i
PhD Dissertation
IMPACT OF TRADE LIBERALIZATION ON ENVIRONMENTAL
QUALITY: A Panel Study of Selected Asian Countries
Submitted by: Naila Jabeen
Supervised by: Dr. Rehana Siddiqui
Dr. Eatzaz Ahmed
Department of Economics
Pakistan Institute of Development Economics
Islamabad
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In the name of ALLAH, the most Merciful and the most Beneficent
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Dedicated to
Aaymah, Aayshah, Aamnah & Fatimah
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Acknowledgement
All praise be to Allah, the Lord of all the worlds.
This dissertation would not have been possible without the guidance and the help
of several individuals who in one or the other way contributed and extended their valued
assistance in the preparation and completion of this dissertation. First, I would like to
express my sincere gratitude to my supervisors. I owe my two supervisors most of what I
have learned during the entire period, not only in Economics, but also in terms of
valuable enhancement in my general knowledge. It is not possible for me to find words to
thank and express gratitude to my teacher and my mentor Dr. Eatzaz Ahmed for teaching
me and guiding me what I should aim for as a researcher. He was always able to find
something remotely interesting inside my vague ideas and encouraged me to work hard
and not give up through the toughest moments. I am indebted to Dr. Rehana Siddiqui, my
supervisor, as well. She was always supportive and helpful whenever I needed someone
for such moral sustenance. Both have been excellent supervisors.
I am also very thankful to my parents and siblings, who have been praying for my
success always and supported me all through my research. I cherish my mother‘s love for
studies and her prayers for my success. I express deep admiration for my father who has
been a permanent source of love, hope, guidance and kindness for me right from the
beginning of my life. I would never be able to sufficiently thank my husband,
Muhammad Saeed Ahmed, for always being there with me and for his insightful
comments at every stage of the research. I would never have achieved what I did during
my research work without his help and intellectual and physical support. He has been an
inspiration throughout my Ph.D work.
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I would take this opportunity for thanking all my teachers from my primary
school onwards, especially, Dr. Eatzaz Ahmad, Dr.Waqar Masood Khan, Dr. Abdul
Qayyum, Dr. Ejaz Ghani, Dr. Wasim Shahid at Pakistan Institute of Development
Economics. I would also like to thank my in-laws, all my colleagues and friends for their
moral support and encouragement. To everyone, I most whole-heartedly say two simple
words: “Thank You”. They, however, bear no responsibility for any errors of omission
and commission, which are entirely mine.
Naila Jabeen
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IMPACT OF TRADE LIBERALIZATION ON ENVIRONMENTAL QUALITY:
A Panel Study of Selected Asian Countries
Table of Contents
Dedication iii
Acknowledgements iv
Table of Contents vi
List of Tables x
List of Figures xi
List of Acronyms and Abbreviations xii
Abstract xvi
Chapter 1 INTRODUCTION 1-10
1.1.Background
1.2.Objectives of the Study
1.3.Organization of the Study
1
9
10
Chapter 2 HISTORICAL ANALYSIS OF TRADE POLICIES 11-27
2.1.Introduction
2.2.Historical Analysis of Trade Profiles
2.3.Country-wise Brief Analysis of Trade Policies
2.3.1. Trade Policies of Pakistan
2.3.2. Trade Policies of India
2.3.3. Trade Policies of Philippines
2.3.4. Trade Policies of Sri Lanka
2.3.5. Trade Policies of Bangladesh
2.3.6. Trade Policies of Thailand
2.3.7. Trade Policies of Malaysia
2.3.8. Trade Policies of Indonesia
2.4.Simple Average Tariff Rates and Cross-Country Rankings
2.5.Conclusion
11
11
14
14
15
16
18
19
21
23
24
26
27
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Chapter 3 REVIEW OF LITERATURE 28-48
3.1.Introduction
3.2.Trade Liberalization Measures
3.3.Trade Liberalization and Environmental Quality: Theory
3.4. Trade Liberalization and Environmental Quality:
Empirical Evidences
3.5.Conclusion
28
28
34
37
48
Chapter 4 ANALYTICAL FRAMEWORK 49-74
4.1. Introduction
4.2. Framework of Analysis: An Outline
4.3. Trade Liberalization Index
4.3.1. Export Supply Model
4.3.2. Import Demand Model
4.3.3. Construction of Trade Liberalization Index
4.4. Trade Liberalization and Environmental Quality
4.4.1. Scale Effect
4.4.2. Composition Effect
4.4.3. Technique Effect
4.4.4. Energy Use
4.4.5. Foreign Investment (FDI)
4.4.6. Human Capital
4.4.7. Democracy
4.4.8. Corruption
4.4.9. Poverty
4.5.Recapitulation of the Model
4.6.Conclusion
49
49
52
52
53
54
55
55
57
59
60
62
63
66
67
69
69
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Chapter 5 DATA SOURCES, CONSTRUCTION OF VARIABLES AND
ECONOMETRIC METHODOLOGY
75-88
5.1. Introduction
5.2. Data Sources
5.3. Construction of Variables
75
75
76
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5.4. Construction of Composite Index of Emissions
5.5. Summary of Construction of Variables
5.6. Econometric Methodology
5.6.1. Specification and Identification of Equations
5.6.2. Generalized Method of Moments (GMM)
5.7.Conclusion
80
81
86
86
87
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Chapter 6 EMPIRICAL FINDINGS-I
CONSTRUCTION OF TRADE LIBERALIZATION INDEX
89-100
6.1. Introduction
6.2. Empirical Results of Trade Equation
6.2.1. The Import Model
6.2.2. The Export Model
6.2.3. Total Trade Model
6.3. Construction of Trade Liberalization Policy Index
6.4. Graphical Analysis of Trade Liberalization Policy Index
6.5.Comparison of Trade Liberalization Policy Index with
alternate Measures of Trade Openness
6.6. Conclusion
89
89
89
91
93
94
95
98
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Chapter 7 EMPIRICAL FINDINGS-II
TRADE LIBERALIZATION AND ENVIRONMENT
QUALITY
101-127
7.1. Introduction
7.2. Overview of the Data
7.3. Estimation and Interpretation of the Model
7.3.1. Carbon Dioxide Emissions Equation
7.3.2. Trade Policy Liberalization Equation
7.3.3. Scale Effect Equation
7.3.4. Technique Effect Equation
7.3.5. Physical Capital Equation
7.3.6. Industrial Share
7.3.7. Energy Use Equation
101
101
103
104
111
112
113
113
114
114
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7.3.8. Foreign Direct Investment Equation
7.3.9. Human Capital Equation
7.3.10. Corruption Equation
7.3.11. Poverty Equation
7.3.12. Democracy Equation
7.4. Comparison of Results of Channel Variables across different
Emissions
7.5. Summary of the Channel Effects
7.6. Comparison of Effects of Trade Liberalization Policy across
Emissions
7.7. Tests based on the Residuals from the Equations of Emissions
7.8. Conclusion
115
116
116
117
118
118
121
124
126
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Chapter 8 FORECASTING ANALYSIS 128-137
8.1.Introduction
8.2.Statistical Measures for Forecasting Evaluation
8.3.Within Sample Forecasts
8.4.Out of Sample Forecasts
8.5.Conclusion
128
128
129
133
137
Chapter 9 SUMMARY, CONCLUSION AND POLICY
IMPLICATIONS
138-142
9.1. Summary
9.2. Conclusion
9.3. Policy Implications
9.4. Limitations of the Study and Way Forward
138
139
141
142
BIBLIOGRAPHY
Appendix-A
Appendix-B
144-158
159
160-166
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List of Tables
Table 2.1 Trade Profiles of Selected Panel Countries 12
Table 2.2 A Snapshot of Trade Policy of Pakistan 15
Table 2.3 A Snapshot of Trade Policy of India 16
Table 2.4 A Snapshot of Trade Policy of Philippines 17
Table 2.5 A Snapshot of Trade Policy of Sri Lanka 19
Table 2.6 A Snapshot of Trade Policy of Bangladesh 20
Table 2.7 A Snapshot of Trade Policy of Thailand 22
Table 2.8 A Snapshot of Trade Policy of Malaysia 24
Table 2.9 A Snapshot of Trade Policy of Indonesia 26
Table 2.10 Simple Average Tariff Rates and Cross-Country Rankings 27
Table 4.1 Expected Effects of trade liberalization on Environmental
Quality
73
Table 5.1 The Normalized Weights for the Construction of Composite
Index of Emissions
81
Table 5.2 Summary of Description, Construction and Sources of
Variables
82
Table 6.1 Correlations between Trade Liberalization Policy Index and
its Components
95
Table 6.2 Correlation Matrix of Trade Liberalization Index and alternate
Measures of Trade Openness
98
Table 7.1 Correlation Matrix for the Main Variables 102
Table 7.2 Empirical Estimates of Complete Model 106
Table 7.3 Comparison of Results across Emissions (CO2, SO2,
composite index of emissions)
120
Table 7.4 Contribution of Trade Policy Liberalization on CO2
Emissions
123
Table 7.5 Comparison of Effects of Trade Liberalization Policy across 125
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Emissions
Table 7.6 Regression of the Residuals from the Equations of Emissions
on the Trade Liberalization Policy Index
126
Table 8.1 Statistical Tests from the Model Validation: Within-Sample
Forecasts
133
Table 8.2 Statistical Tests from the Model Validation: Out-of-Sample
Forecasts
134
List of Figures
Figure 4.1 Graphical Representation of Analytical Framework 51
Figure 6.1 Graphical Representation of Constructed Trade Liberalization
Policy Index
96
Figure 6.2 Country-Wise Graphical Representation of Constructed Trade
Liberalization Policy Index
97
Figure 6.3 Graphical Presentation of constructed Trade Liberalization
Index and alternate Measures of Trade Openness
99
Figure 8.1 Actual and Forecasted Series of the Endogenous Variables
(Within Sample Forecasts)
129
Figure 8.2 Actual and Forecasted Series of the Endogenous Variables
(Out of Sample Forecasts)
134
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List of Acronyms and Abbreviations
2SLS Two Stage Least Squares
AFTA ASEAN Free Trade Area
ASEAN Association of South East Asian Nations
ATR Average Tariff Rates
BAU Business as Usual
BMP Black Market Premium
BOD Biological Oxygen Demand (Organic Water Pollutant)
CDIAC Carbon Dioxide Information Analysis Centre
CO2 Carbon dioxide
CPI Consumer Price Index
ECM Error Correction Method
EDGAR Emissions
EDGAR Emission Database for Global Atmospheric Research
EJVs Equity Joint Ventures
EKC Environmental Kuznets Curve
ERE Environmental Regulation Effect
ESI Environmental Sustainability Index
EU European Union
FAST Free and Secure Trade Agreement
FDI Foreign Direct Investment
FEE Factor Endowment Effect
FEH Factor Endowment Hypothesis
FEMD Foreign Exchange Market Distortions
FTA Free Trade Agreement
GDP Gross Domestic Product
GHG Green House Gases
GMM Generalized Method of Moments
GNI Gross National Income
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GS Government Spending
ICRG International Country Risk Guide
ICRG International Country Risk Guide
IEA International Energy Agency
IFS International Financial Statistics
IV Instrumental Variable
KLE Capital Labour Effect
ME Mean Error
MPE Mean Percentage Error
NAFTA North American Free Trade Agreement
NER Nominal Exchange Rate
NICs Newly Industrialized Countries
NO Nitrogen Monoxide
NOx Nitrogen Oxide
NTBs Non-Tariff Barriers
OECD Organization for Economic Co-operation and Development
OLS Ordinary Least Squares
PCA Principal Component Analysis
PCI Pollution Component of Imports
PCM Principle Component Method
PHE Pollution Heaven Effect
PHH Pollution Heaven Hypothesis
PM-10 Particulate Matter
PRS Political Risk Services
PTA Preferential Trade Agreement
QRs Quantitative Restrictions
REAS Regional Emissions Inventory in Asia
xiv
RER Real Exchange Rate
RMSE Root Mean Square Error
RMSPE Root Mean Square Percentage Error
SAARC South Asian Association of Regional Cooperation
SAFTA South Asian Free Trade Area
SO2 Sulphur dioxide
TIC Theil’s Inequality Coefficient
TOT Terms of Trade
TRIMS Trade Related Investment Measures
UNEP United Nations Environment Program
USA United States of America
WB World Bank
WDI World Development Indicator
WTO World Trade Organization
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xvi
Abstract
The ongoing climatic changes and the global warming have intrigued researchers
to explore the impact of different policies on the environmental quality. In this study, we
focus on the question as to whether freer trade policy is compatible with environmental
quality standards. There is no simple pattern of the association between the trade and
environmental quality. The main objectives are to construct an index of Trade
Liberalization Policy, to investigate the role of the trade liberalization in the
environmental quality by decomposing the scale, composition and technique effect and to
explore the additional social and institutional channels through which trade liberalization
may cause environmental quality.
The estimated empirical findings regarding the effects of the trade liberalization
policy on imports and exports are strong and robust in different model specifications.
Reductions in export and import duties have a significant positive effect on imports and
exports of the panel countries with the overall impact on imports being greater than
exports, while the liberalized trade regime has a significant positive influence on
expanding trade volumes. The empirical findings reveal a mixed but moderate effect of
the trade liberalization policy on the environmental quality. Trade liberalization policy
appears to affect environmental quality differently through different channels. The net
affect also varies across different pollutants.
The trade liberalization policy has a detrimental effect on the environmental
quality, through six out of ten channels. The channels which appear damaging to the
environment include scale effect, energy use, manufacturing, democracy, poverty and
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foreign direct investment. However, the trade policy liberalization benefits environment
through four channels which include technique/income effect, physical capital, human
capital and control over corruption. The net impact of liberalized trade policies is
detrimental to the environment in case of carbon dioxide and composite index of
emissions. However, in case of sulfur dioxide emissions, the overall net impact appears
beneficial to the environment by lowering the SO2 emissions. This study has also
examined the performance of the model by applying standard forecasting techniques such
as within-sample and out-of-sample forecasts. The findings demonstrate that the model
tracks data well and has very small mean prediction errors. The Theil’s Inequality
Coefficient (TIC) also approaches zero in almost all cases. Thus the model can be used as
a tool for carrying out structural analysis, forecasting and policy evaluation.
Overall, in the trade-environment nexus, this study justifies the ambiguity
regarding the impact of the freer trade on the environmental quality through different
channels offering opposing effects. The findings of the present study necessitate the
policy formulation to be multi-dimensional for dealing with simultaneously occurring
positive and negative impacts.
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Chapter 1
INTRODUCTION
1.1. Background
The concept of ‘Sustainable Development’ can be viewed as a cornerstone for the
Environmental Economics. It means to meet the needs of present generations without
compromising the needs of future generations (WCED, 1987). No development can attain
longevity without ensuring the availability of enough natural capital, which could be
handed over to the future generations for enabling them to afford desired standards of
living. In the recent past, well-known scholars and researchers have tried to draw public
attention towards the issue of a rapid growth in the world economy with a lethal potential
to cause irreparable damages to the environment (Daly and Cobb, 1989: Daly, 1996;
Jackson, 2009; NEF, 2010). The concern arises from the two intuitive concepts: the first
is that more output requires more inputs so the earth’s natural resources will soon be
depleted; the second pertains to an aggressive output that causes more emissions
exceeding earth’s overall capacity (Lopez, 1994).
The ongoing climatic changes and the global warming have intrigued researchers
to explore the impact of different policies on the environmental quality. Ever since the
contemporary world started moving towards a freer the trade regime at a faster pace, it
has turned into a global phenomenon almost at par with the environmental quality. The
trade liberalization, on the other hand, has emerged as a disruptive social and economic
process that invariably contributes towards creating winners and losers. It would be naive
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to expect that an increasingly integrated world will consistently provide more benefits
than costs. After free trade has been established and expanded, people got a taste of
integration and globalization, the more complex problems began to emerge, such as
migration, labor standards and environmental degradation (Lofdahl, 2002).
The challenge at present is to explore the models and techniques that integrate
international trade and global environment. This procedure starts by characterizing the
policy debate that took place in the recent past. The then President of the World Trade
Organization, (WTO) sums it up eloquently: “Sweeping generalizations are common
from both the trade and environmentalists community, arguing that is either good for the
environment, full stop, or bad for the environment, full stop, while the real-world
linkages are presumably a little bit of both, or a shade of grey” (qtd. in Economist
1999b).
In this study, we focus on the question as to whether freer trade policy is
compatible with environmental quality standards. International trade can affect climate
change by inducing economic growth through producing and transporting goods, all of
which can lead to an increase in emissions of greenhouse gasses (GHG), which are the
main cause of rising global temperatures.
Trade can potentially drive a green economy by promoting the exchange of goods
and services that are environment friendly, such as: enhancing resource productivity,
creating economic and employment opportunities and contributing towards poverty
alleviation. In case of sloppily designed and poorly managed policies, unrestrained the
trade can, however, lead to environmental degradation, unsustainable use of resources
and seemingly unending wealth-oriented disparities. All of them combined can,
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resultantly, obstruct the transition of a green economy and the objectives of sustainable
development (UNEP, 2012).
Though the literature - theoretical as well as empirical - has become very
sophisticated and is rapidly growing, the fundamental findings of Baumol and Oats
(1988) still provide the basic insights. The main arguments of the simple partial
equilibrium model developed by Baumol and Oats are restated here. Their model
analyses the environmental consequences of freer trade in a two-good and two-country
case. The rich country has strict regulations on safeguarding environment while the poor
country does not. One good can be produced by a dirty, more polluting, process, but need
not to be, and the other one is produced by the non-polluting process. Baumol and Oats
(1980) demonstrated that the decision of poor country to produce dirty good using
polluting production method will force the price of dirtier good below and, hence, its
demand above the socially optimal level. The, resultant, rise in the production of dirtier
good will lead to a higher level of pollution. The findings suggest that when free trade is
combined with differences in environmental regulations among countries, they have the
potential to result into environmental hazards.
The international trade can potentially affect the environmental quality in many
ways. Firstly, the trade might be helpful in shifting production activities from
environmentally less sustainable places to places where it is relatively more sustainable
or the other way round. Secondly, the trade liberalization can bring changes to the
international production, consumption and income patterns and levels. These changes can
also have consequences for the world environment in a number of ways that go beyond
the shifting of production and consumption across countries. Thirdly, the trade may also
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influence the process of economic growth and development by creating additional
opportunities for the cost-effective and profitable use of limited resources.
There is no simple pattern of the association between the trade and environmental
quality. Depending upon the nature of sectors affected by the trade, such as markets,
countries and prevalent policies, trade and trade liberalization policy may be good or bad
for the environment. As a matter of reality – not just a probability - the effects may turn
out to be both simultaneously i.e bad in some ways and good in others. The net impact
can be in either direction conditional to which one dominates the other (Antweiler et al.,
2001; Dean, 2002; Copeland and Taylor, 1994, 2004; Copeland, 2005; and Frankel and
Rose, 2005).
The earlier literature divides the trade effect on environment into three parts;
scale, composition and technique effects (Grossman and Krueger, 1993; Antweiler et al.,
2001; and Copeland and Taylor, 1994, 1995, 2003). Antweiler et al. provide theoretical
framework to explore and analyze the determinants of environmental quality empirically
and to effectively divide them into scale, composition and technique effects. The scale
effect deals with the effect of a rise in production activities (e.g., GDP) on emissions that
can be harmful to the environment. The technique effect refers to the favorable effect of
more strict environmental regulations, which encourage the adoption of environment-
friendly production methods and which are put in place as income growth increases the
demand for a better environment. The composition effect elucidates how the composition
of output - the structure of economy - affects emissions, which is determined by the
comparative advantage of a country as well as by the extent of trade liberalization. This
effect could either be positive or negative, which is dependent on the country’s resource
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abundance and the stringency of its environmental policy. These are, respectively, termed
as the capital–labor effect (KLE) and the environmental regulation effect (ERE). Because
of these conflicting impacts, the theoretical relationship between the trade and emissions
remains ambiguous and warrants an empirical investigation. Since the study used a single
equation reduced form the model, the authors acknowledge that such estimation will not
differentiate the extent to which the trade policy has affected emissions because the trade
policy itself will generate three effects as discussed above. Since the trade affects
environment through these three effects, there is a need to make an in-depth analysis of
these channels. These three effects - scale, technique and composition - can further be
studied to find their respective determinants and to see through which particular
channel(s) the trade openness is more effective in affecting environment.
Relatively less stringent environmental policies indicate that the use of
environment as a factor of production is rather cheaper to firms. According to the
standard Heckscher-Ohlin (HO) the trade model, such a country with relatively lower
factor price ratio (environment related taxes as input prices) or relatively larger physical
stock of a factor (environmental goods as input) is categorized as relatively environment-
abundant economy. Trade openness would then direct to the increased specialization in
pollution concentrated goods. The main concern of the environmentalists about the
detrimental environment-abundant factor is that it would go up and hence all firms
would shift to less pollution-intensive production technologies, known as the ‘technique
effect’.
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If the Inverted–U hypothesis is taken as correct (Grossman and Krueger, 1995)
the amount of environmental damage at every point in time is, however, endogenous and
depends upon the income level of the country. This Hypothesis states that at the lower
level of income, the scale effect outweighs the income and composition effects. Thus, as
a small country grows, it observes a net growth in environmental losses. Over time,
income touches some critical level, and the latter two effects, technique and composition
outweigh the earlier, the scale. Growth then brings about a net decrease in the
environmental damage.
The literature on the environmental consequences of trade liberalization is rich
enough in the sense that many authors have studied different sets of data and utilized
different econometric techniques to study the impact of liberalized trade policies on the
environmental quality. There is still a vast scope for further research in this area.
Theoretical models, however, remain vague in providing channels through which trade
liberalization can affect the environment. Empirical literature suffers from the data
quality issues, particularly for developing countries. There are flaws in the econometric
studies that establish the causality between the two, i.e., free trade and environmental
quality. The absence of the trade openness measures has complicated the situation
further. Studies have not tackled the common problem of simultaneity properly in the
econometric setting and, therefore, delivered often unconvincing and contradictory
results. Owing to all the above mentioned reasons, further in-depth research on all
possible aspects of environmental issues is an imperative.
In this study, we are geared towards making a comprehensive analysis in terms of
the impact of trade openness on the environment with a particular focus on different
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channels to capture the missing relationships in regards to trade liberalization and the
environmental quality in the current literature. Most of the studies available in the
relevant literature use the trade volume share as a measure of trade liberalization which is
not a good measure/indicator. It might be a reason owing to which the trade-environment
relationship remains ambiguous. A better measure of the trade liberalization needs to be
used for obtaining more reliable estimates to determine the extent of this relationship and
to effectively address the ambiguity in the existing literature. Trade openness measures
are prevalent in the literature but they have not been used for analysis in this area so far.
To bridge this gap, there is a need to develop and use a plausible measure that
incorporates the trade policy indicators theoretically. In this study, we shall develop the
trade openness measure which is more relevant for examining the effects of trade
liberalization policy on the environmental quality.
Trade openness has clearly various simultaneous impacts on the environmental
quality. Since these effects can work in opposed directions, it is quite possible that
models consisting of single-equation using single variable to represent the trade
liberalization, may produce counterintuitive outcomes. A multiple-equation system is
more suitable that could capture the effects of freer trade policy on the environmental
quality through multiple simultaneous channels. Furthermore, it can be tested whether the
environmental quality is affected by trade liberalization policy alone or by some other
institutional variables such as democracy, corruption, etc.
Factor endowments that play a major role in pollution can be added in the analysis
to capture their effect in the trade-environment nexus. Since FDI leads to a greater
availability of capital in the economy for production, it can also be included to capture
8
the effect of capital accumulation along with the trade liberalization. Another
contribution of the present study is to incorporate the role of human capital.
The emission intensity is a measure of technique effect. Basically, it measures the
efficiency of productive activities. If scale and composition do not explain completely the
changes in emissions then technique effect can be interpreted as a measure of omitted
variables. Demand for any good is meaningful only when desire is matched with the
purchasing power; the same is the case with environmental good: that is, the absence of
pollution. Desire for a cleaner environment is determined by the level of awareness and
purchasing power as indicated by the income level. So, technique effect may be
decomposed into income effect, human capital effect and environment regulation effect.
We may capture the composition effect by dividing sectors into polluting and
non-polluting parts and/or imports exports into polluting and non-polluting segments. It
might also be helpful to remove the ambiguity in the direction of relationship between the
trade and environment if positive or negative.
The Asian region has shown a good advancement on liberalizing the trade policies
and lowering tariffs since the early 1990s when most of the economies introduced
reforms. These countries have also undertaken substantial initiatives for industrial
deregulation and other institutional reforms. The governments along with the private
sector have recognized that strong exports are inevitable for an overall economic
development and poverty elimination. The export-led growth strategy has become a key
thrust in each country. We are taking a panel data that consists of 8 cross-sections for the
time period of 1971-2011. Cross-section includes the main countries of SAARC and
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ASEAN. The selection of countries among these two regional blocks is based on the
availability of the data on the major variables included in the analysis. This comprises of
Pakistan, India, Bangladesh, Sri Lanka, Malaysia, Indonesia, Thailand and Philippines.
Since the free trade and the environmental quality both are the variables that
evolve over time and have cross-sectional effects, a panel of countries will be considered
to carry out the analysis in this study. The focus will be on the Asian countries, the panel
of SAARC and ASEAN regions because of rising free trade in this region. The data have
been used over the time period of 1971-2011. Among SAARC and ASEAN, only those
member countries are selected for analysis for which data on all critical variables was
available from the same data source to maintain data consistency. These countries have
shown rapid growth even at the face of weaker growth scenario internationally. This
advancement has been a result of accelerating production linkages, promoting integration
with world economy, welcoming foreign investment flows, good advancement on
liberalizing the trade policies and lowering tariffs and hosting commodity boom and
exaggerated demand from the rising middle class of Asian region. The overall results of
all such policies have been remarkably optimistic. These regions have been among the
most dynamic nations globally and have made highly significant socio-economic
developments. While challenges remain, these counties are on the right track.
