The Journal of International Trade & Economic
Development
An International and Comparative Review
ISSN: 0963-8199 (Print) 1469-9559 (Online) Journal homepage: http://www.tandfonline.com/loi/rjte20
Dynamic causality testing for EKC hypothesis,
pollution haven hypothesis and international
trade in India
Ritu Rana & Manoj Sharma
To cite this article: Ritu Rana & Manoj Sharma (2018): Dynamic causality testing for EKC
hypothesis, pollution haven hypothesis and international trade in India, The Journal of International
Trade & Economic Development, DOI: 10.1080/09638199.2018.1542451
To link to this article: https://doi.org/10.1080/09638199.2018.1542451
Published online: 07 Nov 2018.
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THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT
https://doi.org/10.1080/09638199.2018.1542451
Dynamic causality testing for EKC hypothesis, pollution
haven hypothesis and international trade in India
Ritu Rana
and Manoj Sharma
Department of Management & Humanities, National Institute of Technology Hamirpur, Himachal
Pradesh, India
ABSTRACT
This study examines the causality relationships between foreign direct investment (FDI),
economic growth (GDP) and CO2 emissions along with the level of trade (exports and
imports) taking place in India. The study uses data obtained from World Development
Indicators (WDI) of World Bank Group for the period 1982–2013. The study employed
the dynamic multivariate Toda-Yamamoto (TY) approach that uses the modified Wald
(MWALD) test. Among the major findings of the study are: the existence of both Pollution Haven Hypothesis and Environmental Kuznets Curve (EKC) hypothesis in India.
The other findings of the study are: FDI is causing exports; exports are causing imports;
imports are causing CO2 emissions; and finally CO2 emissions and GDP are causing each
other. This finding concludes mainly two things. First, India imports more of pollutionintensive manufactured goods. Second, FDI is causing GDP in India but through CO2
emissions.
KEYWORDS Causality; CO2 emissions; international trade; FDI; Toda-Yamamoto approach; MWALD test
JEL CLASSIFICATIONS A13, C12, F21, O10, Q27, Q31, Q50, Q51
ARTICLE HISTORY Received 23 September 2017; Accepted 26 October 2018
1. Introduction
Due to the emergence and development of liberalisation, privatisation and globalisation
(LPG) of businesses, the economic activities have crossed all the boundaries. Economic
activities are taking place on a large scale to achieve extreme economic growth (EG). EG
is achieved through an increase in economic activities, i.e. manufacturing, extracting,
imports, exports and so on. These days, the economic development of a country is the
first and foremost aim of every country’s policies. For this, the governments of developing or less-developed countries are encouraging and promoting business activities on a
large scale. India is one among them.
Four basic factors are involved in the process of economic development i.e. human
and natural resources and building up of capital and technology (Hitam and Borhan
2012). The developing countries rely mostly on the foreign capital and technology
for their economic development (Rana and Sharma, forthcoming). Over the last two
riturana2222@gmail.com
Department of Management & Humanities, National
CONTACT Ritu Rana
Institute of Technology Hamirpur, Himachal Pradesh 177005, India
© 2018 Informa UK Limited, trading as Taylor & Francis Group
2
R. RANA AND M. SHARMA
decades, foreign direct investment (FDI) flows have considerably increased all around
the world (Seker, Ertugrul, and Cetin 2015). FDI and that too in the manufacturing sector is considered the only means to attract foreign investments in such countries. The
recent step taken by the Indian government in this direction i.e. the ‘Make in India’ campaign can be seen as an example of it. In the words of Bokpin (2017), it has almost become
a cliché that FDIs, to a very large extent, positively influence the economic prospects of
recipient nation. Several relaxations in taxes, environmental standards and regulations
are being provided to foreign investors to increase the level of economic activities. These
economic activities are expected to bring EG ultimately. The global trade (i.e. exports and
imports) is also done on a large scale in developing countries to increase the level of gross
domestic product (GDP). Markets are thus made open to the global investors. Thus it
seems perfectly good for any developing country like India to promote and encourage
huge amounts of investments in business activities.
At the same time, the effects of these activities on the natural environment of the
host country cannot be ruled out. Several manufacturing processes make massive use of
both renewable and non-renewable energy resources apart from other natural resources.
One of the major green house gas (GHG) emissions resulting from combustion of fossil
fuels, known as CO2 emissions, is considered the main cause for global warming and
climatic instability (IPCC 1996). A debate that has attracted much of the global attention in the past few decades is about reducing GHG emissions from the high polluting
emerging economies without hindering the momentum of their economic development
(Alam et al. 2011). Investors from developed countries exploit the resources of developing countries as these countries have free trade policies and relaxed environmental laws.
This becomes clear with the concept of pollution haven hypothesis (PHH) which states
that the firms of developed countries relocate to pollution haven countries in order to
avoid the costs of complying with high environmental standards prevailing in their home
country (Elliott and Shimamoto 2008; Johnson and Turner 2010; Blanco, Gonzalez, and
Ruiz 2011; Shao 2018).
