12/11/2014

POLLUTION HAVENS

Or

FACTOR ENDOWMENT

A STUDY OF AGRICULTURE

SECTOR

A dissertation submitted to the Pakistan institute of

Development Economics, Islamabad in partial

fulfillment of the requirement of the degree of Master of

Philosophy in Environmental Economics

DEPARTMENT OF ENVIRONMENTAL

ECONOMICS

SUBMITTED BY: ASFAND YAR TAREEN

REGISTRATION # 28/M.Phil.-

ENV/PIDE/2012

SUPERVISOR: Dr REHANA SIDDIQUI

ii

DECLARATION

I Asfand Yar Tareen solemnly declare and affirm on oath that I myself have authored this MPhil

Thesis with my own work and means, and I have not used any further means except those I have

explicitly mentioned in this document. All items copied from internet or other written sources have

been properly mentioned in quotation marks and with a reference to the source of citation.

Asfand Yar Tareen

iii

ACKNOWLEDGEMENTS

Praise is only for Allah who created the Universe and the mankind, it is he who taught what I know

of and what I will know of. After my Allah my achievement till this stage was never possible

without the support of my Parents. Secondly I would like to show my gratitude to my supervisor

Dr Rehana Siddiqui whose concern and engagement made me solve the complicated issues arising

at different stages of my thesis. I also like to show my gratitude to Dr Wasim shahid Malik whose

lectures actually made me understand and use economics. Last but not the least am grateful to all

those who helped me in the process of degree and my thesis.

Asfand Yar Tareen

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Contents List of tables and figures ................................................................................................................. v

Abstract .......................................................................................................................................... vi

chapter # 1 ...................................................................................................................................... 1

Introduction .................................................................................................................................... 1

chapter # 2 ...................................................................................................................................... 5

Literature review ............................................................................................................................ 5

chapter # 3 .................................................................................................................................... 11

Theoretical framework ................................................................................................................. 11

chapter # 4 .................................................................................................................................... 21

Data & methodology .................................................................................................................... 21

chapter # 5 .................................................................................................................................... 34

Results & discussions ................................................................................................................... 34

Conclusions & recommendations ................................................................................................ 48

Appendix ....................................................................................................................................... 49

References .................................................................................................................................... 52

v

LIST OF TABLES AND FIGURES

Table 4 1 Descriptive Statistics Of Variables ............................................................................................ 25

Table 5 1 Regression Results ..................................................................................................................... 36

Table 5 2 Sensitivity Analysis ..................................................................................................................... 43

Figure 4 1 Agriculture share of GDP % 2011 ........................................................................................... 22

Figure 4 2 Agricultural Pollution Concentration of Carbon di oxide Equivalent by Country 2011 ...... 23

Figure 4 3 Distribution of Agricultural Pollution Concentration C02 Equivalent ................................. 24

Figure 4 4 Distribution of Agricultural Pollution Concentration log - C02 Equivalent ......................... 24

Diagnostic Test 1 Time Fixed Effect ......................................................................................................... 49

Diagnostic Test 2 Pesaran's test of cross sectional independence ............................................................ 49

Diagnostic Test 3 Wooldridge test for autocorrelation in panel data ....................................................... 50

Diagnostic Test 4 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model

.................................................................................................................................................................... 51

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ABSTRACT

This paper investigates the pattern of trade for agriculture sector using dataset of 20 countries

(developed and developing) from 1980-2011 using decomposition method. This method is helpful

in analyzing how different economic factor effect the agriculture sector while “decomposing the

impact on pollution into “Scale, Composition and Technique effects”. Scale variable used in

analysis is the Agricultural output to its overall agricultural land, Composition comprises of the

Capital to Physical labour ratio, and for Technique Research & Development Expenditures in

Agriculture sector is used as Proxy. This variable is the key variable to finding the pattern of trade.

With high level of Research Expenditures leads to increase in income level hence it will be useful

to determine the pattern of trade difference among the developed and developing countries. The

papers finds that “Pollution Havens” do determine the pattern of trade but also shows that

investment by corporate firms in developing countries are using clean technologies hence reducing

the level of pollution in these developing countries in agriculture sector. And with Trade intensity

pollution concentration are reduced in the agricultural sector thus is beneficial to the environment.

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1

CHAPTER # 1

INTRODUCTION

1.1 STATEMENT OF THE PROBLEM

Some of us are environmentalists, while others are economists, but both are interested in debate of

international trade its impacts in various areas, and its theories. In 1970s these debates lead to its first

stringent environmental regulations in developed countries of the world. Which were continued in the

international trade agreement “North American Free Trade Agreement (NAFTA)” and “Uruguay Round of

the General Agreement on Tariffs & Trade (GATT)” and later with creation of “World Trade Organization

(WTO)”. Such debates of NAFTA and GATT have subsided, but the fundamental issue remains alive of

the trade and environment. Korber (1995); Kuznets (1955) discusses the rising inequality from trade

according to them some economies may benefit from trade while to others it may result in losses and these

losses can occur on the environment; Kuznets (1995) also argues that forces that caused inequality

(difference) includes the trade, labour supply, as well as technology.

Apparently there are two theories that conclude this debate “Pollution havens and Factor Endowment”

environmentalist’s agreement is on “Pollution havens hypothesis”, whereas economists agree with “factor

endowment”. “Pollution havens” suggests countries with low environmental regulations or having low

income level developing countries gets dirties with trade, “factor endowment” on the other hands suggests

with free trade countries get cleaner because more and more industries are transferred to countries that are

capital abundant developed countries having strict environmental regulations. Both of these theories have

its merits and demerits and both state that advantage from trade is determined by country’s factors of

production, competitive advantage, or income. Tobey (1990); Grossman, Gene M Krueger (1995) also

agrees with “factor endowment” as the sole determinant for trade not by differences in their policies.

There are numerous studies conducted to find the effect of trade intensity on Environment; among them are

Grossman, Gene M Krueger (1995); Taylor & Copeland (1994) who developed a theoretical framework

and decomposed the impact from trade intensity into three effects; the Scale, Composition and Technique

2

using concentration levels for SO2 this method is becoming useful to analyse different effects on pollution,

where different interaction terms 1 are also generated which also reveals insightful conclusions. The

empirical work using the same method of Scale, Composition and Technique was further complemented

by Taylor, Werner Antweiler, Brian R Copeland (2001).

The studies conducted however are not sector specific, we know economy is segregated into

different sectors where agriculture is the main sector for developing countries. Agriculture being

the most important sector of the economy highly contributes to GDP in developing economies. In

Pakistan share of agriculture is 14% where for United States this share is 1%2. Trade effect on

different sectors can be different because each sector rely on different sets of inputs comparative

advantage, capital abundance level as well as different level of Income. Grossman and Krueger

study on NAFTA was based on these three effects (but the composition was specific to only one

country Mexico), however this research will complement the same theoretical framework but with

a set of 20 countries having both developed and developing countries. And it also determines how

trade intensity will affect the agricultural sector while determining the patterns of trade and its

various effects from interaction terms3. Interaction terms will play an important role because trade

intensity alone cannot determine pattern of trade, changes in only trade intensity will show the

change in “Scale, Composition, and Technique” domestically. The income4 difference exploited

across developed and developing countries in “technique effect” will be used to find out the effect

1 Interaction terms are generated using Trade Intensity variable with relative Variables of Scale, Composition & Technique; whereas relative variables are generated by dividing each country variable with the Average of that variable dataset. 2 Share of agriculture sector in GDP for each Country explained in coming chapters calculated using this research data set 3 Interaction terms are generated using Trade Intensity variable with relative Variables of Scale, Composition & Technique; whereas relative variables are generated by dividing each country variable with the Average of that variable dataset. 4 For Technique effect Proxy of Research & Development Expenditures are used, this expenditure increases the level of income and it varies across countries, such variation is exploited to find the Pattern of Trade among both Developed and Developing Economies.

3

of trade intensity on environment using agricultural sector and determine whether “Pollution

Havens or Factor Endowment” determine the Pattern of trade. The reason income difference in

“technique effect” is used for analysis is because both theories predict that trade intensity will alter

economy’s composition which depends on countries comparative advantage and both (Grossman

and Krueger 1995) suggests to allow policy to change with level of income.

1.2 RESEARCH QUESTIONS

There are numerous questions often asked when determining the pattern of Trade, “Does increased

growth with induced Trade intensity affect the environment? Does trade follow “Pollution Haven

Hypothesis or Factor endowment” Hypothesis? Do different policies have different effects on

environment”?

1.3 HYPOTHESES.

1. Trade intensity negatively effects the environment.

2. Pattern of trade is determined by “Factor Endowment Hypothesis”

3. FDI negatively effects (Increase in Pollution) the environment in developing countries.

1.4 RESEARCH GAP/SIGNIFICANCE OF THE STUDY

“To contribute to the existing research and fill the gap”, This study will describe and determine the

environmental consequences of trade intensity using agriculture sector and dividing it into “Scale,

Composition and Technique Effects” including relative strengths, magnitudes & the pattern of trade;

“Pollution Havens or Factor Endowments”. Existing contribution to trade and environment is backed by

Azhar, Khalil, & Ahmed (2007); Vilas-Ghiso & Liverman (2007); Zhang (2012), Azhar, Khalil, & Ahmed

(2007) conducted his study in Pakistan which finds the correlation with two dependent pollutants; air and

water; this study is not sector specific which may have different effects from Trade intensity, whereas Vilas-

Ghiso & Liverman (2007) performs his analysis on Mexican agriculture, whereas Zhang (2012) research is

4

on energy sector. In light of above this research will take part in determining trade intensity impact on

agriculture sector, and may contribute to policy.

1.5 ORGANIZATION OF THE STUDY:

Chapter 1 presented the main idea behind the research as well numerous questions often asked by

researchers about trade and environment, the hypothesis and significance of the Study.

Chapter 2 will complements this area of study by discussing various literatures which methodology

is used in other literature, how and what determines the trade patterns as well as discussions of

various results in literature. Chapter 3 will determine the theoretical framework used under study,

whereas data description, procedures used to gather data, and variables definitions will be

explained in Chapter 4, Chapter 5 will show the results and discussions from analysis, and Chapter

6 will contain a conclusions drawn from findings, discussion and recommendation

5

CHAPTER # 2

LITERATURE REVIEW

2.1 TRADE ENVIRONMENT DEBATE

There are numerous debates conducted between economists and environmentalists to discuss the effects of

trade intensity on environment, some have agreed to the benefits of trade intensity while others demand

protective measures before liberalization. These discussions and negotiations were conducted in “North

American free trade agreement (NAFTA)” and the “Uruguay round of general agreement of trade and tariffs

(GATT)”. According to Herman Daly (1993), “growth is as if risky, will lead to degradation of

environment”. These debates were intensified with the creation of “World Trade Organization (WTO)”

which resulted in improvement in solving disputes among the nations and provided a better platform for

negotiations. In 1994 Esty (1994) highlights these disputes in his popular writing “Greening the World

trade”, according to him restriction on trade liberalization is considered a “consummate evil”, but for

“environmentalists ultimate good lies in protection of land, water and air”. Under these negotiations it was

agreed upon that limiting trade as a tax for enforcing environmental settlement will be beneficial, on the

other hand traders see it a discouragement to view such initiatives as unfavourable trade barriers.

“Beyond this ‘‘political economy’’ argument lies a trans-boundary environmental spill overs create a risk

of ‘‘market failure’’ that could undermine the international economic order and compromise the gains from

an open world trading system” Bhagwati, J Srinivasan (1996), 167. Baumol & Wallace (1988) Rules to

control externalities is important for active markets making, and making environment important part of

trade policy. In these discussions there were also several other issues highlighted individually by countries;

India who asked for intellectual property rights; for Nigeria certain pesticides were banned; according to

U.S policies these pesticides may be environmentally harmful but for poor developing countries where

deaths from malaria is quite high, trade-off for such country might lead to different results. Such issues

often arise time to time in these discussions, where new countries who follow environmental regulation

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often come across with several reasons where Environment is considered secondary than growth rate of

their country.