1.2. Objectives of the Study
The precise objectives of the present study are as follows:
1) To construct an index of Trade Liberalization for each country included in the
panel to capture the full effect of the trade liberalization policy
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2) To find the determinants of the channels scale, composition and technique effects
3) To investigate the role of the trade liberalization in the environmental quality by
decomposing the scale, composition and technique effect and to explore the
additional channels through which Trade Liberalization may cause environmental
quality
4) To introduce the role of other institutional variables e.g. democracy, corruption
etc. along with the trade liberalization policy and to explore social indicators, such
as poverty and human capital along with income level in the determination of the
demand for cleaner environment to compliment the technique effect
1.3. The Organization of the Study
After the introduction, the rest of the study is organized as follows: The chapter 2
examines the trade and environmental policies of the panel countries. The chapter 3
presents the review of the existing literature. The analytical framework is outlined in the
chapter 4. The chapter 5 provides information on the data sources, construction of
variables and econometric methodology adopted in the study. As a practical follow-up of
the analytical framework, chapter 6 and 7 provide empirical results of the analysis. The
chapter 8 presents forecasting analysis by using within-sample and out-of-sample
techniques. The conclusion and relevant policy implications emerging from the study are
presented in the chapter 9.
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Chapter 2
HISTORICAL ANALYSIS OF TRADE POLICIES
2.1. Introduction
Trade Policy of a country has numerous consequences for its economy and social
setup. Thus, the significance of trade policy needs to be realized as it plays critical role in
determining the nature of developments within the country. Environmental concern is one
of the latest and highlighted issues. Keeping in view the importance of trade and its
impact on environment, this chapter reviews trade policies of the panel countries included
in the analysis. Thus, in this chapter an effort is made to evaluate the trade liberalization
process that evolved over time and across the panel countries.
2.2. Historical Analysis of Trade Profiles
Even during the period of a fragile economic growth at world level which was
surrounded by risks and reservations, Southern and Southeast Asian economies have
shown rapid growth. This progression has been made by escalating regional production
linkages, integrating with the world economy, reducing investment and trade barriers,
encouraging foreign direct investment (FDI), introducing commodity boom and
intensified demand from the rising middle class of Asian region. The outcomes have been
exceptionally positive; these regions have been among the most dynamic nations globally
and have made highly remarkable socio-economic developments. While challenges
remain, these counties are on the right track.
12
Asian region has made good advancement on liberalizing trade policies and
lowering tariffs since the early 1990s when most of the economies introduced with
structural reforms. These countries have also undertaken substantial initiatives for
industrial deregulation in addition to other organizational reforms. The governments
along with the private sector have recognized that strong exports are inevitable for
inclusive economic development and poverty elimination. Export-led growth has become
a key thrust in each country. Each country in this region has been integrated with the
world economy, as demonstrated by the significant growth in the commodities trade
ratios [(exports+imports) / GDP] and overall trade to GDP ratios given in Table 2.1.
Table 2.1: Trade Profiles of Selected Panel Countries
1980 1990 1995 2000 2005 2010 2011 2012
Merchandise Imports (current US$ million)
Bangladesh 2599 3618 6694 8883 13889 27821.2 36213.9 34132.1
India 14864 23580 34707 51523 142870 350234.1 464462.6 489363.7
Indonesia 10834 21837 40630 43595 75724.93 135323.5 176201.4 190225.2
Malaysia 10820 29258 77691 81963 114625 164622.1 187473.1 196615.4
Pakistan 5350 7411 11515 10864 25357.3 37806.88 44011.81 44157
Philippines 8295 13042 28341 37027 49487.42 58467.8 63692.68 65360
Sri Lanka 2037 2688 5306 7177 8833.67 13511.5 20269 19086.5
Thailand 9214 33045 70786 61924 118177.6 182921 228786.6 247590.1
Merchandise Exports (current US$ million)
Bangladesh 759 1671 3501 6389 9297 19194.4 24439.2 25112.9
India 8586 17969 30630 42379 99616 226350 302905.4 293213.5
Indonesia 21909 25675 45417 65403 86996.06 158074.5 200787.5 188146.1
Malaysia 12958 29452 73914 98229 140980 198612 228086.1 227387.6
Pakistan 2618 5615 8029 9028 16051 21409.5 25382.6 24596
Philippines 5741 8117 17502 39783 41254.68 51496 48305 51995
Sri Lanka 1067 1912 3798 5430 6346.79 8602.1 10236 9480
13
Thailand 6505 23068 56439 69057 110936.4 193305.6 222575.8 229518.8
GDP (current US$ million)
Bangladesh 18114.65 30128.78 37939.75 47124.93 60277.56 100360.1 111905.6 116033.9
India 189594.1 326608 366599.6 476609.1 834215 1708459 1880100 1858745
Indonesia 78013.21 114426.5 202132 165021 285868.6 709190.8 845931.6 876719.3
Malaysia 24937.05 44023.81 88832.45 93789.47 143533.2 247533.5 289258.9 305032.7
Pakistan 23689.7 40010.43 60636.07 73952.37 109502.1 177165.6 213685.9 224880.2
Philippines 32450.4 44311.6 74119.87 81026.29 103066 199589.4 224095.2 250182
Sri Lanka 4024.622 8032.551 13029.7 16330.81 24405.79 49567.52 59178.01 59393.06
Thailand 32353.51 85343.19 168018.6 122725.2 176351.9 318907.9 345672.2 365965.8
Merchandise Imports (% of GDP)
Bangladesh 14.35 12.01 17.64 18.85 23.04 27.72 32.36 29.42
India 7.84 7.22 9.47 10.81 17.13 20.50 24.70 26.33
Indonesia 13.89 19.08 20.10 26.42 26.49 19.08 20.83 21.70
Malaysia 43.39 66.46 87.46 87.39 79.86 66.50 64.81 64.46
Pakistan 22.58 18.52 18.99 14.69 23.16 21.34 20.60 19.64
Philippines 25.56 29.43 38.24 45.70 48.02 29.29 28.42 26.12
Sri Lanka 50.61 33.46 40.72 43.95 36.19 27.26 34.25 32.14
Thailand 28.48 38.72 42.13 50.46 67.01 57.36 66.19 67.65
Merchandise Exports (% of GDP)
Bangladesh 4.19 5.55 9.23 13.56 15.42 19.13 21.84 21.64
India 4.53 5.50 8.36 8.89 11.94 13.25 16.11 15.77
Indonesia 28.08 22.44 22.47 39.63 30.43 22.29 23.74 21.46
Malaysia 51.96 66.90 83.21 104.73 98.22 80.24 78.85 74.55
Pakistan 11.05 14.03 13.24 12.21 14.66 12.08 11.88 10.94
Philippines 17.69 18.32 23.61 49.10 40.03 25.80 21.56 20.78
Sri Lanka 26.51 23.80 29.15 33.25 26.01 17.35 17.30 15.96
Thailand 20.11 27.03 33.59 56.27 62.91 60.61 64.39 62.72
Merchandise Trade (% of GDP)
Bangladesh 18.54 17.55 26.87 32.41 38.47 46.85 54.20 51.06
India 12.37 12.72 17.82 19.70 29.07 33.75 40.82 42.10
Indonesia 41.97 41.52 42.57 66.05 56.92 41.37 44.56 43.16
14
Malaysia 95.35 133.36 170.66 192.12 178.08 146.74 143.66 139.00
Pakistan 33.63 32.56 32.23 26.90 37.82 33.42 32.47 30.57
Philippines 43.25 47.75 61.85 94.80 88.04 55.09 49.98 46.91
Sri Lanka 77.13 57.27 69.87 77.20 62.20 44.61 51.55 48.10
Thailand 48.59 65.75 75.72 106.73 129.92 117.97 130.58 130.37
Trade (% of GDP)
Bangladesh 23.38 19.65 28.21 33.21 39.63 43.42 54.51 55.29
India 15.12 15.24 22.47 26.44 41.31 48.31 54.08 54.73
Indonesia 54.39 49.06 53.96 71.44 63.99 47.49 51.31 50.15
Malaysia 110.96 146.89 192.11 220.41 203.85 169.66 166.79 162.41
Pakistan 36.59 38.91 36.13 28.13 35.25 32.87 32.92 32.59
Philippines 52.04 60.80 80.54 104.73 97.88 71.42 67.59 64.79
Sri Lanka 87.02 68.24 81.64 88.64 73.60 53.06 60.66 59.33
Thailand 54.48 75.78 90.43 124.92 148.25 135.14 149.35 148.83
Source: World Development Indicators, 2013
2.3. Country-Wise Brief Analysis of Trade Policies
2.3.1. Trade Policies of Pakistan (1990-2009)
Trade liberalization in Pakistan started in the 1980s and continued sluggishly but
without serious interruptions until 1996/97 (World Bank, 2004). During 1996/97, a new
and wide-ranging program of trade liberalization was initiated, which continued till
2002/03. Till that year, the overall maximum Customs duty was brought down to 25%.
However, actual protection taxes are a little greater than simple Customs duties due to
differences in the prevalence of an income withholding tax applicable to international
imports and domestic transactions. In the federal budget 2003/04, no major changes to
tariffs were made and there were no formally announced strategies for additional
reductions in tariff rates. However, the government has accomplished a politically
sensitive and ambitious plan of comprehensive liberalization of trade and other policies
15
which affected its agricultural sector. This is in sharp contrast with Bangladesh, India and
Sri Lanka, which are following robust protective policies in their agricultural sector. One
factor affecting trade liberalization policy in Pakistan is the recognition of the huge
volumes of illegitimate imports through India and Afghanistan that has been encouraged
by high protection. Behavior of some important indicators of trade policy is indicated in
table 2.2.
Table 2.2: A Snapshot of Trade Policy of Pakistan
Variable 1990 1995 1998 2001 2005 2009
Tariff rate, applied, simple mean, all
products (%) - 50.09 45.61 20.16 14.61 14.78
Tariff rate, most favoured nation,
simple mean, all products (%) - 50.86 47.06 20.12 14.24 13.9
Taxes on international trade (% of
revenue) 29.63 23.76 17.18 11.18 13.58 8.04
Customs and other import duties (% of
tax revenue) 44.44 31.41 21.69 15.39 18.78 -
Source: World Development Indicators, 2013
2.3.2 Trade Policies of India (1990-2009)
In India, trade liberalization started during 1991/92 and continued during the
1990s up to five years, but it lost its momentum in some crucial areas between from 1997
to 2001 (World Bank, 2004). The large number of QRs (Import licenses and import
quotas) India retained by India to safeguard its producers of consumer goods, were
abolished this period due to outside pressures initiated in the Uruguay Round. However,
many industrial import tariffs increased, anti- dumping activities were encouraged, local
content arrangements (TRIMS) were used in the auto industry and specific duties were
16
applied to protect its textile and garments industry. Till the end of the period specifically,
tariffs for protecting main agricultural produces and agro based-industries were increased
considerably. Considerable tariff reforms restarted, however, by introducing reduction in
the general maximum custom duties from 35% to 30% in the federal budget of 2002-03,
to 25% in the 2003-04 budget and up to 20% on January 8, 2004, with the abolition of
another protecting import tax (the Special Additional Duty). However, the agriculture
sector was exempted from the new trade liberalizing policy: import monopolies traded by
the state are being preserved over the major food grains and tariffs on agricultural sector
have been rising even though the average level of tariffs in industrial sector has been
falling. In recent past, India has again focused on liberalizing its trade policies. The trend
of some indicators of India’s trade policy is reported in the following table which shows
significant decrease in trade restrictions.
Table 2.3: A Snapshot of Trade Policy of India
Variable 1990 1992 1997 2001 2005 2009
Tariff rate, applied, simple mean, all
products (%) 81.56 56.41 28.9 31.86 17.01 11.46
Tariff rate, most favored nation, simple
mean, all products (%) 84.01 55.84 30.08 34.6 19.88 14.03
Taxes on international trade (% of
revenue) 28.60 24.14 21.63 15.65 14.43 11.41
Customs and other import duties (% of
tax revenue) 35.79 31.18 28.62 21.40 17.72 13.33
Source: World Development Indicators, 2013
2.3.3 Trade Policies of Philippines (1989-2010)
Trade policies in Philippines have changed from highly restricted to liberalize
during the past decade (see Table 2.4). Though the simple average of tariffs has declined
17
up to quite below the level of 10%, this coincides with protectionism policy in sensitive
sectors like agriculture. An evidence of creeping protection has recently been observed
through sophisticated NTBs (non-tariff barriers), yet again focused on agriculture related
products. Philippine has also shown backsliding on AFTA (ASEAN Free Trade
Agreement) obligations related to petrochemical products. Restrictions are much higher
on FDI (Foreign Direct Investment) flows and trade in services as compared to trade in
goods. As inscribed into the Philippine constitution, the constraints on foreign ownership
still continue to be the most evident obstacle to market-access. Overall, the government
has shown little zeal for further liberalisation since the occurrence of Asian crisis owing
to increase in internal protectionist pressures.
A weaker regulatory and institutional background at domestic level is possibly the
bigger hurdle to liberalized trade and foreign investment as compared to formal barriers
towards market-access. Post-Asian crisis, broader economic and international trade
policies seem more ad hoc and less focused as compared to the Ramos administration
during which the major liberalisation measures were initiated.
Table 2.4: A Snapshot of Trade Policy of Philippines
Variables 1989 1993 1995 2000 2005 2010
Tariff rate, applied, simple mean, all
products (%) 28.26 22.04 19.79 7.19 5.4 5.31
Tariff rate, most favored nation, simple
mean, all products (%) 28.5 22.92 20.31 7.6 6.27 6.26
Taxes on international trade (% of
revenue) .. 29.97 28.95 18.69 17.52 21.46
Customs and other import duties (% of
tax revenue) .. 33.37 31.43 20.65 20.09 23.70
Source: World Development Indicators, 2013
18
The Philippines has never played an active role like other developing economies
in the WTO (World Trade Organization). Because of the problems in executing Uruguay-
Round treaties and displaying defensiveness on numerous negotiating issues, it has been
hesitant about the Doha Round as well. It has been a principal supporter of excluding
Special Products from liberalisation policy in the net food-importing economies. It has
also been somewhat protective on trade in services sector, liberalization of some
manufactured products and various other issues.
The Philippines is discussing a mutual FTA (Free Trade Agreement) with Japan
and is also involved in combined AFTA negotiations with other countries. FTA policy of
Philippines, like Indonesia, seems ad hoc and reactive, with minute sense of strategic
policy.
2.3.4 Trade Policies of Sri Lanka (1990-2012)
Sri Lanka’s export-oriented textile sector and garment industry have dominated its
trade and its industrial sector. Industrial tariffs are low despite the introduction of an
additional charge to Customs duties and in 1997 all tariffs on textile were abolished and
since then the textile industry has been functioning under free trade settings, both in
providing garments to the domestic market and exporters (World Bank, 2004). There is
substantial safeguard, however, on some of manufacturing products along with
significant protection of some main import substitution agricultural produces, specifically
potatoes, rice, chilies and onions. Sri Lanka’s early adoption of trade liberalization policy
and the appreciation of its currency relative to the Indian currency have generated a huge
and rising trade deficit with India. With the hope of improving this deficit, it signed an
agreement of free trade with India which came in effect from March, 2000. Although, it
19
has helped Sri Lankan exports to India grow faster since then, they were still very small
up to 2002-03 and the mutual trade deficit with India has risen considerably. If the
domestic conflict of Sri Lanka is settled in future and its economy takes off with fast,
export-led development, the strength of its agriculture sector suggests that trade policies
may follow the trend of East Asian economies like Korea, with shrinking agricultural
sector relatively and benefiting from the high protection policies.
Table 2.5: A Snapshot of Trade Policy of Sri Lanka
Variable 1990 1994 1997 2000 2005 2012
Tariff rate, applied, simple mean, all
products (%) 25.76 24.3 20.01 9.5 11.58 8.67
Tariff rate, most favored nation, simple
mean, all products (%) 27.87 25.17 21.12 9.46 11.37 8.83
Taxes on international trade (% of
revenue) 26.02 19.10 15.54 11.08 13.67 20.31
Customs and other import duties (% of
tax revenue) 27.44 22.73 18.76 13.14 13.47 23.84
Source: World Development Indicators, 2013
2.3.5. Trade Policies of Bangladesh (1989-2008)
Bangladesh has a very large garment industry which is export-oriented and
established during 1980s. It has shown rapid growth since the 1990s to the present-day
(World Bank, 2004). Several of the industrial industries, however, supplying the national
market, are still heavily sheltered with tariffs ranging from 50% to over 100%. Like
India, the trade liberalization showed slower growth in Bangladesh since 1995.
20
Table 2.6: A Snapshot of Trade Policy of Bangladesh
Variable 1989 1994 1999 2002 2005 2008
Tariff rate, applied, simple mean, all
products (%) 105.36 84.9 22.26 20.98 15.47 13.89
Tariff rate, most favored nation, simple
mean, all products (%) 114.01 82.79 21.78 20.67 15.31 14.68
Taxes on international trade (% of
revenue) .. .. .. 29.93 32.39 26.55
Customs and other import duties (% of
tax revenue) .. .. .. 42.51 42.51 ..
Source: World Development Indicators, 2013
Customs duties have been decreased but these reductions were counterbalanced
by the implementation of other protective taxes on imports. Till 2000-01, these taxes
were more than one-third of total Customs revenues from import tariffs. Additionally,
Bangladesh has continued to retain a number of QRs, some apparently for trade reasons,
with the purpose to safeguard large local businesses, particularly the textile fabric related
producers. The basic extreme customs duty was reduced in federal budget 2002-03 along
with elimination of one of the para-tariffs. Further reduction was introduced in the basic
maximum level of Customs duty in budget 2003-04, but this decline was offset by more
than increases in other para-tariffs. During early 2004, Bangladesh was the most
protected country of the South Asian region as shown by its average un-weighted
protective import tariffs, with higher taxes particularly in agriculture sector. To which
extent these procedures essentially empower domestic firms to escalate their prices is,
however, indeterminate because of the illegal imports in large volumes, specifically from
India. These prohibited imports comprise traditional smuggling across borders that
21
bypass the Customs check posts and a larger volume; commonly considered to be
“official” smuggling, coming through the seaport and land port Customs check-posts,
including under-invoicing and other mis-declarations, in spite of the procedures of pre-
shipment inspection organizations.
2.3.6. Trade Policies of Thailand (1989-2009)
As compared to other old ASEAN member countries, Thailand still retains higher
protection measures. The average tariff of Thailand is considerably higher, with larger
tariff spreading and intensification; non-tariff barriers (NTBs) are significant; and tariff
barriers in services sector are substantial. Under the Thaksin administration (from 2001-
2006), no significant liberalisation of the economic policies has occurred.
Among ASEAN member nations, Thailand was the first one to follow Singapore
on the FTA (Free Trade Agreement) pathway. FTAs have now given top priority in
Thailand’s trade policy with dominated political consideration and exchanging resources.
However, the insight and efficacy of its trade policy is very much debatable.
Political resolve and representation is in abundance but the concern to economic
policy is not as much obvious. Thoughtful planning and analysis are missing in
evaluating the costs and benefits of prospective liberalizing agreements, selecting the
right trade partners and articulating the negotiating points. There seems to be little plans
and knowledge of how the FTAs are going to adjust with the wide-ranging national
agenda of the economy. The negotiations with the USA are expected to deliver somewhat
more significant, in the face of likely intense pressure by US to further liberalize Thai
22
markets. However, Thailand is not expected to catch much in return. Amongst several
factors, this is the one why the US-Thai dialogues have run into inland political problems.
Table 2.7: A Snapshot of Trade Policy of Thailand
Variable 1989 1993 1995 2000 2003 2005 2009
Tariff rate, applied, simple mean, all
products (%) 36.95 42.64 20.86 16.86 13.63 10.51 11.22
Tariff rate, most favoured nation, simple
mean, all products (%) 38.77 44.81 22.86 18.42 15.35 11.92 10.42
Taxes on international trade (% of
revenue) .. .. .. .. 9.72 7.44 4.56
Customs and other import duties (% of
tax revenue) .. .. .. .. 12.01 8.78 5.58
Source: World Development Indicators, 2013
The existing obsession with FTAs has considerably diverted Thai devotion away
from the WTO (World Trade Organization). Thailand approached the Doha Round well
below its weight. “Its positions on the major negotiating matters are mixed but mostly
pragmatic. Top priorities of Thailand are to gain market access for exports of its
agricultural and some industrial-goods. But due to protectionism policies at home, it also
has displayed defensive positions in all market-access discussions.”
On the whole, the current FTA policy appears “to be concerned more about partial
sectoral treaties than overall ambitious liberalization. This has caused distraction from
both essential domestic restructurings and from multidimensional liberalization in the
WTO framework.
23
2.3.7. Trade Policies of Malaysia (1988-2009)
With relatively open trade policies by developing-country standards, Malaysia is
considered one of most globalized economies in the world (see Table 2.8). It is still
marked with, however, peak import tariffs, tariff rate escalations and various NTBs (non-
tariff barriers) in politically sensitive goods sectors and protection” measures in the
services sector. Protectionism can also be perceived in the background of Bumiputera’s
policies to differentiate in favor of the Malay majority. The outcome is a dualistic
economy which is competitive with FDI-driven industrial export sectors on one hand and
is inefficient with import-competing local sectors on the other hand, enjoying high rates
of actual protection.
Usually, the Malaysian leaders “have reconciled the burdens of globalization
policy and the directives of Malay-dominated internal politics through professional
rationality. Dr. Mahathir’s government turned into more protective, however, after the
Asian crisis, particularly in the WTO. Trade policy fluctuated unpredictably, though it
has changed back slightly towards businesslike practicality since Abdullah Badawi
became prime minister.”
In the Doha Round, Malaysia has shown mixed positions. Its top priority has been
market access for exports of its manufactured-goods and processed-palm-oil – not least to
other developing economies. Agriculture “sector is overall of deteriorating importance as
a negotiating matter. Malaysia is, however been defensive on services sector. It was
protective and strict on developing-country concerns such as Special and Differential
Treatment (S&D) and on the Singapore issues but it has displayed much flexibility after
Cancun.”
24
Malaysia was a little late to follow the trend of FTAs. But now, it has started
negotiating mutually with India and Japan. It is part of joint negotiations of ASEAN with
third countries as well. Malaysia’s major “trade-policy task is to liberalize pockets of
protectionism through trade plus FDI opening and local governing reforms. This is
essentially a matter for unilateral accomplishment. But it can be supplemented by a
positive, flexible, market-access-oriented attitude in the WTO and by strong
implementation of WTO-plus FTAs. The hazard is that an overly protective and Third-
Worldist standpoint in the WTO,” along with weaker, trade-light FTAs, could divert
attention from essential reforms at domestic level.
Table 2.8: A Snapshot of Trade Policy of Malaysia
Variable 1988 1991 1996 2001 2006 2009
Tariff rate, applied, simple mean, all
products (%) 14.07 13.62 9.87 7.54 6.28 6.75
Tariff rate, most favoured nation,
simple mean, all products (%) 13.42 13.42 9.88 8.34 7.18 8.6
Taxes on international trade (% of
revenue) .. .. 12.15 5.08 4.08 2.06
Customs and other import duties (% of
tax revenue) .. .. 12.47 5.09 3.09 1.99
Source: World Development Indicators, 2013
2.3.8. Trade Policies of Indonesia (1989-2009)
Trade policies of Indonesia have oscillated from high protection to liberalized
regime in a relatively short period (Table 2.9). Its average un-weighted tariff rates have
declined “to under 10%. The IMF Structural Adjustment Program (SAP) settled with the
Indonesian government during 1998 has considerably accelerated trade plus FDI
liberalization and strengthened” governing reforms at domestic level in both goods and
25
services sectors. However, the agriculture sector is particularly marked with higher tariffs
rates and tariff escalations. The evidence of creeping protection policy has also been
observed recently through higher NTBs (non-tariff barriers) mainly on agricultural
commodities, steel and textiles sector products. The government’s zeal for further
openness has noticeably diminished in current years. These developments related to
trade-policy might be placed in the context of post-Asian crisis marked with severe
economic and political instability, with weak institutional setup. The high-cost “local
regulatory and official environment, unreliable implementation of property rights and
contracts, corruption, weaker government administration, minimum-wage and other labor
market laws, now pose greater obstacles to trade and FDI as compared to traditional
barriers to market-access. Additionally, the firefighting atmosphere after the occurrence
of Asian crisis prohibited a clear emphasis on priorities of trade and broader economic
policy. Thus, the trade policy seems more adhoc than it did before 1997.”
Indonesia has been less dynamic in the WTO than Thailand, Malaysia and
Singapore. Somewhat weaker capacity of trade-policy initiatives and domestic
firefighting issues has prevented it from contributing effectually in the Doha Round.
Rising domestic protection policy pressures in addition to these factors, have led to an
overall defensive stance in the round. “Indonesia’s top and dominant priority has been to
get exemption to a list of “special products” (like staples such as rice and sugar), from
liberalization. It has also been relatively protective on services sector, openness of some
manufactured products and on the Singapore matters. This has conceded its capacity to
promote market access for exports of its tropical-products and industrial-goods to” other
developed and other developing economies. Indonesia is part of combined ASEAN’s
26
FTA negotiations with other countries and has also displayed a concern in negotiating
bilateral FTAs, though the latter stance is in response to the policies of Thailand and
Singapore. Thus, Indonesia’s FTA policy, so far, seems volatile and ad hoc, showing
little sense of strategic planning.
Table 2.9: A Snapshot of Trade Policy of Indonesia
Variable 1989 1993 1996 1999 2005 2009
Tariff rate, applied, simple mean, all
products (%) 18.74 16.74 10.76 9.86 6.00 5.24
Tariff rate, most favoured nation,
simple mean, all products (%) 22.18 17.92 12.35 11.19 6.95 6.81
Taxes on international trade (% of
revenue) .. 5.55 3.49 2.74 3.14 2.10
Customs and other import duties (% of
tax revenue) .. 6.10 3.75 2.45 4.38 ..
Source: World Development Indicators, 2013
2.4. Simple Average Tariff Rates and Cross-Country Rankings
The World Bank Report (2004) ranks countries for their respective trade policies
on the basis of simple average tariff rates. Table 2.10 enlists the information pertaining to
the selected panel of this study. It provides an immediate comparison of trade
liberalization scenario across countries. In all product case, Bangladesh has the lowest
rank, within selected panel, with highest average tariff rates of 26.5% while Philippines
got the highest rank with minimal average tariffs of 5.1%. In agriculture sector case, the
picture is quite changed. Here, India is ranked at the lowest with highest average tariff
rates of 40.1% and Malaysia is placed at the highest rank with lowest rates of average
tariffs (3% only).