In the quest for EG, Indian government also has created more favourable operating environments for investors (both domestic and foreign) through tax reductions or
exemptions, relaxed labour laws and relaxations to natural environmental regulations,
etc. Thus, the determinants of EG have already attained enough attention and several
efforts are being made to promote them. But, increased economic activities in India have
driven several patterns of environmental destruction. India is the fourth largest country
in the world in emitting total GHG although it is one of the lowest countries emitting per
capita GHG (Alam et al. 2011). As per Alam et al. (2011), the reason behind India’s being
against any commitment for legally binding agreement on its GHG emissions could be
because of the commonly held perception that reduction in environmental pollution
may hinder EG.
On the contrary, in the core of the EG-environment relationship, there exists a concept known as the Environmental Kuznets Curve (EKC) Hypothesis or the inverted
U-shaped curve. EKC hypothesis supports the view that growth will ultimately also contribute to the solution for environmental problems in the developing world (Shafik and
Bandyopadhyay 1992; Panayotou 1993; Kuznets 1995; Johnson and Turner 2010). Grossman and Krueger (1995) found the reason behind this is due to the people becoming
more concerned about the quality of their environment as their income levels rise.
This paper is an attempt to examine the existence of PHH and, at the same time,
EKC hypothesis in Indian context because India is also trying to attract more of FDI for
it’s EG while taking environmental sustainability as a crucial concern in between. For
THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT
3
this purpose, it becomes important to understand the causal relationships between the
determinants of EG and the environmental degradation in India. Hence, examination
of causality relationships among FDI, EG and CO2 emissions along with indicators of
international trade (exports and imports) has been taken for the study. Such examination
is crucial to formulate strategies to cope up with the environmental issues.
2. Existing literature and our contribution
2.1. Existing literature
The causal relationships between FDI, EG, environmental pollution and indicators of
international trade have been well documented in the literature. The finance literature
consists of a well-debated rationale for the need of foreign investment in less-developed
countries due to several benefits it provides to the host country e.g. bridging the internal resource and savings gap, increasing managerial abilities, reducing foreign exchange
shortage and improving balance of payment (Aliyu 2005). There are studies that have
linked FDI with the environmental pollution on the basis of PHH and have tried to find
out the causal relationship among them (e.g. Jorgenson, Dick, and Mahutga 2007; Seker,
Ertugrul, and Cetin 2015; Shahbaz et al. 2015; Bokpin 2017). The indicators of global
trade, i.e. exports and imports, have also been used to examine such relationships (e.g.
Hitam and Borhan 2012; Chakraborty and Mukherjee 2013; Sawhney and Majumder
2015; Sawhney and Rastogi 2015). A wide range of literature is devoted to the analysis of environmental pollution represented by either carbon dioxide (CO2 ) emissions
or sulphur dioxide (SO2 ) and nitrous oxide (N2 O) (Seker, Ertugrul, and Cetin 2015).
Several studies have linked environmental pollution to EG (expressed in terms of GDP)
and have examined the causal relationships among them (e.g. Choi, Heshmati, and Cho
2010; Alam et al. 2011; Hitam and Borhan 2012) along with examining the existence of
Environmental Kuznets Curve (EKC) Hypothesis.
Regarding the literature, on PHH, Elliott and Shimamoto (2008) are of the opinion
that less-developed countries could competitively undercut each other’s environmental
regulations to attract FDI. Blanco, Gonzalez, and Ruiz (2011) explains that this condition
may result in these countries becoming “pollution havens”, where multinational corporations (MNCs) locate operations to save on environmental-related costs. Shao (2018)
also found that low-income countries use lax environmental regulations in the absence
of FDI-attracting factors such as infrastructure and skilled labor. As per him, these lowincome countries emerge as innocent pollution havens because they cannot afford the
costs of implementing and monitoring environmental regulations. Thus, he found that
dirty foreign investments flow to low-income countries. Acharyya (2009) also found
quite large impact of FDI on CO2 emissions in India. Rana and Sharma (forthcoming)
concluded mainly the existence of PHH in India.
On the contrary, Dietzenbacher and Mukhopadhyay (2007) found that India considerably gains from extra trade. Using input–output analysis, they examined whether India
can be regarded as a pollution haven but found that India has moved further away from
being a pollution haven.
The inverted-U-shaped curve was first introduced by Simon Kuznets in December, 1954 while delivering a presidential address entitled ‘EG and income inequality’ in
the 67th annual meeting of American Economic Association. In 1990s, Kuznets Curve
took a new form of relationship. Grossman and Krueger (1991) showed that per capita
income and environmental degradation follow the same inverted U-shaped relationship
4
R. RANA AND M. SHARMA
as original Kuznets Curve in their study. Later, this relationship was supported by
several studies like World Bank 1992 Development Report (Shafik and Bandyopadhyay 1992) and ILO discussion paper (Panayotou 1993). As per Kuznets (1995), as EG
increases, income distribution becomes more uneven at first, then stabilises at middleincome levels and finally evens out again. Similarly, EKC hypothesis states that as EG
increases income levels, environmental degradation also increases at first, then stabilises
at middle-income levels and finally declines with higher income levels. Selden and Song
(1994), while analysing the relationship between income and air pollutants, also supported the existence of EKC hypothesis. As per Dinda (2004), EKC now has become a
vehicle for describing the relationship between income and environmental quality.