2.2 DIFFERENT RESEARCH METHODOLOGIES

The effect of trade on environment is studied by numerous researchers. They have used different theories

and methodologies to prove the effect of trade intensity on economy as well as on environment, there

empirical testing’s show different results based on their analysis and type of country or industry used in

dataset; Tobey (1990) used the HOV Model for its empirical analysis, he uses 11 resource endowments of

different industries that relies heavily on resources from environment such as mining, metals, paper pulps,

chemicals etc ; this HOV model focuses on multiple commodity model &multiple factor of production.

According to him; he found stringent environmental regulations in 1970 have not affected the patterns of

trade such as “Pollution havens” does not determine the pattern of trade.

Stokey (1998) studies the long run growth using an inverted U-shape relationship between income and per

capita income. He found that tax and voucher scheme seems to have advantage over direct regulation

because of their availability for “capital accumulation”

Gale & Mendez (1998), discusses the empirical relationship between trade and environment, they included

SO2 in their analysis to capture the effects of scale, trade and policy, they find that increase in country

activity has negative effect on environment, whose relation was not inverted U-shaped, and also finds that

trade policy didn’t show a significant effect; its effects were ambiguous. Pollution rose with its abundance

of capital.

Grossman & Krueger (1991); Copeland & Taylor (1995) uses a decomposition method of “Scale,

Composition and Technique effect” to determine effect of trade intensity, they found that air quality has

deteriorated with trade. Taylor & Copeland (1994); Taylor, Werner Antweiler, Brian R Copeland (2001);

Vilas-Ghiso & Liverman (2007) also decomposition method of “Scale, Composition and Technique effect”

to determine effect of trade liberalization, according to this theory countries with higher income specializes

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in relatively clean factors of production than countries with lower income level which produces dirty goods,

they also found that “factor endowment” plays a significant role in determining the “pattern of trade”

.

Strutt & Anderson (2000) a case study that investigates Indonesia, it uses a GTAP model, GTAP model is

used in projecting the outcome. This model investigates to explore the policy induced effect from economic

activity, projecting its level of changes in composition of output and in techniques of production with its

change in environmental indicators. “This model uses air as well as water pollution in its analysis, it projects

different pollutions; for air it includes the carbon di oxide, sulphur , Nitrogen, for water it includes BOD,

COD, DS, SS these pollutions are generated from Paddy rice, livestock, food processing, textiles, clothing,

paper products, chemicals, rubber, manufactures and households, and projections were made for 2010 and

2020”. To determine policy induced effect, using same pollutants as well as decomposed data of sectors,

projection of reduction of pollution under Uruguay Round table were also made.

2.3 POLLUTION AS DEPENDANT VARIABLE

There are many studies which use Pollution as Dependent variable. Earlier studies includes Akbostanci,

Tunç, & Türüt-Asik; McGuire (1982); Trade and Environment, Volume 6: A Theoretical Enquiry (1980);

Copeland & Taylor (1995); Copeland & Taylor (2003); Pethig (1976), Pethig uses one primary sector as

an input, whereas Rudiger uses two primary inputs, Macguire uses a sector model with two primary sectors

as an input. Recent work includes the Copeland and Taylor uses it for many good with many factors and

many different pollutants.

2.4 TRADE PATTERN

“Pollution havens” as detailed by most environmentalist is the sole determinant of pattern of trade,

Costanza et al (1995) finds simple correlation between national income level of country and its policy level.

Other studies determine the pattern of trade and “pollution havens”, Mani & Wheeler (1998). They argued

that “pollution havens” is low wage pollution havens, which shows that environmental regulations increase

with income. According to this research there are two causes of increase in industrial pollution regulations.

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Firstl demand for better environment increases with its income level, secondly developed government

institutions are better capable of implementing their environmental regulations. They estimated their results

for dirty industries such as Pulp and Paper and Non-Metallic Mineral products, Iron & Steel, Non Ferrous

Metals, Chemicals, and compared it with clean industries such as Non-electrical machinery, Textiles,

Transport equipment and Instruments. They also found that “pollution havens” do not have major

significance because “a major part of increase in dirty sector share is highly income elastic, and with

continuous income growth elasticity has declined”. They also found other factors that have also affected

them such as energy price shocks and energy subsidies in developing countries. Thus showing

environmental regulations increases with income and plays a significant role in shifts from dirty to clean

sector.

Taylor & Copeland (1994); Taylor, Werner Antweiler, Brian R Copeland (2001); Azhar, Khalil, & Ahmed

(2007) decomposes their model into “Scale, Composition and Technique effects”, Werner Antweiler,

Brian R. Copeland uses the panel data for its estimmates on different cities, they also measure elasticity

estimates to determine the pattern of trade. According to them “factor endowment” plays a significant role

in pattern of trade. Their results rely on several estimates from effect of Trade liberalization, which includes

the Trade induced compostion effect, and results from various interaction terms. In the end they argued that

trade liberlization reduces Pollution intensity and “factor endowment” plays a significant role in transfer of

industries from poor to rich countries.

2.5 STUDIES ACROSS PAKISTAN

Azhar, Khalil, & Ahmed (2007) conducted their research in Pakistan they use the same method of

decomposing its model into “Scale, Composition and Technique effect” which uses two pollutants water

and air pollutant, they use Vector error correction method to find the relationship, according to them air and

water pollution holds a significant effect on level of pollution between the selected countries.

Trade intensity or Trade liberalization has been an important variable to determine the effect of Trade in

analysis, several measures have been used to find the effect of trade liberalization Leamer (1988), among

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them is the most commonly used (Exports + Imports)/Gdp, this variable is used in known studies as given

above this includes Taylor, Werner Antweiler, Brian R Copeland (2001); Azhar, Khalil, & Ahmed (2007).

Whereas Acharyya (2009); Gamper-Rabindran & Jha (2004) uses dummy variable before and after

liberalization period to measure the effect on environment.

2.6 MEASURE OF FDI TO FIND THE EFFECT OF TRADE ON ENVIRONMENT

FDI is one of the greatest source of transfers from one country to other. It can be in the form of FDI flows

i.e. in form of income and FDI stocks that can be in form of machinery equipment etc. FDI does have a

significant effect on countries, if these transfer are in the right clean industry it will results in expansion of

industry and reducing the overall pollution level in the world as well as in the economy and vice versa. FDI

can benefit in various forms it includes “capital transfer, skills and technology, market access and

promotion”. Studies which use FDI to measure effects on environment are Acharyya (2009); Damijan et al

(2003) Acharrya uses the industry level panel data to find the effect of FDI he find co-integration to estimate

the relationship. Results from this study show positive relationship between inflow of FDI and GDP

Growth. He also points out that “without having proper empirical estimates on the relationship between

sectorial decomposition of FDI inflows and sectorial decomposition to environmental damages it is

premature to conclude either way, it is because pollution intensities and emissions differ across

sectors”.Acharyya (2009), 11. Whereas Damijan uses (Research and Development Expenditures (RnD) and

FDI as its measure in transition economies. According to him technology is being tranferred through FDI,

but on the other hand other than the FDI is the RnD which acts as a vehicle to growth in clean goods, there

are “four ways technology can be transferred from foreign investment this includes the 1. Institution effect

2. Competition effect 3. Foreign knowledge effect 4. Training effect”.

Muhammad, Samia, & Talat (2011) investigates a nonlinear relationship between FDI effect on

environment using data of 110 developed and developing countries using emissions from energy the linear

and nonlinear terms are included in the data, results show environmental degradation increase with FDI

and that environmental Kuznets curve is relevant in the data set used. Kuznets curve shows that with

increase in income first environmental degradation increases but as time passes and income further

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increases this results in reduction of degradation of environment. The theoretical framework which shows

“environmental Kuznets curve which is inverted U-shaped is possible has to follow certain conditions with

increase in income”. Other studies GFredriksson (1999), chap 5 also prove Kuznets curve which uses both

air and water pollution data for developed and developing countries to measure the industrial pollution in

Economic development, they found that air pollution results were consistent with Kuznets curve but water

pollution gave ambiguous results he used total industrial pollution data to the share of manufacturing

output.

CONCLUSION

Above studies show that in some economies, industries and sectors “Pollution Haven” is proven while in

other “factor endowment” determines its pattern of trade. These studies are however lacking the important

sector agriculture. Neglecting this sector will results in numerous consequences and raise cost in future and

trade being one instrument that may result in increase in pollution in environment or help reduce pollution.

“The two theories “Pollution havens or Factor Endowment” can be used together to determine the mix of

trade that may not result in losses to environment because the sole purpose of policy would be optimum

output with less damage to the environment or reduction in damage from either policies or mix of policies”.

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CHAPTER # 3

THEORETICAL FRAMEWORK

The review of literature helped in analysis of the theoretical model which was completely explained by

Taylor, Werner Antweiler, Brian R Copeland (2001). This theoretical framework circulates around some

major Questions: How trade effects the environment? Is “factor endowment” the major source of pattern of

trade for agriculture sector or “Pollution haven Hypothesis” is the major impediment force in determining

the pattern of trade? And how endogenous pollution policy or change in income will effect the environment

through trade patterns? Trade patterns are determined from two methods “Factor endowment” as

economists suggests that capital intensive countries will relocate to more developed countries with openness

to trade. Whereas “Pollution Haven Hypothesis” states, relative countries with lower income, will become

dirtier with trade with relocation to developing countries.

Gale & Mendez (1998); Taylor & Copeland (1994); Copeland & Taylor (1995); Taylor, Werner Antweiler,

Brian R Copeland (2001) decomposed Pollution level into “Scale, Composition and Technique effect”.

They found positive relationship by exploiting the panel structure. Trade intensity resulting in increase in

income leads to reduction in pollution level. There do exists a positive effect due to scalar increase resulting

in increase in pollution level and “composition effect” resulting in positive effect, whereas “technique

effect” results in reduction of pollution level. The technique outpace the scale and “composition effect”.

They also pointed out trade induced changes in composition of nations output, but there is less evidence to

believe that intensity effects composition of output in all countries equally this is because composition of

output depends on comparative advantage of the countries and their relative strength of the three effects of

scale, composition and technique.

As discussed pollution consequences of income growth depends on trade induced income change created

from capital accumulation, however differences exists; capital accumulation promotes production of dirty

goods and neutral technological progress do not. These consequences are dependent on sources of growth.

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3.1a ECONOMIC THEORY

There are N agents in an economy, which produces goods from two industries with two primary factor of

production; capital and physical labour K & L. These two industries produce two output but one industry

which is the capital intensive industry produces dirty good whereas the other industry which is labour

intensive industry produces clean good; dirty industry is the industry X and pollution is generated from this

industry as its by product, whereas clean industry Y, there also exists constant returns to scale in production

technology. We describe the unit cost function as:

𝐶𝑋 (𝑤, 𝑟)

𝐶𝑌(𝑤, 𝑟).