27
Table 2.10: Selected Panel Countries: Simple Average Tariff Rates and
Cross-Country Rankings
Data Year All Products (134 Countries) Agriculture (134 Countries)
Country Average
Tariff* (%) Rank Country
Average
Tariff* (%) Rank
2004-05 Bangladesh 26.5 5 India 40.1 7
2004-05 India 22.2 10 Bangladesh 32.1 10
2004-05 Pakistan 18.5 19 Sri Lanka 28.1 12
2002 Thailand 14.7 35 Pakistan 19.9 39
2003-04 Sri Lanka 13.4 42 Thailand 16.2 56
2002 Malaysia 8.8 86 Philippines 10.5 101
2002 Indonesia 7.2 99 Indonesia 8.4 115
2003 Philippines 5.1 120 Malaysia 3 129
Source: World Bank (2004), Trade Policies in South Asia: An Overview, Report No.
29949, page 35, *Tariff rates are inclusive of customs duties and other general and
selective protective levies (para-tariffs).
2.5. Conclusion
This chapter highlights some important aspects of foreign trade policies of
selected panel countries during recent past. The discussion elaborates that almost all
countries have undergone the trade liberalization process though its intensity and time
differs somewhat. Earlier to the trade liberalization scenario, countries were working in a
trade protected environment. Tariffs and Non-tariff Barriers (NTBs) were used along
with other trade restricting measures to protect the nascent industrial units. Later on, most
of the Asian countries opened their borders for international trade by reducing import and
export taxes and eliminating other prevailing NTBs. It helped to reduce the anti-export
bias and brought an increase in output growth.
28
Chapter 3
REVIEW OF LITERATURE
3.1. Introduction
Over the past few decades, a lot of work has been done to explore and analyze the
trade-environment nexus. A good deal of progress has been made in improving
methodologies for reviews of trade liberalization and environmental assessments. One of
the emerging lessons from this review of literature is that we should not await a perfect
way of assessing the complex and dynamic nature of trade-environment linkages. This
study represents an effort in this regard. Section 3.2 of this chapter is devoted to the
existing measures of trade policy liberalization employed in the literature. This section
will be helpful in the selection and construction of appropriate measure for trade
liberalization. Section 3.3 provides theoretical literature which links trade liberalization to
the environmental quality. Section 3.4 is devoted to the review of empirical literature on
the relationship. This section presents recent and frequently cited empirical studies
analyzing the extent to which outward orientation policy affects environmental quality.
Section 3.5 summarizes and concludes the discussion.
3.2. Trade Liberalization Measures
Most of the studies available in the relevant literature use trade volume share as a
measure of trade liberalization which is not correct. Increases in trade volumes may not
necessarily related to freer trade policies rather it might be affected by a number of other
factors like economic growth, population size, geo-political situation and capital flows,
29
etc. It might be a reason due to which trade-environment relationship remains ambiguous.
While examining relationship of trade policy with economic growth or some other macro
level variables, researchers have tried to measure trade liberalization in a number of
ways. Trade openness measures mostly prevalent in literature are discussed here.
Flow based measures of trade liberalization are the most common in literature.
These include mostly total trade to GDP ratio, import and export penetration ratios
(Balassa, 1978; Tyler, 1981; Romer, 1993). These variables are advantageous because of
capturing a broader definition of trade liberalization. Moreover, data on these variables is
easily available for a larger set of countries (Yanikkaya, 2003; Alsenia et al., 2000).
These measures are criticized for not necessarily related to trade policy and being
affected by other structural factors like geographical aspects, population size, economic
size and foreign capital flows etc.
Balassa (1985) has constructed openness index for 43 countries by utilizing the
difference between actual and predicted export volumes for a time period of 1973-79.
This index is named as ‘structure adjusted trade intensity index’ by Pritchett (1991).
Regression residuals are considered as trade orientation measure with positive values as
an indication of outward-oriented policy and vice versa. Pritchett (1991) has criticized
this measure for not having sound theoretical background and for ignoring important
variables of labor force and capital accumulation in its regression analysis.
Keeping in view the above criticism, Leamer (1988) developed relatively more
sophisticated indicators for trade policy; openness measures and intervention measures.
Pritchett (1991) has named this index as ‘endowment adjusted intensity ratio’. Leamer
30
considers more than nine variables which include capital, different type of labor and land,
oil, distance to markets etc. The residuals are taken as an indicator of restricted trade.
Leamer’s indices are advantageous being objective, continuous and comparable across
different countries (Edwards, 1989a; 1992; Santos-Paulino, 2005). However, these
measures have some limitations as well, including sensitivity to construction, limitations
in measuring endowments and for one time period i.e 1982 only (Pritchett, 1991).
Lee (1993) constructed trade openness index using pooled data consisting of 81
countries for a period of 1960-85. He regressed the import ratio on tariff rates, black
market premium (BMP), structural variables and other natural trade barriers by using the
Instrumental Variable (IV) technique. Lee’s measure of trade liberalization was criticized
for not considering the non-tariff barriers (NTBs). To overcome this drawback, Pritchett
(1991) developed an openness measure incorporating NTBs coverage ratio for the year
1985 over a cross-section of 72 countries (less developed countries). However, the
coefficient of NTBs variable appeared in contrast to theory and also statistically
insignificant.
Bhagwati (1978) and Krueger (1978b) use the term ‘bias’ to categorize a country
regarding its trade policy. It is calculated as ratio of effective exchange rate paid by
importers to effective exchange rate applied to exporters. Since it is a continuous
measure, it has advantage of avoiding dichotomized analysis of trade policy regimes
(Edward, 1989a; 1993). A large number of studies use black market premium (BMP)
over the official exchange rate as a measure of trade restriction (World Development
Report, 1991; Sala-i-Martin, 1997; Lee et al., 2004; Edwards, 1992, 1998). The argument
31
for using BMP as a measure of restricted trade is that foreign exchange restrictions act as
a barrier to trade. However, Levine and Renelt (1992) and Rodriguez and Rodrik (2001)
contended that it is unwise to use this variable as an indicator of any one policy due to the
higher correlation between the black market premium and a number of other policies and
outcomes such as high inflation, acute external debt problems, a high degree of
corruption, a less trustworthy bureaucracy and inefficient law enforcement.
One of the most important indicators of trade liberalization policy is Wacziarg’s
(2001) index of trade policy. He has constructed this index for pooled data of 54
countries and time period of 1970-89. The index is a weighted average of import duty
revenues, Sachs and Warner’s (1995a) dummy and NTB coverage ratio. Weights are
generated through regression analysis. This index has the merit that it deals with the issue
of measurement errors in observed and potential trade. It also overcomes the collinearity
problem between policy and gravity variables. It is a continuous and objective measure
which is comparable across countries.
Tariff revenue is one of the most widely used indicators of trade liberalization
(see, among others, Clemens and Williamson, 2001; Edwards, 1992, 1998; Lee et al.,
2004; Wacziarg, 2001). This measure is preferred over tariff rates because data on tariff
revenues is easily available and it is not an ad-hoc measure. It is a better indicator of trade
restriction because revenues are, by construction, weighted by imports and exports
(Pritchet and Sethi, 1994). Moreover, it is very complex to aggregate tariff rates correctly
(Anderson and Neary, 1996).
32
Rates of protection both nominal and effective have also been developed and used
to measure trade openness (Corden, 1966; Balassa, 1965). These are, respectively,
calculated as difference between domestic and international price levels of finished
products and difference between value addition in domestic and world prices expressed
as a ratio to the world level. This measure incorporates role of distortion sourced by
tariffs and NTBs. However, it is criticized for being discontinuous and requiring a very
large data set for its calculation (Edwards, 1992, 1993).
Another direct measure of restrictive trade policies is coverage ratio which
accounts for the existence of nontariff barriers (NTBs). This measure is equally important
along-with tariffs and many studies have used this variable to measure trade restrictions
(Vamvakidis, 2002; Wacziarg, 2001; Edwards, 1998). Coverage ratios are computed
either as percentage of imports sheltered by trade barriers or as percentage of products
restricted by import licenses. This measure of nontariff barriers has also some limitations.
As it only suggests that restrictions exist but severity of trade restrictions is not indicated.
Likewise, coverage ratios combine the effects of many nontariff barriers like quotas,
quality controls, licenses, which may exercise different impacts on imports. All such
factors limit the effectiveness of coverage ratio as a measure NTBs (Pritchett and Sethi,
1994 and Yannikaya, 2003).
Another important measure trade policy used in literature is Dollar’s (1992) index
for trade openness. He constructs this index for a period of 1976-85 for 117 countries
which is based on concept of relative price levels taking United States as a benchmark
country. According to this index, the country having higher price levels over a long time
period may be considered as trade protected country (Pritchett, 1991).
33
Sachs and Warner (1995a) develop the index of trade policy for 111 countries
over the period of 1970-89. It is a dichotomous measure which takes value of one if an
economy is open and zero if it is closed. Sachs and Warner’s index is constructed using
five major variables related to trade policy like tariffs, NTBs, socialist country status,
black market premium and monopoly power in major exporting sectors. Due to
considering additional variables along with just tariff and non-tariff measures, this index
has been used in a number of studies (Parikh and Stirbu, 2004; Wacziarg and Welch,
2003). This index has disadvantage of being subjective and dichotomous. Variables other
than tariff rates and NTBs relates to institutional characteristics more than trade policy
itself (Greenway et al., 1998; Harrison and Hanson, 1999).
Other important measures of trade liberalization include Greenway and Nam’s
(1998) measure of trade openness, Economic Freedom Index of Heritage Foundation
since 1995 and Guttmann and Richards’ (2004) index. Most of them have limited use in
literature because of being subjective and not based on sound economic theory.
Having discussed briefly a number of trade liberalization measures used in the
literature, it is clear that every openness measure contains methodological issues. Thus,
we cannot completely accept one measure over the other nor can we discard any measure
altogether. Nonetheless, the openness measures which are constructed on reasonable
theoretical grounds can be employed to create a consistent trade policy measure. Under
this wisdom, Wacziarg’s (2001) trade policy openness index is found to be theoretically
plausible. Wacziarg’s (2001) trade policy liberalization index is a weighted average of
three variables, i.e., imports duty rates, Pre-Uruguay NTB coverage ratio and Sachs &
34
Warner (1995a) dichotomous variable. The weights used to develop the index came from
a regression of trade volumes (as a ratio to GDP) on these three indicators plus some
gravity variables i.e. population, area and per capita income growth.
An important benefit to this method is that it circumvents both the harms of
measurement errors as the deviation of actual and potential trade shares (because it is not
constructed as a residual) and the problem of collinearity between trade policy variables
and other determinants of trade volumes. It also confines the possible effects of excluded
variables in the equation that can determine trade volumes, insofar as these excluded
variables may be assumed to bear a weak connection with the policy variables which are
incorporated in the regression equation.
3.3. Trade Liberalization and Environmental Quality: Theory
Literature has suggested that there is no one-to-one relationship regarding
linkages between trade liberalization and environmental quality. Researchers have mainly
distributed the impact into three main categories named as scale, composition and
technique effects (Grossman and Krueger, 1992, 1995; Antwieler et al., 2001). Since
each one of the above channels can either be detrimental or beneficial to the environment,
the net outcome remains ambiguous apriori.
Though the theoretical and empirical literature has become very sophisticated and
is rapidly growing, the fundamental findings of Baumol and Oats (1988) still provide the
basic insights. The main arguments of simple partial equilibrium model developed by
them are restated here. Their model analyses the environmental consequences of freer
trade in a two good and two country case. The rich country has strict regulations
35
regarding environment while the poor country has not. One good can be produced by a
dirty process and the other one is produced by the non-polluting process. Baumol and
Oats demonstrated that the decision of poor country to produce dirty good using polluting
production method will force the price of dirtier good below and hence its demand above
the socially optimal level. It will consequent upon the production of dirtier good more
than the socially optimal which will lead to higher pollution. The findings suggest that
when open trade is combined with differences in environmental regulations among
countries, it will result in environmental hazards.
Bommer et al., (1999) argue that trade liberalization is harmful to the
environment when researchers focus only on changes in production patterns and
environmental policy is taken as exogenously given. By using the political optimization
model, they show that if environmental policy is considered as politically endogenous
then trade liberalization appears to be mutually compatible with environmental quality.
Dean (2002) investigated the trade-environment relationship from a different
perspective. He developed a simultaneous equation system by combining the literature on
trade openness and economic growth and EKC. By using the pooled data set for Chinese
provinces for 1987-95, he has showed that trade liberalization harmed the environmental
quality via terms of trade effect and improved it via income effect. Further simulation
suggested that the net effect was environmentally beneficial in case of China.
Using a model of intra-industry trade between two countries in a monopolistic
competitive setting, Anouliès (2010) shows that trade openness affect the incentives of
36
regulating polluting industries. He concludes that trade integration is welfare improving
only if countries are cooperating on environmental regulation.
Antweiler et al. (2001) is of the view that relevant economic theory provides little
rationale to believe that trade liberalization affects all countries in the same way. It is
important to analyze the interactions among scale, technique and composition effects
along-with diversified national characteristics (Antweiler, 2001; Copeland and Taylor,
2004).
Theoretical analysis has highlighted that national characteristics and government
policies have the potential to alter the overall impact of trade integration. Copeland and
Taylor (2004) are of the view that stringent regulations regarding environmental
standards create distortions in comparative advantages by affecting trade flows and plant
locations. Deacon and Muller (2004) discuss the role of governance in impeding the
technique effect. Corruption renders governments unresponsive to public demands for
better environmental quality. Other studies also support that bad governance may cause
environmental degradation by reducing efficiency of environmental policies (Damnia et
al., 2003 and Welsch, 2004).
Dinda (2005) provides a possible theoretical justification for the existence of
Environmental Kuznets Curve (EKC) by using endogenous growth framework. Less
developed economies damage their environment by utilizing whole of their capital stock.
His model suggests that one part of capital should be allocated for pollution abatement.
At earlier stage of development, countries lack such investment which becomes available
37
at higher growth levels. It helps to control environmental degradation at later stages of
development.
3.4. Trade Liberalization and Environmental Quality: Empirical
Evidence
A brief review of the existing literature suggests that empirical substance on the
relationship of trade liberalization with environmental quality is still far-away from
clarity. The methodologies used to investigate the hypothesis broadly differ as do the
results (Copeland and Taylor, 2004).
Rock (1996) studied the environmental implications of open trade policies in
developing countries. He “has challenged the conventional wisdom that freer trade can
lead to a ‘win-win’ situation and showed that developing countries with more outward-
oriented policies have higher pollution intensities as compared to those with inward-
oriented policies. He used panel data for the period 1973 to 1985 over a sample of rich
and poor countries. The dependent variable was the pollution intensity or, more precisely,
the toxic chemical intensity. Since there is no simple, widely agreed upon and available
measure of trade orientation, four measures of trade orientation were used.” These
include dummy variable for open and closed economies, growth rate of export shares and
the growth rates of exports and dollar index. The first three of these four measures have
positive and significant impact on pollution intensity variable indicating that trade has
detrimental impact on environmental quality.
Copeland and Taylor (1997) tested the trade-induced degradation hypothesis
which states that international trade “can play a key role in initiating a vicious cycle in
which trade-induced environmental degradation begets income losses, and not gains.
38
Moreover, these income losses can then lead to further degradation (Daly, 1993). In a
simple two sector dynamic model, they examined the consequences of free trade when
government pollution policy is myopic and showed that free trade may have large
environmental consequences but only in certain conditions. Trade can create large
consequences because in autarky both domestic price adjustment and endogenous policy
responses work to insulate the economy from extremely clean or extremely dirty
equilibria. In free trade, domestic prices are linked to world prices and the entire burden
of adjustment now falls on policy responses. In some cases, however, adjustments in
pollution policy are over-whelmed by market driven changes in the composition of
national output. As a result, free trade can set in motion a negatively” reinforcing cycle of
real income loss and environmental degradation that could not occur in autarky.
Johansson et al. (2006) assessed the impact of liberalizing agricultural trade on
the agri-environment of United States. They used a comparative-static, spatial and market
equilibrium model termed as the U.S. Regional Agricultural Sector Math Programming
Model. Indicators of agri-environment were Sheet, Rill, Wind Erosion, Phosphorus Lost
from Crop Production and Nitrogen Lost from Crop Production. The analysis showed
that the environmental impact of hypothesized elimination of all trade barriers was small
in aggregate- less than 1%- but with important variations across regions.
Baek (2009) examined dynamic “relationships among trade, income and the
environment for both developed and developing countries using the co-integration
analysis. Results suggest that trade and income growth appear to increase environmental
quality (measured in terms of SO2 emissions) in developed countries, whereas they have
39
damaging effects on environment in most developing countries. It is also established that
for developed countries, the causal relationship seems to run from trade and income to
the environment — a change in trade and income growth causes a resultant change in
environmental quality.” On the other hand, for most developing countries, the causality is
found to move from the environment to trade and income; however, the reverse causal
relationship holds for China.
Galeotti et al. (2009) questioned the robustness of traditional integration and co-
integration techniques according to which EKC was a dead concept. They used the
system fractional integration and co-integration technique to test the existence of EKC
because these techniques are more flexible in determining the order of integration. They
used the controversial case of carbon dioxide for 24 OECD countries for the period of
1960 to 2002. The results showed that EKC comes back into life relative to traditional
integration/co-integration tests. However, the EKC hypothesis still remained a fragile
concept.
Oryan et al. (2010) analyzed quantitatively the socio-economic and environmental
impact of free trade agreements for Chili. Using a Dynamic General Equilibrium
framework, the consequences of unilateral liberalization and trade agreements with
European Union (EU) and United States (USA) are compared with the business as usual
(BAU) situation. The simulation based on Chilean coefficients showed that absolute
levels of CO2 and PM-10 emissions increased significantly under BAU between 2005
and 2020: CO2 increases by 90 per cent and PM-10 by 80 per cent. Trade Liberalization
changed this situation but not dramatically. Results showed that both PM-10 and CO2
40
decrease relative to BAU but not very significantly. In the long-run negative composition
effect dominated the positive scale effect. Under the more foreign investment (FDI)
scenario, both emissions increased by 1.5% in the long-run. In this case positive scale
effect dominates the negative composition effect. Under freer trade scenario, income for
all quintiles increased, prices declined, wages increased and have a positive effect on the
real disposable income for all households. It also has a positive effect on distribution of
incomes.
Khalil et al. (2011) analyzed the long-run relationship between environmental
quality and trade liberalization using the co-integration technique and error correction
method (ECM). They concluded on that there exists a long-run relation between the both.
Freer trade was found to have a detrimental impact on the environmental quality
measured as CO2 emissions and Arable Land.
Nasir et al. (2011) investigated “the relationship between carbon emissions,
income, energy consumption, and foreign trade in Pakistan for the period 1972–2008.
Applying the Johansen method of co-integration, the study established that there was a
quadratic long-run relationship between carbon emissions and income, confirming the
existence of Environmental Kuznets Curve for Pakistan. Moreover, both energy
consumption and foreign trade are found to have positive impact on emissions. However,
the short-run results have denied the presence of the EKC (Environmental Kuznets
Curve). The short-run results are unique to the existing literature in the sense that none of
the long-run determinants of” emissions is significant.
41
Fernandez et al. (2011) applied a fixed-effects model to examine the impact of
trade and environmental policies on air quality at ports along the U.S. Mexico border.
They had controlled for other factors influencing air quality, such as air quality of cities
near the border, volume of traffic flows and congestion. Results showed that the air
quality improved after 2004, when the diesel engine policy was applied. The Free and
Secure Trade Program (FAST) policy did reduced the PM10 and CO pollutants.
Dean (2002) investigated the trade-environment relation from a different
perspective. He developed a simultaneous equation system by combining the literature on
trade openness and economic growth and EKC. By using the pooled data set for Chinese
provinces, he has showed that trade liberalization harmed the environmental quality via
terms of trade effect and improved it via income effect. Further simulation suggested that
the net effect was positive for the environmental measures.
Galeotti et al (2006) reconsidered the evidence on EKC for CO2 by assessing
how robust it is when the analysis is conducted in a different parametric setup and
emissions data from a different source like International Energy Agency is used. It was
concluded on the basis of econometric results that existence of EKC does not depend on
the source of data in case of Co2. Using an alternative functional form, inverted-U type
relationship found for the group of OECD countries. But in case of non-OECD countries,
results depend on the source of data. EKC is slowly concave according to the IEA data
but more bell-shaped according to the CDIAC dataset.
Naughton (2010) investigated the five globalization variables (FDI, neighboring
countries wealth, cross border pollution and participation in international environmental
42
treaties) on SO2 and NO emissions. Using dataset of Europe from 1980 to 2000, spatial
autoregressive model was estimated. It was concluded that the omission of globalization
variables changed the included coefficients significantly. Including all five variables,
study showed that trade lowers emissions.
Galeotti (2003) chose an optimal growth model based on Nordhaus’s RICE
model, designed for climate change policy analysis and carried out simulations to
characterize the relationship between economic growth and emissions. Results showed
that the model did not produce an inverted-U type relationship for per capita CO2
pollution and income. Emissions strongly increase in unregulated regions with
productivity enhancing technical change. Due to green technical change, changes in the
emissions intensity induce a reduction of positive slope of income-environment
relationship but not helpful to turn it negative. But when regulation was introduced
(emission limits and emission trading), then growth and emissions tend to decouple.
Grether et al. (2008) investigated the role of trade in world-wide SO2
manufacturing. They had decomposed growth in scale, technique and composition effect
using data for 62 countries with 7 manufacturing sectors for time period of 1990-2000.
Results showed that trade had contributed to 2-3% decrease in world SO2 emissions. By
comparing with the no-trade (autarky) benchmark, trade has contributed 3-10% increase
in emissions. Adding the transport related emissions, trade has contributed 16% increase
during 1990 and 13% increase during 2000 in world emissions as compared to autarky
benchmark. Decrease in 2000 as compared to 1990 was mainly due to shifting of dirty
production towards cleaner countries.
43
Grether et al. (2009) constructed data bases on SO2 intensities in an exercise of
growth decomposition of world emissions. For the sample consisting of 62 countries over
the period of 1990-2000, estimates showed that manufacturing activity increased by 10%
and emission declined by 10%. Large countries like China and India were clean in terms
of emissions per labor unit but dirty in terms of emissions per dollar.
Lu (2010) estimated annual SO2 emissions for China since 2000 using
technology-based method. They showed that due to the use of improved technology,
emissions growth had slowed down. SO2 emissions increased by 53% from 2000 to 2006
at an annual growth rate of 7.3% but it declined from 2006 onward. Decline was mainly
due to the use of Flue-Gas Desulfurization (FGD) device in power plants in response to
the new government policy. The decline in growth was also reflected in decreasing trends
in SO2 and other indicators over East Asia.
Galeotti et al. (2009) investigated the relationship between CO2 emissions and
GDP and has forecasted on the basis of results. They found that the empirical relationship
between the two is better described by the non-linear functions e.g Gamma and Weibull
specifications as compared to usual linear and log-linear functional forms. Despite the
decreasing marginal propensity to pollute, forecasted values showed that future emission
will increase at global level. The average world CO2 growth was estimated as 2.2%
annually between 2000 and 2020. It will grow at an annual rate of 3.3% during the same
period for developing countries at the edge of industrialization.
Lovely et al. (2011) examined that technological innovation influences the
environmental regulations in non-innovating countries with a particular focus on coal
44
based power plants. They had used the adoption of environmental regulation as a
dependant variable opposed to some measure of environmental quality. Results showed
that an increased excess to technology via trade increased the likelihood that a country
will adopt environmental regulation. Though, richer countries adopt first, developing
countries adopt at rather earlier stages as compared to the developed countries. SO, EKC
is satisfied, peak is being shifted to the left.
Cole et al. (2003) had examined the determinants of trade induced composition
effect. Econometric results concluded that trade induced composition effect is small as
compared to the scale and technique effects and the direct composition effect. Magnitude
and sign of the results depend on the pollutants and whether it is measured in terms of per
capita emissions or pollution intensities. In case of per capita emissions results were mix
but in case of pollution intensities, trade had a positive impact on environment.
Lee et al. (2005) had examined the income effect on different measures of
environment and environmental sustainability, controlling for population density and
civil-political liberty. By using the ESI (Environmental Sustainability Index, constructed
by the University of Yale) and decomposing them in pollution measures and other
measures of environmental sustainability, it was concluded that at higher income levels
pollution measures tend to improve but other eco-efficiency measures decline at higher
income levels. They concluded that EKC should be renamed as Pollution Kuznets Curve.
Managi et al. (2009) had investigated the trade-environment relationship to find
the overall impact by treating income and trade as endogenous and using instrumental
variable approach. They found that overall impact depended on the country and pollutant
45
itself. Results for non-OECD countries showed that 1% increase in trade openness caused
0.92% increase in SO2 emissions, 0.88% increase in CO2 emissions and 0.16% decrease
in BOD emissions. While in case of OECD countries, it caused 2.23% decrease in SO2
emissions, 0.19% decrease in CO2 emissions and 2.22% decrease in BOD emissions.
There was a sharp difference in results for CO2 and SO2 between OECD and non-OECD
countries. Trade openness had beneficial impact on environment of OECD countries but
it had detrimental impact in case of non-OECD countries. They also found that there is
difference between short-run and long-run elasticities which implies that dynamics
should be taken into account. Again, trade openness affected environment via ERE and
KLE (FEE) and former had been stronger than the latter.
Cole (2006) had empirically estimated the impact of trade liberalization on energy
use. They had utilized the theoretical principles given by Antweiler et al. (2001). Sample
consisted of 32 developed and developing countries over the period of 1975-1995.
Results suggested that positive scale effect dominated the negative technique effect
leaving trade openness affecting energy use positively in the mean country of the sample.
Feridun (2006) investigated the role of trade liberalization on pollution and
resource depletion in Nigeria. They found that pollution was positively related to trade
openness measure and scale of the economy while it was negatively related to income
and composition. For resource depletion case, trade openness, scale, incomes were found
positively while composition was negatively related.
46
Marzio Galeotti et al. (2004) surveyed the literature regarding interactions
between climate and trade policies. The concept of trade here used is broader than just
exchange of goods and services. It was found that different measures of economic
globalization affect environmental quality in several ways and through multiple channels.
From the perspective of trade liberalization, major policy issues like foreign investment,
technology diffusion and trade expansion provide the primary impulse of economic
integration.
Grether et al. (2006) had measured the pollution content of trade and decomposed
that into further three components. One was the ‘deep component’ which consisted of the
variables of gravity model other than trade. Other two parts were factor endowments and
environmental policies which were of main interest in the present scenario. Analysis had
been done for ten pollutants covering 48 countries and 79 ISIC 4-digit sectors over the
period of 1986-88. Results supported the PHE because of the stricter environmental
standards in the North, PCI (pollution component of imports) increased. But,
simultaneously, due to the factor endowment effect (FEE), PCI decreased because North
is well endowed with the capital and pollution intensive activities are also capital
intensive. Since, most of the trade at world level is intra-regional with high share of
North to North trade; PHE and FEE were small as compared to the other deep
determinants of trade.
