naila jabeen dr. rehana siddiqui dr. eatzaz...

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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

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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

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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

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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

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Chapter 5 DATA SOURCES, CONSTRUCTION OF VARIABLES AND

ECONOMETRIC METHODOLOGY

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5.1. Introduction

5.2. Data Sources

5.3. Construction of Variables

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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

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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

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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

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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

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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

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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

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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

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Table 5.1 The Normalized Weights for the Construction of Composite

Index of Emissions

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Table 5.2 Summary of Description, Construction and Sources of

Variables

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Table 6.1 Correlations between Trade Liberalization Policy Index and

its Components

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Table 6.2 Correlation Matrix of Trade Liberalization Index and alternate

Measures of Trade Openness

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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)

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Table 7.4 Contribution of Trade Policy Liberalization on CO2

Emissions

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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

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Table 8.1 Statistical Tests from the Model Validation: Within-Sample

Forecasts

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Table 8.2 Statistical Tests from the Model Validation: Out-of-Sample

Forecasts

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List of Figures

Figure 4.1 Graphical Representation of Analytical Framework 51

Figure 6.1 Graphical Representation of Constructed Trade Liberalization

Policy Index

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Figure 6.2 Country-Wise Graphical Representation of Constructed Trade

Liberalization Policy Index

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Figure 6.3 Graphical Presentation of constructed Trade Liberalization

Index and alternate Measures of Trade Openness

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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)

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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

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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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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

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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 -


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


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


revenue) 28.60 24.14 21.63 15.65 14.43 11.41


tax revenue) 35.79 31.18 28.62 21.40 17.72 13.33


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


products (%) 28.26 22.04 19.79 7.19 5.4 5.31




revenue) .. 29.97 28.95 18.69 17.52 21.46


tax revenue) .. 33.37 31.43 20.65 20.09 23.70


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


products (%) 25.76 24.3 20.01 9.5 11.58 8.67




revenue) 26.02 19.10 15.54 11.08 13.67 20.31


tax revenue) 27.44 22.73 18.76 13.14 13.47 23.84


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


products (%) 105.36 84.9 22.26 20.98 15.47 13.89




revenue) .. .. .. 29.93 32.39 26.55


tax revenue) .. .. .. 42.51 42.51 ..


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


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


revenue) .. .. .. .. 9.72 7.44 4.56


tax revenue) .. .. .. .. 12.01 8.78 5.58


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


products (%) 14.07 13.62 9.87 7.54 6.28 6.75


simple mean, all products (%) 13.42 13.42 9.88 8.34 7.18 8.6


revenue) .. .. 12.15 5.08 4.08 2.06


tax revenue) .. .. 12.47 5.09 3.09 1.99


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


products (%) 18.74 16.74 10.76 9.86 6.00 5.24


simple mean, all products (%) 22.18 17.92 12.35 11.19 6.95 6.81


revenue) .. 5.55 3.49 2.74 3.14 2.10


tax revenue) .. 6.10 3.75 2.45 4.38 ..


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


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


Pit∗ , Pit = IFS, Country


(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


Mit = IFS, Country


TRFxit Average export

duties TRFit

x = D it

x

Xit

Dit x = Gross export duties (millions of


Xit = value of exports

Dit x = GFS, country


Xit = IFS, Country


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


Pit m = IFS, Country


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


so2it = EDGAR, online

Popit = WDI, online

N2Oit Nitrous Oxide

emissions per capita

N2Oit = n2oit / Popit so2it = Total Sulpher Dioxide

emissions


n2oit = EDGAR, online

Popit = WDI, online

84

CH4it Methane emissions

per capita

CH4it = ch4it / Popit ch4it = Total Methane emissions


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


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


source

Index for corruption ranges from 0

(the maximum level of corruption) to

6 (the minimum)


Risk Guide (ICRG)

GSit Government

Stability

GSit is proxied by an index


source

Index for government stability ranges

from 0 (the most unstable) to 12


Risk Guide (ICRG)

85

LOit Law & Order LOit is proxied by the index


source

Index of law & order ranges from 0

(the worst conditions of law & order)

to 6


Risk Guide (ICRG)

FDit Fiscal Deficit FDit = GFDit / NGDPit GFDit = Government Fiscal Deficit

(millions of local currency units)

NGDPit = nominal GDP (million of


GFDit = IFS, Country


NGDPit = IFS, Country


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


NYind

it = WDI, online



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


Conit = WDI, online



BQit Bureaucratic Quality BQit is proxied by an index


source.

The index ranges from 0 (poor

quality) to 4 (best quality)


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



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



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)*

-


108


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)*

- - -


109


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


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)*


(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)*


(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)*



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



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


Scale Effect Technique Effect


-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


Manufacturing Human Capital


Democracy Corruption


-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


Composite Index of Emissions Physical Capital


Scale Effect Technique Effect


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


Manufacturing Human Capital


Democracy Corruption


-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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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

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