Y is a Numeraire and relative price of X is denoted by P

𝑃 = Ω𝑃𝑤 --------------------------------------(3.1)

“Since countries differ in their location and trade barriers domestic prices are not identical to World prices

Ω, represents trade frictions and PW is common relative price of X. Ω > 1 if X is imported & Ω < 1 if X is

exported”

3.2a POLLUTION ABATEMENT

Pollution emission is denoted by E, which is produced from emission of X, but firms have access to identical

factor intensities as all other activities in the industry, hence we treat X units as inputs in abatement, if firm

has gross output of firm is 𝑥 units and allocates 𝑥𝑎 units to abatement then net output is:

𝑥𝑛 = 𝑥(1 − 𝜙)

𝜙 = 𝑥𝑎 /𝑥

Where 𝜙 represents the “intensity of abatement”. “If Pollution is proportional to output and abatement is

constant returns to its activity level, then Pollution emissions can be written as”

𝐸 = 𝑒(𝜙)𝑥-------------------------------------- (3.2)

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Where 𝑒(𝜙) is emission per unit of X output and is decreasing in 𝜙.To reduce pollution government uses

taxes 𝜏, the firm creates profits 𝜋𝑥 for a firm by producing X are given by revenue less factor payments,

pollution taxes and abatement costs. Using (3.1) and our definition of 𝜙, we may write profits as

𝜋𝑥 = 𝑃𝑁 𝑥 − 𝑤𝐿𝑥 − 𝑟𝑘𝑥-----------------------------(3.3)

𝑃𝑁 = 𝑃(1 − 𝜙) − 𝜏𝑒(𝜙)

𝑃𝑁 is the net producer price of gross output. Because there exists constant return to scale, hence

the output of an individual firm is indeterminate, for any level of output, and the first order

condition for the choice of 𝜙 is

𝑃 = −𝜏 𝑒/(𝜙)----------------------------------------(3.4)

Where 𝜙 = 𝜙(𝜏

𝑝),𝜙/ > 0

Then emission per unit of output is

𝑒 = 𝑒 (𝜏

𝑃)---------------------------------------------(3.5)

Where 𝑒/ < 0. The production side equilibrium condition are (3.2) and (3.4) and the zero profit

and employment conditions are

𝑝𝑁 = 𝐶𝑥 (𝑤, 𝑟) 1 = 𝐶𝑦 (𝑤, 𝑟)

𝑘 = 𝐶𝑟𝑋 𝑥 + 𝐶𝑟

𝑦 𝑦 𝐿 = 𝐶𝑤

𝑥 + 𝐶𝑤𝑦

-------(3.6)

3.3a CONSUMERS PREFERENCES

There are two groups in the society that differ in their preferences the Green who are environmental

friendly 𝑁𝑔 and the Browns 𝑁𝑏 who care less about the environment in their preferences.

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𝑁𝑏 = 𝑁 − 𝑁𝑔

Each consumer maximizes his utility given its pollution level so the indirect utility function for

the 𝑖th is written as:

𝑉𝑖 (𝑃,𝐺

𝑁, Ε) =

𝑘(𝐺

𝑁)

𝜗(𝑃)− 𝜌𝑖Ε---------------------------(3.7)

𝐹𝑜𝑟 𝑖 = (𝑔, 𝑏), 𝑤ℎ𝑒𝑟𝑒 𝜌𝑔 > 𝜌𝑏 ≥ 0

G is income per capital (So (𝐺

𝑁) is the income per capita which is obtained through agricultural

sector production), Where 𝜗(𝑝) price index and k is increasing and concave. Pollution is a pure public

bad, but green suffers a greater disutility than Browns. We can write this as:

𝐼 =

𝐺𝑁

𝜗(𝑃)

3.4a GOVERNMENT POLICY

Since there are two sets of preferences for environment, Government uses its policy to maximize the level

of Utility for both groups it is possible through:

𝑀𝑎𝑥𝜏 𝑁[ 𝛽[𝑉𝑔 + (1 − 𝛽)𝑉𝑏)]-------------------------(3.8)

where 𝛽 is the weight Government puts on Green individuals, but a more realistic logic tells that

Government attitude varies towards caring for consumer preferences varies across regions of both

developed and developing economies, developing economies care less about the environment.

To maximize the utility based on these different groups across different countries. Utility is maximized,

subject to “Private sector behavior, production possibilities, fixed world prices and fixed trade frictions”

Private sector behavior is represented by research and development expenditure for agriculture sector giving

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us maximized revenue as 𝑅(𝑝𝑁 , 𝐾, 𝐿) Overall income in private sector is Private sector revenue plus

rebated taxes.

𝐺 = 𝑅(𝑝𝑁 , 𝐾, 𝐿) + 𝜏𝐸

First order condition this yields:

𝜇/ (𝐼)𝑑𝐼

𝑑𝜏− [𝛽𝜌𝑔 + (1 − 𝛽)𝜌𝑏]

𝑑𝐸

𝑑𝜏 = 0

With fixed world prices we have:

𝑑𝐼

𝑑𝜏= 1/𝑁𝛿(𝑝)[𝑅𝑝𝑛

𝑑𝑝𝑁

𝑑𝜏+ 𝐸 + 𝜏

𝑑𝐸

𝑑𝜏]

=𝜏

𝑁𝜈(𝑝) (

𝑑𝐸

𝑑𝜏)

Rearranging will yield Samuelson rule:

𝜏 = 𝑁[𝛽𝑀𝐷𝑔(𝑝, 𝐼) + (1 − 𝛽)𝑀𝐷𝑏(𝑝, 𝐼)]

𝑊ℎ𝑒𝑟𝑒 𝑀𝐷𝑖(𝑝, 𝐼) = 𝜌𝑖𝜈(𝑝)/𝜇/

Above is the marginal damage per person where 𝐷𝑙𝑖 > 0 , Simplifying allows us to rewrite (3.9) as:

𝑡 = 𝜏𝛼(𝑝, 𝐼)----------------------------------------- (3.9)

Country Type 𝑡 is

𝑡 = 𝛽𝑁𝜌𝑔 + (1 − 𝛽)𝑁𝜌𝑏

And effective marginal damage is:

𝑡α(𝑝, 𝐼)

Pollution policy therefore varies to Government type and economic conditions.

16

3.5a DEMAND AND SUPPLY OF POLLUTION

There are different economic factors that affect the supply and demand for pollution, decomposing these

into “scale, composition and technique effects” is useful in this regard. As discussed previously private

sector demand for pollution is implicitly defined by (3.2), to rewrite demand in a more convenient way for

our empirical work, Economies scale is defined as “an economy’s scale as the value of national output of

agriculture sector at base year world prices” Scale is measured as

𝑆 = 𝑝𝑥𝑜𝑥 + 𝑝𝑦

𝑜𝑦---------------------------------(3.11)

Pollution Emissions can be written as:

𝐸 = 𝑒𝑥 = 𝑒𝜑𝑆----------------------------------(3.12)

Where 𝜑 is the share of x, above equation shows a decomposition, pollution depends on pollution intensity

of dirty industry e(𝜙), the relative importance of dirty industry in the economy 𝜑 and the overall scale of

the economy S. In differential equation form,

𝐸 = 𝑆^ + 𝜑^ + 𝑒^--------------------------------(3.13)

Where hats denote percentage change, the 1st is Scale, “it measure the increase in pollution generated if the

economy were simply Scaled up holding constant the mix of goods produced 𝜑 and production techniques

e(𝜙)" i.e. if the endowments in a particular economy are increased by 10% and if there exists no change

in output or emission level than we would see a 10% increase in pollution level. Then is the Composition

and technique effects. “Composition effect” can be defined as if we hold the Scale and emissions level of

economy constant than economy which uses more of these goods pollute more. And “all else constant an

increase in emission intensity will increase pollution.”

Quantity index is used as a measure of the level of output as Scale, because of the change in prices which

creates opposing effects of “Composition and Technique effects”, so it makes it compulsory to divide each

into separate determinants. By using (3.6) we can solve for total output share 𝜑 as function of

17

capital/labour, 𝑘 = 𝐾/𝐿, net producers price 𝑝𝑁 and base year prices, Composition of output is given

by 𝜑 = 𝜑(𝑘, 𝑝𝑁).

And we have “composition effect” given by:

𝜑^ = 휀𝜑𝑘𝑘^ + 휀𝜑𝑝 𝑝^𝑁-----------------------------(3.14)

Next to differentiate 𝑝𝑁 we use 3.1 and 3.4 to find

𝑝^𝑁 = (Ὠ^ + 𝑃^𝑤^)(1 + 𝛼) − 𝑏𝑡^---------------------(3.15)

𝑏 = 𝐸(𝜙)𝑏𝜏

Now using 3.1 and 3.5 we find

𝑒^ = 휀𝑒,𝑝

𝑇 (Ὠ^ + 𝑝^𝑤

− 𝑡^)------------------------(3.16)

Where the elasticity of emission intensity with respect to p/𝜏>0. Then combining (3.13-3.16) we obtain

decomposition of demand for private sector.

𝐸^ = 𝑆^ + 휀𝜑,𝜅𝑘^ + [(1 + 𝑏)휀𝜑,𝑝 + 휀𝜑,𝑝

𝑇 ] Ω^ + [(1 + 𝑏)휀𝜌,𝑝 + 휀𝑒,𝜑/𝑇]𝑝^𝑤 −

[𝑏휀𝜌,𝑝 + 휀𝑒,𝑝

𝜏] 𝜏^)-----------------------------------------(3.17)

All elasticities are positive. “If we draw derived demand in {z, T} space, then (3.17) shows that an increase

in Scale, Capital abundance, or the world prices of pollution demand curve to the right. A reduction in trade

frictions shifts the pollution demand curve to the right for dirty good exporter, but to the left for a dirty

good importer”. An increase in pollution tax reduces the demand for pollution through two channels. Firstly

it lowers the demand for pollution by raising abatement cost and by lowering emissions level per unit of X

produced. Secondly “higher pollution taxes will lower producers price of X and creates a shift in the

composition of output that lowers X output of any given emission intensity”. Supply of pollution depends

18

on government policy who sets the price. So decomposition of pollution supply is from 3.1 and 3.10 and

measured as:

𝜏^ = 𝜏^ + 휀𝑀𝐷,𝑝Ω^ + 휀𝑀𝐷,𝑝Ω𝑝^𝑤 + 휀𝑀𝐷,𝐼 𝐼^--------------(3.18)

휀𝑀𝐷,𝑝 > 0 𝑎𝑛𝑑 휀𝑀𝐷,𝐼 > 0

If (3.18) is drawn is two space than increase in investment level (which is the expenditure in research and

development) and relative prices shifts pollution supply upwards. For example if we give more weight to

Greens than Browns, than policy will become more stringent and supply of pollution will shift upward.

Similarly an increase in real income increases the demand for environmental quality shift the pollution

supply upward as well. “An increase in pollution supply makes consumption of market goods more

expensive relative to environmental protection”. This may create pure substitution effect. 휀𝑀𝐷,𝑝 > 0

3.6a “TRADE INDUCED COMPOSITION EFFECT”

PROPOSITION:

Consider two economies that differ in their Trade frictions: (i) if both countries export polluting good, then

pollution is higher in country with lower Trade frictions; (ii) vice versa. This isolates the Trade induced

“composition effect”, although the sign of the effect differs across countries. For an exporter 𝛽 rises with

free trade and raises relative price of dirty good X. This results in a shift of dirty good exporter demand to

the right and pollution supply upward. Pollution demand shifts out towards X; and emission intensities

increase because abatement is now more costly. “This shift in pollution supply lessens the increase in

pollution as pure substitution effect of the goods price increase leads the government to raise the pollution

tax”. However demand side effect results in supply side substitution effect and also increases pollution.

Consequently emissions must rise. This increase in emission represents the trade induced “composition

effect”. Proposition therefore also implies that, if we look at cross countries and hold emissions constant,

one may not find openness per se related in any systematic way to emissions. Proposition is helpful in two

aspects. It captures partial effect of Trade liberalization, and the results from proposition are in fact

19

conditional on Trade patterns but Proposition itself is invisible on determinants of Pattern of Trade. These

issues are treated below. If pollution havens determines the pattern of trade in dirty goods, a positive value

would specify that increase in Composition would increase pollution demand hence shifting the curve

rightward which increase concentration level, whereas negative effect that is reduction in demand for

pollution would shift production possibility curve inward hence reducing pollution level” Taylor, Werner

Antweiler, Brian R Copeland (2001), 895.