Frankel and Rose (2002) analyzed the causality between trade openness and some
measure of environmental quality. They argued that the link between the both might be
due to endogeniety of trade and not causality. They had found support for EKC.
McAusland (2008) discussed the direct and indirect effects of globalization (trade and
47
FDI) on the environmental quality. Direct effects on environment include the emissions
and environmental damage during the transportation of goods.
Hossain (2011) tested the causal relationships among CO2 emissions, energy
consumption, economic growth, trade liberalization and urbanization. The panel analysis
consisted of NICs (Newly Industrialized Countries) for the time period (1971-2007).
Results showed the presence of co integration vector among the variables. Granger
Causality test verified that there is only short-run unidirectional causal relationship
among the variables and causality runs from economic growth and trade openness to CO2
emissions, from growth to energy consumption, from trade to growth, from urbanization
to growth and from trade openness to urbanization. Long run elasticity of CO2 emissions
with respect to energy use was greater than the short run elasticity. But in case of other
variables, environmental quality is a normal good.
Bommer et al. (1999) analyzed theoretically and empirically on the data set of US
trade sector that free trade policy was beneficial to the environment when environmental
policy is endogenous. The improved environmental quality was an optimal response to
the trade liberalization policy.
Judith M. Dean et al. (2009) had tested for PHH by estimating the determinants of
location choices for Equity Joint Ventures (EJVs) in China. Results showed that EJVs in
highly polluting industries funded by Hong Kong, Taiwan and Macao were attracted to
the lower environmental standards. But, on contrast, EJVs by other than these sources
were not attracted by the lower environmental standards, negating the PHH.
48
3.5. Conclusion
This chapter shows “that the empirical evidence on trade-environment remained
mixed and controversial despite using sophisticated econometric techniques and taking
into accounts important theoretical advancements. The existing literature demonstrates no
consensus regarding the effects of trade policy liberalization on environmental quality.”
These mixed results have created the need for further research in the area in a
more rigorous and comprehensive way. It is also learnt that there is limited work on the
trade liberalization-environment relationship for the SAARC and ASEAN regions. The
lack of empirical work in this area is quite astonishing given that trade policy reforms are
part of almost all macroeconomic policy decisions in this area. Since very few studies
have been conducted in this particular region in a panel data framework, exploring the
additional channels in trade-environment nexus, “the present study is expected to make a
significant contribution to the existing knowledge. The study is an endeavor to improve
upon the flaws and discrepancies related to the subject matter and to provide a better
rationale for the trade policy and environmental quality relationship.
49
Chapter 4
ANALYTICAL FRAMEWORK
4.1. Introduction
This chapter provides theoretical base for our empirical study as it develops
theoretical model that will be empirically analyzed in the succeeding chapters. The
chapter consists of four sections. Section 4.2 outlines briefly the theoretical framework.
Section 4.3 discusses the effects of trade policy liberalization on imports and exports and
provides the theoretical base for construction of trade policy liberalization index. Second
4.4 presents the theoretical underpinnings for construction of our empirical model to
examine the relationship between trade policy liberalization and environmental quality.
For that purpose, different channel variables are discussed through which trade policy
liberalization is presumed to affect environmental quality. Section 4.5 recapitulates the
whole model and section 4.6 concludes the discussion.
4.2. Framework of Analysis: An outline
This section provides a brief sketch of our theoretical framework which will be
elaborated in subsequent sections of this chapter. The proposed theoretical frame is
summarized in the organogram given on next page. Our theoretical framework, basically,
consists of two parts. In the first part, we are going to model trade equations to ascertain
the effect of trade liberalization policies on exports and imports. The trade liberalization
policy index will be constructed from these trade equations. In the second part, by using
50
this index, we shall model the effect of trade policy liberalization on environmental
quality through different channel variables.
Trade liberalization has both the direct and indirect effects on environmental
quality. Direct Effects are those that are related to the physical movement of traded goods
e.g emissions from transportation, bio-diversity losses and leakages in sea waters. The
indirect effects are traditionally categorized as the scale, composition and technique
effects (McAusland, 2008). The focus of present study is on indirect effects and not on
the direct effects.
Indirect effects are those that come through some variables other than trade
itself. Trade liberalization affects environmental quality through many channel variables.
Grossman (1995) distinguished three channels through which economic growth might
influence environmental quality. Most of the studies so far, have focused on the
economic determinants of environmental quality. This study contributes by exploring
some additional socio-economic and political channels in addition to the traditional scale,
composition and technique effects. Debate on environmental consequences of trade
liberalization provides ambiguous results. This is a challenge for the researchers and
suggests being more explicit about the linkages between trade liberalization and
environmental quality by specifying more clearly the channels related to these variables.
In this section, the theoretical basis of the channel variables are discussed which are
supposed to link trade liberalization with environmental quality. These channel variables
are hypothesized to account for most of the changes in environmental quality due to the
trade liberalization. Unless, we shed light on the causal mechanism involved, the work
will be of little use in serving us understand how trade liberalization affects environment.
51
Theoretical predictions of the effects of globalization are often ambiguous. Therefore,
empirical work must provide evidence of the actual impact on the environment (sources
of the ambiguities are discussed by Copeland and Taylor, 2003).
Figure 4.1: Graphical Representation of Analytical Framework
Scale Effect
Trade
Liberalization
Index
Composition
Effect-I (Physical Capital)
Technique
Effect
Human
Capital
Democracy
Energy Use
Foreign
Investment
Environmental
Quality
Corruption
Poverty Composition
Effect-II (Manufacturing)
Trade Equations
Import
Equation
Export
Equation
Trade Share
Equation
52
4.3. Trade Liberalization Index
In this part we will model trade equations to examine the effect of trade
liberalization policy on exports, imports and trade balance. We will follow Wacziarg
(2001) for construction of the index because it appears theoretically sound and overcomes
most of the drawbacks involved in many of the trade openness measures. From these
trade equations, we will construct a trade liberalization index which will be used to model
the effects of trade liberalization policy on environmental quality through various channel
variables.
4.3.1. Exports Supply Function
Traditionally, exports supply depends on international competitiveness
(Senhadji, 1998; Perraton, 2003), which is measured by relative prices at home and
abroad (RER). It also depends on world income (Y*).
X = f (RER, Y*)
By introducing the trade liberalization measures tariffs on exports (Tariffx) and a
liberalization dummy variable (D), the augmented form will be given as under (Wacziarg,
2001).
X = f (RER, Y*, Tariffx, D)
D is the dummy variable to incorporate the effects of non-tariff barriers. The
tariff rate is an additional measure of trade liberalization. Changes in tariff rates are,
however, incomparable across time as the tariff base changes, widening the total tariff
lines (Yen, 2009). Therefore, we have used average tariff rate (ATR) proxied by import
tax revenue divided by total imports. To check robustness of the results, the role of terms
53
of trade (TOT) and foreign exchange market distortions (FEMD) is also included
(Sarmad & Mahmood, 1985).
X = f (RER, Y*, Tariff, D, ToT, FEMD)
By adding some interacting terms on right hand side and expressing exports as a
ratio of GDP, the following equation for export supply will be estimated.
it
x
it
FEMDToTYDRERDDTRFYRERY
X 87
*
6543
*
210 **
(4.1)
4.3.2. Imports Demand Function
Traditionally, imports demand depends on international competitiveness, which
is measured by relative prices at home and abroad (RER). It also depends on domestic
product or income level (Y) (Arize and Malindretos, 2012).
M = f (RER, Y)
By introducing the trade liberalization measures tariffs on imports (TariffM
) and
a liberalization dummy variable (D), the augmented form will be given as under
(Wacziarg, 2001).
M = f (RER, Y, TariffM
, D)
To check robustness of the results, the role of terms of trade (TOT) and foreign
exchange market distortions (FEMD) is also included.
M = f (RER, Y, TariffM
, D, ToT, FEMD)
54
By adding some interacting terms on right hand side and expressing imports as a
ratio of GDP, the following equation for import demand will be estimated.
it
M
it
FEMDToTYDRERDDTRFYRERY
M 876543210 **
(4.2)
4.3.3. Construction of Trade Liberalization Index
By using the above mentioned export and import ratio equations, total trade
equation can be given as under.
it
XM
it
FEMDToT
YDYDRERDDTRFTRFYYRERY
TR
1110
*9876543210 ****
(4.3)
By estimating the above trade ratio equation, we will obtain coefficient estimates
of the variables. For the sake of construction of trade liberalization index, we will pick
parameter estimates of trade policy variables i.e export duties (TRFX), import duties
(TRFM
) and trade liberalization dummy (D) to incorporate the effect of non-tariff
barriers. These estimated values will be used as weights assigned to each trade policy
variable and index will be constructed as follows.
(4.4)
TLI is Trade Liberalization Index. Each weight indicates the power of a variable
with which it influences total trade. Thus, multiplying each trade policy variable with its
weight (the predicted coefficient for the entire sample period) and adding up all these
series will provide us with a trade liberalization index. This index is equal to that part of
trade shares which is attributable to the effective impact of trade policies.
DTRFTRFTLI XM654
55
An important benefit of this method is that it overcomes problems of
measurement errors in constructing trade policy liberalization index as a deviation of the
actual and potential trade shares and the harms of collinearity between trade policy
variables and other determinants of trade volume.
4.4. Trade Liberalization and Environmental Quality
Appendix-B provides the mathematical derivation of the baseline theoretical
model analyzed in this study.
4.4.1. Scale Effect
Most of the economic activity damages the environment whether in extracting
the raw materials from the nature, harvesting renewable resources or generating
pollution/emissions through production process. Increase in the scale of the economic
activity means increasing the damage to the environment unless regulations are in place.
Regarding trade environment nexus, Scale Effect refers to the increase in the
production and consumption after trade has been liberalized. It means that the overall size
expands in open economies which have consequences for the environmental quality.
There is consensus in the literature that more production is detrimental to the
environment at the initial stage. For example, NAFTA made it possible to increase
manufacturing in Mexico, which had created environmental problems near border areas.
But there is debate regarding environmental consequences at the later stage of the
economic growth. This is known as the famous Environmental Kuznets Curve (EKC)
hypothesis (Grossman, 1991). The basic EKC hypothesis is that an ‘inverted U’
relationship exists between some measure of environmental degradation and income per
56
capita. The underlying argument is that beginning at very low per capita income levels
environmental degradation is low but as national incomes rises, environmental
degradation increases until, at a certain level of income, environmental degradation
begins to decline for further increases in income. There are many factors that have been
hypothesized to account for the EKC relationship including changes in the mix of
outputs, the mix of outputs, greater production efficiency and decreased levels of
emissions per unit of output (Stern, 2003).
It has again two parts. Trade effects economic growth (size of the economy) and
economic growth in turn causes impact on environment. Studies regarding trade’s effect
on growth and studies regarding growth’s effect on environmental quality are discussed
here.
There exists extensive theoretical literature aiming on the relations between the
economic growth and the environmental degradation (Gradus and Smulders 1993;
Grimaud 1999; Mohtadi 1996; Hettich 1998; Dinda 2005; Rosendahl 1996, Endress,
Roumasset and Zhou 2005; Perez and Ruiz 2007; Ricci 2007; and Grimaud and
Tournemaine 2007). Most of the authors assume that pollution levels are a function of
aggregate production levels. In these “models, the degradation of environmental quality
either depresses the utility of the consumer or lowers the productivity of the factors of
production. Most of these models are constructed in a one sector Ramsey-Solow
structure. Environmental degradation is regarded as the social byproduct of the use of
modernized machineries in the production sector because the operation of these
modernized machines involves the use of pollution increasing raw materials like oil, coal
etc.” Lopez (1992) suggests a model capturing two cases: when the environment is an
57
input in future production and when it is not. Output decreases are necessary to decrease
pollution in the latter case. However, in the former case, growth can cause a decrease in
pollution since the environment has an opportunity cost in future production.
Even if trade affects growth positively, there is a possibility that growth might
be greener that it may not cause harm to the environment through introduction of better
technologies and enhanced affordability. We need to test if growth caused by trade is
detrimental to the environment or not. It all depends on the comparative advantage of the
country whether it lies in pollution intensive goods or cleaner goods.
4.4.2. Composition Effect
Trade may affect composition in two ways; by allowing physical capital
accumulation and by changing the sectoral output shares, so we may decompose
composition effect in two of these parts.
The composition effect refers to the changes in the structure of the economy as a
result of trade liberalization. It may, in turn, influence environmental quality favorably or
adversely depending upon the comparative advantage of the country. There are two main
hypothesis regarding comparative advantage in trade-environment nexus; Pollution
Heaven Hypothesis (PHH) and Factor Endowment Hypothesis (FEH). The composition
effect is the channel through which the Pollution Heaven Hypothesis (PHH) would cause
the pollution levels to rise or decrease. PHH postulates that due to the lax environmental
standards, developing countries have a comparative advantage in pollution intensive
commodities. In freer trade conditions, they specialize in dirty goods and serve as a
pollution heaven for developed nations. So according to PHH, trade induced composition
58
effect will be affecting environmental quality adversely in developing countries and
favorably in developed countries.
By contrast, the factor endowment hypothesis (FEH) states that source of
comparative advantage lies in the factor abundance and not in the environmental
standards. Environmental regulations either do not affect or affect little the trade patterns.
According to FEH, capital intensive countries have a comparative advantage in capital-
intensive good which are more polluting as compared to labor-intensive goods (Mani and
Wheeler 1998; Antweiler et al 2001). Some authors like Bretschger and Smulders (2007),
Mohtadi (1996), Hettich (1998), Perez and Ruiz (2007) etc. assume a direct relationship
between the stock of physical capital and the level of environmental pollution when
whole physical capital stock is entirely utilized. If this is the case then developed
countries should specialize in capital-intensive sectors while developing countries should
specialize in labor-intensive goods. Gale and Mendez (1998) attempted to assess the
importance of composition effects in predicting cross-country differences in pollution
levels. Their results suggest a strong link between capital abundance and pollution
concentrations even after controlling for incomes per capita. If capital accumulation
means replacement of old machines by new eco-friendly machines, then environmental
pollution should change negatively with rise in capital accumulation.
Theoretical literature puts forward that physical capital accumulation is a critical
channel through which trade liberalization can affect growth of the economy (Parikh and
Stirbu, 2004; Wacziarg and Welch, 2003; Wacziarg, 2001; Harrison, 1996; Levine and
Renelt, 1992). Since freer trade leads to factor price equalization, when labor-abundant
countries engage in freer trade, they experience a rise in wage to rental ratio as a result of
59
increase in wage rate and decrease in price of capital. Translated into a dynamic
framework, this will encourage a rise in investment leading to capital accumulation. The
gains of trade liberalization in case of developing closed economies are large because
most of these countries are labor-abundant. Literature has acknowledged a number of
channels between trade liberalization and physical investment (Baldwin and Seghezza,
1996). Trade liberalization affects investment through the size of market. New firms
bring about fixed investment in exports market. With the removal of restrictions,
encourage the imports of intermediate capital goods while lower tariffs increase the rate
of return by reducing the cost (Romer, 1994; Murphy et al., 1989). These imported
capital goods are enriched with modern technologies, which further fuel economic
growth (Wacziarg, 2001; Lee, 1995). Traded sector is relatively more capital intensive
than the non-traded sector and the competition in the international market for capital
goods lowers the price of capital, which promotes the capital accumulation process.
Trade liberalization also increases capital stock and investment through efficiency gains
(Baldwin, 1992).
4.4.3. Technique Effect
Technique effect means the emission intensity. Most of the literature takes
income levels to proxy technique effect. Technique effect is the second process along
with the scale effect, that together result in EKC. Grossman and Krueger (1993) are the
first one to use the concepts of scale, composition and technique. The original technique
effect in the Grossman (1995) is the average emission intensity. Technological progress
often accompanies economic growth. That gradually and generally leads to the
substitution of obsolete and dirtier technologies with new and cleaner ones leading to
60
lower emission intensity. It has a positive effect on environment which is known as the
technique effect of trade liberalization on environmental quality. In the light of the micro
and macro level studies’ evidence that income and environmental quality are positively
correlated, it appears quite logical that income gains from trade will translate into greater
demand for environmental quality. One possible channel, through which individuals
express this demand, is through calls for tighter environmental regulations. We expect the
relationship between per capita income and pollution to be negative since increasing
economic prosperity leads to high public demand for pollution abatement and provides
the necessary resources to achieve it.
Gains from trade hypothesis suggests that trade has a positive impact on income
levels. Empirical testing has also verified the positive technique effect. Antweiler et al.
(2001) find technique elasticity between -1.577 and -0.905 using data on SO2
concentrations for 108 cities of 43 countries. On the basis of their findings, if trade
liberalization raises income level by 1%, SO2 concentrations will reduce about 0.9% to
1.6% due to the technique effect. Dean (2002) finds that 1% decrease in trade restrictions
will cause 0.09% increase in income growth which in turn reduces emissions (COD in
China) by 0.03%.
4.4.4. Energy Use
The neglect of energy use in the trade-environment debate is very surprising.
Only a few studies investigate the impact of trade liberalization on the energy use.
Energy use, particularly, the burning of fossil fuels is the major cause of many pollutants.
Since the impact of trade liberalization on air pollution vary across different pollutants
61
depending upon the pollutant-specific characteristics (Cole and Elliot, 2003), it is
obviously very crucial to understand the underlying causes of air pollution namely energy
use. Energy consumption should be accounted for when analyzing the impact of trade
liberalization on environmental quality. Trade itself fosters economic growth which has
consequences on the energy consumption. Increased energy use is fueled largely by
industrial and trade expansion. Cole (2006) investigates the impacts of trade liberalization
on energy use and energy intensity using the theoretical principles given by Antweiler et
al. (2001). He finds that trade liberalization has positive impact on both energy use and
energy intensity for the mean country. Trade liberalization increases energy use in a
country with higher capital-labor ratio and low income level, while it decreases the
energy use in a country with lower capital-labor ratio and higher income levels. Baek
and Kim (2011) find that causality holds from trade liberalization to energy use and
fluctuations in trade liberalization cause changes in energy use.
Baek and Kim (2011) find a positive long-run association between
environmental quality (CO2 emissions) and energy consumption which indicates that air
pollution is likely to increase as a country’s energy consumption increases. Amin et al.
(2012) find that in Bangladesh energy consumption lead to increase pollution. Alam et al.
(2011) find that in case of India, bidirectional causality exists between energy
consumption and CO2 emissions both in the long run and in the short run. Most of the
studies using different data sets and different econometric techniques confirm empirically
the existence of long-run relationship between energy use and environmental quality (e.g
Apergis and Payne, 2010; Wolde-Rufael, 2010; Saboori and Solaymani, 2010; Soytas et
al. 2007).
62
4.4.5. Foreign Investment (FDI)
Foreign investment is another possible channel through which trade
liberalization can affect the environmental quality. Does FDI foster trade liberalization?
The answer is not that straightforward. It depends on the type of FDI or other factors like
size and conditions of it. FDI acts both as a substitute and a compliment to trade
liberalization. It works as substitute because goods are produced that cannot be imported
due to restricted trade. Freer trade helps investors to get the imports at lower costs. In this
way, FDI works as a compliment of trade liberalization. Theoretical literature suggests
that FDI is encouraged in open economies as compared to the closed economies
(Bhagwati, 1978; Singh and Jun, 1995). There are many empirical studies that tried to
verify the exact nature of relationship between the both. Several of them end up with
different often conflicting results.
Many studies are conducted to investigate the economics of FDI so far.
Theoretical work into this area can broadly be grouped into two parts. One part provides
the rationale of FDI-Growth nexus (Lucas 1988, Romer 1986, Rebelo 1991, Helphman
and Grossman 1991). Other part has linked FDI to environment known as FDI-
Environment nexus (Pethig 1976, Copeland and Taylor 1994 and 1995, Porter and van
der Linde 1995). Under this nexus, two main phenomenon are investigated; Pollution
Heaven Hypothesis (PHH) and Porter Hypothesis. PHH asserts that under globalization
conditions relatively lower environmental standards in developing countries serve as a
comparative advantage to attract foreign capital in pollution intensive sectors. On the
other hand, the Porter hypothesis claims that, since environmental quality is a normal
63
good, as income increases with FDI inflows, developing countries tend to adopt more
strict environmental regulations (Porter and van der Linde 1995).
Empirical testing provides mixed results regarding the relationship between FDI
and environmental quality. Copeland and Taylor (1994) were the first to model this PHH
and their work was supported by other studies like He (2006), Spatareanu (2007), Cave
and Blomquist (2008) and MacDermott (2009b). However, other studies like
Jayadevappa and Chhatre (2000) were unable to support this claim. Research undertaken
by Dean (1992), Wheeler and Moddy (1992), Zarsky (1999), Eskeland and Harrison
(2003), Smarzynska and Wei (2004), and Dean, et al. (2005) found little evidence for the
pollution haven hypothesis. Baek and Kim (2011) find FDI to have little long-run effect
on the environment in both developed and developing countries. Conversely, some
studies support ‘the pollution halo hypothesis, which means that FDI brings improvement
in environmental performance of developing countries. Blackman and Wu (1998) find
that foreign investment in power sector in China increased energy efficiency and reduced
emissions. Letchumanan and Kodama’s (2000) case study argues anecdotal evidence of a
transfer of cleaner products and processes by a foreign investor to a developing host
country. Eskeland and Harrison’s (2003) study claims that foreign firms are significantly
more energy efficient and adopt cleaner types of energy than local firms.
4.4.6. Human Capital
Human Capital is another possible and relatively less explored channel through
which trade liberalization can affect environmental quality. Trade, through this channel
64
can lead to the green growth as opposed to the channel of physical capital. Trade
liberalization has positive impact on human capital accumulation by modifying the
relative returns to factor inputs.
In a theoretic model, Findlay and Kierzkowski (1983) and Lucas (1988) find that
trade liberalization will discourage human capital formation in developing countries. In
models of technology transfer and firm heterogeneity, trade liberalization will enhance
human capital formation in developing countries. In particular case of developing
countries, Lai (2010) predicts that trade liberalization may or may not have a differential
impact on human capital formation. He empirically finds that trade liberalization has a
differential effect on human capital formulation. In high-literacy developing countries,
which have comparative advantage in producing moderately skill-intensive goods, trade
liberalization is human capital enhancing. But trade liberalization discourages human
capital formation in low-literacy developing countries, which have become increasingly
specialized in producing labor-intensive goods.
Human Capital may also be a prerequisite for a higher demand of a clean
environment. Hence, environmental quality is supposed to be related to the level of
education or human capital in a country (Lamla, 2006). Education makes the people
aware of the environmental issues and of the need of environmental protection.
Therefore, educated people can protect the environment on scientific basis. The positive
relationship between human capital accumulation and environmental quality has also
been supported by empirical testing. Several studies include measures of education as
control variables in their respective setup (Torras and Boyce, 1998; Klick, 2002). Many
studies have supported a positive correlation between education and environmental
65
concern (Schahn and Hotzer 1990; Arcury and Christianson 1990; Howell and Laska
1992; Scott and Willits 1994). Shen and Saijo (2007) also find in a pooled study that high
income and high education level are positively related to environmental concern by
improving awareness and affordability. Torras and Boyce (1998) tested impact of
income, literacy rate, Gini coefficient (income inequality) on environmental pollution.
Results confirmed that the literacy rate has a significant negative effect on pollution
particularly in low income countries. Petrosillo et.al (2007) finds that the tourists’
attitudes during a visit to Marine protected areas depend greatly on their education level.
Clarke and Maantay (2006) concluded that the participation rate of the people in the
recycling program conducted in New York City and its neighborhood is highly dependent
on the education level of the participators.
Though human capital accumulation is one of the determinants of environmental
quality, human capital accumulation itself is also affected by the environmental quality.
Pollution casts negative effects on human health and decreases their learning ability.
Noise pollution causes disturbances at the academic institutes. Margulis (1991) finds
substantial positive association between lead in air and blood lead levels. He further
displays that children with higher blood lead levels have a lesser cognitive development
and necessitate additional education. Kauppi (2006) finds that methyl mercury, whose
contact to human comes from fish intake, may lower the learning capacity of the children.
Air pollution also causes problems associated with eye sight and functioning of the brain.
Gradus and Smulders (1993) consider this negative effect of environmental pollution in
an otherwise alike Lucas (1988) model.
66
4.4.7. Democracy
Development of a country depends very much on the role of institutions (Rodrik,
1999, 2000; Rodrik et al., 2002; Hall and Jones, 1999; North, 1991). It is found that the
countries with stronger institutions to handle the conflicts regarding trade gain more from
trade liberalization (Rodrik, 1998a). Democracy is one amongst them. It is an important
channel through which trade liberalization can affect the environmental quality.
Advocates of trade liberalization are of the view that it brings prosperity which in turn
fosters democracy and help in providing the social and cultural values for environmental
protection (Salinas, 1994). Opponents of trade liberalization argue that it weakens the
process of democracy and environmental protection by eroding national controls over
domestic policies (Khor, 1993).
Literature has documented a positive effect of trade liberalization on democracy
since open economies has been found more democratic than the closed economies
(Lopez-Cordova and Meissner, 2005). Theoretically this link is established on the
argument that trade liberalization promotes growth which, in turn, foster democracy by
strengthening the middle class demanding expanded political rights (Lipset, 1959).
According to some other studies, the opposite may also hold (Li and Reuveny, 2003). In
general, trade liberalization is argued to have positive effect on democratic process
through integration with advanced and democratic nations and greater demand from the
international institutions. However, the ultimate effect of trade liberalization on the
democracy is ambiguous and hence requires empirical testing.
67
The relationship between democracy and environmental quality has been found
somewhat uncertain. The literature regarding political determinants of environmental
quality is relatively limited and still developing. However, a consensus seems to be
developing that democracy leads to higher environmental quality. Thomas Drosdowski,
(2005) has theoretically analyzed the effect of democracy on the environmental quality.
They conclude that under the median-voter hypothesis, perfect democracy would
establish a compromise on these three issues between both extremes, i.e. moderate
pollution, growth and inefficiency. This finding is contrasted with one of the conclusions
of Eriksson and Persson (2003), specifically that power shifting to the less wealthy
individuals materializes in less abatement and more pollution. Carlsson and Lundstrom
(2001) discover a negative impact of political freedom on CO2 emissions. Deacon (1999)
finds a negative relationship between democracy and lead levels. Bernauer and Koubi
(2004) find a positive and quite robust relationship between democracy and
environmental quality.