However, some studies imply that EKC may not hold at all times and for all indicators
of environmental degradation e.g. Lopez (1994), John et al. (1995), McConnell (1997)
and Stokey (1998). Stern (2003) argues that the early studies on EKC indicate that an
inverted U-shaped relation of environmental degradation with income applies to local
pollutants rather than global pollutants like carbon dioxide.
Several studies have focused on examining the possible determinants of the EKC relationship. These determining variables include: ‘trade’ (e.g. Rock 1996; Rothman 1998),
‘political freedom’ (e.g. Torras and Boyce 1998), ‘FDI’ (Hitam and Borhan 2012; Shahbaz et al. 2015) ‘density of economic activity’ (e.g. Kaufman et al. 1998) and ‘economic
structure’ (e.g. Rock 1996; Suri and Chapman 1998).
2.2. Our contribution
Xepapadeas (2005) found that the importance of causal relationships among foreign capital accumulation, environmental pollution and other growth indicators is very high in
the development theory. While FDI’s effects on EG and CO2 emissions have been tested
several times (using various causality testing techniques), no unanimity has still been
achieved regarding the dynamic causal relationship among them which also included
indicators of international trade (i.e. imports and exports) as ancillary independent variables in the same framework, especially for India. There are only a few studies that
evaluate the dynamic causality relationship between them for India in particular.
The study made by Alam et al. (2011) is one of the remarkable studies in examining the causality relationships taken specifically for India. Their study was, however,
focused mainly on examining the causal relationships between only three variables:
income growth, CO2 emissions and energy consumption. They did not throw any light
on the role of FDI and international trade in such relationships. EKC hypothesis and
PHH were also not examined particularly. Thus, our study can be differentiated from
the paper of Alam et al. (2011), though the methodology used is same.
The study by Linh and Lin (2015) also examined the dynamic causality relationships
among CO2 emissions, energy consumption, EG and FDI in 12 most populous Asian
countries including India. Their study was focused on and limited to a few variables
and the causality testing methodology is different from what we have used in our study.
We have examined a wider aspect of the causal relationships between FDI, EG and CO2
emissions while taking the international trade indicators as important variables in such
a relationship.
Similarly, Rana and Sharma (forthcoming) though used the same methodology to
examine the FDI-EG-CO2 relationships but did not examine the existence of the EKC
hypothesis.
THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT
5
Our study is important for the following four reasons:
• Knowledge on the effects of FDI on EG and at the same time on natural environment
(symbolised by CO2 emissions) of India. This is important as India is one among the
emerging economies; and FDI is getting attracted to India in huge amounts to take
personal benefits from the lax environmental regulations that India is providing.
• Knowledge on the role of international trade indicators, namely exports and imports,
in FDI-EG-CO2 relationship.
• Examination of existence of PHH, and at the same time, EKC hypothesis in India.
This is the unique and important part of our study because both of these hypotheses,
to the best of our knowledge, have never ever been examined alongside specifically
for India.
• Examining the EKC hypothesis using the Toda-Yamamoto (TY) multivariate
approach. This is also an important part of the study. Literature comprises of testing
for EKC hypothesis through various methodologies with a squared term of income in
the model (e.g. Choi, Heshmati, and Cho 2010; Hitam and Borhan 2012; Chakraborty
and Mukherjee 2013; Sinha and Bhatt 2017). But, to the best of our knowledge, TY
approach has never ever been used for this purpose.
This study examines the causal relationships among FDI-EG-CO2 emissions and international trade indicators for India with a dynamic multivariate TY approach for the
period 1982–2013 (32 years).
3. The model and the methodology
3.1. The model
Initially, the study uses a linear regression model to examine the relationship between
FDI, economic growth, indicators of international trade and environmental degradation
in India during 1982–2013. CO2 emissions are taken as the representative of environmental degradation because CO2 emissions are considered as one of the major global
pollutants. The initial model is:
CO2 = f (GDP, GDP2 , FDI, EXP, IMP)
(1)
and the equation of the model is:
lnCO2t = β0 + β1 (lnGDP)t + β2 (lnGDP)2t + β3 (lnFDI)t
+ β4 (lnEXP)t + β5 (lnIMP)t + ut
(2)
where CO2t is carbon dioxide emissions (metric tons per capita); GDPt is gross domestic
product per capita (current US$); GDP2t is the squared term of GDPt ; FDIt is net inflows
of foreign direct investment (current US$); EXPt is exports (% of GDP); IMPt is imports
(% of GDP); and ut is the error term or the stochastic random variable; all at time t.