3.7a TRADE INTENSITY IMPACT

To measure the full impact of trade liberalization, one must account for change in real income, the Scale

and its Composition. Differentiating (3.12) with respect to Ω, holding world prices, country type and factor

endowments constant, to find

𝑑𝐸

𝑑Ω Ω

𝐸= 𝜋1 (

𝑑𝑆

𝑑Ω) (

Ω

𝑆) − 𝜋3 (

𝑑𝐼

𝑑Ω) (

Ω

𝐼) + 𝜋4----------------(3.19)

A fall in trade frictions produces a “scale effect” and a “technique effect” and the “trade induced

composition effect”. These three effect determine the environmental consequences for trade. Below figure

1 shows the dirty good exporter to a fall in trade frictions, in bottom panel we depict the pollution

consequences of reduction in trade frictions. Before reduction in trade barriers production is at point A,

whereas world prices is pw and net Price is pN. Since we have assumed that country is exporter of dirty

good and therefore has consumption at point to A along the economy budget constraint. Note the values in

world prices of domestic output at A measures the Scale of economy.

In the bottom panel we depict the equilibrium level of pollution both before and after trade frictions, as we

know 𝐸 = 𝑒(𝜑)𝑥. Hence production is at A and intensity is at e (𝜑), pollution is given by EA . when

restrictions on trade falls, world prices and domestic prices becomes equal to each other and production

level moves to point C and where producers price is PN. At point C real income is higher and there is a

change in the techniques that is used in production. Likewise emission will fall to e (𝜑𝐶) and overall

20

pollution falls to EC. This methodology divides the movement from EA to EC into three components.

Holding all else constant first through Scale and the Techniques, trade creates a change in composition of

output by movement from A to B. Corresponding to this movement results in an increase in pollution from

EA to EB

. Showing same as the “Trade induced composition effect.” “Scale effect” is the movement in the

top panel from Point B to Point C. Resulting in Increase in level of pollution is from EB to ES. Finally

technique effect is the fall in pollution from ES to EC as producers switch to clean technology that produces

lower level of emission.

Figure 1 Impact of Trade Liberalization on Environment

pw

PN

B

C

pw

PNt

A

XC XA

E=e(ὨC)x

E=e(ὨA)x

EC

EC

EB

ES

yC

yA

Composition

Scale

Technique

Source : Taylor, Werner Antweiler, Brian R Copeland (2001), 9

21

CHAPTER # 4

DATA & METHODOLOGY

In the study secondary data is used. Selection of most of the data is from Food and Agriculture

Organization (FAO) database. FAO has an extensive database for agriculture, and fisheries, it not

just includes the production data of agriculture but also many other variables of interest, including

agricultural emissions level. FAO has recently updated its emissions database, which is now

extended to 2011 and projections are also made for year 2050. Other useful variables includes,

inward FDI stock and Research & development expenditure. Agriculture sector is considered an

important sector for most economies to grow, this sector contributes to GDP as well as full fill the

world requirement for food. Developing countries heavily rely on agriculture sector, shown in

figure 4.1 in case of Pakistan agriculture contribution to GDP is 14%, for Bangladesh its 10%

whereas for developed countries this percentage is quite less; for Japan and United States Of

America which is 1%.

4.1 SAMPLE

The data acquired for analysis is balanced data analysis. The data used as the sample for my study comprises

a list of countries which are taken from State Bank of Pakistan, it includes the major cumulative trading

partners of Pakistan although these partners do not specify as the agriculture trading partners, but since we

are using the cumulative country list it might compensate for choosing agriculture trading partners, however

even if one do not agree with this list it still is enough for our analysis to find out the pattern of trade because

to determine the pattern of trade we need a list of developed and developing countries which are already

present in this cumulative list. This list contain both developed and developing countries but I dropped 5

countries because data from those countries were missing. The selected countries include; “(Australia,

Bangladesh, China, Germany, India, Indonesia, Italy, Japan, Malaysia, Morocco, Netherlands, Pakistan,

South Africa, Spain, Sri-lanka, Thailand, Turkey, United Kingdom, United States, and Vietnam)” The list

22

contains major countries but due to data unavailability, five countries were dropped, which includes Saudi

Arabia, Korea, Oman, UAE and Singapore. The data of selected countries varies from 1980-2011. So there

are twenty Cross sections and time series of thirty two years.

Figure 4 1 Agriculture share of GDP % 2011

4.2 DEPENDANT VARIABLE

4.2.1 POLLUTANT DESCRIPTION

Anthropogenic activities are considered the main cause of pollution, it affect humans through direct as well

as indirect sources FAO (2001); our dependent variable “agricultural pollutant is linked readily with two,

main pollutants which are generated from greenhouse gas emissions (GHG), plants absorb up to only 50%

of nitrogen fertilizers applied to land, such inefficiency which results in non-carbon dioxide gases are the

methane (CH4) and Nitrous Oxide (N2O), that are generated from production of crops, livestock and

management of activities. Carbon dioxide emissions are mainly due from cultivated organic soil5. Figure

5 http://faostat3.fao.org/faostat-gateway/go/to/download/G1/*/E>

Australia1%

Bangladesh10%

China6%

Germany0%

India10%

indonesia8%

Italy1%Japan

1%

Malaysia7%

Morocco9%

Netherland1%

Pakistan14%

South Africa0%

Spain1%

Srilanka7%

Thainland7%

Turkey5%

UK0%

US1%

Vietnam11%

Australia Bangladesh China Germany Indiaindonesia Italy Japan Malaysia MoroccoNetherland Pakistan South Africa Spain SrilankaThainland Turkey UK US Vietnam

Source: Author

23

4.2 shows that china has the greatest share of agricultural pollutant and the second largest share is that of

India whereas Pakistan has a small share as compared to China and India. Such pollution levels are based

on 2011 estimates. Each observation comprises of giga grams of carbon di oxide equivalent concentrations

of 20 countries from period 1980-2011. And since our data set also comprises of China, India, US, UK as

major countries, the level of concentration from these countries remain high than the other developing

countries. The agricultural pollutant is the total agriculture pollutant in Giga-grams of carbon-di-oxide

equivalent, it comprises of Nitrous oxide (N20) and Methane (CH4), which are generated from management

activities, livestock and crops production. Nitrous oxide is also generated from use of burning of fossil

fuels. Since our pollutant are closely linked to these sectors results may be of interest to public policy.

Throughout this analysis and writing the term agricultural pollutant, pollution intensities, and concentration

will mean the same as pollution level.

Figure 4 2 Agricultural Pollution Concentration of Carbon di oxide Equivalent by Country 2011

Australia8%

Bangladesh3%

China32%

Germany2%

India27%

Indonesia6%

Italy1%

Japan1%

Malaysia1%

Morocco1%

Netherland1%

Pakistan6%

South Africa1%

Spain1% Srilanka

0%

Thailand3% Turkey

2%

UK2% US

0%Vietnam

2%

Australia Bangladesh China Germany India Indonesia Italy

Japan Malaysia Morocco Netherland Pakistan South Africa Spain

Srilanka Thailand Turkey UK US Vietnam

Source: Author

24

4.2.2 CARBON DI OXIDE EQUIVALENT

Carbon di oxide equivalent abbreviated as (CO2 - eq) is used to compare the emission from different

greenhouse gases according to their “global warming potential (GWP)”. “It converts gases to the equivalent

amount of carbon di oxide from its global warming potential”6

GGCDE= (Giga grams of Pollutant) * (GWP of gas)

I also checked the distribution

of my dependent variable,

Figure 4.3 shows the original

plot of data which is skewed

towards left. To have better

results and fix this distribution I

took the log of my complete data,

which give normal distribution

as shown in Figure 4.4, “some

values are shown at the left

hand bar which I consider are

due to temperature variations

which are not observable

through pollutants data”

6 http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Glossary:CO2_equivalent

Figure 4 3 Distribution of Agricultural Pollution Concentration C02 Equivalent

Figure 4 4 Distribution of Agricultural Pollution Concentration log - C02 Equivalent

25

Table 4 1 Descriptive Statistics Of Variables

Variables

Units OBS

Mean

Std. Dev.

Min

Max

Sources of Data Collection

Panel ID

1- 20 640 10.5 5.770791 1 20 State Bank of Pakistan

Year 1980-2011 (32 Years)

640 1995.5 9.240314 1980 2011

logCO2 Log of Gg of CO2 equivalent

640 10.41287

2.046926 2.76569

13.635 FAO

Scale = Value Added

Agri/ Agri land Area

Absolute Value/1000

(Hec) Constant 2005

640 1514520

2539146 928 1.34E+07 UN STATS7

Composition = Capital to labor ratio K/L

USD Million / absolute value in 1000 Constant 2005

640 47.67546

69.26572 0.82372

322.1626 FAO

Technique = RND

USD Million Constant 2005

588 2.13E+08

9.30E+08 0.02002

7.18E+09 FAO/ASTI/ OECD STATS

TI= Exports+ imports/Gdp

Export/imports = 1000 USD, GDP = 1000 USD Constant 2005

640 0.543307

0.399823 0.07998

2.07254 WDI

Fdi Stock /Capital

USD Million Constant 2005

526 136608.2

342774.9 0.16571

2804588 FAO

Temperature

Average Degree Celsius

640 18.99555

6.322251 6.84746

28.9219 GHCNM v3

Precipitat~n Coefficient of

variation

640 1.0277

31

0.433186 0.1201

9

2.77778 GHCNM v2

Time 1-32 640 16.5 9.240314 1 32 Years

Fertilizers Tonns 460 4121077

7178563 0 3.96e+07 FAO

26

4.3 INDEPENDENT VARIABLE

4.3.1 “SCALE EFFECT”

The first and foremost independent variable in analysis is the Scale of a country which is output of

agriculture, it is derived from value added of Agriculture (which is in absolute terms), divided by

agricultural land (1000 (Ha)) area of particular country i.e. this data is available completely from UN

Statistics.

“It measures the increase in pollution as if economy were simply scaled up holding constant the mix of

goods produced and production technique, example if there exists constant returns to scale & endowments

increases by 10% and if relative prices or emission intensities do not change, then pollution will increase

by 10% as well8 ”.

4.3.2 “COMPOSTION EFFECT”

Comparative advantage leads to specialization of countries, “comparative advantage stems from changes

in relative size of the economic sectors following a reduction in trade barriers9”. “The change in the share

of the dirty good in national output, if we hold scale of the economy and emission intensities constant, than

economy devotes more resources to producing the polluting good will pollute more10”

For “Composition effect” ratio of capital to physical labour is used; this is an important variable which

determines the comparative advantage of countries. Countries that are highly capital intensive are dirty

industries relative to labour intensive industries . The data of “composition effect” is derived by obtaining

capital “K” (USD million) was divided by total agricultural labour force “L” (Absolute value in 1000) to

give us the capital to labour ratio (K/L). “Relative capital abundance is derived by dividing each country’s

capital abundance by average of countries in the dataset of the given year.”11.

8 Taylor, Werner Antweiler, Brian R Copeland (2001), 882 9 GFredriksson (1999), 2 10 (Taylor, Werner Antweiler, Brian R. Copeland, 2001, p. 882) 11 Taylor, Werner Antweiler, Brian R Copeland (2001), 892

27

4.3.2a CAPITAL STOCK

Capital stock is defined as the Gross Capital Stock it includes “The activity of crop or animal husbandry,

this measure includes the assets used in the production process covering land development; livestock,

machinery, equipment and structures for livestock”. “The gross fixed capital stock is the value, at a point

of time, of assets held by the farmer with each asset valued at “as new“ prices, at the prices for new assets

of the same type, regardless of the age and actual condition of the assets. The gross capital stock database

is the sum of individual physical assets = (land development + livestock + machinery + equipment +

structures for livestock)”12. The data used from FAO is the Gross investment, which is used to create capital

stock series, depreciation rate for this purpose is taken at 5%. The depreciation rate of 5% is taken for the

sake of simplicity this rate is different across countries as well as across sectors. The procedure to generate

capital stock is given in different papers but the simplest one I find is explained in Hal (2010)

4.3.3 TECHNIQUE EFFECT

Technique effect can be defined as the change in methods of production followed by trade intensity.