4.4.8. Corruption
Trade liberalization is also supposed to affect environmental quality through the
channel of institutional quality which is captured via level of corruption. Literature
provides four main channels through which trade liberalization causes corruption levels.
First one is the less and fewer trade restrictions (Krueger, 1974; Gatti, 1999). Second is
enhanced foreign competition (Ades and Di Tella, 1999). Third is increase in
international investment (Wei, 2000; Larrain and Tavares, 2004). Fourth channel is lesser
opportunities for bureaucrats to demand bribes. Trade liberalization has a negative impact
on poverty which in turn reduces corruption. If inequality increases along with trade
68
liberalization then it leads to increase in corruption since there is positive correlation
between inequality and corruption. Trade liberalization also reduces corruption by
promoting democracy. Winters (2004) argues that when trade policies are less restrictive,
the incentive for corruption lowers. In general, trade liberalization is supposed to lower
the corruption level. Torrez (2002) finds that the negative relationship between trade
liberalization and corruption is not empirically supported by all the available data sets.
According to him, the negative association between freer trade and corruption level is
theoretically strong but empirically weak.
Debate regarding impact of corruption on environmental quality provides mixed
insights. Damania et al (2003) develop a political economy model for the endogenous
environmental policy determination. They find that the effect of trade policy depends on
governmental corruption; less corruption is associated with the increase in environmental
policy stringency. Deacon and Mueller (2004) argue that corrupt governance may impede
the technique effect by rendering governments unresponsive to public demands for
greater environmental quality. Damania et al (2003) and Welsch (2004) also find that
corruption can cause environmental degradation by reducing the effectiveness of
environmental regulations. Leitao (2006) investigated the relationship between EKC and
corruption levels and find that countries with higher level of corruption face EKC turning
point at higher level of incomes. Theory of environmental policy formation developed by
Fredriksson and Svensson (2003) predicts that corruption reduces the stringency of
environmental regulations. They find it consistent with empirical results.
69
4.4.9. Poverty
The linkages between trade and poverty are not as direct and immediate as the
linkages between poverty and national policies on education, health, land reforms, micro-
credits, infrastructure, governance, and so on. Nor does trade compare to other
international polices, such as debt relief, vaccination programs, or research on tropical
(malaria) and other diseases (AIDS) that set back developing countries. Trade can
nevertheless affect the income opportunities of the poor in a number of ways; some
positive and some negative (WTO, 1999).
Poverty is considered to cause environmental degradation and also being
affected by it simultaneously. The poor people, who rely on natural resources more than
the rich, deplete natural resources faster as they have no real prospects of gaining access
to other types of resources. Since, they depend on nature; they are more vulnerable to
environmental problems.
4.5. Recapitulation of the Model
While the link between income growth and the environment is important, trade
may alter environmental outcomes through a variety of other channels. The empirical
reduced form relationship has been widely explored in near past but it provides little
insights regarding the relationship between trade liberalization and environmental quality
and most of the time provides ambiguous results. This study investigates the relationship
by using a structural model for environmental quality. It consists of one environmental
quality equation (based on the Antweiler et al 2001 model), one trade liberalization
policy equation and remaining eight equations are of channel variables. The
environmental quality equation includes endogenous and exogenous variables.
70
Endogenous variables are the channel variables i.e scale (growth), composition (physical
capital accumulation and/or sectoral shares), technique (emission intensity and/or income
levels), Foreign Direct Investment, Human Capital, Energy use, democracy, corruption.
Exogenous variables are the population density and environmental policy. Channel
equations also contain endogenous and exogenous variable. The equations of our model
are mentioned as below.
Emissions Equation
itititititit
ititititititit
EPCompPDCorH
EnergyDemoFDIPovTechInvSZ
12111098
76543210 (4.5)
Trade Liberalization Index Equation
itititititititit POPTOTFEMDRERISSTLI 6543
int
210 (4.6)
Scale Effect Equation
itititititit
itititititititit
SEnergyLODemoGS
CorFDIHZLKTLIS
int
12111098
76543210 (4.7)
Technique Effect Equation
ititititititititit EnergyKLRHFDIEPTLITech 76543210
(4.8)
Capital Accumulation Equation
ititit
itititititititit
FDLOGSDCC
CORDemoRERHSFDITLIKL
111098
7654
int
3210
(4.9)
71
Manufacturing Share Equation
ititititititititind InvEnergyInfraKLFDITLIY 6543210
(4.10)
Energy Use Equation
itit
itititititind
itit
POP
FDIRSKLYTLIEnergy
7
6543210 (4.11)
Human Capital Equation
itititit
itititititititit
ZUrbEdu
DRDemoInvRSFDITLIH
11109
8765
int
4210
(4.12)
Democracy Equation
itititit
itititititititit
UrbFDIIMR
PolConLEXLOGCKLSTLIDemo
1098
76543
int
210
(4.13)
Corruption Equation
ititit
itititititititit
BQFDI
DemoUrbHGCInvSTLICOR
98
76543
int
210
(4.14)
Foreign Investment Equation
itititit
ititititititititit
WLOGC
aInfraRERIHGSInvSTLIFI
1098
76543
int
210 (4.15)
Poverty Equation
ititititind
itit
itititititititit
ZUrbYWLO
raFDIHGCInvSTLIPov
12111098
76543
int
210 inflog(4.16)
Variables used in above model are defined as under:
Zit = Emission (CO2, SO2, Composite Index of Emissions)
72
TLIit = Trade Liberalization Index
Hit = Human Capital (H=AYS*LF*LFPR by saima nawaz)
EPit = Environmental Protection Measure
FDIit = Foreign Direct Investment
Sit = Scale Effect (Gross Domestic Product)
Techit = Technique Effect
Rit = Per capita income
Demoit = Democracy
Pov it = Poverty
CORit = Corruption
KLit = Capital Labor Ratio
Compit = Composition of the economy (industrial share)
RERit = Real Exchange Rate
POPit = Population
FEMDit = Foreign Exchange Market Distortions
TOTit = Terms of Trade
PDit = Population Density
POLCONit = Political Constraints
LEXit = Life Expectancy at Birth
IMRit = Infant Mortality Rate
GCit = Government Consumption
LOit = Law and Order
BQ = Bureaucratic Quality
73
Energyit = Energy Use
DEit = Defense Expenditures
DRit = Dependency Ratio
DCCit = Domestic Credit Creation
Urbit = Urbanization Rate
Infrait = Infrastructure
FDit = Foreign Deficit
Wit = Averages Wages
Table 4.1 summarizes the theoretically expected effects of trade liberalization policy on
channel variables and the effects of channel variables on environmental quality.
Table 4.1: Expected Effects of trade liberalization on Environmental Quality
Channel Variables Effect of Trade
Liberalization on
Channel
Effect of
Channel on
Environmental
Quality
Effect of trade
liberalization on
Environmental
Quality
Scale + +/- +/-
Industrial Share (compo-I) +/- - +/-
Capital Accumulation
(compo-II) + +/- +/-
Technique (income) + + +
Human Capital + + +
Foreign Direct Investment +/- +/- +/-
Democracy +/- +/- +/-
Corruption +/- - +/-
Poverty +/- + +/-
Energy Use + - -
Total effect +/-
74
4.6. Conclusion
This chapter elucidates the theoretical framework of our study. It discusses the
effects of trade liberalization policy on exports and imports and outlines the methodology
for the construction of trade liberalization policy index. It also presents in detail the
effects of trade liberalization policy on environmental quality. For this purpose, this
chapter has developed ten prominent channel variables through which trade liberalization
policy is expected to affect environmental quality. These channel variables are presumed
to capture most of the effects of trade policy liberalization analysis.
75
Chapter 5
DATA SOURCES, CONSTRUCTION OF VARIABLES
AND ECONOMETRIC METHODOLOGY
5.1. Introduction
This chapter provides thorough details about sources of the required data,
construction and explanation of variables and econometric methodology to be used in the
subsequent chapters. For this purpose, the chapter is divided into five sections. Section
5.2 provides details about the data sources. Section 5.3 discusses the construction and
explanation of variables. Section 5.4 elucidates the econometric methodology (estimation
techniques) to be pursued, along with its justification. Section 5.5 concludes the chapter.
Thus, this chapter, on the whole, develops econometric and statistical foundations for the
following empirical chapters.
5.2. Data Sources
We are taking panel data that consists of 8 cross-sections for the time period of
1971-2011. Cross-section includes main countries of SAARC and ASEAN. The selection
of countries is based on the availability of the data on the main variables included in the
analysis. This comprises of Pakistan, India, Bangladesh, Sri Lanka, Indonesia, Malaysia,
Philippines and Thailand. Singapore is dropped from the list because of the data
unavailability on one of the critical variable like tax revenues from imports and exports
duties. Since, our panel consists of countries that are independent entities; data are
collected from the common international sources to maintain the same definition and
structure of the variables. We have used annual data for the selected panel of countries.
76
Most of the data has been collected from World Development Indicators (WDI)
published by the World Bank, International Financial Statistics (IFS) and Government
Financial Statistics (GFS) published by the International Monetary Fund. Data on export
tariff revenues and import tariff revenues are taken from GFS manuals. Data of
institutional quality and governance have been collected from International Country Risk
Guide (ICRG) published by The PRS (Political Risk Services) Group, New York. Data
on Human Capital are collected from Barro & Lee (2011). Since, we are considering air
quality as a measure of environmental quality; data on emissions is taken from different
sources as World Development Indicators (WDI), Regional Emission Inventory in Asia
(REAS) and Emissions Database for Global Atmospheric Research (EDGAR).
The problem of data unavailability is very common in the field of empirical
estimations. Our analysis is also facing the problem of data limitations. Missing data or
data gaps are filled by using extrapolations/interpolations techniques. Suitable proxies
from secondary sources are used for the variables on which data is totally unavailable.
We have taken 2000 as a common base year for all relevant variables to facilitate the
interpretation of results.
5.3. Construction of Variables
This section elaborates the construction of some variables on which data is not
readily available from secondary sources. Data on some of the variables are directly
available, so they need not to be constructed. This includes data on population, Infant
Mortality Rate, Life Expectancy at Birth, Nominal Exchange Rate, Democracy,
77
Corruption, Law & Order, Political Constraints, etc. Construction of some main variables
that are not directly available is explained as under.
Terms of Trade (TOTit)
Terms of Trade are defined as the value of country’s exports in relation to that of
its imports. It is calculated by taking ratio of price of exportable to price of importable
and then multiplying by 100. Mathematically,
𝑇𝑂𝑇𝑖𝑡 = 𝑃𝑖𝑡𝑋 ∗ 100/ 𝑃𝑖𝑡
𝑀 (5.1)
Here TOTit is the Terms of trade, while 𝑃𝑖𝑡𝑋 is the unit value index of exports and 𝑃𝑖𝑡
𝑀 is
the unit value index of imports.
Fiscal Deficit
In literature, some of the studies has used ratio of primary deficit to GDP as a
measure of fiscal deficit (e.g Alsenia et al., 1999). However, we are taking overall
government fiscal deficit to GDP ratio to proxy the fiscal deficit. Mathematically,
FDit = GFDit
NGDPit (5.2)
Here FDit is the Fiscal Deficit as a ratio to GDP, GFDitis government fiscal deficit and
NGDPitis nominal GDP.
Foreign Exchange Market Distortions
Foreign exchange market distortions are expected to affect trade. In literature,
mostly, black market premium is used to proxy such distortions (e.g Wacziarg, 2001;
Barro, 1995). We are constructing this proxy as a percentage ratio of difference between
market and official exchange rate to official exchange rate. Symbolically,
FEMDit = [MRit− ORit
ORit] 100 (5.3)
78
Here, FEMDit is foreign exchange market distortions, MRitis market exchange
rate ORitis official exchange rate (expressed in terms of domestic currency units per unit
of US dollar).
Real Exchange Rate
Real exchange rate is defined as the exchange rate of currencies after adjusting for
the relative inflation in both the countries. Symbolically,
RERit = NERit . ( Pit
∗
Pit) (5.4)
Here RERit is real exchange rate, NER it is nominal exchange rate expressed as the
local currency units per US dollar, P*it(Pit) is the foreign(domestic) price level.
Foreign Investment Inflows
Different studies have constructed foreign investment in different ways. Some has
taken foreign direct investment as a ratio of gross fixed capital accumulation (Antweiler
et al., 2001; Botric and Skuflic, 2006). Some has taken FDI as a ratio to GDP to measure
foreign investment inflows (Zakaria, 2010; Wacziarg, 2001). We are also following the
second approach. Mathematically,
FDIit = FIit
NGDPit (5.5)
Here FDIit stands for Foreign Direct Investment taken as a ration to nominal GDP,
FI it is foreign direct investment and NGDP it is nominal gross domestic product.
Human Capital
Human capital plays important role in determination of many socio-economic
conditions. Its measurement, however, is not that easy. Some studies has considered
literacy rates as a measure of human capital, while some others have taken average year
79
of secondary education to proxy human capital (Tavares and Wacziarg, 2001; Pelligrini
and Grlegh, 2004). Following Zakaria (2010), we are taking secondary school enrollment
rates as a proxy for human capital. Mathematically,
Hit = SSEit
POPit 10−14 (5.6)
Here Hit is human capital, SSE it is secondary school enrollments and POP it is
population of 10 to 14 years of age group.
Average Import Duties
The tariff rates are important measure of trade liberalization, however, changes in
tariff rates are incomparable across time as the tariff base has changed widening the total
tariff lines (Yen, 2009). Because of this problem, we are considering average import
duties as a proxy for tariffs on import (Zakaria, 2010; Wacziarg, 2001). It is calculated by
taking total import duties as a percentage of total imports. Symbolically,
TRFit M =
D it M
Mit (5.7)
Here TRFit M is average import duties, D it
M is gross (total) import duties and Mit is
total value of imports.
Average Export Duties
Just like average import duties, average export duties are used as a proxy for trade
restrictions on exports. It is calculated by taking total export duties as a percentage of
total value of exports. Symbolically,
TRFit X =
D it X
Xit (5.8)
80
Here TRFit X is average export duties, D it
X is gross (total) export duties and Xit is
total value of exports.
Dummy for Trade Liberalization
A dummy variable is indicator of some particular situation or presence of some
attributes. In present study, we are using a dummy variable for trade liberalization status
of countries included in the panel. In literature, different dummies has been used, one of
the most commonly known is the dummy constructed by Sachs and Warner (1995a) on
the basis of five different trade policy variables. Signing of WTO is also taken as the
dummy variable in some studies to proxy removal of non-tariff barriers. It takes value of
1 for 1995 onwards. We have merged both of above dummies in one by taking value of 1
if both are 1, 0 otherwise.
5.4. Composite Index of Emissions
Since air quality is affected by different kind of emissions, therefore, using
different kind of indicators, we have developed a composite index of air quality. The
technique of Principal Component Method (PCM) has been applied to construct this
index of air quality. The PCM specifies how much variance of a variable is explained by
a specific principal component. The principal component is derived by computing the
eigenvalues of the sample covariance matrix. These eigenvalues are the variances of the
variables (different kind of emissions in this case) therefore the number of principal
components is equal to the number of variables. Typically most of the variance is
explained by the first principal component and therefore its value is used for computation
of the index. The main advantage of PCM is that the weights to be assigned to the
variables are determined by the data itself.
81
Using PCM, we have developed an air quality index. The normalized weights
used in the construction of index are given in Table 5.1. The aggregate index of air
quality shows that individual indicators of air quality demonstrate similar pattern. The
composite index is used in the regressions models to capture the air quality. We have also
examined the separate impact of some important components of the index in the analysis.
The results of regression analysis are reported in chapter 7.
Table 5.1: The normalized weights used in the construction of Composite Air
Quality Index
Country Name Carbon
dioxide (CO2)
Nitrous oxide
(N2O)
Methane
(CH4)
Sulpher
dioxide (SO2)
Bangladesh 0.279 0.271 0.258 0.192
India 0.255 0.235 0.254 0.255
Indonesia 0.171 0.263 0.282 0.284
Malaysia 0.009 0.270 0.346 0.376
Pakistan 0.268 0.198 0.269 0.266
Philippines 0.262 0.170 0.293 0.274
Sri Lanka 0.321 0.037 0.322 0.320
Thailand 0.249 0.221 0.270 0.260
Note: The weights have been derived using Principle Component Analysis (PCA)
5.5. Summary of Construction of Variables
Table 5.2 provides summary of construction of all variables used in the present
study along with the other details on respective data sources.
82
Table 5.2: Summary of Description, Construction and Sources of Variables
Variable Description Construction Explanation Source
Mit Value of imports Mit Mit = value of imports in US$ terms at
constant 2000 prices
Mit = IFS, Country
pages (various issues)
Xit Value of exports Xit Xit = value of exports Xit = IFS, Country
pages (various issues)
RER it Real exchange rate RERit = NERit (
Pit∗
Pit )
NER it = Nominal exchange rate
(units of domestic currency per US
dollar)
Pit∗ (Pit) = World (domestic) Consumer
price index (2000 as base year)
NERit = IFS, Country
pages (various issues)
Pit∗ , Pit = IFS, Country
pages (various issues)
(In case of Bangladesh,
GDP deflator is used
instead of CPI)
RERIit Real Exchange Rate
Index RERIit =
RERit
RERit (2000)
RER it = Real exchange rate
(calculated as above)
RER it(2000) = Real exchange rate at
2000
RER it = Real exchange
rate (self calculations)
Y*
World Domestic
Product (WDP)
Y* = NWDP
* NWDP
* = Nominal WDP NWDP
* = WDI, online
Y
Domestic Income
(GDP)
Y = NGDP
NGDP = Domestic nominal GDP NGDP = WDI, online
83
TRFm
it Average import
duties TRFit
m = Dit
m
Mit
Dit m = Gross import duties (millions of
local currency units)
Mit = value of imports
Dit m = GFS, country
pages (various issues)
Mit = IFS, Country
pages (various issues)
TRFxit Average export
duties TRFit
x = D it
x
Xit
Dit x = Gross export duties (millions of
local currency units)
Xit = value of exports
Dit x = GFS, country
pages (various issues)
Xit = IFS, Country
pages (various issues)
TOTit Terms of Trade 𝑇𝑂𝑇𝑖𝑡 = [
𝑃𝑖𝑡𝑋
𝑃𝑖𝑡𝑀] ∗ 100
Pit x = Unit Price value of exports
Pit m = Unit Price value of imports
Pit x = IFS, Country
pages (various issues)
Pit m = IFS, Country
pages (various issues)
Zit Emissions per capita Zit = co2it / Popit co2it = Total Carbon Dioxide
emissions
Popit = Total Population
co2it = WDI, online
Popit = WDI, online
SO2it Sulpher Dioxide
emissions per capita
SO2it = SO2it / Popit so2it = Total Sulpher Dioxide
emissions
Popit = Total Population
so2it = EDGAR, online
Popit = WDI, online
N2Oit Nitrous Oxide
emissions per capita
N2Oit = n2oit / Popit so2it = Total Sulpher Dioxide
emissions
Popit = Total Population
n2oit = EDGAR, online
Popit = WDI, online
84
CH4it Methane emissions
per capita
CH4it = ch4it / Popit ch4it = Total Methane emissions
Popit = Total Population
ch4it = EDGAR, online
Popit = WDI, online
FEMDit Proxied by black
market premium FEMDit = [
MRit − ORit
ORit] ∗ 100
MRit = Market exchange rate
ORit = Official Exchange rate
MRit = IFS, Country
pages (various issues)
ORit = WDI, online
Dit Trade liberalization
dummy
Dit = 1 if D1it and D2it both are
1, zero otherwise
D1it = constructed by Sachs and
Warner (1995a), 1 for the period of
liberalization and 0 otherwise
D2it = 1 for the period of signing
WTO and hereafter, 0 otherwise
D1it = Sachs and
Warner (1995a),
Wacziarg and Welch
(2003)
D2it = WTO website
Sint
it Initial GDP Sint
it = RGDPit-8
POPit Population Popit = Total population Popit = WDI, online
R it Per capita income
Demo it Democracy Demo it is proxied by an index
which is directly taken from data
source
Index for democracy ranges from -10
(complete autarky) to 10 (complete
democracy)
International Country
Risk Guide (ICRG)
Cor it Corruption Cor it is proxied by an index
which is directly taken from data
source
Index for corruption ranges from 0
(the maximum level of corruption) to
6 (the minimum)
International Country
Risk Guide (ICRG)
GSit Government
Stability
GSit is proxied by an index
which is directly taken from data
source
Index for government stability ranges
from 0 (the most unstable) to 12
International Country
Risk Guide (ICRG)
85
LOit Law & Order LOit is proxied by the index
which is directly taken from data
source
Index of law & order ranges from 0
(the worst conditions of law & order)
to 6
International Country
Risk Guide (ICRG)
FDit Fiscal Deficit FDit = GFDit / NGDPit GFDit = Government Fiscal Deficit
(millions of local currency units)
NGDPit = nominal GDP (million of
local currency units)
GFDit = IFS, Country
pages (various issues)
NGDPit = IFS, Country
pages (various issues)
Yind
it Share of Industrial
production in GDP
Yind
it = NYind
it / NGDPit NYind
it = nominal value of industrial
output
NGDPit = nominal GDP (million of
local currency units)
NYind
it = WDI, online
NGDPit = IFS, Country
pages (various issues)
DRit Dependency Ratio 𝐷𝑅𝑖𝑡 = [
𝑝𝑜𝑝𝑖𝑡 0−14 + 𝑝𝑜𝑝𝑖𝑡 65+
𝑝𝑜𝑝𝑖𝑡 15−64]
POPit 0-14 = population ages 0-14
POPit 65+ = population ages 65+
POPit 15-64 = population ages 15-64
POPit 0-14 = WDI,
online
POPit 65+ = WDI, online
POPit 15-64 = WDI,
online
LEXit Life Expectancy at
birth
LEXit is directly taken from data
source.
LEXit = life expectancy at birth
(number of years)
LEXit = WDI, online
PolConit Political Constraints
Index
PolConit is proxied by
POLCON-V score which is
directly taken from data source.
POLCON-V is an index that ranges
between 0 (no constraints) to 1 (full
constraints)
Henisz (2000) dataset
(updated version: 2010)
86
IMRit Infant Mortality Rate IMRit is directly taken from the
data source
IMRit = infant mortality rate (ratio of
infants dying before 1-year of age to
1000 live births)
IMRit = WDI, online
Urbit Urbanization Rate 𝑈𝑟𝑏𝑖𝑡 =
𝑃𝑜𝑝𝑖𝑡𝑢
𝑝𝑜𝑝𝑖𝑡⁄
Popuit = Urban population
Popit = Total population
Popuit = WDI, online
Popit = WDI, online
H it Secondary School
enrollment rate
H it=Sec.Enr / POP10-14 Pop10-14 = Population between age
group 10-14
Pop10-14 = WDI, online
Sec.Enr = Barro & Lee
(2011)
GCit Government
consumption
expenditures
GCit= Conit /GDPit Conit = consumption expenditures of a
country
GDPit = Nominal GDP(million of
local currency units)
Conit = WDI, online
NGDPit = IFS, Country
pages (various issues)
BQit Bureaucratic Quality BQit is proxied by an index
which is directly taken from data
source.
The index ranges from 0 (poor
quality) to 4 (best quality)
International Country
Risk Guide (ICRG)
Infra it Length of Roads Infrait = Total length of roads in
Kilometers
Infrait = Total length of roads in
Kilometers
Infrait = WDI, online
87
5.6. Econometric Methodology
5.6.1. Specification and Identification of Equations
We have taken a large number of exogenous variables in our model to overcome
the Liu’s (1960) omitted variable bias concern. However, the minor determinants of
dependent variables are referred to the error terms of respective equations. We have taken
a set of endogenous and exogenous variables based on the existing theoretical literature.
In every equation, the number of exclusions is sufficient for the order condition of the
identification issue to be satisfied. The rank condition can safely be assumed to hold in a
model of this size. Our individual equations and hence the whole system is supposed to
be over-identified. Therefore, to estimate this over-identified system and to tackle the
simultaneity problem, we shall apply a reliable instrumental variable estimation
technique like Generalized Method of Moments (GMM) to estimate our model.
5.6.2. Generalized Method of Moments (GMM)
Generalized Method of Moments (GMM) estimator which is also known as
minimum variance estimator is single equation as well as system estimator. It is proposed
by Arellano and Bond (1991) and Arellano (1993) and is applied to over-identified
models. It is basically a generalization of the Method of Moments (MM) estimators. Like
Two-Stage Least Squares (2SLS), it is also an instrumental variable estimator, which
selects parameter estimates such that the correlations between the instruments and
disturbances are as close to zero as possible. Since, it utilizes all (including the excess)
moments by minimizing their difference from zero; it is a step further to 2SLS estimators.
88
It also makes use of the variance-covariance matrix of all moments to account for
heteroskedasticity and autocorrelation and gives more weight to that moment which
possesses small variance. In this way, GMM provides consistent and asymptotically
efficient results.
The popularity of GMM estimator over the other estimators of its category stems
from different facts. Firstly, GMM nests many standard estimators and provides a useful
framework for their comparison and evaluation. Secondly, it provides a simple alternative
to other estimators, especially when it is difficult to write down the Maximum Likelihood
Estimator. Thirdly, GMM is a robust estimator because it does not require the exact
distribution of the disturbances. Fourthly, GMM is consistent and asymptotically
unbiased estimator regardless of the weighting matrix used. When the correct weighting
matrix is used, GMM is also asymptotically efficient in the class of estimators defined by
the orthogonality conditions. Furthermore, if an equation is exactly identified then GMM
will collapse to 2SLS and the same holds with homoskedastic errors. However, GMM is
an asymptotic or large-sample estimator which is rarely efficient in finite samples. The
validity of instruments is verified by applying the classical Hanson’s J- test of the over-
identified restrictions.
5.5. Conclusion
This chapter discusses the details of data sources, construction and explanation of
variables and econometric technique to be used in subsequent chapters of empirical
analysis. It has also elaborated the justification for the choice of Generalized Method of
Moments (GMM) among other estimators of this type. In brief, this chapter provides the
econometric underpinnings for succeeding chapters.