3.2. Data
The data on all the variables used for the study is sourced from the globally reliable source
i.e. World Development Indicators (WDI) of World Bank Group (2016) Data Reports for
India. The data is selected upto the year 2013 as per its availability at the time of the study.
6
R. RANA AND M. SHARMA
3.3. The methodology
Several methodologies exist in literature for testing causality such as Sims causality
(1972), Granger (non-) causality (Engle and Granger 1987) and causality in Johansen
and Juselius (1990) ECM to name a few. The main econometrics methodology used
in this paper is the Modified Wald (MWALD) Test proposed by Toda and Yamamoto
(1995). TY multivariate approach for testing the causality has its advantages over others in the sense that it can be used even when the variables are integrated of different
orders. It requires the estimation of an augmented VAR irrespective of whether the time
series is I(0) or I(1) and whether it cointegrated or not (Ahmed 2015). Also, if the system has a unit root, the conventional OLS of VAR in level-based Wald statistics have
non-standard asymptotic distribution that may involve annoying parameters (Toda and
Phillips 1993). TY procedure put restrictions on the parameters of VAR (l) from an augmented VAR (l + imax ) model, where l is the optimal lag length and imax is the maximum
order of integration of variables (Alam et al. 2011). A general VAR (l + imax ) model in
TY approach is written as:
yt = α + β1 yt−1 + . . . + βl+imax yt−(l+imax ) + εt
(3)
where yt consists of K endogenous variables, α is a vector of intercept terms, β are coefficient matrices and εt are white noise residuals. The null hypothesis in TY causality
is based on zero restriction on first l parameters (H0 : β 1 = . . . = β l = 0) of the kth
element of yt . Until l ≥ imax , the model is valid (Kurozumi and Yamamoto 2000).
3.4. The sequencing of tests performed
At first, a graphical representation of the data series is made. Next, the appropriate maximum lag length (l) for the variables has been chosen for the study. A VAR (l) in levels
of the data is then set up including an intercept in each equation. The residuals were
then examined to see whether the VAR in levels is well specified. AR Roots Table and
AR Roots Graph are used to examine the stability of the VAR model so formed. Serial
Correlation Lagrange’s Multiplier (LM) test is used for examining serial independence,
White Heteroskedasticity (No Cross Terms) test is used to examine homoskedasticity
and normality is examined with the help of J-B (Jarque-Bera) statistic through Cholesky
of covariance (Lutkepohl) method. Then, the maximum order of integration (imax ) of the
variables is obtained with the help of unit root tests. All unit root tests with an optimal lag
length are performed with deterministic elements, i.e. a time trend and a constant based
on the graphical representation of all the data series. The unit root tests performed in
the study include Augmented Dickey Fuller (ADF) unit root test and Phillips and Perron
(PP) unit root test for obtaining robust results. Next, the decision regarding an appropriate cointegration testing technique is made to determine any cointegrating equations
among the variables so that an Error Correction Model (ECM) be introduced. Finally, a
levels VAR model is re-estimated with imax additional lags (i.e. l + imax lags in total) of
each of the variables into each of the equations to examine the causality among variables.
4. Results and discussions
This section provides the results of various tests performed along with discussions
related to the results.
THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT
7
4.1. Graphical representation of data series
Before moving further and going into rigorous statistical and econometric analysis, a
graphical representation of the time series of all the concerned variables is very important. Such graphical representation is crucial to determine the direction in which the
analysis should move ahead for getting accurate results. Figure 1 provides the trend in
the data of all the variables of interest through their graphical representation.
Figure 1 shows that all the variables do not evolve around zero mean, they actually
fluctuate around some other mean. It also depicts that all the variables have a slightly
or largely upwards moving trend in them. It is clear from the graphical representation
that the extent of FDI has increased since 1980s in India due to several initiatives taken
by the Indian government to attract more FDI. With the introduction of new economic
policy in 1991 and subsequent reform process, India has witnessed a change in the flow
and direction of FDI into the country (Dutta and Sarma 2008).
Similarly, the share of both exports and imports in India’s GDP has increased over
time. In the words of FICCI (2016), during the last 25 years, India’s exports have
increased more than 17 times, from US$ 18.1 billion in 1990–1991 to US$ 309 billion
Figure 1. Graphical representation of all the data series.
8
R. RANA AND M. SHARMA
in 2014–2015, and India’s imports have increased 19 times, from US$ 23.5 billion in
1990–1991 to US$ 447 billion in 2014–2015. India’s share in global exports has moved up
from mere 0.6% in the early nineties to 1.7% currently. Likewise, India’s share in global
imports has increased from around 0.6% during the early nineties to 2.4% currently.
Given the extent of trade, the impacts of international trade indicators on India (both
positive and negative) are thus quite obvious. In the words of Trading Economics (World
Bank), GDP in India averaged 545.81 USD Billion from 1960 until 2017, reaching an
all time high of 2597.49 USD Billion in 2017 and a record low of 36.54 USD Billion in
1960. Similarly, the rising concentrations of greenhouse gases (GHGs) of anthropogenic
origin in the atmosphere such as carbon dioxide (CO2 ), methane (CH4 ) and nitrous
oxide (N2 O) have increased, since the late nineteenth century (Sharma, Bhattacharya,
and Garg 2006).
