 1995 and 2010
1995
2000
2005
2010
year
CO2 emissions (metric tons per capita)
lb/ub
Figure 4. Number of countries that received the green ODA between 2002 and 2012
39
0
Green ODA (million $)
50
100
150
200
Figure 5. Annual mean, minimum, and maximum of green ODA
2002
2004
2006
2008
2010
2012
year
Green ODA (million $)
lb/ub
Figure 6. Annual mean, maximum, and minimum of alternative and nuclear energy
6
alternative and nuclear energy (%)
8
10
12
14
ratio of total primary energy production
2000
2002
2006
2004
2008
2010
year
alternative and nuclear energy (%)
40
lb/ub