89
Chapter 6
EMPIRICAL RESULTS - I
CONSTRUCTION OF TRADE LIBERALIZATION POLICY INDEX
6.1. Introduction
Following the analytical framework and econometric methodology explained in
preceding chapters, we are now able to present and explain the results of statistical tests
and models. This chapter consists of three sections. Section 6.2 reports the empirical
findings of the effects of trade liberalization policies on imports and exports shares along
with respective interpretations. On the basis of these results, section 6.3 constructs trade
liberalization policy index that will be used in analyzing trade-environment relationship
in the next chapter. Section 6.4 presents graphical analysis of the trade liberalization
index developed in the previous section. Section 6.5 concludes the chapter.
6.2. Empirical Results of Trade Equations
This part presents and explains the empirical findings of trade equations.
6.2.1. The Import Model (The Import to Income Ratio Model)
To study the effects of trade liberalization policy on imports, the import to income
ratio model is estimated by using Generalized Method of Moments (GMM). It is helpful
in controlling for the potential endogeniety of the explanatory variables. Lagged values of
the explanatory variables are used as the instruments. This part provides results of import
demand (expressed as a ratio to nominal GDP) equation described in chapter 4 of
Analytical Framework. We have mentioned results of the model with general to specific
90
technique. Most of the coefficients are according to the expectations except income
effect.
M_share = 1.8091 – 2.62E-07 Y – 0.006 TRF_M + 0.230 DUM – 0.232 RER + 0.001 TOT
(0.30)* (0.00)* (0.00)* (0.14)** (0.10)* (0.00)*
– 0.035 Dependency – 0.166 DUM*RER
(0.00)* (0.17)
R2 – adjusted = 0.72, S.E of regression = 0.09, J – Statistics (Prob) = 18.69
(0.16)
D.W = 1.71
Notes: Values in parentheses denote underlying standard errors (S.E). The S.E significant
at 5% and 10% levels of significance are indicated by * and ** respectively.
Most of the coefficients of trade related variables are statistically different from
zero and also carry the theoretically expected signs. Average import duties have a
significant negative effect on import demand. Its coefficient is -0.006 with 3% level of
significance. Implementation of trade liberalization policy has a significant positive effect
on import demand. Size of the economy has a negative effect on imports. Though it
contradicts the expectations, however, the simple correlation between Imports-GDP ratio
and GDP itself (-0.12 with p-vlaue=0.04) points to the negative relationship between the
both. In our given panel this may hold true because with increased and innovated
production at home demand for imported goods may lower down. This can also be true
because here import demand is determined by some other variables more significantly.
Another possible justification of this negative sign is that imports increase less than
proportionally with increase in income. Since most of the selected countries are oil
importing which render their imports relatively inelastic. The results in this case seem not
quite surprising.
91
Terms of trade holds positive sign as expected. With terms of trade appreciation,
imports become relatively cheaper that has a positive effect on its demand. Real exchange
rate has a negative and statistically significant effect on import demand. Liberalization
policy affects import demand negatively through real exchange rate channel but it is not
statistically significant.
6.2.2. The Export Model (The Export to Income Ratio Model)
This part presents findings of export supply (expressed as a ratio to nominal GDP)
equation discussed in preceding chapter (Analytical Framework). By following the
general to specific rule in selection of variables, the results of augmented version are
given as under.
X_share = 0.4304 + 6.41E-10 WY – 0.0164 TRF_X + 0.109 DUM + 8.23E-05 RER
(0.04)* (0.00) (0.00)* (0.02)* (0.00)**
– 0.001 TOT – 0.0004 Density + 7.35E-05 DUM*RER
(0.00)* (0.00)* (0.00)**
R2 – adjusted = 0.75, S.E of regression = 0.11, J – Statistics (Prob) = 18.05 (0.21)
D.W = 1.82
Notes: Values in parentheses denote underlying standard errors (S.E). The S.E significant
at 5% and 10% levels of significance are indicated by * and ** respectively.
Most of the variables hold theoretically expected signs which confirm that the
response of exports share to changes in world income, real exchange rate, average export
duties, trade policy reforms, terms of trade etc is in the expected direction. World
nominal income has a positive effect on export demand but it is not statistically
significant. Price level has a positive and significant effect on exports though its
coefficient is relatively small. Low magnitude of price coefficient is also found in
92
literature (Senhadji and Montenegro, 1999; Perraton, 2003). It shows the possibility of
losing export revenues trying to make itself competitive through the policy of devaluation
of the real exchange rate.
The average export taxes measure the effects of policy distortions on exports. Its
coefficient of -0.016 is negative and statistically significant though small in magnitude.
The minimal effect can be attributed to the fact that most of export tariff reforms have
already been occurred during the period under consideration. Therefore, any further tariff
reductions have the smaller effect. As regards the impact of trade liberalization policy
reforms, results verify that they affect exports favorably. The direct effect of trade
liberalization policy is 0.109 which indicates that elimination of distortions has a positive
and statistically significant impact on export performance. Liberalization policy affects
export demand positively and significantly through real exchange rate channel. It means
that as the countries under consideration become more liberalized, depreciation leads to
increase export demand.
The coefficient of Terms of Trade is negative as expected and statistically
different from zero. With terms of trade improvement, exports become expensive
relatively which has a negative impact on its demand. Results indicate that one unit
increase in terms of trade lead to 0.001 unit reduction in export demand. Population
density also has a negative impact on exports performance which is sign of supply side
constraints.
93
6.2.3. Total Trade Model (Total Trade to Income Ratio Model)
This part presents the empirical findings of total trade to income ratio model
(given by equation in previous chapter). Results of this part will be used to construct
trade liberalization policy index by allocating weights to relevant components (given by
equation...). Findings are as under:
(X+M)/Y = 1.7063 – 2.62E-07 Y + 6.41E-10 WY – 0.006 TRF_M – 0.0164 TRF_X
(0.29)* (0.00)* (0.00) (0.00)* (0.00)*
+ 0.34 DUM – 0.232 RER– 0.00007 TOT – 0.035 Dependency
(0.12)* (0.10)* (0.00) (0.00)*
– 0.0004 Density + 0.166DUM*RER
(0.00)* (0.03)*
R2 – adjusted = 0.73, S.E of regression = 0.11, J – Statistics (prob) = 17.09 (0.23)
D.W = 1.59
Notes: Values in parentheses denote underlying standard errors (S.E). The S.E significant
at 5% and 10% levels of significance are indicated by * and ** respectively.
The empirical findings reveal that increase in world income, decrease in domestic
income and real exchange rate will cause an increase in total trade to GDP ratio. Increase
in average import and export duties will have a significant negative impact on trade
intensity (total trade to GDP ratio). Liberalization policy positively and significantly
affects total trade. It indicates that liberalization policy provides a favorable environment
for trade to occur. Terms of trade has a negative impact on trade intensity though it is not
statistically significant. Real exchange rate appreciation has a negative impact on trade
intensity directly but it has a positive effect as the economies have adopted liberalized
94
policies. In other words, liberalization policy affects total trade positively and
significantly through real exchange rate channel.
6.3. Construction of Trade Liberalization Policy Index
Chapter 4 explains the details about construction of trade liberalization policy
index. This part is about actual computation of the index. The previous section provides
results of total trade to income ratio model. These results are utilized here to allocate
weights to the three components of trade liberalization policy index which are import
tariffs, export tariffs and trade liberalization status (Sachs-Warner and WTO). Selection
of these variables is based on earlier studies which have tried to incorporate both tariff
and non-tariff barriers (Wacziarg, 2001; Zakriya, 2010; Arshad et al., 2012).
Signs of the relevant variables are according to the expectations. Taxes on imports
and exports get negative weights while liberalization status receives a positive weight.
For each cross-section and time period, the index is constructed as under:
TLI = – 0.006 TRF_M – 0.016 TRF_X + 0.34 DUM
Correlation analysis is performed to get an idea of the relevant weights attached to
each component. Table 6.1 displays the correlation coefficients among the resulting trade
liberalization policy index and its different components. It shows that all correlations
with the index are quite high and in the expected direction. Liberalization status has a
positive correlation and it receives the greatest weight in the construction of index. It is
followed by average import duties and average export duties which have negative
95
correlation with the index. Liberalization status is negatively correlated with both average
import and export duties.
Table 6.1: Correlation Matrix of Trade Liberalization Policy Index and its
Components
TLI Import Duties Export Duties Liberalization Status
TLI 1
Import Duties -0.599 1
Export Duties -0.469 -0.009 1
Liberalization Status 0.955 -0.404 -0.359 1
6.4. Graphical Analysis of Trade Liberalization Policy Index
This part presents the graphical analysis of the trade liberalization policy index
that has been constructed in the previous section. It will be helpful to verify if the overall
trend of the index is supported by the historical analysis of policies. The following graph
(Fig 6.1) shows the mean index for all the cross sections.
It shows an upward trend starting from 1971 onwards. The positive trend is an
indicator of relaxing tariff and non-tariff barriers. The jump at 1995 displays the fact that
most of the countries observed liberalized trade policy during 90’s. The dummy
introduced also takes a value of 1 at 1995 time period for most of the countries.
96
Figure 6.1: Graphical Representation of Constructed Trade Liberalization Policy
Index
Graphical presentation of Trade Liberalization Policy Index for all the countries
under consideration is given as under. It verifies that overall trend is positive with a little
fluctuation for indivisual counries.
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
97
Figure 6.2: Country-wise Graphical Representation of Constructed Trade
Liberalization Policy Index
-0.2
-0.1
0
0.1
0.2
0.3
19
71
19
74
19
77
19
80
19
83
19
86
19
89
19
92
19
95
19
98
20
01
20
04
20
07
20
10
Bangladesh
-0.6
-0.4
-0.2
0
0.2
0.4
19
71
19
74
19
77
19
80
19
83
19
86
19
89
19
92
19
95
19
98
20
01
20
04
20
07
20
10
India
-0.4
-0.2
0
0.2
0.4
19
71
19
74
19
77
19
80
19
83
19
86
19
89
19
92
19
95
19
98
20
01
20
04
20
07
20
10
Pakistan
-1
-0.5
0
0.5
19
71
19
75
19
79
19
83
19
87
19
91
19
95
19
99
20
03
20
07
20
11
Sri Lanka
-0.4
-0.2
0
0.2
0.4
19
71
19
74
19
77
19
80
19
83
19
86
19
89
19
92
19
95
19
98
20
01
20
04
20
07
20
10
Thailand
-0.4
-0.2
0
0.2
0.4
19
71
19
75
19
79
19
83
19
87
19
91
19
95
19
99
20
03
20
07
20
11
Philippines
-0.4
-0.2
0
0.2
0.4
19
71
19
74
19
77
19
80
19
83
19
86
19
89
19
92
19
95
19
98
20
01
20
04
20
07
20
10
Malaysia
-0.2-0.1
00.10.20.30.4
19
71
19
75
19
79
19
83
19
87
19
91
19
95
19
99
20
03
20
07
20
11
Indonesia
98
6.5. Comparison of the Trade Liberalization Policy Index with alternate
measures of Trade Openness
As discussed in Chapter 3 of literature review, there are many other alternative measures
of trade openness that have been used in different research studies. To check robustness
and consistency of the trade policy index constructed in this study, a correlation analysis
and graphical plots of this index along with alternative measures have been presented
here. Table 6.2 presents correlation matrix of trade policy index and other conventional
measures of trade openness.
Table 6.2: Correlation Matrix of Trade Liberalization Policy Index (TLI) and
alternative Measures
Alternate Measures of Trade Openness Correlation Coefficients
Trade Intensity 0.416
(0.00)
Imports to GDP ratio 0.402
(0.00)
Exports to GDP ratio 0.417
(0.00)
Taxes on Trade -0.849
(0.00)
Taxes on Imports -0.663
(0.00)
Taxes on Exports -0.677
(0.00)
All the correlation coefficients carry the expected signs i.e liberalized trade policy is
positively correlated with trade volumes and negatively correlated with trade restrictions
in the form of imports and exports duties and other restrictions on trade. The graphical
plots of trade liberalization policy index with alternative measures of trade openness are
99
presented in Fig 6.3. These graphical illustrations are also indicative of the robustness of
the constructed index of trade liberalization policy.
Figure 6.3: Graphical Representation of Constructed Trade Liberalization Policy
Index and alternative measures of Trade Openness
0
50
100
150
200
-.4 -.3 -.2 -.1 .0 .1
TLI
TI
0.0
0.2
0.4
0.6
0.8
1.0
-.4 -.3 -.2 -.1 .0 .1
TLI
M_
SH
AR
E
0.0
0.2
0.4
0.6
0.8
1.0
1.2
-.4 -.3 -.2 -.1 .0 .1
TLI
X_
SH
AR
E
0
10
20
30
40
-.4 -.3 -.2 -.1 .0 .1
TLI
TR
F_
T
100
6.6. Conclusion
This chapter has constructed the trade liberalization policy index following the
methodology outlined in the first part of Analytical Framework chapter. The empirical
findings reveal that trade liberalization has promoted both exports and imports. Increase
in average tariffs on exports and imports have a negative impact on exports and imports
respectively, while liberalization status indicator affects trade volumes positively. Trade
liberalization policy index is constructed by allocating weights to these three components.
The correlation matrix shows that in the construction of index, liberalization status
receives greatest weight. It is followed by average import duties and average export
duties. The constructed liberalization index seems theoretically plausible. Its empirical
confirmation is explored in the next chapter.
0
20
40
60
80
-.4 -.3 -.2 -.1 .0 .1
TLI
TR
F_
M
0
10
20
30
40
-.4 -.3 -.2 -.1 .0 .1
TLI
TR
F_
X
101
Chapter 7 EMPIRICAL RESULTS - II
TRADE LIBERALIZATION AND ENVIRONMENTAL
QUALITY
7.1. Introduction
This chapter presents empirical findings of the system of equations mentioned in
chapter-4 of Analytical Framework and using the trade liberalization policy index
developed in the previous chapter. The chapter is divided into four sections. Section 7.2
outlines overview and some preliminary aspects of the dataset given through correlation
matrix. Section 7.3 presents and discusses the empirical results of the model in detail by
taking into account the technical aspects. Section 7.4 provides comparison of results of
channel variables across different emission types like CO2, SO2 and composite index of
emissions. Section 7.5 discusses summary of the effects through channel variables and
contribution of trade liberalization policy in CO2 emissions (which is taken as baseline
measure of environmental quality). Section 7.6 provides comparison of effects of
liberalized trade policy across emission types. Section 7.7 gives results of tests based on
the residuals from the regression equations. Finally, section 7.8 concludes the chapter.
7.2. Overview of the Data
This part of the chapter presents broader characteristics and nature of the data. It
provides results of correlations among the variables of the present analysis. “Table 7.1
presents correlation matrix.” First two columns are most relevant and interesting. The
first column presents unconditional linear relationship among environmental quality and
channel variables. It also
102
Table 7.1: Correlation Matrix for the Main Variables
C
O2 E
mis
sion
s
Tra
de
Poli
cy
Ind
ex
Sca
le E
ffec
t
Tec
hn
iqu
e E
ffec
t
Ph
ysi
cal
Cap
ital
Man
ufa
ctu
rin
g
En
ergy U
se
Fore
ign
Dir
ect
Inves
tmen
t
Hu
man
Cap
ital
Dem
ocra
cy
Corr
up
tion
Pover
ty
CO2 Emissions 1
-
Trade Policy Index 0.355
(0.000)
1.000
-
Scale Effect 0.098
(0.172)
0.596
(0.000)
1.000
-
Technique Effect 0.831
(0.000)
0.423
(0.000)
0.412
(0.000)
1.000
-
Physical Capital 0.827
(0.000)
0.393
(0.000)
0.356
(0.000)
0.982
(0.000)
1.000
-
Manufacturing 0.712
(0.000)
0.383
(0.000)
0.425
(0.000)
0.822
(0.000)
0.815
(0.000)
1.000
-
Energy Use 0.960
(0.000)
0.329
(0.000)
0.073
(0.311)
0.888
(0.000)
0.902
(0.000)
0.722
(0.000)
1.000
-
Foreign Direct Investment 0.707
(0.000)
0.589
(0.000)
0.328
(0.000)
0.830
(0.000)
0.848
(0.000)
0.598
(0.000)
0.777
(0.000)
1.000
-
Human Capital -0.037
(0.608)
0.205
(0.006)
0.002
(0.973)
-0.356
(0.000)
-0.382
(0.000)
-0.113
(0.116)
-0.155
(0.030)
-0.272
(0.000)
1.000
-
Democracy 0.047
(0.518)
0.045
(0.535)
0.379
(0.000)
0.114
(0.114)
0.148
(0.039)
0.327
(0.000)
0.020
(0.778)
-0.027
(0.705)
0.102
(0.157)
1.000
-
Corruption 0.239
(0.000)
-0.092
(0.193)
0.084
(0.246)
0.305
(0.000)
0.346
(0.000)
0.207
(0.003)
0.278
(0.000)
0.303
(0.000)
-0.231
(0.001)
0.301
(0.000)
1.000
-
Poverty -0.789
(0.000)
-0.325
(0.000)
-0.425
(0.000)
-0.933
(0.000)
-0.905
(0.000)
-0.739
(0.000)
-0.840
(0.000)
-0.693
(0.000)
0.378
(0.000)
-0.146
(0.042)
-0.295
(0.000) 1
Note: Values in parenthesis represent underlying p-values.
103
includes trade liberalization policy index. The signs of these correlations are consistent
with the literature (expectations). The second column provides correlations of trade
liberalization policy index with all the channel variables. The signs are again consistent
with priors and correlations are relatively higher. These high simple/linear correlations
again validate the selection of channel variables in the present study. Correlation results
of first two columns, collectively, provide some insights into the direction of effects
between trade liberalization and environmental quality through all the channels. For
example trade liberalization positively correlates with scale effect and scale effect is
associated with emissions positively. It indicates that trade liberalization has positive
effect on emissions through the channel of scale effect. Taken at the face value, these
simple/linear correlations suggest that trade liberalization affects emissions
(environmental quality) through all the channel variables. Since these correlations are
unconditional and based on one to one linear relationship, it does not provide some
meaningful interpretation. It is necessary to control for other determinants as well. The
results may portray a different picture when we turn to conditional analysis and control
for potential endogeniety bias.
7.3. Estimation and Interpretation of the Complete Model
After putting arduous effort, the parameters of the model have been estimated by
using the estimation technique of Generalized Method of Movements (GMM). “This
method achieves consistency by applying appropriate instrumenting and efficiency
through optimal weighting. Separate set of instruments has been used for each equation
which are basically the difference of lagged values of the variables of that particular
equation and other suitable exogenous variables. The choice between the FEM and the
104
REM is established using the Hausman test. The results from Hausman test favour the
‘fixed effects’ specification. Therefore, we have used the fixed effects model with time
and cross section fixed effects in estimating the models. The variables which appear
insignificant in repeated estimations have been excluded from the model. This exercise
has helped to get a model in which results are not sensitive to model specifications. The
main findings of the model are presented by Table 7.2. The selected specification is based
on the existing empirical and theoretical literature on the determinants of various
endogenous variables under study.
With the exception of the corruption equation, the explanatory power of the
model is above 75%. Autoregressive (AR) process has been applied to remove
autocorrelation. Values of Durban-Watson (DW) statistics are reasonably close to the
desired value of two in almost all equations which is an indication of absence of
autocorrelation problem in the model. Results of each equation are explained below.
7.3.1. Carbon Dioxide (CO2) Emissions Equation
The results of the CO2 emissions equation are closely resembled with the findings
of the existing literature on trade-environment nexus. The coefficient of scale effect on
CO2 emissions is 0.56 and statistically significantly positive. It shows that one
percentage point rise in scale of the economy bring 0.56 percentage points rise in
emissions keeping other determinants constants. Numerous researchers favor this line of
argument. See for example Grossman (1991), Dinda (2005), Ricci (2007) and Grimaud
and Tournemaine (2007).
105
Technique effect decreases the emissions, thus increasing environmental quality. Physical
Capital to labor, which is one part of composition effect on inputs side, has a positive and
statistically significant coefficient. It points towards that enhanced capital is
environmental friendly. This point estimate implies that one percent increase in this
variable leads to 0.32 percent decline in emissions per capita. The estimated coefficient
on foreign direct investment is positive and statistically significant. It is more than three
times of its standard error. The coefficient is, however, economically low in magnitude.
One percent increase in FDI leads to 0.03 percent rise in CO2 emissions. With increase in
foreign direct investment, CO2 emissions increase, ceteris paribus. This result is also
supported by Copeland and Taylor (1994), He (2006) and Spatareanu (2007).
As expected, the coefficient on energy use is positive and highly significant; more
than four times of its standard error. The result is in accord with Baek and Kim (2011)
and Amen et al (2012). The significant positive effect implies that rise in energy use is
detrimental to environmental quality by increasing the CO2 emissions. The empirical
results show that human capital is environment friendly. Its coefficient is highly
significant (about six times of its standard errors) and appears with negative sign. A rise
of one percent in human capital will lower the emissions by 0.29 percent.
As expected, manufacturing share has an increasing effect on pollution.
Coefficient of democracy again appears with positive sign leading to higher emissions,
which results in lower environmental quality. The findings are consistent with Thomas
Drosdowski (2005). Rise in poverty has an increasing effect on emissions. The
coefficient is statistically significant with one percentage point increase in poverty
generating 0.08 percentage points rise in emissions, ceteris paribus.
106
Table: 7.2: Empirical Estimates of Complete Model
Variable C
arb
on
Dio
xid
e
Tra
de
Lib
erali
zati
on
In
dex
Sca
le E
ffec
t
Ph
ysi
cal
Cap
ital
Man
ufa
ctu
r
ing
Sh
are
Tec
hn
iqu
e
Eff
ect
En
ergy U
se
Dem
ocra
cy
Corr
up
tion
Fore
ign
Dir
ect
Inves
tmen
t
Pover
ty
Hu
man
Cap
ital
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Constant@ -0.636 (1.200)
1.469 (2.843)
21.932 (20.716)
-0.162 (0.219)
0.642 (0.445)**
2.750 (1.523)**
0.278 (0.260)
-4.029 (1.276)*
-9.139 (2.631)*
-13.025 (3.807)*
8.499 (1.489)*
2.117 (0.539)*
Carbon Dioxide - -0.202
(0.101)* - - - - - - - - - -
Sulpher Dioxide - - - - - - - - - 0.049
(0.036) -0.059
(0.023)* -
Trade
Liberalization
Index
- - 0.133
(0.072)**
0.062 (0.033)*
0.077 (0.029)*
0.265 (0.168)**
0.060 (0.035)**
0.116 (0.059)*
0.384 (0.127)*
0.313 (0.128)*
0.285 (0.091)*
0.141 (0.035)*
Scale Effect
0.557 (0.111)*
- - - - 0.284
(0.055)*
-0.090 (0.043)*
- - - - -
Physical Capital -0.320
(0.082)* -
0.111 (0.119)
- 0.069
(0.046)**
0.179 (0.078)*
0.079 (0.039)*
0.059 (0.051)
-0.211 (0.110)*
0.312 (0.162)*
-0.209 (0.030)*
-0.064 (0.017)*
Manufacturing
Share
0.129 (0.087)**
- - - - - 0.050
(0.075) - - -
0.299 (0.054)*
-
Table 7.2 continues…
107
Table 7.2 (continued…): Empirical Estimates of Complete Model
Variable C
arb
on
Dio
xid
e
Tra
de
Lib
era
liza
tio
n I
nd
ex
Sca
le E
ffec
t
Ph
ysi
cal
Ca
pit
al
Ma
nu
fact
uri
ng
Sh
are
Tec
hn
iqu
e
Eff
ect
En
erg
y U
se
Dem
ocr
acy
Co
rru
pti
on
Fo
reig
n
Dir
ect
Inv
estm
ent
Po
ver
ty
Hu
ma
n
Ca
pit
al
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Technique
Effect
-0.307 (0.157)*
- - - - - 0.135
(0.071)** - - - - -
Energy Use
0.627 (0.145)*
- 0.419
(0.171)* -
-0.164 (0.065)*
0.183 (0.071)*
- - - - - -
Democracy 0.011
(0.007)** -
-0.033 (0.016)*
0.036 (0.006)*
-0.026 (0.006)*
-0.109 (0.030)*
- - 0.134
(0.067)*
-0.185 (0.084)*
0.058 (0.028)*
0.008* (0.006)
Corruption
-0.023 (0.007)*
- 0.021
(0.008)*
-0.005 (0.012)
0.013 (0.006)*
- - 0.041
(0.005)* -
0.133 (0.033)*
- -
Foreign Direct
Investment
0.029 (0.009)*
- 0.040
(0.022)**
0.014 (0.007)*
-0.028 (0.009)*
0.004 (0.011)
-0.008 (0.008)
0.105 (0.059)**
- - 0.087
(0.012)*
0.009 (0.002)*
Poverty
0.084 (0.032)*
- - - - - - - - - - -
Human
Capital
-0.299 (0.053)*
- 0.065
(0.096) -
0.020 (0.015)
-0.094 (0.085)
- - 0.700
(0.217)*
0.391 (0.151)*
-0.398 (0.069)*
-
Table 7.2 continues…
108
Table 7.2 (continued…): Empirical Estimates of Complete Model
Variable C
arb
on
Dio
xid
e
Tra
de
Lib
erali
zati
on
In
dex
Sca
le E
ffec
t
Ph
ysi
cal
Cap
ital
Man
ufa
ctu
ri
ng
Sh
are
Tec
hn
iqu
e
Eff
ect
En
ergy U
se
Dem
ocra
cy
Corr
up
tion
Fore
ign
Dir
ect
Inves
tmen
t
Pover
ty
Hu
man
Cap
ital
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Initial Income Per
Capita -
-0.362 (0.196)**
-0.074 (0.067)
-0.068 (0.025)*
- - - - 0.198
(0.154) - -
0.114 (0.014)*
Real Exchange
Rate -
0.267 (0.093)*
- -0.079
(0.034)* - - - - -
-0.367 (0.058)*
- -
Foreign Exchange
Market
Distortions
- 0.114
(0.079) - - - - - - - - - -
Terms of Trade - -0.284
(0.100)* - - - - - - - - - -
Government
Consumption
- - - - - 0.038 (0.035)
- - - - -0.163 (0.046)*
-
Infrastructure - - - - 0.205
(0.096)* - - - - - - -
Population Density - -1.549
(0.621)* - - - - - -
-0.908 (0.357)*
- - -
Table 7.2 continues…
109
Table 7.2 (continued…): Empirical Estimates of Complete Model
Variable
Carb
on
Dio
xid
e
Tra
de
Lib
erali
zati
o
n I
nd
ex
Sca
le E
ffec
t
Ph
ysi
cal
Cap
ital
Man
ufa
ctu
ri
ng
Sh
are
Tec
hn
iqu
e
Eff
ect
En
ergy U
se
Dem
ocra
cy
Corr
up
tion
Fore
ign
Dir
ect
Inves
tmen
t
Pover
ty
Hu
man
Cap
ital
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Dependency
Ratio - - - - - - - - - - -
0.233 (0.059)*
Infant Mortality
Rate - - - - - - -
0.019 (0.006)*
- - - -0.003
(0.001)*
Law & Order
- - -
0.015 (0.004)*
- - - -0.055
(0.009)* -
0.038 (0.018)*
- -
Government
Stability - - -
0.005 (0.003)**
- - - - - - - -
Bureaucratic
Quality - - - - - - - -
0.060 (0.031)*
- - -
Brown Policy
0.037 (0.015)*
- - - - - - - - - - -
Urbanization
-0.136 (0.126)
- - - - - 0.205
(0.083)*
1.106 (0.431)*
- - - 0.119
(0.032)*
110
Table 7.2 (continued…): Empirical Estimates of Complete Model
Variable
Carb
on
Dio
xid
e
Tra
de
Lib
erali
zati
o
n I
nd
ex
Sca
le E
ffec
t
Ph
ysi
cal
Cap
ital
Man
ufa
ctu
ri
ng
Sh
are
Tec
hn
iqu
e
Eff
ect
En
ergy U
se
Dem
ocra
cy
Corr
up
tion
Fore
ign
Dir
ect
Ind
ex
Pover
ty
Hu
man
Cap
ital
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Fiscal Deficit
- - -
0.012 (0.002)*
- - - - - - - -
Lagged
Dependent
Variable
0.676 (0.072)
*
- - 1.119
(0.025)*
1.113 (0.070)*
- 0.735
(0.066)*
0.829 (0.032)*
1.214 (0.088)*
0.801 (0.066)*
0.979 (0.039)*
0.810 (0.040)
*
AR(1) - 0.877
(0.050)*
0.998 (0.003)*
- - 0.755
(0.039)* - - - - - -
R2 0.986 0.900 0.998 0.959 0.976 0.957 0.907 0.787 0.698 0.983 0.892 0.945
R2 – Adjusted 0.975 0.893 0.991 0.943 0.974 0.952 0.891 0.766 0.665 0.981 0.886 0.932
DW 1.642 1.903 1.731 1.720 1.457 1.918 1.530 1.922 1.916 1.768 1.612 1.785
J-Statistics
Prob. (J-Stats)
15.293 (0.226)
21.382 (0.339)
18.082 (0.587)
10.524 (0.230)
23.389 (0.237)
15.654 (0.680)
6.633 (0.622)
15.600 (0.657)
11.578 (0.238)
15.794 (0.521)
11.161 (0.851)
11.117 (0.357)
No. of
Observations 194 188 194 166 168 235 228 194 188 189 194 194
Notes: Values in parentheses denote underlying standard errors (S.E). The S.E significant at 5% and 10% levels of significance are
indicated by * and ** respectively.