4.2. Optimal lag selection
To select the optimum lag for the study, four different lag selection criterion were
examined, namely, Final Prediction Error (FPE), Akaike information Criterion (AIC),
Schwarz Information Criterion (SIC) and Hannan-Quinn (HQ) Criterion. The various
information criteria suggest that we should have a maximum lag length of 2 (except SC)
for each variable (Table 1). Hence, following the results obtained from various criteria,
optimum lag (l) selected for the study is 2 (subject to testing).
4.3. Residual diagnostics of VAR (2) at level
A VAR (2) model in the levels of the data, including an intercept in each equation is then
set up, as l = 2 here. The residuals were then examined to make sure that the VAR is well
specified. Results of the various tests performed on the residuals are shown in Table 2.
The estimated model is found to be dynamically stable as checked by the AR Roots Graph
and the information provided by the AR Roots Table (mentioned in Table 2). White Heteroskedasticity (No Cross Terms) test has the null hypothesis of “no heteroskedasticity”
which cannot be rejected in our results. Similarly, the null hypothesis under J-B Statistics
of Cholesky of Covariance (Lutkepohl) test is “residuals are multivariate normal”. The
null in this case too cannot be rejected in our results. The null hypothesis under Serial
Table 1. Optimum lag selection.
Lag
0
1
2
FPE
AIC
SC
HQ
1.14e–09
6.23e–14
3.13e–14a
–3.563505
–13.43253
–14.43511a
–3.283266
–11.47086a
–10.79200
–3.473854
–12.80497
–13.26965a
a Indicates lag order selected by the criterion.
Table 2. Diagnostic test results of VAR (2) in levels.
Diagnostic test
Serial correlation LM (with l = 2)
White Heteroskedasticity (No Cross Terms)
Cholesky of Covariance (Lutkepohl)
VAR Stability (AR Roots Table and Graph)
Test statistic
p-Value
0.937497 (Rao F-stat)
473.5773 (Chi-Sq.)
8.093121 (J-B Stat)
No root lies outside the unit circle
.5771
.3447
.7778
–
THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT
9
correlation LM test is “no serial correlation exists” that again, cannot be rejected. Hence,
it is evident from the results that there was no serial correlation found among the residuals of the VAR (2) equations; and the residuals were found to be homoskedastic and
normally distributed. Also, the VAR (2) in levels model is dynamically stable.
4.4. Unit root tests
In the context of time series regression, the idea that historical relationships can be generalized to the future is formalized by the concept of stationarity. A procedure to formally
test for non-stationarity was devised by Dickey and Fuller (1979, 1981) known as the
Dickey-Fuller unit root test and then an augmented Dickey Fuller (ADF) unit root test
that has been used in the study to obtain maximum order of integration (imax ) of the
variables, before estimating the TY model. PP unit root test has also been used for the
study because Phillips and Perron (1988) is of the opinion that ADF unit root test under
rejects when the time-series are subject to both: a deterministic trend and an exogenous
trend. The null hypothesis under both the tests is “the variable has a unit root” which
means, the variable is non-stationary. Through the graphical representation of the data
series, it becomes evident that all the series have a trend in them and they do not evolve
around zero mean, they actually fluctuate around some other mean. Thus, deterministic
elements i.e. a time trend and a constant have been included in the unit root tests. Table
3 displays the p-values of ADF and PP unit root tests results for the data series of all the
variables at levels and at first differences.
The ADF unit root test results indicate that lnCO2 , lnGDP, (lnGDP)2 and lnIMP are
non-stationary at level but are integrated of first-order i.e. I(1) at 1% significance level
whereas, lnFDI is I(0), at 5% significance level and lnEXP is I(0) at 10% significance level.
So, maximum order of integration as per ADF unit root test is 1.
The PP unit root test results indicate that lnCO2 , lnGDP, (lnGDP)2 , lnEXP and lnIMP
are non-stationary at level but are integrated of first order, which means they all are I(1).
but lnFDI is I(0) or stationary at level. So, maximum order of integration as per PP unit
root test is also 1.
Concluding both the unit root tests performed on the variables, imax has been
determined as 1 in this study.
4.5. Cointegration
It is important to mention here that cointegration result does not affect the final results of
the causality analysis under TY multivariate approach (Giles 2011). But a cointegration
Table 3. ADF and PP unit root tests results.
ADF
PP
Variable
At level
At first difference
Order of
integration
At level
At first difference
Order of
integration
lnCO2
lnGDP
(lnGDP)2
lnFDI
lnEXP
lnIMP
0.2487
0.9031
0.9243
0.0350**
0.0910*
0.1720
0.0046***
0.0008***
0.0012***
–
–
0.0015***
I(1)
I(1)
I(1)
I(0)
I(0)
I(1)
0.6234
0.9031
0.9243
0.0350**
0.1048
0.1721
0.0043***
0.0008***
0.0012***
–
0.0000***
0.0016***
I(1)
I(1)
I(1)
I(0)
I(1)
I(1)
Note: ***, ** and * indicate significance level at 1%, 5% and 10%, respectively
10
R. RANA AND M. SHARMA
Table 4. Diagnostic test results of residuals of ARDL model with two lags.
