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
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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
iv
Contents List of tables and figures ................................................................................................................. v
Abstract .......................................................................................................................................... vi
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.
vii
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
6
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
7
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.
8
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
9
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
10
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”.
11
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.
12
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
Australia Bangladesh China Germany Indiaindonesia Italy Japan Malaysia MoroccoNetherland Pakistan South Africa Spain SrilankaThainland Turkey UK US Vietnam
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
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.
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>
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.
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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.
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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
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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
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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
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level of pollution it shows that with one percent increase pollution concentrations are reduced from 155%
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