@ Country specific effects are reported in Appendix-A.
111
Control over corruption has a diminishing effect on emissions, which implies that
corrupt economies are relatively more polluting. The result is in accord with Damania et
al (2003) and Welsch (2004). As expected, the lenient environmental policy has an
increasing impact on CO2 emissions. The coefficient on browner kind of environmental
policy is positive and statistically significant. Foreign Direct Investment has a
significantly positive impact on CO2 emissions though it is not very high in its
magnitude. A one percentage increase in its size generates 0.03 percentage point rise in
emissions level. Urbanization, as a control variable, appears with negative sign on its
coefficient though it is not statistically significant. In a nutshell, most of the coefficients
appear consistent with the existing literature.
7.3.2. Trade Policy Liberalization Equation
The equation of trade liberalization policy is incorporated in the mode to deal
with the problem of endogeniety and to check the possibility of reverse causation
between trade liberalization policy and environmental quality. It will provide efficiency
gains without affecting the parameter estimates of rest of the equations. The findings are
in accordance with the theoretical expectations. In particular, real exchange rate and
economic size of the country affect trade liberalization positively and significantly. Initial
income per capita is found to cause a negative affect, which supports the convergence
theory (the tendency of less developed countries to grow more quickly than more
developed economies). Black market premium has a favorable effect on trade
liberalization but it is statistically insignificant. Size of the country, proxied by
population, is also found consistent with the existing literature. It bears a negative and
112
statistically significant sign suggesting that larger countries are likely to follow the
inward oriented policies.
The estimate of environmental quality (proxied by per capita emissions of carbon
dioxide) is negative and statistically significant. Thus, we find a proof of reverse
causation between environmental quality and trade liberalization.
7.3.3. Scale Effect Equation
The results suggest that scale effect (size of the economy) is positively related to
the trade liberalization policy. A one unit increase measured in trade liberalization index
increases scale of the economy by 0.13 percent. This result is supported by numerous
authors like Grossman (1991), Copeland and Tailor (2003) and Antweiler et al (2001).
Other determinants also affect the size of the economy in the predicted direction. Both
human capital and physical capital exert a positive impact on economic size though they
are insignificant statistically. The estimated positive coefficient on foreign direct capital
(by taking foreign direct capital stock as a ratio of domestic capital) suggests that it has a
favorable effect on size of the economy. Initial economic size has a negative relation with
current size of the economy, which supports the conditional convergence theory. Other
negative factors include democracy and corruption. Both are statistically significant.
Negative effect of democracy on economy is not surprising and consistent with the
studies of Persson and Tabellini (1992), Barro (1996) and Tavares and Wacziarg (2001).
Negative and significant effect of corruption on economic growth is consistent with the
view that corruption lowers the marginal product of capital by acting as a tax on
investment proceeds (Mauro, 1995; Barreto, 1996; Tanzi, 1997). Energy use per capita
has been found to cast significant positive impact on economic growth.
113
7.3.4. Technique (Income) Effect Equation
Results demonstrate that most of the variables bear theoretically expected signs.
The t1echnique effect (proxied by per capita gross national income) appears to be
affected positively by trade liberalization index. A one unit increase in the index leads to
0.26 percent rise in per capita income. This finding is also supported by Antweiler et al.
(2001) and Copeland and Taylor (2003). Capital to labor ratio is found to exert a positive
and significant impact on technique effect. Negative determinants include democracy and
human capital accumulation though the latter is not significant statistically. Coefficient of
scale of the economy is positive and significant suggesting a strong favorable relation
between size of the economy and technique effect. Energy use per capita also has
affirmative effect on per capita income. It is found significant statistically. Other positive
factors are government consumption expenditures and foreign direct investment but both
of them appear to be statistically insignificant.
7.3.5. Physical Capital (Composition Effect-I) Equation
Trade liberalization policy appears to have a significant positive effect on
physical capital accumulation (defined as the capital stock to labor ratio). One point
increase in trade liberalization index is associated with 0.06 percent increase in capital-
labor ratio. Though, the coefficient is significant statistically, it is small in economic
terms (lower in value). As far as other determinants are concerned, most of them are
statistically significant and theoretically consistent. Capital accumulation is positively
and significantly correlated with initial income. As expected, democracy is also
conducive to capital accumulation. Foreign direct investment is also favorable for capital-
labor ratio which shows that FDI plays a complimentary role rather than a substitute.
114
Other encouraging factors include government stability, fiscal deficit and better law and
order situation. As per our expectations, corruption is disadvantageous for investment to
flourish though it is not statistically significant.
7.3.6. Industrial Share (Composition Effect-II) Equation
Composition of the economy can be one of the main channels through which
trade liberalization may affect environmental quality. Depending upon the comparative
advantage, trade liberalization may cause a positive or negative impact on manufacturing
share. In the present study, it appears to have a significant positive effect on
manufacturing share. Estimates show that a one unit increase in trade liberalization index
causes 0.08 percent rise in manufacturing share of the economy. As far as other
determinants are concerned, capital-labor ratio and human capital accumulation has a
positive effect on manufacturing share though the later one is not significant statistically.
Foreign direct investment appears to have a negative effect of industrial share. Other
negative factors include democracy and energy use per capita. Not surprisingly,
corruption has a significant positive impact on manufacturing. Corruption or palm-
greasing serves as a facilitator to speed up the procedure of documentation etc and hence
promotes manufacturing. Infrastructure is proxied by railway network coverage which
appears with positive sign suggesting a beneficial effect on industry to grow.
7.3.7. Energy Use Equation
Trade liberalization has a significant positive effect on energy use per capita. A
one point increase in trade liberalization index leads to 0.06 percent increase in energy
use per capita and it is statistically significant. It indicates that increased trade resulting
115
from liberalized policies results into higher energy demands. Other determinants of
energy use are also in line with theoretical expectations. Energy use is positively
associated with per capita income. Economic size of the country has negative effect on
energy use suggesting that economic growth is not energy intensive. Domestic
investment has positive and significant (statistically) impact. Other positive factors
include urbanization and manufacturing share though the latter is not statistically
significant. As the urban population increases, energy demand and usage accompanies
this rise. Foreign direct investment as a ratio to capital stock has a negative but
statistically insignificant relationship with energy use per capita.
7.3.8. Foreign Direct Investment Equation
Trade liberalization policy has a positive impact on foreign direct investment,
which shows that foreign investment appears to be a compliment rather than a substitute
to trade liberalization. Economically speaking, one point rise in trade liberalization index
brings increase in foreign direct investment by 0.31 percent. The findings are consistent
with Coe et al. (1997), Jun (1995) and Bhagwati (1978) who argue that open economies
attract more FDI as compared to the closed economies.
Most of the signs of estimated coefficients are according to theoretical
expectations. Initial per capita income positively and significantly affects foreign direct
investment. Domestic capital labor ratio appears to have a favorable effect on foreign
investment, which supports the view that domestic investment is a compliment to foreign
investment. Human capital and improved situation of law and order are encouraging
factors for FDI. Other negative determinants include democracy and corruption.
Corruption distorts foreign investment inflows. Another interesting result is regarding
116
environmental quality (proxied by per capita emissions). Rise in domestic sulpher dioxide
emissions is associated positively with FDI, which supports the well-known Pollution
Haven Hypothesis (PHH).
7.3.9. Human Capital Equation
Human capital is found to be positively affected by trade liberalization policy.
The estimate is statistically significant though small in magnitude. Ceteris paribus, a one
point increase in trade liberalization policy is likely to increase human capital by 0.14
percent. It suggests that trade liberalization consequent upon development of human
capital through increased competitiveness. Findings of other determinants of human
capital are also consistent with the previous literature. Initial income per capita has a
strong positive effect on human capital. Domestic investment has a negative impact on
human capital. However, foreign direct investment bears a significant positive sign.
Human capital is also positively and significantly associated with urbanization,
democracy and dependency ratio. Urbanization provides a favorable environment for
development of human capital by improved education institutions and wider
opportunities. Democratic governments are public representatives with major focus on
human development. Enhanced responsibility in terms of increased dependency ratio,
serves as a pushing force for human capital. Human capital is negatively associated with
infant mortality rate. Corruption and environmental quality are found to have negative
but insignificant effect hence not included in final results.
7.3.10. Corruption Equation
Control over corruption is found to be negatively affected by trade liberalization
index. These findings are consistent with one school of thought in this regard like
117
Gurgur-Shah (2005) and You and Khagram (2005). A one unit rise in trade liberalization
index leads to 0.38 percentage point decrease in control over corruption. Empirical
findings regarding other determinants are also in line with dominant literature in this
field. Human Capital has a strong positive effect on lowering the corruption levels.
Again, democracy also appears with positive coefficient which is statistically significant.
The factors contributing negatively to control over corruption include physical capital
and population density. These findings are strongly conformed by the existing theoretical
and empirical literature. As expected, bureaucratic quality has a significant positive
impact on lowering the corruption levels.
7.3.11. Poverty Equation
Poverty equation displays a positive and significant (statistically) effect of trade
liberalization policy on poverty level. A one unit increase in trade liberalization leads to
0.29 percent increase in poverty and this effect is statistically significant. Empirical
findings regarding other determinants of poverty are also consistent with previous
literature. Physical capital accumulation has a highly significant negative effect on
poverty. Other poverty reducing factors include human capital accumulation, government
consumption expenditures and lack of environmental quality (proxied by per capita
sulpher dioxide emissions). Inflow of foreign direct investment is positively correlated to
poverty level. Democracy has also poverty enhancing effect which is statistically
significant as well. Increase in manufacturing share of the economy leads to a rise in
poverty. It implies that industrialization has poverty enhancing consequences at-least at
the earlier stage of development.
118
7.3.12. Democracy Equation
The estimates reveal that trade liberalization policy contributes positively to
democratic process in South Asian and South East Asian countries. One unit increase in
trade liberalization policy index leads to 0.12 percent rise in democratic index. The
coefficient is statistically significant. Thus, as per theoretical predictions, liberalizing
foreign trade will assist the democratic process.
As far as other control variables are concerned, most of the variables carry
expected signs. Results show that variable of corruption appears with positive sign
indicating that lack of corruption is positively correlated with democracy. Physical capital
accumulation, foreign direct investment, urbanization and infant mortality rate are found
to strengthen democracy while law and order distorts democracy. Although, from
statistical point of view the model appears to perform well, from theoretical point of view
it is not equally good as signs of two variables are against theoretical expectations. This
includes variables of infant mortality rate and law and order.
7.4. Comparison of Results of Channel Variables across different
Emissions (Carbon Dioxide, Sulpher Dioxide, Composite Index of
Emissions)
Following the tradition in trade-environment nexus, Carbon Dioxide and Sulpher
Dioxide has been used to proxy the environmental quality. These two are among the most
important components of emissions. CO2 is a Green House Gas (GHG) that is very
harmful for the environment and one of the root causes of Global Warming. SO2 is not
GHG but it has severe consequences, acid rain is one of them. We have also constructed a
composite index for environmental quality by applying Principal Component Analysis
119
(PCA) on different pollutants including CO2, SO2, N2O, CH4 etc. It has an advantage of
utilizing additional information about the air quality which might be more helpful in the
present analysis.
Table 7.3 presents effects of channel variables on Carbon dioxide (CO2), Sulpher
dioxide (SO2) and composite index of emissions. Empirical results of CO2 emissions
have already been discussed in section 7.3.1 in detail. To check robustness and
consistency of empirical findings of the base model (CO2 in this case), the model has
been estimated for Sulpher dioxide and the composite index of emissions. Results of the
effects of the channel variables on these emissions have been discussed in Table 7.3 for
the sake of comparison.
In case of SO2 emissions, the coefficient of scale effect appears positive and statistically
significant. As expected, the technique effect (income effect) appears with negative sign
indicating favorable effect on environment by decreasing emissions. The channel of
physical capital also has a negative coefficient in case of SO2 emissions. It points towards
the use of environment friendly technology. The estimated coefficient of foreign
investment is positive and statistically significant which validates the existence of
Pollution Heaven Hypothesis. SO2 emissions increase with rise in energy use. The
coefficient of energy use turns positive and statistically different from zero. Human
capital appears environment friendly by decreasing SO2 emissions. Democracy and
Poverty also have a detrimental effect on environment with positive and statistically
significant coefficients. Manufacturing share has an increasing effect on the emissions
though its estimated coefficient is not statistically significant. The control over corruption
has a diminishing impact on SO2 emissions which implies that the corrupt economies are
120
Table: 7.3. Comparison of Results across Emissions (CO2, SO2, Composite Index of Emissions)
Variables
Effect of Channel
Variables on CO2
Emissions
Effect of Channel
Variables on SO2
Emissions
Effect of Channel
Variables on Composite
Index of Emissions
(1) (2) (3)
Intercept -0.636
(1.200)
16.622
(6.932)*
-1.664
(1.231)
Scale 0.557
(0.111)*
3.549
(1.465)*
0.528
(0.122)*
Technique -0.307
(0.157)*
-2.267
(1.207)**
-0.246
(0.166)**
Physical Capital -0.320
(0.082)*
-2.470
(0.515)*
-0.351
(0.088)*
Energy Use 0.627
(0.145)*
2.107
(0.430)*
0.709
(0.147)*
Human Capital -0.299
(0.053)*
-2.212
(0.769)*
-0.280
(0.067)*
Democracy 0.011
(0.007)**
0.076
(0.024)*
0.011
(0.008)
Poverty 0.084
(0.032)*
0.196
(0.155)
0.098
(0.031)*
FDI 0.029
(0.009)*
0.249
(0.038)*
0.028
(0.010)*
Corruption -0.023
(0.007)*
-0.105
(0.025)*
-0.026
(0.007)*
Manufacturing 0.129
(0.087)**
0.469
(0.390)
0.108
(0.087)
Brown Policy 0.037
(0.015)*
0.151
(0.068)*
0.037
(0.017)*
Urbanization -0.136
(0.126) -
0.183
(0.117)
Lagged Dependent
Variable
0.676
(0.072)*
0.492
(0.087)*
0.644
(0.072)*
R2 – Adjusted 0.995 0.920 0.995
D.W 1.642 1.701 1.623
J. Statistics
(Prob. of J.stats)
15.293
(0.226)
8.055
(0.561)
22.613
(0.283)
Notes: Values in parentheses denote underlying standard errors (S.E). The S.E significant at 5%
and 10% levels of significance are indicated by * and ** respectively.
121
more polluting. As expected, the impact of lenient and lax environmental government
policies is detrimental to the environment.In case of the composite index of emissions,
scale effect again appears positive and statistical significant. The technique effect shows
favorable environmental impact by lowering the emissions. Emissions are reduced by the
channel of physical capital which has a negative coefficient. The coefficient of energy
use has a positive value which is statistically different from zero. Human capital has an
advantageous impact on environmental quality. Democracy, poverty and foreign direct
investment all have damaging effect on environment by increasing the emissions. The
channel of manufacturing share appears with positive sign but its coefficient is not
statistically different from zero. The lax environmental policy again has harmful
consequences for environmental quality.
As evident from the above discussion, most of the empirical results of channel
variables are consistent across different pollutants with a little variation in statistical
significance. Comparison of the results is presented in Table 7.3. The country specific
effects are reported in Appendix-A.
7.5. Summary of the Channel Effects
The summary of the channel effects of trade liberalization policy on Carbon dioxide
(CO2) emissions, based on the results given in Table 7.2, is presented in Table 7.4. It
reports impact of trade liberalization policy on each channel variable (column-1) and then
the effect of each channel variable on CO2 emissions (column-2). The last column gives
product of the two coefficients along with their standard errors. The table depicts trade
liberalization policy significantly affects environmental quality through all ten channel
variables. All these partial effects are summed to get a net effect.
122
According to this table, trade liberalization policy affects CO2 emissions
positively (leading to deteriorated environmental quality) through six out of ten channels.
The channels which appears damaging to the environment include scale effect, energy
use, manufacturing, democracy, poverty and foreign direct investment. Trade policy
liberalization benefits environment by decreasing emissions through four channels which
include technique/income effect, physical capital, human capital and control over
corruption.
To summarize, our model provides strong evidence in favor of the detrimental
effect of trade liberalization policy on environmental quality in terms of carbon dioxide
emissions. The findings are consistent with other studies like Baumol and Oats (1988),
Copeland and Taylor (1997), Daly (1993) and Jungho Baek (2009). The net effect is an
increase in emissions which is damaging for the environment quality. According to the
parametric value, one percentage point increase in trade liberalization policy would cause
a 0.115 percentage point increase in carbon dioxide emissions per capita once all of the
channels of influence are brought into the picture.
123
Table 7.4: Contribution of Trade Policy Liberalization on CO2 Emissions
Channel Variable Effect of Trade
Liberalization on the
Channel
Effect of the
channel on CO2
Emissions
Effect of Trade
Liberalization on
CO2 Emissions
(1) (2) (3)
Scale 0.133
(0.072)**
0.557
(0.111)*
0.080
(0.040)*
Technique 0.265
(0.168)**
-0.307
(0.157)*
-0.081
(0.046)**
physical Capital 0.062
(0.033)*
-0.32
(0.082)*
-0.020
(0.008)*
Energy Use 0.06
(0.035)**
0.627
(0.145)*
0.040
(0.005)*
Human Capital 0.141
(0.035)*
-0.299
(0.053)*
-0.042
(0.005)8
Democracy 0.116
(0.059)*
0.011
(0.007)**
0.013
(0.007)*
Poverty 0.285
(0.091)*
0.084
(0.032)*
0.034
(0.020)**
FDI 0.313
(0.128)*
0.029
(0.009)*
0.091
(0.022)*
Corruption 0.384
(0.127)*
-0.023
(0.007)*
-0.009
(0.002)*
Manufacturing 0.077
(0.029)*
0.129
(0.087)**
0.010
(0.003)*
Total Net Effect 0.115
(0.021)*
Notes: Values in parentheses denote underlying standard errors (S.E). The S.E significant at 5%
and 10% levels of significance are indicated by * and ** respectively.
124
7.6. Comparison of Effects of Trade Liberalization Policy on Carbon
dioxide, Sulpher dioxide and Composite Index of Emissions
This part presents comparison of net effects of trade policy liberalization on
different types of emissions like Carbon Dioxide (CO2), Sulpher dioxide (SO2) and
Composite index of emissions. The net impact of trade liberalization policy on SO2 and
composite index of emissions through channel variables is generated just like explained
earlier in case of CO2 emissions.
The results reveal that almost all channel variables have an impact in the same
direction across different type of emissions. However, the net impact varies in direction
and magnitude. The net effect of trade liberalization policy is harmful for environment in
case of carbon dioxide emissions and the composite index of emissions. However, in case
of SO2 emissions, technique effect is so dominating that it turns the net effect favorable
for environmental quality by reducing emissions of sulpher dioxide. This finding of lower
SO2 emissions suggest that as an economy becomes more liberalized, it tends to have
stringent environmental standards, which are also consistent with the results of Grether,
et al. (2007). The findings of Antweiler, et al. (1998) and Birdsall and Wheeler (1992)
also validate the favorable impact of liberalized trade policies in terms of lower sulpher
dioxide emissions.
125
Table: 7.5. Comparison of Effects of Trade Liberalization Policy across Emissions
Channel Variable Effect of Trade
Liberalization on
CO2 Emissions
Effect of Trade
Liberalization on
SO2 Emissions
Effect osf Trade
Liberalization on
Composite Index of
Emissions
(1) (2) (3)
Scale 0.080
(0.040)*
0.492
(0.204)*
0.079
(0.039)*
Technique -0.081
(0.046)**
-0.501
(0.212)*
-0.065
(0.019)*
physical Capital -0.020
(0.008)*
-0.133
(0.014)*
-0.022
(0.010)*
Energy Use 0.040
(0.005)*
0.126
(0.014)*
0.053
(0.027)*
Human Capital -0.042
(0.005)8
-0.302
(0.081)*
-0.039
(0.017)*
Democracy 0.013
(0.007)*
0.009
(0.000)*
0.013
(0.007)**
Poverty 0.034
(0.020)**
0.056
(0.008)*
0.028
(0.003)*
FDI 0.091
(0.022)*
0.078
(0.005)*
0.088
(0.012)*
Corruption -0.009
(0.002)*
-0.044
(0.002)*
-0.010
(0.002)*
Manufacturing 0.010
(0.003)*
0.036
(0.012)*
0.010
(0.003)*
Total Net Effect 0.115
(0.021)*
-0.183
(0.055)*
0.134
(0.016)*
Notes: Values in parentheses denote underlying standard errors (S.E). The S.E significant
at 5% and 10% levels of significance are indicated by * and ** respectively.
126
7.7. Tests Based on the Residuals from the Equations of Emissions
To formally test the possibility of omission of any important channel variable
from the model, we regressed residual vector from the equations of emissions on trade
liberalization policy index. This will show statistically significant estimates if any
important channel variable is omitted from the regression equations. The results given in
Table 7.6, however, indicate that this is not the case. In case of all the emissions, the
residual effect of trade liberalization index is highly insignificant. This strengthens our
confidence in the robustness of the model.
Table 7.6: Regression of the Residuals from the Equations of Emissions on the
Trade Liberalization Policy Index
Variables CO2 Emissions SO2 Emissions Index of
Emissions
(1) (2) (3)
Constant -0.002
(0.005)
-0.008
(0.214)
-0.003
(0.004)
Trade Policy
Liberalization
0.021
(0.020)
0.082
(0.089)
0.023
(0.021)
R2 0.005 0.004 0.007
No. of observations 194 188 194
Notes: Values in parentheses denote underlying standard errors (S.E). The S.E significant
at 5% and 10% levels of significance are indicated by * and ** respectively.
127
7.8. Conclusion
This chapter empirically explores the effect of trade liberalization policy on
environmental quality through different channel variables. The results indicate that there
are ten broad pathways through which trade liberalization policy has an indirect impact
on emissions either positively or negatively. The results also indicate that positive effects
(increase in emissions) on emissions dominate in case of CO2 emissions and composite
index of emissions, however, in case of SO2 emissions negative effects (reduction in
emissions). This result is robust to alternative specifications.
128
Chapter 8
FORECASTING ANALYSIS
8.1. Introduction
It is very crucial to evaluate the performance of a macro-econometric model
having done the regression analysis. Different criterion can be applied for this purpose.
This chapter examines the performance of regression analysis done in the preceding
chapter through data forecasts. The chapter is divided into four sections. Section 8.2
discusses relevant measures for evaluating forecasting accuracy which will be applied in
subsequent parts of this chapter. Section 8.3 provides the results of within sample
forecasts. Section 8.4 is about testing model’s performance using out of sample
forecasting technique. Graphical analysis of forecasted and actual time series data helps
to verify if both are moving in the same direction or not.
8.2. Statistical Measures for Forecasting Evaluation
It is desirable to check the predictability having estimated a model so that it may be used
by the policy makers for policy purpose if it fulfills the minimum criterions of tracking
ability. We need to calculate the forecast bias; the tendency of a forecasting method to
over or under predict. There are many evaluation methods to measure the forecasting
error i.e the ways to quantify the difference between values implied by an estimator and
the true values of the quantity being estimated. Some of the most common measures of
predictive accuracy are: ME (Mean Error), MPE (Mean Percentage Error), RMSE (Root
Mean Square Error), RMSPE (Root Mean Square Percentage Error) and TIC (Theil’s
129
Inequality Co-efficient). The smaller the error, the better the forecasting ability of the
model according to these criterions. Formulas for the above measures are given as under.