Diagnostic test
Serial correlation LM (with l = 2)
White heteroskedasticity (No Cross Terms)
Normality
Test statistic
p-Value
0.630198 (F-stat)
0.586753 (F-stat)
1.725126 (J-B Stat)
.5570
.8439
.4221
test is performed only to provide a possible cross-check on the validity of the final
results. Johansen (1988) proposes the trace statistics and maximum eigenvalue statistics wherein, if two or more of the time-series have the same order of integration then
the test examines if they are cointegrated. Johansen cointegration test suffers a limitation that it requires all the time-series to have the same order of integration. It cannot
be performed when some of the variables are I(0) and some are I(1). Having the same
order of integration is not always possible because of different unit root tests providing
different orders of integration even for exactly the same time-series.
Another cointegration testing technique is ARDL bounds test which has been used in
the study. This methodology of Pesaran and Shin (1999) and Pesaran, Shin, and Smith
(2001) is preferred over Johansen cointegration test because it can be used with a mixture
of I(0) and I(1) data. A general ARDL model is written as follows:
Yt = β0 +
k
βi yt−i +
i=1
k
δi xt−i + ϕ1 yt−1 + ϕ2 xt−1 + μt
(4)
i=0
where β i and δ i are short-run coefficients; ϕ 1 and ϕ 2 are ARDL long-run coefficients; μt
is the disturbance term and k is the number of lags.
An ARDL model with 2 lags was set up which was found to be dynamically stable (based on Recursive Estimates, CUSUM test). Also, the residuals of the model
were found to be normally distributed, homoskedastic and free from autocorrelation
(Table 4).
In Pesaran’s Table with unrestricted intercept and unrestricted trend, the upper
bound value at 5% significance level is 4.00 with k = 6, or the number of explanatory
variables as 6. In our study, the Wald test for coefficient restrictions showed the F-statistic
of 10.69463 with a p-value of .0007. The value F-statistic obtained is higher than the
upper bound value of Pesaran’s Table. Hence, the null hypothesis of “no cointegration”
is rejected. It is thus concluded that all the variables are cointegrated and exhibit long-run
relationship among them.
4.6. Error correction model
Once the variables are found to be cointegrated, an Error Correction Model (ECM)
needs to be set up. An ECM which is applicable with all ARDL models is as follows:
Yt = β0 +
n
i=1
βi yt−i +
n
δi xt−i − ϕzt−1 + μt
(5)
i=0
where ϕ is the coefficient of Error Correction Term (ECT); and zt−1 (lagged residuals
series) is the ECT which is equal to b0 + b1 xt−1 − yt−1 .
THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT
11
Table 5. Diagnostic test results of ECM.
Diagnostic test
Serial correlation LM (with l = 2)
White heteroskedasticity (No Cross Terms)
Normality
Test statistic
p-Value
0.030323 (F-stat)
1.079826 (F-stat)
1.393289 (J-B Stat)
.9702
.4389
.4983
Notice the negative sign of the ϕ, which signifies that the result of the coefficient ϕ
must have a negative sign along with being significant.
An ECM with 2 lags was set up which was found to be dynamically stable (based on
Recursive Estimates, CUSUM test). Also, the residuals of the model were found to be
normally distributed, homoskedastic and free from autocorrelation (Table 5).
The value of ECT coefficient ϕ came up as –0.535259 with a p-value .0076. As the
value obtained has a negative sign and is statistically significant at even 1% significance
level, we can say, the system is getting adjusted towards long-run equilibrium at the speed
of 53.52%. Thus, the speed of adjustment towards equilibrium is 53.52%.
4.7. Granger causality
Finally, a VAR model in levels is then set up again but with 1 additional lag (i.e.
l + imax = 2 + 1 = 3 lags) of each of the variables into each of the equations this time
as imax is 1. Granger (non-) causality is then tested using a standard Wald test with the
hypothesis that the coefficients of (only) the first l lagged values of variables are zero
in the equations of the other variables. The model in equation (3) can be written in six
different equations, i.e. for all the six variables under consideration, e.g. for the variable
lnCO2 , the equation can be written as follows:
lnCO2t = α1 +
l
β1i lnCO2t +
i=1
+
imax
imax
γ2i lnGDPt−j +
l
l
δ2i (lnGDP)2 t−j +
+
imax
γ1i lnGDPt
i=1
δ1i (lnGDP)2 t
l
ϕ1i lnFDIt +
i=1
ϑ1i lnEXPt +
i=1
l
i=1
j=l+1
+
β2i lnCO2t−j +
j=l+1
j=l+1
+
imax
imax
ϑ2i lnEXPt−j +
j=l+1
ρ2i lnIMPt−j +
imax
ϕ2i lnFDIt−j
(6)
j=l+1
l
ρ1i lnIMPt
i=1
t
j=l+1
Similarly, the other five different equations can be written for all the five remaining variables under consideration. The coefficients for the ‘extra’ lag are not included while
performing the Wald tests. They are there just to fix up the asymptotics. It is important
to note that the Wald test statistics will be asymptotically chi-square distributed with 2
12
R. RANA AND M. SHARMA
Table 6. Granger causality results.