Pollution emissions do not necessarily stay constant, its intensity depends on a number of other components

that includes increase in income due to trade intensity, investment liberalization which may bring newer

technologies, relative price of intermediate inputs; a “race to bottom” in which trade with foreign countries

may result in setting lower environmental standards due to political pressure or for protection of domestic

industries, however if consumer demand cleaner goods, trade liberalization may reduce pollution level,

instead stimulating “race to the top” and incentive from lobby groups to pressure government to ease

environmental regulations and if sectorial “Composition effect” results in a shift to more pollution intensive

sectors, both environmental and industry interest intensifies their efforts to receive favours from

environmental policy makers – at higher output level more is at stake, both in terms of profits and

12 http://faostat.fao.org/site/660/DesktopDefault.aspx?PageID=660#ancor

Note: Summary statistics are given in Third Chapter of my Dissertation

28

environmental degradation. GFredriksson (1999), 2. In short holding all else constant, reduction in emission

intensity through any possible means such as research and development, policy options or increase in

income will reduce pollution.

Technique effect to be measured is taken from a proxy variable as Research and development expenditure

(RND, USD million), values of Research and Development Expenditure are in Current 2005 which are

necessary to be corrected because all my data are in Constant 2005. To convert from Current to Constant

2005 GDP Deflator 13 is used. Data of GDP deflator are available in World Development Indicators whose

base year do vary country to country. To rescale the 2010 data to 2005 by first creating an index dividing

each year of the constant 2010 series by its 2005 value of each country leaving 2005 values equal to 1. Then

multiplying each year's index result by the corresponding 2005 current U.S. dollar price value which is give

us Constant 2005 values.

“Relative RND is obtained by dividing each country’s RND by the average data of dataset for given year,

where “data average” as described above are my selected countries”. Data of RND is available in FAO

database and for most OECD countries data is available at OECD Stats. For RND some of the data were

missing, which were simple ignored.

4.3.4 TRADE INTENSITY

Trade intensity variable which is the total exports of particular country plus total imports of particular

country divided by particular country GDP, exports and imports of agriculture trade was not chosen for the

reason because I didn’t find any particular agriculture trade intensity variable14 although that would specify

for agricultural sector. Trade intensity occur due to fall of prices when both domestic and world prices

become equal this is mainly due to fall of trade barriers such as from tariffs or quotas.. Trade intensity

measure is created in various ways some use dummy variables while other use (Exports + Imports) /GDP,

13 https://datahelpdesk.worldbank.org/knowledgebase/articles/114946-how-can-i-rescale-a-series-to-a-different-base-yea> 14 Agriculture Trade intensity Variable was measured and Evaluated, but not presented in the research, the results from this measure was giving some very unreliable.

29

this measure is most commonly used, it is also used by Taylor, Werner Antweiler, Brian R Copeland (2001);

Azhar, Khalil, & Ahmed (2007).

4.3.5 POLLUTION HAVENS HYPOTHESIS

Trade encourages reallocation of pollution intensive industries from countries having strict environmental

policies to less stringent ones, when tightening of environmental policies creates strong effect in allocation,

trade flows and when such countries having low income do not indulge themselves in highly abatement

technologies they get dirtier with trade this results in an effect known as “Pollution havens”. This may occur

because countries may have low level of income or they are facing international competitiveness which

may affect their domestic industries hence domestic country lowers their environmental strictness.

Suppose there is an increase in trade intensity between countries having different environmental regulation.

So Countries that tend to have lower environmental standards develop a comparative advantage in dirty

goods production; as dirty industries from high income countries tend to move to countries having low

environmental regulations Taylor, Werner Antweiler, Brian R Copeland (2001), 877

4.3.6 “FACTOR ENDOWMENT HYPOTHESIS”

“Factor endowment” suggest dirty capital intensive countries from low income countries will relocate to

relatively capital abundant high income developed countries with trade; resulting in decrease in level of

pollution ” Taylor, Werner Antweiler, Brian R Copeland (2001), 877

4.3.7 FOREIGN DIRECT INVESTMENT

Foreign direct investment is investment from abroad in one’s economy. This investment can come in

economy through different ways, it can be through multinationals corporations investing in particular

country in a particular sector. Foreign direct investment can come in form of flows or in form of stocks;

foreign direct investment through stocks can results in transfer of technology, this technological transfer

depends on the country’s methods of production thus transfer as FDI stock can be clean or dirty according

to donor country method of production.

FAO now maintains FDI data, this data comprises of FDI flows as well as FDI stocks, FDI inflow of stock

data contains almost all the countries, others countries data are easily available on OECD stats. FDI inflows

30

for stocks are divided by each country’s Capital stock in this research, call this measure “FDI intensity15”.

This data is also available in current 2005 US$ which was converted to constant 2005 using same

methodology as for converting current RND expenditures to constant RND expenditure. FDI database does

contain some negative as well as missing values which are simply ignored. With all the missing and negative

values we have more than 80% of the data which will be sufficient for analysis and may not create bias in

my analysis. “FDI intensity” variable will play an important role in determining, whether technology

transfers are of clean goods or dirty goods from various countries.

4.3.8 TEMPERATURE & PRECIPITATION

Temperature and Precipitation data are also used in my analysis. The data from all the stations for 20

countries are not available, although each country meteorological department maintains this data but

considering its cost of acquiring it data from all stations could not be used, therefore an open source data

with complete time series is used for analysis. Data for temperature is taken from GHCNM v316, this data is

available in raw form. Data of temperature from each station is taken and yearly averaged to find country’s

average temperature for that year. (Each station data is placed vertically than averaged for given years).

Temperature does play a significant role in determining whether temperature has any effect on pollution

dispersion or not. The data of average temperature17 is in degree Celsius, and for precipitation level we use

the same complete data this data is also maintained as an open source in raw form in GHCNM v218.

Coefficient of variation measure is used to determine the effects from Precipitation in my analysis.

4.3.9 FERTILIZERS

The consumption of fertilizers data is used for sensitivity analysis, this data is also available from FAO

stats website as described. This data however is available from only 1980-2002. Fertilizers data is used as

for testing further proving that “factor endowment” do not play role in determining the pattern of Trade.

15 Taylor, Werner Antweiler, Brian R Copeland (2001), para 6 16 http://www.ncdc.noaa.gov/ghcnm/v3.php> 17 Averaging country data without taking into considering the areas where agriculture production takes place is short

coming of this research 18 http://www.ncdc.noaa.gov/ghcnm/v2.php>

31

4.3.10 DUMMY VARIABLES

Dummies for developed and developing countries is also used in the analysis, these dummies are used with

variables as an interaction term to find out the flow of increase and decrease in pollution level after trade

intensity or as transfer of pollutants. This interaction term with RND and FDI will be quite useful in

determining “pollution havens”.

For data analysis; software such as STATA is used for analysis, since my data is panel based that includes

cross sections as my countries and time series as years. There are two main estimation models for panel,

fixed and random effect models.To find whether model is a fixed or random effect model; we use Hausman

test for identification. STATA includes some very extensive packages for estimation of panel. I also

measured the time fixed affect for our data, and cross sectional dependence test for Analysis. The degree

of confidence was measured at three levels 19.To find whether “pollution havens” play a role or “factor

endowment” we used FDI as one of our estimations as an explanatory variable. For sensitivity analysis I

used the data from 1980-2002 for estimation, I added one more explanatory variable; the use of fertilizer in

agriculture sector.

ESTIMATING EQUATIONS

1:

𝑬 = 𝑿𝒋𝒌𝒕/

𝚿 + 𝒀𝒊𝒋𝒌𝒕/

𝚼 + 𝒆𝒊𝒋𝒌𝒕

𝑿𝒋𝒌𝒕/

𝚿 = 𝚿𝑶 + 𝚿𝟏𝑺𝑪𝑨𝑳𝑬𝒋𝒌𝒕 + 𝚿𝟐 (𝑲

𝑳) + 𝚿𝟑𝑹𝑵𝑫 + 𝚿𝟒𝒌𝒕𝑻𝑰 + 𝚿𝟓𝑭𝑫𝑰

𝚿𝟒𝒌𝒕 = 𝝍𝟎 + 𝝍𝟏𝑹𝑬𝑳.𝑲

𝑳 𝒌𝒕+ 𝝍𝟐𝑹𝑬𝑳.

𝑲

𝑳 𝒌𝒕

𝟐

+ 𝝍𝟑𝑹𝑬𝑳. 𝑹𝑵𝑫𝒌𝒕 + 𝝍𝟒𝑹𝑬𝑳. 𝑹𝑵𝑫𝒌𝒕𝟐

+ 𝝍𝟓𝑹𝑬𝑳.𝑲

𝑳 𝒌𝒕 𝑹𝑬𝑳. 𝑹𝑵𝑫𝒌𝒕

19 (*** Significance at 99.1% level of confidence ,** Significance at 95%,* Significance at 90% level of confidence)

32

2:

𝑬 = 𝑿𝒋𝒌𝒕/

𝚿 + 𝒀𝒊𝒋𝒌𝒕/

𝚼 + 𝒆𝒊𝒋𝒌𝒕

𝑿𝒋𝒌𝒕/

𝚿 = 𝚿𝑶 + 𝚿𝟏𝑺𝑪𝑨𝑳𝑬 + 𝚿𝟐 (𝑲

𝑳) + 𝚿𝟒𝑻𝑰 + 𝚿𝟓𝑭𝑫𝑰 + 𝚿𝟔 (

𝑲

𝑳

𝟐

) + 𝚿𝟕𝑹𝑵𝑫𝟐

𝚿𝒌𝒕 = 𝝍𝟎 + 𝝍𝟏𝑹𝑬𝑳.𝑲

𝑳 𝒌𝒕+ 𝝍𝟐𝑹𝑬𝑳.

𝑲

𝑳 𝒌𝒕

𝟐

+ 𝝍𝟑𝑹𝑬𝑳. 𝑹𝑵𝑫𝒌𝒕 + 𝝍𝟒𝑹𝑬𝑳. 𝑹𝑵𝑫𝒌𝒕𝟐

+ 𝝍𝟓𝑹𝑬𝑳.𝑲

𝑳 𝒌𝒕 𝑹𝑬𝑳. 𝑹𝑵𝑫𝒌𝒕

3:

𝑬 = 𝑿𝒋𝒌𝒕/

𝚿 + 𝒀𝒊𝒋𝒌𝒕/

𝚼 + 𝒆𝒊𝒋𝒌𝒕

𝑿𝒋𝒌𝒕/

𝚿 = 𝚿𝑶 + 𝚿𝟏𝑺𝑪𝑨𝑳𝑬 + 𝚿𝟐 (𝑲

𝑳) + 𝚿𝟑𝑹𝑵𝑫 + 𝚿𝟒𝑻𝑰 + 𝚿𝟓𝑭𝑫𝑰 + +𝚿𝟔 (

𝑲

𝑳

𝟐

)

+ 𝚿𝟕𝑹𝑵𝑫𝟐 + 𝚿𝟖𝑺𝑪𝑨𝑳𝑬𝒋𝒌𝒕𝟐

𝚿𝟒𝒌𝒕 = 𝝍𝟎 + 𝝍𝟏𝑹𝑬𝑳. 𝑲/𝑳𝒌𝒕 + 𝝍𝟐𝑹𝑬𝑳. 𝑲/𝑳𝒌𝒕𝟐 + 𝝍𝟑𝑹𝑬𝑳. 𝑹𝑵𝑫𝒌𝒕

+ 𝝍𝟒𝑹𝑬𝑳. 𝑹𝑵𝑫𝒌𝒕𝟐 + 𝝍𝟓𝑹𝑬𝑳. 𝑲/𝑳𝒌𝒕 𝑹𝑬𝑳. 𝑹𝑵𝑫𝒌𝒕

𝑌𝑖𝑗𝑘𝑡/

Υ contain the climatic variables such as temperature and precipitation which may affect the production

of crops and may result in increase or decrease in pollution level. Above three model contains the

unobservable terms to account for theses exclusions individual effects for 𝑒𝑖𝑗𝑘𝑡 given by:

𝜺𝒊𝒋𝒌𝒕 = 𝜺𝒕 + 𝜾𝒊𝒋𝒕 + 𝝂𝒊𝒋𝒌𝒕

“휀𝑡 𝑖s the time specific effect, 𝜄𝑖𝑗𝑡 is a site specific affect and 𝜈𝑖𝑗𝑘𝑡 is an idiosyncratic error20 for

observation stations “i” in city “j” in country k in year t ”.