𝑀𝐸 =1
𝑇 ∗ 𝑁∑ ∑(𝑦𝑖𝑡
^ − 𝑦𝑖𝑡
𝑇
𝑡=1
)
𝑁
𝑛=1
𝑀𝑃𝐸 =1
𝑇 ∗ 𝑁∑ ∑ [
𝑦𝑖𝑡^ − 𝑦𝑖𝑡
𝑦𝑖𝑡]
𝑇
𝑡=1
𝑁
𝑛=1
𝑅𝑀𝑆𝐸 = √1
𝑇 ∗ 𝑁∑ ∑(𝑦𝑖𝑡
^ − 𝑦𝑖𝑡
𝑇
𝑡=1
)
𝑁
𝑛=1
2
𝑅𝑀𝑆𝑃𝐸 = √1
𝑇 ∗ 𝑁∑ ∑ [
𝑦𝑖𝑡^ − 𝑦𝑖𝑡
𝑦𝑖𝑡]
𝑇
𝑡=1
𝑁
𝑛=1
2
𝑇𝐼𝐶 =√ 1
𝑇 ∗ 𝑁∑ ∑ (𝑦𝑖𝑡
^ − 𝑦𝑖𝑡𝑇𝑡=1 )𝑁
𝑛=1
2
√ 1𝑇 ∗ 𝑁
∑ ∑ (𝑦𝑖𝑡^𝑇
𝑡=1 )𝑁𝑛=1
2
+ √1
𝑇 ∗ 𝑁∑ ∑ (𝑦𝑖𝑡
𝑇𝑡=1 )𝑁
𝑛=1
2
Theil’s inequality coefficient (TIC) is also known as Theil’s U. It provides a measure of
how well an estimated time series compares to corresponding actual time series. Its
numerical value ranges between 0 to1. If Theil’s coefficient equals zero then we have the
perfect fit and a value of one means that the forecast is no better than a naïve guess.
8.3. Within Sample Forecasts
Within-sample forecasts are used to compare the actual data with the data predicted by
the estimated model. The forecasted performance of the model is examined by using
within-sample
130
Figure 8.1: Actual and Forecasted Series of the Endogenous Variables
(Within-Sample Forecasts)
CO2 Emissions SO2 Emissions
― Actual . . . Forecasted ― Actual . . . Forecasted
Composite Index of Emissions Physical Capital
― Actual . . . Forecasted ― Actual . . . Forecasted
Scale Effect Technique Effect
― Actual . . . Forecasted ― Actual . . . Forecasted
-1.2-1
-0.8-0.6-0.4-0.2
00.20.40.6
1 4 7 10 13 16 19 22 25 28 31 34 37 40
-8
-7
-6
-5
-4
-3
-2
1 4 7 10 13 16 19 22 25 28 31 34 37 40
-3
-2.5
-2
-1.5
-1
-0.5
0
1 4 7 10 13 16 19 22 25 28 31 34 37 407
7.5
8
8.5
9
9.5
1 4 7 10 13 16 19 22 25 28 31 34 37 40
9.5
10
10.5
11
11.5
12
12.5
13
13.5
1 4 7 10 13 16 19 22 25 28 31 34 37 400
1
2
3
4
5
6
7
8
1 4 7 10 13 16 19 22 25 28 31 34 37 40
131
Foreign Direct Investment Energy Use
― Actual . . . Forecasted ― Actual . . . Forecasted
Manufacturing Human Capital
― Actual . . . Forecasted ― Actual . . . Forecasted
Democracy Corruption
― Actual . . . Forecasted ― Actual . . . Forecasted
-10
-8
-6
-4
-2
0
1 4 7 10 13 16 19 22 25 28 31 34 37 404.8
5.2
5.6
6
6.4
6.8
1 4 7 10 13 16 19 22 25 28 31 34 37 40
0.5
1
1.5
2
2.5
3
3.5
4
1 4 7 10 13 16 19 22 25 28 31 34 37 4017
17.4
17.8
18.2
18.6
19
19.4
19.8
1 4 7 10 13 16 19 22 25 28 31 34 37 40
2
2.5
3
3.5
4
4.5
5
1 4 7 10 13 16 19 22 25 28 31 34 37 40
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
123456789101112131415161718192021222324252627
132
Poverty
― Actual . . . Forecasted
forecasts. Figure 8.1 consists of the graphs of actual and predicted values of the
endogenous variables. It is quite apparent that the forecasted series appear to reproduce
the general long run behavior of the historical data. Short run fluctuations in some of the
cases are, however, are not reproduced well as there is minor over / under prediction in
some of the variables. Overall, predicted values of the majority of the variables track their
actual values well.
By using the forecasted and actual values for each endogenous variable, ME (Mean
Error), MPE (Mean Percentage Error), RMSE (Root Mean Square Error), RMSPE (Root
Mean Square Percentage Error) and TIC (Theil’s Inequality Coefficient) are calculated.
Table 8.1 provides the calculated values of these statistics along with the mean values of
each endogenous variable. The systematic bias statistic for each endogenous variable,
measured by ME and MPE, shows that the model is estimated fairly well. The deviation
for all the variables is less than two percent which depicts that the predicted values track
the historical values quite suitably. The value of Theil’s Inequality Coefficient (TIC) is
almost zero for all of the endogenous variables, again indicating the higher predictability
of the model.
0
1
2
3
4
5
1 4 7 10 13 16 19 22 25 28 31 34 37 40
133
Table 8.1: Statistical Tests from the Model Validation: Within-Sample Forecasts
Actual
Mean
Forecasted
Mean SME SMPE RMSE RMSPE TIC
CO2 Emissions -0.306 -0.271 0.035 0.143 0.051 1.776 0.092
SO2 Emissions -5.258 -5.623 -0.364 0.07 0.416 0.079 0.007
Emissions Index -1.753 -1.73 0.023 -0.014 0.05 0.025 0.008
Scale 11.763 11.78 0.017 0.001 0.115 0.01 0.000
Physical Capital 8.191 8.191 0.000 0.000 0.056 0.007 0.003
Manufacturing 2.914 3.029 0.115 0.041 0.154 0.055 0.009
Technique Effect 6.555 6.559 0.003 0.000 0.043 0.007 0.001
FDI -4.589 -4.341 0.248 -0.041 0.479 0.074 0.011
Human capital 18.531 18.53 -0.001 0.000 0.08 0.004 0.002
Democracy 3.96 4.055 0.095 0.027 0.193 0.054 0.006
Corruption 3.451 3.397 -0.054 -0.017 0.185 0.056 0.008
Energy Use 6.094 6.067 -0.028 -0.005 0.046 0.007 0.001
Poverty 3.785 3.661 -0.124 -0.032 0.158 0.039 0.006
8.4. Out of Sample Forecasts
In out-of-sample forecasts historical data is used to forecast forward. “Empirical
estimates based on out-of-sample forecasts are generally considered better and more
trustworthy than the evidence based on within-sample forecasts performance (White,
2000b). The latter can be more sensitive to the presence of outliers and data mining. Out-
of-sample forecasts also better reflect the information available to the forecaster in real
time. This has led many researchers to regard it as an ultimate test of a forecasting model
(Stock & Watson, 2007).”
To perform an out-of-sample forecast, the model is re-estimated using data from
1971 through 2005 truncating the sample period by 5 years. The values of endogenous
variables are then forecasted on the basis of actual information available on the
134
exogenous variables. The results of the out-of-sample forecasts illustrated graphically in
Figure 8.2 reveal that the predictions from the model are reasonably trustworthy.
Figure 8.2: Actual and Forecasted Series of the Endogenous Variables
(Out-of-Sample Forecasts)
CO2 Emissions SO2 Emissions
― Actual . . . Forecasted ― Actual . . . Forecasted
Composite Index of Emissions Physical Capital
― Actual . . . Forecasted ― Actual . . . Forecasted
Scale Effect Technique Effect
― Actual . . . Forecasted ― Actual . . . Forecasted
0
0.1
0.2
0.3
0.4
0.5
0.6
2007 2008 2009 2010 2011
-6
-5.5
-5
-4.5
-4
-3.5
2004 2005 2006 2007 2008
-1.5
-1.3
-1.1
-0.9
2005 2006 2007 2008 20098.2
8.5
8.8
9.1
9.4
2007 2008 2009 2010 2011
3
5
7
9
11
13
15
17
2007 2008 2009 2010 2011
6.3
7
7.7
2007 2008 2009 2010 2011
135
Foreign Direct Investment Energy Use
― Actual . . . Forecasted ― Actual . . . Forecasted
Manufacturing Human Capital
― Actual . . . Forecasted ― Actual . . . Forecasted
Democracy Corruption
― Actual . . . Forecasted ― Actual . . . Forecasted
-3.5
-3.1
-2.7
-2.3
-1.9
-1.5
2007 2008 2009 2010 2011
6.1
6.2
6.3
6.4
6.5
6.6
2007 2008 2009 2010 2011
2
2.5
3
3.5
4
2007 2008 2009 2010 201118.7
18.9
19.1
19.3
19.5
2007 2008 2009 2010 2011
3.5
3.7
3.9
4.1
4.3
4.5
4.7
4.9
2007 2008 2009 2010 2011
2.8
3.2
3.6
4
4.4
2005 2006 2007 2008 2009
136
Poverty
― Actual . . . Forecasted
The out-of-sample forecasts errors are given in Table 8.2. The statistical results are
consistent with the graphical analysis. It shows that out-of-sample forecast errors are
generally in line with the within-sample forecast errors despite the fact that the former
errors are expected to be larger than the latter. The significantly smaller value of TIC also
depicts the better predictability.
Table 8.2: Statistical Tests from the Model Validation: Out-of-Sample Forecasts
Actual
Mean
Forecasted
Mean SME SMPE RMSE RMSPE TIC
CO2 Emissions 0.311 0.33 0.019 0.058 0.031 0.098 0.152
SO2 Emissions -5.084 -4.971 0.113 0.021 2.09 0.375 0.039
Emissions Index -1.169 -1.151 0.018 1.86 0.37 8.016 0.088
Scale Effect 12.66 12.218 -0.442 -0.035 0.932 0.074 0.003
Physical Capital 8.825 8.858 0.034 0.004 0.034 0.004 0.000
Manufacturing 3.067 3.181 0.114 0.021 0.144 0.032 0.007
Technique Effect 7.134 7.129 -0.005 0.003 0.312 0.042 0.003
FDI -2.59 -2.451 0.139 -0.058 0.412 0.173 0.031
Human capital 19.215 19.223 0.008 0.000 0.012 0.001 0.000
Democracy 4.314 4.274 -0.04 -0.007 0.608 0.149 0.016
Corruption 3.579 3.587 0.009 0.000 0.563 0.151 0.022
Energy Use 6.479 6.468 -0.011 0.002 0.121 0.019 0.001
Poverty 3.314 3.285 -0.028 0.029 0.375 0.164 0.015
2
2.5
3
3.5
4
2006 2007 2008 2009 2010
137
8.5. Conclusion
The literature provides many criteria for evaluating the performance of a
macroeconometric model with each having some problems in its application. This chapter
examines the performance of our model by using data forecasting techniques. Graphs
have been constructed to illustrate if the predicted values go in the same direction as the
actual values. The model used in this study has shown great tractability along with the
quite small mean errors and TIC tending to be zero, almost. Thus, the model can be used
as a tool for carrying out structural analysis, forecasting and policy formulation.
138
Chapter 9
SUMMARY, CONCLUSION AND POLICY IMPLICATIONS
9.1. Summary
The trade liberalization is a disruptive social and economic process that invariably
creates winners and losers. It has led to concerns over the sustainability of environment.
It will be naive to expect that an increasingly integrated world will invariably provide
more benefits than costs. The existing literature demonstrates no consensus regarding the
effects of the trade policy liberalization on environmental quality. Keeping in view the
ambiguity in the trade-environment nexus, this study is the first endeavor in the trade-
environment nexus for exploring the other socioeconomic and institutional channel
variables in addition to the most traditional scale, technique and composition effects. The
present study develops the trade openness measure which is more relevant to examine
effects of the trade liberalization policy on environmental quality.
Since very few studies have been conducted in Asian region in a panel data
framework, the present study is expected to make a significant contribution to the
existing knowledge by exploring the additional channels in the trade-environment nexus.
In this scenario, this study has attempted to test the phenomenon using panel data of
selected SAARC and ASEAN member countries for which the required data is available.
To avoid the endogeniety bias, the Generalized Method of Moments (GMM) / Dynamic
Panel Data has been applied. Since the selection of the panel is not random, the fixed
139
effects model has been applied in the present study, which tackles the cross-sectional
heterogeneity.
A trade policy liberalization index has been constructed by using the estimates of
both import and export models. This index is the weighted average of three major
indicators of the trade policy named as import duties, export duties and dummy for
liberalization status indicator (Sachs-Warner and WTO). The weights indicate that the
liberalization status receives the greater weight in construction of the index followed by
import and export duties. The constructed index turns out to be empirically consistent and
sound (Wacziag, 2001; Zakaria, 2011). The graphical plot of the index indicates more or
less analogous pattern for the panel countries.
9.2. Conclusion
The study examines the effects of the trade liberalization policy on imports and
exports of the panel countries. The estimated empirical findings are strong and robust in
different model specifications. Reductions in export and import duties have a significant
positive effect on imports and exports of the panel countries with the overall impact on
imports being greater than exports. Moreover, the liberalization dummy, which is a
measure of more liberalized trade regime and is used to proxy the non-tariff barriers, has
a significant positive influence on expanding trade volumes. This result is consistent with
the previous studies. Estimated parameters of other variables are in accordance with the
findings of previous literature. Most of the variables affect in the expected direction and,
in general, are statistically significant.
140
The study has also evaluated the impact of the trade liberalization policy on the
environmental quality by exploring different channel variables. The empirical findings
reveal a mixed but moderate effect of the trade liberalization policy on the environmental
quality, which is in conformity of the existing literature. Trade liberalization policy
appears to affect environmental quality differently through different channels. The net
affect also varies across different pollutants.
The trade liberalization policy affects CO2, SO2 and composite index of emissions
positively, leading to deteriorated environmental quality, through six out of ten channels.
The channels which appear damaging to the environment include scale effect, energy use,
manufacturing, democracy, poverty and foreign direct investment. However, the trade
policy liberalization benefits environment by decreasing emissions through four channels
which include technique/income effect, physical capital, human capital and control over
corruption. The net impact of liberalized trade policies is detrimental to the environment
in case of carbon dioxide and composite index of emissions. However, in case of sulfur
dioxide emissions, the overall net impact appears beneficial to the environment by
lowering the SO2 emissions.
It will be pertinent to point out that despite having some common characteristics;
each country in the panel differs somewhat in terms of trade and environment policies.
The Asian economies have made a good progress on liberalizing trade regimes and
cutting tariffs since the early 1990s. The discussion in the chapter 2 of this study
elaborates that almost all countries have undergone the trade liberalization process
though its intensity and time differs somewhat. The fixed effects model has been applied
in the present study which tackles the cross-sectional heterogeneity. Depending upon the
141
different levels of the trade liberalization and the stringency of environmental policies,
the estimated impacts will be more pronounced for relatively more open economies.
This study has also examined the performance of the model by applying standard
forecasting techniques such as within-sample and out-of-sample forecasts. The graphs for
within-sample and out-of-sample forecasts are constructed to determine whether the
forecasted values go in the same direction as the actual values. The model tracks data
well and has very small mean prediction errors. The Theil’s Inequality Coefficient (TIC)
also approaches zero in almost all cases. Thus the model can be used as a tool for
carrying out structural analysis, forecasting and policy evaluation.
9.3. Policy Implications:
This study offers useful insights regarding interaction between the trade
liberalization policy and the environmental quality. The estimated model has some policy
relevance for policy makers. In the wake of the trade liberalization, excessive imports
over exports have created the balance of payments problems, which have serious policy
implications. One particular policy implication points to imports and exports: they should
be liberalized in such a manner that a balance is achieved between the both.
In the trade-environment nexus, this study has arrived at some interesting
conclusions, which are mixed in nature. It justifies the ambiguity regarding the impact of
the free trade on the environmental quality through different channels offering opposing
effects. Overall, the findings of the present study necessitate the policy formulation to be
multi-dimensional for dealing with simultaneously occurring positive and negative
impacts. In order to cope with the negative (adverse) impacts of the trade liberalization,
142
the channels indicating negative impacts of the trade openness policy need an appropriate
policy formulation in those areas to minimize the adverse effects. The negative effects
emerging through some of the channels are indispensable but in some of the cases can be
mitigated by appropriate policy formulation. For example, the trade liberalization causes
increase in energy usage which in turn escalates emissions. In this particular case, for
improving the environmental conditions and reducing emissions, renewable energy
sources, which are environment friendly, may be promoted by governments.
The channels indicating a positive (favourable) impact on the environmental
quality includes income per capita, human capital and lowering the corruption levels.
This highlights the issues, such as importance and constructive role of awareness as well
as affordability and governance in improving the environmental conditions. Such aspects
need to be promoted through appropriate policy response.
In nutshell, there is a dire need to further invest in humans in terms of knowledge
and income levels to help them in becoming aware of the environmental problems and
afford them to say ‘No’ to environmentally hazardous goods. In the wake of ongoing
climatic change, environmental issues should be given a paramount consideration while
designing institutional and policy reforms.
9.4. Limitations of the Study and the Way-Forward
The analysis employed in this study has several limitations that create ample
opportunities for further research in this area. It is generously accepted that the present
study is not by any means the last word on whether the freer trade is good or bad for the
selected Asian countries’ environment. Yet the strength of the analysis contained in this
143
study helps in pointing out the ways in identifying different channels through which the
trade liberalization can affect environment either positively or negatively. Since a very
little is known about the relative pollution intensities in different sectors across countries,
results of the study should be taken as indicative and exploratory rather than final.
More exhaustive research work is needed, however, to assess the impact of the
trade liberalization policy on the environmental quality by further disaggregating the
channels variables and using the micro level data for individual countries. Another
possible extension of this study might be to utilize the more accurate data on pollution
intensities in different traded sectors across countries. Moreover, the data on the
environmental quality is limited, which causes restrictions on the part of researcher
towards performing a comprehensive analysis. The future research might be focussed in
utilizing wide-ranging data comprising of all sorts of indicators including air quality,
water quality and solid waste material.
144
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159
Appendix-A: Country Specific Effects
Variables C
arb
on
Dio
xid
e
Tra
de
Lib
erali
zati
on
Ind
ex
Sca
le E
ffec
t
Ph
ysi
cal
Cap
ital
Man
ufa
ctu
rin
g
Sh
are
Tec
hn
iqu
e
Eff
ect
En
ergy U
se
Dem
ocra
cy
Corr
up
tion
Fore
ign
Dir
ect
Inves
tmen
t
Pover
ty
Hu
man
Cap
ital
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Countries’ Fixed Effects Estimates (Differential Intercepts of cross-sections)
Bangladesh -0.671 -0.908 2.485 0.211 0.634 -0.482 0.012 0.070 -0.189 0.126 -0.148 0.176
India 0.347 0.006 -0.671 0.104 -0.172 -0.278 0.125 -0.101 -1.669 -0.561 0.833 0.515
Indonesia 0.385 0.855 -0.541 -0.014 -0.441 -0.016 0.031 -0.212 -0.531 -0.213 0.283 0.152
Malaysia 0.262 0.537 -0.699 -0.144 -0.280 0.673 -0.064 -0.307 1.118 0.075 -0.365 -0.455
Pakistan -0.074 0.466 -0.694 0.032 0.049 -0.274 -0.022 -0.783 0.200 0.539 -0.194 -0.005
Phillipines -0.032 -0.110 -0.164 -0.032 0.087 0.126 -0.140 -0.266 -0.018 -0.144 0.053 -0.122
Sri-Lanka -0.703 -0.192 0.201 0.076 0.394 -0.206 0.108 1.161 0.750 0.473 -0.479 -0.177
Thailand 0.300 0.218 0.580 -0.084 -0.244 0.380 0.026 0.427 0.054 -0.305 0.016 0.011
160
Appendix-B: The Baseline Model
Model Derivation
Producers Side:
N population of economic agents
A small open economy
Two factors of production K & L
Two final goods X & Y
X is capital intensive that pollutes the environment
Y is labor intensive and does not pollute
Constant Returns to Scale (CRS) and hence production technology can be described by a
unit cost functions cx (w , r ), cy (w , r )
Y is numeraire good with Py = 1
Relative price of X is P = Px/Py
Trade Barriers exists i.e Pd ≠ Pw
pp d where β measures the trade frictions
β >1 implies Pd > Pw so country imports good X
β < 1 implies Pd < P
w so country exports good X
β = 1 implies Pd = P
w there are no trade barriers
xez Z is proportional to x, e is decreasing in θ i.e. as the abatement
techniques increase emission levels decrease
x
xa xa units of X used in abatement technology
τ are pollution emissions taxes imposed by the govt.
Producers are faced with the problem of profit maximization i.e
xxyx
Nx rKwLLKxP ),(
Where )()1( ePP N
First order condition for the choice of θ implies:
0)( eP
)( eP
161
Hence 0/ withp
And pee / with 0e
Consumer Side:
Consumers are faced with the problem of utility maximization
Their utility depends on consumer goods X & Y and also on the pollution
Consumers differ in their preferences over pollution
Two categories: Ng are Green consumers who care greatly about the environment
(Greens) and Nb = N – Ng are Brown consumers (Browns) who care less about the
environment.
Each consumer maximizes utility, treating pollution as given. Their indirect utility
functions of the i’th group is given as:
zp
NGuzNGpV ii
_
/,/,
i = {g , b} and δg > δb ≥ 0
G = National Income, G/N = per capita income
ρ(p) = Price index
Real per capita income can be defined as:
I = p
NG
/
So accordingly,
zIuV ii _
Government Side:
The government chooses a tax that maximizes the weighted sum of each group’s utility
i.e.
bg VVN 1max
λ is the weight that government put on the greens. It is an indicator of the
government policy regarding environment. Higher the λ the more concerned is the
central authority on the cleaner environment.
The overall income of the economy is given by
162
zLKpRG N ,,
Here R is private sector revenue. It is considered as the function of net prices, capital
endowment and the labor endowment, while, z is the government tax revenue.
Solving the government’s maximization problem:
bg VVN 1max
or ]_[1]_[max zIuzIuN bg
Now, the 1st order conditions for the choice of τ are as under:
0]1[
d
dz
d
dIIuN bg
(12)
d
dz
d
dIIu bg ]1[ (13)
Since I = p
NG
/
pN
zLKpRI
N
.
,,
d
dzZ
d
dP
P
R
PNd
dI N
N..
)(.
1
From maximization of overall income of the economy,
0.
Z
d
dP
P
R N
N
So,
d
dz
PNd
dI.
)(. (14)
By replacing eq(14) into eq(13), we get
d
dz
d
dz
PNIu bg ]1[.
)(.
bg
PNIu
1
)(.
163
Iu
PN bg
]1)[(.
Iu
P
Iu
PN
bg )(.1)(.
),(.1),( IPMDIPMDN bbgg
),(1 IPN bg
),(. IPT (15)
Where T = bgN 1 refers to country type that includes both the consumers are
governments preferences regarding the environment.
Pollution supply and its Decomposition:
Equation (15) is pollution supply that in effect given by the government policy that sets
the price for polluting.
Eq. (1) wd pp
wd PP ˆˆˆ (1)~
From eq. (15)
IpT IMDpMD ,,ˆˆˆ
Putting eq. (1)~ we can get the decomposition of the pollution supply.
IpT IMDpMDpMD ,,,ˆˆˆˆ (16)
Here both elasticities are positive.
164
Pollution Demand and its Decomposition:
Now the purpose is to derive an equation that links trade to the pollution emissions. As
already described in chapter 1, trade affects the pollution levels through three channels:
scale, technique, and composition effects. Now, scale of the economy can be defined as:
ypxpS yx
00 (17)
Where scale S is determined by the value of the economy’s output at base year prices p0.
Choosing units so the base year prices are unity, the emission levels this can be written
as:
exz (2)
Or Sez (18)
Here φ denotes the proportion of X commodity in the total output. The above equation
provides the dependence of the pollution levels on the following:
Pollution level is determined by the pollution intensity e of the dirty industry,
It depends on the overall scale S of the economy,
It depends on the relative importance of the pollution-producing commodity φ in
the industry.
After taking natural log of (13) and the differentiating we have,
eSz ˆˆˆˆ (19)
Here ‘۸’ denotes percentage change. The above equation gives us a simple division of the
pollution levels into the scale effect, technique effect and the composition effect (here
hats denote percentage changes). But because a change in prices creates opposing
composition and technique effects, it is necessary to divide each into its more primitive
determinants.
We can solve for the share of X in total output φ as a function of the capital labor ratio ĸ
= K/L, relative prices pd, pollution taxes τ and base year world prices (suppressed here).
Output supplies depend on pollution taxes only through their effect on emission
intensities. That is, we can write
LKpepxx dd ,,/, (20)
165
LKpepyy dd ,,/, (21)
Given base year world prices (normalized to one) and the linear homogeneity of supplies
in K and L, the composition of output can be written as:
),,(1/
/ ep
yx
yx
yx
x d
(16)
Differentiating the composition effect yields
eP e
d
pˆ (22)
Here all elasticities are positive.
Since pee / with 0e
Taking log and differentiating eq. (5)
dpe (5)~
Now, putting eq. (1)~ wd PP ˆˆˆ in eq. (5)
~ we get,
pe (23)
Substituting eq. (23) in eq. (22),
ˆˆˆˆ PP e
d
p (24)
Putting eq. (23) & eq. (24) in eq. (19),
eSz ˆˆˆˆ
(19)
ˆˆˆˆˆˆˆˆˆ
,,,
PPPSz ep
eepep pSz ,,,,,, 11ˆ1ˆˆ (25)
This gives us the decomposition of demand for pollution.
166
Final Equation:
Combining the equations (25) and (16), a reduced form equation can be obtained, that
links pollution emissions to some economic variables.
IpT
pSz
IMDpMDpMD
eepep
,,,
,,,,,,
ˆˆˆ
11ˆ1ˆˆ
IT
pSz
IMDee
PMdepPMdep
ˆ.ˆ1ˆ1
11ˆ11ˆˆ
,,,
,,,,,,,
TpRkSz ˆˆˆˆˆˆ654321
(26)
Where µ1 = 1 >0
µ2 = , > 0
0]1[ ,,3 RMDp
0)]1)(1([ ,,,4 pMDep
0)]1)(1([ ,,,5 pMDep
0]1[ ,6 e