MWALD test
Dependent
variable
CO2
GDP
(GDP)2
FDI
CO2
_
26.8014***
(0.0000)
30.0114***
(0.0000)
18.4268***
(0.0001)
3.2001
(0.2019)
8.2522**
(0.0161)
GDP
5.2056*
(0.0741)
4.1084
(0.1282)
0.7769
(0.6781)
0.0464
(0.9771)
3.1962
(0.2023)
_
2.6368
(0.2676)
_
1.9379
(0.3795)
1.6241
(0.4439)
_
3.3037
(0.1917)
2.7982
(0.2468)
0.3526
(0.8384)
_
0.6940
(0.7068)
0.5181
(0.7718)
0.9022
(0.6369)
1.8665
(0.3933)
_
(GDP)2
FDI
EXP
IMP
2.1350
(0.3439)
0.1586
(0.9238)
2.7527
(0.2525)
0.4597
(0.7947)
0.1953
(0.9070)
2.3409
(0.3102)
0.5695
(0.7522)
11.4113***
(0.0033)
2.3001
(0.3166)
EXP
15.3484***
(0.0005)
IMP
Causality
inference
CO2 ← GDP
CO2 ← (GDP)2
CO2 ← FDI
CO2 ← IMP
GDP ← CO2
_
_
EXP ← FDI
IMP ← EXP
Notes: ***, ** and * indicate significance level at 1%, 5% and 10%, respectively; p-values are in parentheses; and
← denotes a uni-directional causality
degrees of freedom under the null and the extra lag that was introduced by adding imax
to l, is not included in the test results. Rejection of the null implies a rejection of Granger
(non-) causality i.e. a rejection supports the presence of Granger causality. The results of
the Granger (non-) causality test are shown in Table 6.
4.8. Notable findings
The study is mainly focused on the variables FDI, economic growth and CO2 emissions;
thus the concentration of the explanations will be basically on these variables. Below are
the main findings of the study:
(a) No causality between GDP and FDI: This finding shows that neither FDI is causing
economic growth in India nor the level of economic growth is attracting FDI in
India. This finding can be validated by our other finding that follows.
(b) Uni-directional causality from FDI to CO2 emissions: This finding indicates that
FDI is one of the causing factors for CO2 emissions in India. It confirms the existence of PHH in India. By looking at this and the previous finding of the study it can
be concluded that it is the relaxed environmental regulations, not the level of economic growth that is attracting FDI to India. This is an alarming finding for Indian
government.
(c) Bi-directional causality between CO2 emissions and GDP: Causality from GDP
to CO2 signifies that the rate of economic growth is becoming one of the causes of
environmental degradation in India. Similarly, causality from CO2 to GDP indicates
that the economic growth in India is driven by compromising on environmental
sustainability.
(d) Uni-directional causality from GDP2 to CO2 emissions: The finding that GDP2 will
cause CO2 emissions in India but CO2 emissions will not cause GDP2 proves the
existence of EKC hypothesis in India. In EKC hypothesis, it is assumed that after
achieving a certain level of economic growth the environmental degradation starts
THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT
13
to decline. First, GDP is observed to be positively related with CO2 (in finding ‘c’),
indicating that economic growth increases environmental degradation. The square
of the GDP term (GDP2 ) is found to be negatively related with CO2 (with coefficient
of GDP2 bearing a negative sign and being significant at the third extra lag). This
uni-directional causality from GDP2 to CO2 emissions shows that a high level of
economic growth will cause the amount of CO2 emissions in India negatively. Similarly, no causality from CO2 to GDP2 strengthens the existence of EKC hypothesis
for India.
Among other findings are:
• FDI is causing exports; exports are causing imports, and in return, imports are
causing CO2 emissions. This finding reveals that FDI has an impact on India’s
foreign trade as FDI is causing the volume of exports. These exports are forcing
India to import more, may be import of technology, raw materials or even certain finished products that help in manufacturing for exports. But these imports are
not environmentally sustainable as they are becoming one of the causes for CO2
emissions.
• Causality from FDI to exports; exports to imports; imports to CO2 and finally from
CO2 to GDP concludes mainly that FDI is causing both CO2 and GDP. FDI is causing
CO2 not just directly (finding “b”) but also indirectly through the volume of foreign
trade. Similarly, FDI is causing GDP, though not directly, but in an indirect manner
through the volume of trade and CO2 emissions, confirming the existence of PHH in
India.
4.9. Comparison of findings with existing literature
Acharyya (2009) found a positive long-run impact of FDI on GDP growth of India
and Rana and Sharma (forthcoming) found uni-directional causality from GDP to FDI.