20 Measurement error

33

EXPLANATION OF EMPIRICAL MODELS

The empirical model is derived from theory as shown in (3.12) and (3.13) which separate scale,

composition and technique effect. In the First model A scale (absolute value/1000 Hectares); represents the

output in the agricultural sector which is derived from value added agricultural output divided by total

agricultural cultivated area. The second is the capital to physical labour ratio which represents composition

effect, third shows the technique effect which is obtained from use of proxy variable from research and

development expenditure. The other variables in model comprises of relative terms which is obtained by

dividing each country values by data average.

In the second model; a modification is made to model A; the square of independent variables are taken,

such squares are taken because impact of research and development expenditure on pollution also depends

on the existing level of capital to labour as well as existing expenditures of research and development, this

amendment also represents factor accumulation.

In the third model, another modification is made by taking square of scale, this is taken because

“nonlinearity” in the impact of scale could arise from non homotheticities in production and consumption”

“Feasible Generalized Least Square” Feasible Generalized Least Square was chosen after confirming from

Hausman test and several diagnostic tests of my analysis

34

CHAPTER # 5

RESULTS & DISCUSSIONS

My main variables in the model include the Scale, Composition and Technique effect, of the three models

described above. The results show both tests fixed and random effects are significant. To choose among the

models selection is made through “Hausman test”. Fixed effect model using Least Square Dummy variable

approach (LSDV) which show significant results; the probability statistics are significant at *** 99% level

of confidence for all three models. This proves that fixed effect model is the appropriate model for analysis,

but to be sure random effect model is also to be tested for analysis.

As fixed and random effect models are appropriate models for panel data analysis, both are to be tested for

this purpose. I first tested fixed effect model using Least Square Dummy variable approach (LSDV) which

show significant results; the probability statistics are significant at 99% level of confidence for all three

models. This proves fixed effect model as an appropriate model for analysis, but for surety random effect

model is also to be tested for analysis. To check for random effect I use Breusch and Pagan Langragian

multiplier (LM) test, which is applied after estimations for random effect model we find consistent and

significant results for random. Hence proves that random effect model is also the appropriate model for

analysis, therefore we find that there is evidence of significant difference across countries. Since both of

my fixed as well as random effects results are significant I need to identify the appropriate model for

analysis, hence I apply Hausman test for identification. Hausman test helps choose between fixed and

random effect when both fixed and random effects are significantly present. “The Null hypothesis for

Hausman test is model preferred is random effect model rather than fixed effect model”, it tests whether

errors are correlated with the regressors, and the null hypothesis is the errors are not correlated with each

other. In estimating the Hausman test. I faced problem in STATA which gave an error SUEST (Seemingly

unrelated estimation). SUEST is not solved for panel hence a solution is given by, Schaffer & Stillman

(2011), which gave results at 99% level of significance for all the models that Fixed Effect is the appropriate

model for analysis. Hence we can use “Fixed effect model” to find the results. The results of Hausman and

35

LR test are given in Table 5.1. I have also used elasticity estimates for my analysis, elasticity

estimates are calculated through mean elasticities, these estimates are useful to determine the

magnitudes effects from increase in scale, composition and technique effect. To check for other

problems several diagnostic tests are also performed to determine the feasible or appropriate test for

analysis21.From Diagnostic test I have come to result that FGLS is my appropriate model for analysis. To

determine the level of confidence I use three degrees of level of confidence.

*** 99.1 percent level of confidence

** 95 percent level of confidence

* 90 percent level of confidence

5.1.1 SCALE EFFECT

The first and foremost independent variable is the Scale variable. In table 5.1 which show results from

regression of FGLS. In the first model I find a strong negative relationships between Scale of Agriculture

activities (Value added agriculture / Agricultural land of particular Country) to its effect on agricultural

pollutant (i.e. Carbon di oxide equivalent concentrations). These results are consistent with all three model

showing a negative relationship. It means that with increase in Scale of agricultural production level of

pollution will decrease. The results of scale are off-course from my expectations; because according to

theory increase in production will increase the level of pollution in the economy, but as my data set contains

developing countries which are labour intensive which produces clean good22, and developed countries

which are technologically advance; there per hectare production remains high leading to low pollution

concentration than their production level hence leading to overall negative “scale effect” on pollution. The

effect of agricultural production is quite small on pollution reduction. Elasticity estimates for Scale are

measured for all the three models. The elasticity estimates in my model A shows that with one percent

increase in Scale of agriculture there will be 11.2% reduction in agriculture pollution. But in Model B there

21 The results from Diagnostic tests are given in the appendix 22 For details book Trade and Environment by Brian R. Copeland and M. Scott taylor

36

is a little increase in level of pollution this may be because Composition effect outweigh “Scale effect” or

due to further increase in Production, so in Model B my elasticity estimates show that with one percent

increase in Scale of agriculture there will be a reduction of 9.8% of agriculture pollution. This reduction is

less than Model A but as we move on to model three these estimates are further changed, the elasticity

estimates on one percent increase in production results is 38.3%. So the overall elasticity estimates vary

between 11.2% - 33.3%, these estimates as well as the level of significance differs in random effect and

along the models. In random effect only the second model shows the significant results and have a totally

different sign compared to that in “Fixed effect; FGLS model”. The elasticity estimates for random effects

are significant in model B which shows that one percent increase in production there would be about 20%

reduction in level of pollution. The model C of fixed and random effects also contain a square of Scale

which shows factor accumulation and that further increasing the Scale2 will create a positive effect of

increase in Pollution. The elasticity estimates vary “between 9.7 to 38%”.

Table 5 1 Regression Results

Variables Fixed Effect Model Random Effect Model

Test Row A B C A B C

Scale -7.45e-08*** -6.46e-08*** -2.54E-07*** 2.67E-08 1.33E-07*** 9.72E-08

(6.32E-09) (8.93E-09) (4.37E-08) (6.33E-08) (3.40E-08) (1.03E-07)

Scale2 1.64e-11*** -7.36E-12

(3.59E-12) (7.28E-12)

Composition -0.00288*** -0.0101*** -0.0165*** -0.00226 -0.0158*** 0.000531

(0.00115) (0.00289) (0.00276) (0.00203) (0.00258) (0.00492)

Composition2 3.81E-05** 6.02E-05*** 4.23E-05*** -

6.96E-06

-(0.0000127) (0.0000117) (0.0000106) (0.0000199)

Technique

-4.08e-09*** -6.53e-09*** -6.33e-09*** -9.90E-10 -2.96e-09** -5.16e-09***

(3.13E-10) (2.30E-10) (2.26E-10) (5.93E-10) (1.09E-09) (1.11E-09)

Technique 2 1.15e-18*** 1.12e-18*** 5.20e-19* 9.08e-19***

(Tech) (8.31E-20) (8.19E-20) (2.05E-19) (2.38E-19)

37

FDI stock/Capital

(FDI) -0.0138* -0.015* -0.0217** -0.0041 -0.0121*** -0.00412

(0.00695) (0.00787) (0.00815) (0.00521) (0.0026) (0.00487)

Trade Intensity (TI)

-0.616*** -0.697*** -0.818*** 0.499*** -0.946 0.488***

(0.118) (0.123) (0.124) (0.143) (0.1) (0.14)

TI*rel Composition

-0.0337*** -0.00974* 0.0412** -0.0272 -0.0779*** -0.0363

(0.00847) (0.0125) (0.0146) (0.0148) (0.00943) (0.0203)

TI*rel Composition2

0.00109*** 0.000498* -9.97E-05

0.000423*

-0.000706***

0.000534

(0.000159) (0.000223) (0.000228) (0.000192) (0.000154) (0.000305)

TI*relTech 1.11e-08** 1.23e-08*** 1.17e-08*** 2.38E-09 4.67E-09 8.87e-09**

(3.98E-09) (1.51E-09) (1.50E-09) (3.32E-09) (2.41E-09) (3.34E-09)

TI*relTech2 5.57e-19** -2.97e-18*** -2.88e-18*** 1.76E-19 -1.33e-18* -2.39e-18***

(1.96E-19) (2.67E-19) (2.63E-19) (1.57E-19) (5.75E-19) (6.97E-19)

TI*RelTech* RelComposition

-6.40E-11 -1.67E-11 -1.55E-11 -1.73E-11 9.67E-13 3.34E-12

(4.47E-11) (1.77E-11) (1.75E-11) (3.41E-11) (1.79E-11) (3.31E-11)

Temperature -0.0200** -0.0351*** -0.0311*** 0.00794 0.0352** -0.0023

(0.00723) (0.00888) (0.00851) (0.0245) (0.013) (0.0221)

Precipitation 0.328*** 0.340*** 0.298*** -0.0273 -0.0693 -0.0237

(0.0658) (0.0676) (0.0642) (0.104) (0.0528) (0.0971)

Ti*Tech* Developing

0.000298*** 0.000298*** 0.000309*** 0.0000139 0.000105*** 0.0000132

(0.0000522) (0.0000485) (0.0000487) (0.0000259) (0.0000123) (0.0000244)

Hausman/Chi^2 56.12*** 33.75*** 40.19***

LM Test/Chi^2 5078.16*** 5453.92*** 5356.48***

ELASTICITY ESTIMATES

Variables Fixed Effect Model Random Effect Model

Scale elasticity -

0.1124544***

- 0.0975109**

* - 0.3838488*** 0.0402506

0.2014471***

0.1467267

(0.00953) (0.01349) (0.06591) (0.09558) (0.05127) (0.15612)

Composition elasticity

- 0.1451595**

*

- 0.508007***

- 0.8329255*** -

0.1137916*

- 0.7948659**

* 0.026747

(0.05774) (0.14585) (0.13902) (0.10227) (0.13014) (0.24799)

Technique elasticity

- 0.8688637**

*

- 1.388144***

- 1.346751*** -0.2106947 -

0.6290638***

- 1.097017***

(0.06667) (0.04901) (0.04798) (0.12611) (0.23114) (0.0065646)

38

FDI Elasticity -

0.0219621***

- 0.0239137**

* - 0.0346692*** -0.0065362

- 0.0192992**

* -0.0065646

(0.01108) (0.01255) (0.01299) (0.0083) (0.00415) (0.00777)

TI*Technique* Developing

0.0525609***

0.0525207***

0.0545298*** 0.0024573 0.0185911**

* 0.0023207

(0.00921) (0.00856) (0.0086) (0.00458) (0.00216) (0.0043)

N 588 588 588 588 588 588

_cons 11.43*** 11.80*** 11.98*** 10.28*** 10.48*** 10.60***

-0.148 -0.192 -0.187 -0.6 -0.396 -0.523

LR Test/ chi2 775.3*** 786.3*** 942*** 31.93*** 211.6*** 48.03***

Note: The 1st value of the variable shows the coefficient value while the second value represents the standard error whereas Steric shows the level of Confidence

5.1.2 “COMPOSITION EFFECT”

In model A with one percent increase in composition in the economy results in 14% reduction in overall

level of pollution. The variable capital to labour ratio which is “composition effect” shows the country’s

comparative advantage, in model B the elasticity estimates further improve resulting in 51% reduction and

in the last model it shows 83% reduction in pollution level. “composition effect” is an important variable,

this variable shows the cross country differences; “comparative advantage” According to theory;

developing countries are labour intensive and since they are labour intensive they are involved in production

of labour intensive clean goods, whereas; developed countries production results in production of capital

intensive dirty goods. My analysis contains both developed and developing countries the overall negative

“composition effect” on pollution results due to large labour force that is involved in agriculture from

developing countries; which produced clean intensive goods whereas developed economies uses advance

technologies or advance capital as a result their production remains high in proportion to creating pollution

resulting in overall reduction in level of pollution in my data set. Further increasing in level of capital to

labour ratio such as the square of “composition effect” shows will increase concentration levels further

creates a positive relationship hence leading to increase in pollution level.