But, in our study, we did not find any causality between FDI and GDP. Our finding of
FDI causing GDP in an indirect manner through CO2 emissions is consistent with the
findings of Rana and Sharma (forthcoming).
The causality found from FDI to CO2 emissions is in line with the findings of
Acharyya (2009) and Rana and Sharma (forthcoming). As found by Chakraborty and
Mukherjee (2013), it is thus concluded that foreign investment inflows create adverse
environmental consequences. Hence, PHH exists in India. This finding is though contrary to the findings of Dietzenbacher and Mukhopadhyay (2007) who found that India
has moved further away from being a pollution haven.
The bi-directional causality found between CO2 emissions and GDP is in contrast to
the findings of Alam et al. (2011) and Rana and Sharma (forthcoming). Alam et al. (2011)
found no evidence of causality between income and CO2 emissions in either direction, whereas, Rana and Sharma (forthcoming) found uni-directional causality from
CO2 emissions to GDP. Although, this finding is in line with the findings of Dinda and
Coondoo (2006) as they too found a bi-directional causal relationship between GDP and
CO2 .
Our finding of ‘existence of EKC hypothesis in India’ through a squared term of GDP
is consistent with the finding of Chakraborty and Mukherjee (2013). They also used
14
R. RANA AND M. SHARMA
the squared term of Per Capita Income (PCI) in their study and concluded that environmental sustainability of a country improves with its economic prosperity. Sinha and
Bhatt (2017) also found the existence of EKC hypothesis in India using a squared term
of GDP.
In our study, though we did not find any direct causality from exports to CO2 emissions but an indirect causality was found from exports to imports and then imports
to CO2 emissions. Chakraborty and Mukherjee (2013) also found that greater export
inclination of primary and manufacturing products are potentially associated with
environmental degradation.
The finding that imports are causing CO2 emissions validates the findings of Sawhney and Rastogi (2015). Sawhney and Rastogi (2015) found that India has remained
a net importer of pollution-intensive manufactured goods on the whole. Sawhney and
Majumder (2015) also found that the escalating domestic demand for metal and use of
relatively labour-intensive technology are significant factors behind India’s import of
metallic wastes from different source countries.
5. Conclusions and recommendations
This study investigates dynamic Granger (non-) causality relationship among FDI, economic growth and CO2 emissions along with the level of trade taking place in India.
The results from the TY Model conclude mainly the existence of both Pollution Haven
Hypothesis (PHH) and EKC hypothesis in India.
The uni-directional causality from GDP2 to CO2 emissions explains the existence of
EKC hypothesis in India. It indicates that India can open its markets for foreign investors
and can continue to reduce trade barriers for a free trade environment to accumulate
capital because in the long-run the environmental degradation tends to decline. But
at the same time, the uni-directional causality from FDI to CO2 emissions and the bidirectional causality between CO2 emissions and GDP were found in the study. Both of
these findings validate the existence of PHH. The finding of ‘no causality between FDI
and GDP’ directly but an indirect ‘causality from FDI to GDP’ through international
trade and CO2 , also confirms the existence of PHH. FDI helps improve the economic
growth of India but only through the way of environmental destruction. Hence, there
is a need for Indian government to frame stricter environmental policies and enforce
stringent environmental laws and regulations.
FDI is also increasing the volume of exports in India which is considered a helpful
means to increase the rate of economic growth in terms of GDP. These exports, in turn,
are causing imports in India. Hence, FDI is causing the volume of trade directly and
indirectly. Imports are causing CO2 emissions, so a check on the volume and quality of
imports is needed from Indian government.
Concluding all the findings, it can be advised that India can open its markets for
foreign investors to accumulate eco-friendly economic growth in long-run subject to a
certain level of environmental check. Newer, greener and environment-friendly technological processes can be brought in India by developed countries if a strict check is
made while allowing foreign firms to invest in India and controlling the investment
in dirty industries. An encouragement should also be given to local innovations. Also,
incentives and subsidies can also be provided to manufacturing industries for complying with the environmental standards and for lower CO2 emissions during a specific
period. Dirty investments lacking advanced and cleaner technologies and causing CO2
THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT
15
emissions require a careful monitoring. A careful examination by Indian government
is also needed on investors that enjoy and accumulate personal benefits due to the
relaxed environmental laws and other free trade environments offered by the Indian
government.
Despite the notable findings, the study suffers data availability limitations. The data
for all the variables is taken for the years 1982 to 2013 as per their availability upto 2013,
whereas the ‘Make in India’ program of Indian government was launched in the year
2014 that increased the amount of economic activities on a large scale. At the same time,
Indian government is assuring its people of “Zero Defect Zero Effect”. Hence the impacts
of ‘Make in India’ (both negative and positive) on all the variables are not taken into
account that may have both long-run and short-run effects on economy as-well-as the
natural environment of India. Future studies can be expected to take these impacts into
consideration.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Ritu Rana
http://orcid.org/0000-0002-2241-0807
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