39

5.1.3 “TRADE INDUCED COMPOSITION EFFECT”

Trade induced composition effect is the effect on composition due to trade intensity, trade induced

composition effect depends on comparative advantage of the country as well as on the sign of trade induced

“composition effect” is different across different countries23. If the country is more polluting than the results

from trade will also results in more pollution but if country has less polluting industries it may result in

decrease in level of pollution. Trade Induced Composition Effect are different across different model, in

model A and B it shows significant and decreasing results in pollution level but in model C it shows

significant results with positive increase in level of pollution. The reason that model C has a positive sign

is because Scale was added in it which shows “Scale effect” over Technique effect.

5.1.4 TECHNIQUE EFFECT

The results from technique effects are also important for my analysis, since these are the extra expenditures

which are used in reduction or improving techniques in production which in case increases the level of

income across different countries. The results from “technique effect” or “trade induced technique effects”

are important to determine the pattern of trade. “technique effect” show consistent results for all three

models, the elasticity from Model A shows that with one percent increase in “technique effect” creates 86%

reduction in pollution level whereas with other models this estimate reduces up to 13%. The range of

technique varies “between” 86% to 138%. But with further increase in Research and development

expenditure will result in increase in pollution levels. This may be because the “technique effect” showed

a negative sign, this sign is consistent according to theory. Composition induced increase in pollution level

5.1.5 “TRADE INDUCED TECHNIQUE EFFECT”

In table 5.1 Trade induced technique effect is the main variable that determines the patterns of trade whether

its “pollution havens” or “factor endowment”, if “factor endowment” were the accurate measure for our

trade pattern we would see a strong positive relationship between pollution concentration and “trade

induced technique effect” in developed countries. “Rich countries would have comparative advantage in

23 Effect of “trade induced Composition effect” is explained in the Chapter 3 of Theoretical Framework.

40

dirty good and we would see a positive shift in pollution demand, where as if “pollution havens” were the

appropriate measure for our analysis we would see a strong positive relationship for developing countries,

developed countries would have a comparative advantage in clean goods leading to negative effect on

pollution concentrations. I use an interaction dummy for Developing countries which show positive

increase in pollution, the elasticity for “trade induced technique effect” dummy estimates vary “between

0.52 to 0.54%”. Hence we come to the conclusion that dirty goods production increase with trade

liberalization in developing countries and there is transfer of dirty goods or factor of production to

developing economies”. Taylor, Werner Antweiler, Brian R Copeland (2001), 895.

“Considering both capital to labour ratio as well as research and development expenditure variable is

important in determining country’s comparative advantage, a positive value would specify that increase in

Composition would increase pollution demand hence shifting the curve rightward which increase

concentration level, whereas negative effect that is reduction in demand for pollution would shift production

possibility curve inward hence reducing pollution level” Taylor, Werner Antweiler, Brian R Copeland

(2001), 895.

5.1.6 TRADE INTENSITY

Next in table 5.1 consider the variables of trade intensity, this variable is a combination of (Exports +

Imports) /GDP. This variable is also used with several other variables as an interaction term to determine

the “pollution havens” or “factor endowment” for analysis. The sign and level of significance vary across

models. Trade intensity shows a negative relationship between pollution level and trade.

5.1.7 TEMPERATURE &PRECIPITATION

In the model it also indicates two site specific variables the temperature and precipitation, it is observed

that with rise in temperature results in more and more disbursement or dissemination of pollution whereas

high precipitation concentration results in low washing of concentrations24 .

24 Taylor, Werner Antweiler, Brian R Copeland (2001), 894

41

CONCLUSIONS

My finding shows that increase in Trade intensity does reduce pollution levels, and “Pollution havens” play

an important role in determining the pattern of trade the results are also similar in Acharyya (2009); Strutt

& Anderson (2000) where they find that “pollution havens” do play role and dirty industries are transferred

to their developing country, agriculture being an important sector of the economy mostly for developing

countries is being polluted with trade and “pollution havens “explains this phenomena, but the overall

pollution is reduced due to “Trade Intensity”

5.2 SENSITIVITY ANALYSIS

Dividing the determinants of pollution in Scale, Composition and Technique is useful to determine the

“Pollution havens” and “factor endowment” as done in the previous section, but to further test these results

and to do “sensitivity analysis” another variable of fertilizers consumption data was added this is useful

because earlier analysis contain only one factor endowment variable capital to labour ratio whereas

fertilizers consumption can also increase the level of agricultural pollution; adding this shows “factor

endowment” expected increase in endowments should have a positive effect or increase in pollution levels.

This fertilizer data was available from 1980-2002. So I used data only from 1980-2002 for my analysis, for

this purpose I also reduced the Time series of my other variables till 2002 and performed my analysis on

this data, this method of performing or reducing time series is also performed in Taylor, Werner Antweiler,

Brian R Copeland (2001). And I have only used Model C to find out the results for “sensitivity analysis”,

so purposively I am using Trade intensity Research and Development dummy and FDI stock intensity

Dummy for developing for my sensitivity analysis, these all dummies are not used in a single model but is

used in different models.

5.2.1 “SCALE EFFECT”

Increase in scale will have a negative effect on pollution concentrations as shown in table. This result is

consistent with our earlier results. The reason for its negative sign is the same as developing countries are

more labour intensive whereas developed countries produce more output than level of concentrations and

due to their advancement in technology their concentrations levels are low as compared to their output. The

42

elasticity estimates also show that with one percent increase in Scale the pollution level declines by 32%.

The other Scale measures that include the FDI country dummy given in table below in Column 2 and

Column 5 shows that with increase in Scale of agriculture production there is a reduction of pollution levels.

The elasticity estimates show that with one percent increase in Scale there is a 31% reduction in pollution

concentrations, the same results are also available for fertilizers data and fertilizers country dummy shows

that with one percent increase there is 20% decrease of pollution level according to my data set.

5.2.2 “COMPOSITION EFFECT”

“Composition effect” which determines the country’s comparative advantage is an important variable the

results from “Composition effect” are indifferent than our analysis than before the signs are negative but

are not significant, but the elasticity estimates show significant results hence showing and proving or earlier

analysis that with increase capital to labour there is a reduction in pollution levels. But when I used FDI

country dummy in second model of my sensitivity analysis it shows concentrations are increased with

increase in Composition. It shows 17% increase as more stocks are introduced in model through fertilizers

as well as FDI stock. Since the FDI is entering as a stock (Stock=Capital) if the stock is dirty industry

pollution increased overall. In the next model I used fertilizers in separate model, the composition effect

from using fertilizers data shows that pollution level is reduced less than the other analysis, so with

increased use of fertilizers results in more agricultural pollution due to use of fertilizers but also results in

reduction of pollution level. Hence technically with its one percent increase results in 16% reduction of

pollution level. So I have come to the conclusion that FDI stock transfer to developing countries in more of

a clean technology because it reduces pollution concentration more than the other models.

5.2.3 “TRADE INDUCED COMPOSITION EFFECT”

Trade induced composition effect shows increase in pollution levels. This result is consistent with my last

effects of trade induced composition effect which shows the same pattern of increase in pollution.

5.2.4 “TECHNIQUE EFFECT”

“Technique effect” results are also same as before, increase in research and development expenditure offset

these effects from Capital to labour and from “Scale effect” creating a negative effect which reduces the

43

level of pollution it shows that with one percent increase pollution concentrations are reduced from 155%

to 190%

Table 5 2 Sensitivity Analysis

FIXED EFFECT RANDOM EFFECT

Model

Technique Model FDI Model Fertilizers

Model Technique

Model FDI Model

Fertilizers

Scale

- 2.26E-07

***

- 2.78E-07

***

- 1.46E-07

***

-1.22E-08 2.34E-07 4.13E-08

(3.24E-08) (4.80E-08) (2.53E-08) (7.09E-08) (1.49E-07) (9.74E-08)

Scale2 1.34e-11*** 1.32e-11*** 6.67e-12** -2.55E-12 -1.32E-11 -4.66E-12

(2.74E-12) (3.77E-12) (2.10E-12) (4.88E-12) (9.11E-12) (6.81E-12)

Composition -0.00392 0.0185*** -0.00374 -0.0206*** -0.0358** -0.00437

(0.00217) (0.00536) (0.00205) (0.00358) (0.0118) (0.00517)

Composition2 9.81E-06

-9.17E-05 ***

1.21E-05 5.29E-05** 0.000178** 9.21E-06

(0.0000081) (0.0000267) (0.00000853) (0.000017) (0.0000596) (0.0000236)

Technique (Tech)

-8.83e-09*** -9.12e-09*** -1.00e-08*** -5.80e-09*** -6.33e-09***

-6.59e-09***

(4.66E-10) (5.10E-10) (7.10E-10) (9.50E-10) (1.33E-09) (1.21E-09)

Technique2 (Tech)2

1.70e-18*** 1.77e-18*** 1.89e-18*** 1.16e-18*** 1.22e-18*** 1.31e-18***

(2.04E-19) (2.15E-19) (2.96E-19) (2.29E-19) (3.29E-19) (3.02E-19)

FDI Stock -0.0281*** 0.00163 -0.0263*** -0.0111*** -0.0258 -0.00419

(0.00615) (0.00791) (0.00712) (0.00305) (0.027) (0.00437)

Trade Intensity

(TI) -1.488*** -0.842*** -0.940*** -1.604 -0.114 -0.0075

(0.17) (0.127) (0.125) (0.1) (0.119) (0.108)

TI*Composition

0.0720*** -0.0670** 0.0263* 0.154*** 0.120** 0.00725

(0.0142) (0.0255) (0.0122) (0.0138) (0.0419) (0.0227)

TI*Composition2

-0.000189 0.00187*** 0.000202 -0.00122*** -0.00223** 0.00028

(0.000159) (0.000541) (0.000178) (0.000202) (0.000849) (0.000316)

TI*RelTech 2.14e-08*** 2.01e-08** 2.62e-08** 1.16e-08* 1.90e-08* 1.46E-08

(6.41E-09) -6.77E-09 (9.27E-09) (5.58E-09) (8.44E-09) (7.89E-09)

TI*RelTech2 -4.34e-18** -4.77e-18** -4.49e-18* -3.36e-18** -3.27E-18 -3.78e-18*

44

(1.49E-18) (1.57E-18) (2.15E-18) (1.25E-18) (1.85E-18) (1.74E-18)

TI*RelComp*Tech

-9.79E-11 -5.37E-11 -1.57E-10 -1.22E-11 -1.18E-10 -2.75E-11

(1.39E-10) (1.46E-10) (2.00E-10) (1.11E-10) (1.68E-10) (1.59E-10)

Temperature 0.0365*** 0.0602*** 0.0310*** 0.0192 -0.0109 -0.00321

(0.00647) (0.012) (0.00578) (0.0132) (0.0204) (0.0173)

Precipitation 0.0253 0.0172 0.0661 -0.066 0.00333 -0.026

(0.0512) (0.0602) (0.053) (0.0588) (0.0901) (0.0812)

Fertilizers 0.000000114**

* 0.000000107*** 0.000000190*** 3.92e-08*** 3.89e-08** 2.35E-08

(7.94E-09) (7.56E-09) (2.20E-08) (7.41E-09) (1.45E-08) (4.26E-08)

TI*Tech*Developing

-0.000514 0.000335*

(0.000439) (0.00016)

FDI*Developing

-0.0499*** 0.0235

(0.011) (0.0274)

Fertilizers*Developing

-8.79e-08*** 3.95E-09

(2.19E-08) (4.29E-08)

ELASTICITY

FIXED EFFECT RANDOM EFFECT

Scale elastcity -

0.32986*** -

0.3177832*** - 0.205589*** -0.0177428 0.2680595 0.0584065

(0.04723) (0.05494) (0.03577) (0.10334) (0.1707) (0.13766)

Composition elasticity

- 0.179107**

0.7794779*** -

0.1621584**

- 0.9417957**

*

- 1.508969**

* -0.1895892

(0.09909) (0.22606) (0.08879) (0.16383) (0.4978) (0.22402)

Technique elasticity

- 1.552276***

- 1.907084*** - 1.654812*** -

1.018415***

- 1.323473**

*

- 1.086992**

*

(0.08191) (0.10658) (0.1172) (0.16689) (0.27752) (0.19933)

FDI elasticity -

0.0351699*** 0.0024438*** - 0.031065***

- 0.0139322**

* -0.0385473 -0.0049496

(0.00769) (0.01183) (0.00841) (0.00382) (0.04038) (0.00516)

Fertilizers 0.4923529*** 0.454006*** 0.7848296*** 0.1692285**

* 0.1653126*

** 0.0970424

(0.03426) (0.03217) (0.09068) (0.03195) (0.06173) (0.17547)

TI*Tech*Develping

-0.023124 0.0150992**

(0.01976) (0.00719)

45

FDI elasticity Developing

- 0.052** (0.01147)

0.0244389 (0.02853)

Fertilizers elasticity

Developing

- 0.2204388***

(0.0)

0.0099079 (0.0)

0.05484 0.10762

_cons 10.50*** 9.853*** 10.41*** 11.10*** 10.90*** 10.77***

(0.149) (0.279) (0.116) (0.335) (0.496) (0.425)

sigma_u

_cons

0.875***

1.054***

0.991***

sigma_e -0.211*** -0.171***

_cons 0.232***

N 432 363 460 432 0.347 0.33

Note: The 1st value of the variable shows the coefficient value while the second value represents the standard error whereas Steric shows the level of Confidence

5.2.5 “TRADE INDUCED TECHNIQUE EFFECT”

“Trade induced technique effect” is an important variable that helps us determine the pattern of trade. Trade

induced “technique effect” itself shows significant results with increase in expenditures in R&D results in

reduction of pollution level. It is also used as an interaction term between country dummy for developing

countries. The “trade induced technique effect” shows a decline of -2.3% but does not show significant

result hence proving that “Pollution Havens” determine the Pattern of Trade and because there is an

increment of fertilizers in the model it has resulted and more focus on towards “factor endowment” rather

than “Pollution Havens”. To further confirm it, the FDI stock dummy for developing can show us the exact

pattern of trade which is discussed next. “

5.2.6 “FDI INTENSITY”

FDI inward stock of agriculture is also an important variable. It confirms my analysis of trade pattern. This

variable data is also used from 1980-2002, by definition this variable is FDI inflows of stock in a particular

country divided by capital level of that country, which is mostly known as “FDI Intensity”, since the stock

data is available to us we can find whether it has a positive effect on developing and developed economies

or a negative one, FDI inward stock itself along with country dummy can play a significant role in

46

determining, my results it show that FDI has a negative effect it shows increase in “FDI intensity’ reduces

the level of pollution, the elasticity estimates are between 2% to 3%, for my second model of FDI stock

dummy it shows a significant increase in pollution level of 0.0024438% but for developing countries it

shows reduction in pollution level hence showing that FDI inward stock is clean technology the elasticity

is 0.052%, hence proving from FDI table that technology transfer is clean. The reason is “if multinational

corporations have common production methods in both developed and developing countries for

engineering, quality control, or other reasons, then the pollution intensity of their production will be

determined by the income per capita of the source country. As a result, a larger multinational presence in a

poor country may mean it is cleaner, all else equal; however, there is an alternative hypothesis working in

the other direction. If multinationals locate in poor countries because of their lax environmental protection,

then we may instead find a positive relationship between foreign direct investment (FDI) and pollution”

Taylor, Werner Antweiler, Brian R Copeland (2001), 898. Hence we come to the conclusions that

multinationals who use common production technique produces along with benefits from research produces

negative effect from FDI Intensity to Pollution concentrations. These results are shown in Table 5.2 Column

(3) and (6).

5.2.7 FERTILIZERS USE

“Increase in endowments should have a positive effect on pollution concentrations”. To find out whether

this is true we add fertilizer consumption variable that is used in agriculture sector, the data of consumption

of fertilizer is available from FAO25 but the data varies from 1980-2002, so for our analysis we will use

same time series for our analysis. We also use the dummy for developing countries to find out the effect

fertilizers have on the pollution level. The results from my analysis show that fertilizers have a positive

effect on increasing the level of pollution, increase in fertilizers consumption increase pollution

concentration, the elasticity shows that with one percent increase in “FDI intensity” pollution concentration

increases by 49% to 78% and for developing countries I find that pollution concentration is reduced with

25 http://faostat3.fao.org/faostat-gateway/go/to/download/R/RA/E

47

the use of fertilizers probably due to use to enhanced techniques or capital that is transferred in developing

countries.

5.2.8 TEMPERATURE AND PRECIPITATION

My sensitivity analysis show quite different results than my normal model estimations, since in sensitivity

analysis I am using fertilizers data and FDI intensity for developing country dummy, this shows that with

increase in fertilizers temperature increase in temperature results in an increase in pollution dispersion

whereas for precipitation it also shows positive effect, without considering the seasonal variation of

precipitation it shows that increase in precipitation is not causing pollution level to reduce.

CONCLUSIONS

The results from sensitivity analysis shows that “Pollution havens” determine the pattern of trade.

Industries that are now entering through FDI stocks in the developing countries are corporation

which are using the same clean technology as they do in developed countries. Therefore, the

overall level of pollution is reduced in the world or at least between these trading partners in my

data set.

48

CONCLUSIONS & RECOMMENDATIONS

My research focuses on investigating the effect of trade intensity on the environment using agriculture

sector while determining the pattern of trade. This sector is an important sector for most of the Developing

economies, and since long discussions on effects have shown that trade liberalization may lead to transfer

of pollution or pollution causing industries to poor developing countries. My study has identified this

pattern of trade and may clear it for agriculture sector. Evidently from my data I have come to the conclusion

that most of my variables shows that with increase in Scale, Composition and Technique results in reduction

of pollution level in my data set, hence it shows that my first Null hypothesis is rejected and trade intensity

does not pollute and reduces the level of pollution. And to prove my second hypothesis which determines

the pattern of trade shows that “Pollution Havens” determine the pattern of Trade, which according to

definition tells us that with increase in trade intensity results in shift of industry from Developed to poor

developing countries but my further analysis also tells me that with increase in FDI stock to Developing

countries results in transfer of clean technology to the industries which is mainly from Corporations using

same factors of production in developed countries, hence rejecting my third claim of my analysis. This

overall analysis hence finds an ideal means for us. Environmentalists can agree on it that FDI for developing

countries are clean technology from multinationals which is an appropriate measure of production in

developing economies. It will not only help reduce pollution but also improve the overall level of trade.

49

APPENDIX DIAGNOSTICS TESTS

TIME FIXED EFFECT

To test whether time fixed effects are needed, I use J Lloyd Blackwell (2005), it is a joint test to see if the

dummies for all years are equal to zero. Time fixed effect is used while running “fixed effect model” for

analysis. The probability values for all three models are given below which shows they are significant and

is needed for analysis

Diagnostic Test 1 Time Fixed Effect

Time Fixed Effects for Normal Models(Data without fertilizers & Year 1980-2011)

A B C

r2 0.5303 0.5224 0.5297

Chi^2 199.86*** 185.74*** 112.58***

“CROSS SECTIONAL DEPENDENCE TEST”

Pasaran Cross-Sectional Hoyos & Sarafidis (2006) dependence test is check whether residuals are

Correlated with each other. “This cross-sectional dependence is not an issue for micro panels but for long

panels (such as time= over 20-30 years) it may be a problem; to solve “Cross Sectional Dependence”

problem Driscoll and Kraay are used”. These results are consistent for all three models. The results show

that for all three models there does not exist any “Cross sectional dependence” among the countries, the

results are also same with the “Sensitivity analysis” data.

Diagnostic Test 2 Pesaran's test of cross sectional independence

“Cross Sectional Dependence” for Original Data “Cross sectional” Test for sensitivity Data

Model A Ti*Tech~Develping

2.564, Pr = 0.0104 2.090, Pr = 0.0366

ABV off-diagonal elements = 0.468 ABV of off-diagonal elements = 0.410

Model B FDI_Develop~g

1.852, Pr = 0.0640 0.869, Pr = 0.3846

ABV of off-diagonal elements = 0.466 ABV of off-diagonal elements = 0.452

50

Model C Fertilizers_Develop~g

1.531, Pr = 0.1257 4.512, Pr = 0.0000

ABV of off-diagonal elements = 0.468 ABV of off-diagonal elements = 0.417

“SERIAL CORRELATION”

“Serial Correlation” is applied to Macro panels, serial correlation problem is common with longer time

series, this problems causes standard errors to be smaller and also resulting in higher R-square, the Null

hypothesis is there is no correlation. To test for “Serial Correlation” Drukker (2003) “Lagrange-Multiplier”

test is used for analysis and the results shows that serial correlation is also present in all three models and

even in my “sensitivity analysis” data.

Diagnostic Test 3 Wooldridge test for autocorrelation in panel data

“Serial Correlation” Test With Original Data “Serial Correlation” Test With Sensitivity Data

Model A TI*Tech_Develop~g

H0: no first-order autocorrelation H0: no first-order autocorrelation

F(1, 19) = 11.598 F( 1, 19) = 20.753

Prob > F = 0.0030 Prob > F = 0.0002

Model B FDI

H0: no first-order autocorrelation H0: no first-order autocorrelation

F( 1, 19) = 13.694 F( 1, 19) = 22.196

Prob > F = 0.0015 Prob > F = 0.0002

Model C FERTILIZERS

H0: no first-order autocorrelation H0: no first-order autocorrelation

F( 1, 19) = 13.636 F( 1, 19) = 22.910

Prob > F = 0.0015 Prob > F = 0.0001

51

“HETEROSKEDASTICITY” TEST

Problem of “Heteroskedasticity” may also arise in the data for that purpose I also tested my data for Hetero

for both Original data and found that Problem of Heteroskedasticity was also present in my data. The results

are presented below in the Table.

Diagnostic Test 4 Modified Wald test for groupwise heteroskedasticity in fixed effect regression

model

Test for Hetero for Original Data Test of Hetero for Sensitivity Data

MODEL A TI*Tech*Developing

H0: sigma(i)^2 = sigma^2 for all i

chi2 (20) = 5086.97 H0: sigma(i)^2 = sigma^2 for all i

Prob>chi2 = 0.0000 chi2 (20) = 5482.08

Prob>chi2 = 0.0000

MODEL B FDI

H0: sigma(i)^2 = sigma^2 for all i

chi2 (20) = 4958.00 H0: sigma(i)^2 = sigma^2 for all i

Prob>chi2 = 0.0000 chi2 (20) = 19648.09

Prob>chi2 = 0.0000

Model C Fertilizers

H0: sigma(i)^2 = sigma^2 for all i H0: sigma(i)^2 = sigma^2 for all i

chi2 (20) = 4801.07 chi2 (20) = 45087.07

Prob>chi2 = 0.0000 Prob>chi2 = 0.0000

After running Diagnostic tests and keeping the mind that my Hausman test shows “Fixed effect” as my

appropriate model. And to solve for all the Problems of “Heteroskedasticity, Serial correlation, Time fixed

effect” I find that these can be solved with Feasible Generalized Least Square method for Panel J Lloyd

Blackwell (2005); the results are explained in the results section.

52

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