An Attempt at Solving
the
Trade-Environment Puzzle
Student Name: Beatrice Locatelli
Student Number: 410685
Supervisor Name: Dr. Jean Marie Viaene
Co-reader Name: Dr. Hans de Kruijk
1
Abstract
This thesis analyses the link between trade and different environmental indicators, in
particular regarding air pollution. Exploiting the instrumental variable method to solve
for the endogeneity of openness, to be able to inspect the causal relationship , the study
tries to untie the different effects trhough which liberalisation affects pollution in a
sample including all the countries for which data is available. Depending on the
pollutant, 165 is the minimum number of nations included in each regression. The
research makes use of a panel data, covering the years from 1990 to 2011, and the
Gravity Equation is the instrument used in the analysis, since it is identified as the best
performing tool to solve the endogeneity of trade. Furthermore, the link between
income growth and pollution, known as the Environmental Kuznets Curve, is
investigated as its complementarity with trade in the determination of pollution levels
in countries is of topical importance. Results show evidence for an inverse U-shaped
relationship between pollution and income, supporting the EKC hypothesis.
Furthermore, for the majority of pollutants, it appears that trade has a positive effect on
the environment, contributing to the reduction of pollution levels.
2
Index
1. Introduction; 3
2. Pollutants Description; 4
3. Literature review; 8
3.1 - The relationship between pollution and income; 8
3.2 - The relationship between pollution and trade; 11
3.3 - The relationship between trade and environmental regulation; 15
4. Hypotheses formulation; 16
5. The Endogeneity Issue; 18
5.1 – Description of the problem; 18
5.2 - The Gravity Model; 19
5.3 - The income equation; 23
6. Pollutants and Data Description; 24
7. Methodology; 26
7.1 - Constructing the instrument; 26
7.2 - Fitting the Data in the model; 27
7.3 - The instrument’s quality; 28
7.4 - The environmental damage equation; 30
7.5 - Robustness checks; 31
8. Results; 33
9. Results Implications; 39
10. Conclusions; 41
11. References; 42
12. Appendix; 45
3
1. Introduction
The debate over the consequences of trade on environment has been open for the last
fifteen years, and still there is no general consensus on how liberalization influences
environmental quality. One of the main challenges consists in correctly identifying the
channels through which the two elements are connected, to be then able to model a
correct specification when the analysis moves onto an empirical level. In particular this
relationship has become focus of attention for policy makers, needing to decide whether
to enhance growth through liberalization or impose higher environmental standards for
production.
In this sight, there are two main streams of thought: the race to the bottom and
the gains from trade hypotheses (Frankel and Rose, 2002). The race to the bottom
hypothesis assumes that countries gain a comparative advantage in the pollution
intensive sectors lowering their environmental standards when they open up to trade.
Doing so, they are able to compete internationally with more developed countries which
are already better integrated in the global markets. This hypothesis implies a world level
of environmental regulation less than optimal, and the assumption is not in common
with the pollution haven hypothesis. This predicts that countries with strict
environmental regulation will relocate pollution intensive production activities where
regulation is laxer. Generally, no evidence supporting the Pollution Haven Hypothesis
has been found (Jaffe, 1995; Tobey, 1990). The gains from trade hypothesis, on the other
hand, assumes that increased levels of liberalization bring about technology spillovers,
so the production moves faster towards cleaner methods, and the population also
becomes more aware of environmental concerns, moving its preferences in the direction
of clean goods.
We know from standard trade theory that trade is a growth enhancing factor, and
it alters the composition of national output bringing countries to move towards
increased allocative efficiency (Levine and Renelt, 1992). It is also a common finding
that increasing output has a detrimental effect on the environment (Grossman and
Krueger, 1994). One of the aims of this thesis is to test if at higher income levels, growth
4
still has the effect of increasing pollution. The impact that improved liberalization has on
pollution depends also on what kind of environmental policies is being enforced.
The focal aim of this thesis is to analyse the impact of international trade on
country-level pollution. Furthermore, this work widens the existing literature exploring
the trade-pollution causality and using an extended dataset: the use of a panel data to
take in account the time-varying dimension, combined with the instrumental variable
method to control for trade endogeneity , and the wide set of countries being analysed
constitute altogether the singularity of the work. By using a panel data this analysis can
disentangle the three effects through which trade affects the environment: the scale,
composition and technique effect. This research also tries to explore income
endogeneity, but after attempts to follow and improve the existing literature, a good way
to instrument for it is not found.
Overall, regarding the set of pollutants analysed, no evidence is found supporting
the hypothesis that trade worsens pollution levels. Furthermore, results indicate the
presence of an Environmental Kuznets Curve, meaning that on the long run, at high
levels of income, growth is not detrimental for the environment.
The thesis is structured as following: chapter 2 describes the different pollutants
analysed in the empirical part, then an outline of the relevant literature will be
presented to explore the work that has already been carried out on the topic in chapter
3. Following this, the hypotheses that will be tested are defined; Chapter 5 deals with the
endogeneity of trade and describes the method used to solve it, and after this a
description of the data is provided. In Chapter 7 the main methodology is framed,
followed by results their and policies implications, in chapter 8 and 9. The last part
draws some general conclusions on the analysis.
1. Pollutants Description
The pollutants taken in consideration in my research have some characteristics which
classify them as useful for the analysis. They must be a resulting spillover from
production of goods, they must be emitted in different quantities by different sectors,
5
must have local (at a country level) effects, must be regulated by local or international
policies, must be available from a wide set of countries with different characteristics
(developed, developing, open or closed economies) (Antweiler et al., 1998). A list of the
different pollutants I’m analyzing follows, with a brief description of it and relevant
information.
𝑪𝑶𝟐 , Carbon Dioxide, is the first pollutant I’m analysing. It is a gas naturally
present in the atmosphere, but it’s also produced through some of the primary human
industrial production activities such as fossils fuels combustion, energy production,
transportation, and land use. It is classified as a greenhouse gas and is one of the main
pollutants produced by human activities. It accounts for 57% of greenhouse gases
emissions1. It is also a major source of water pollution, since it dissolves in oceans and
forms carbonic acid. The first regulations for Carbon Dioxide were released in the US in
1994. The pollutant is the reference gas against which other greenhouse gases are
measured, so its Global Warming Potential2 is set to 1.
Sulphur Dioxide is another gas produced by fossil fuels combustion. It is present
in nature from volcanoes and decaying organic matter. It is estimated that almost 99%
of the Sulphur Dioxide present in the atmosphere is a result of human sources3. The gas
is strongly associated with electricity production activities, and since these activities are
typically more capital than labor intensive, a good proxy for this pollutant might be
capital intensive industries. Technologies for reducing emissions of Sulphur Dioxide are
available even if costly, and the pollutant is regulated by EPA since 1971.
GHG stands for Greenhouse Gases, which are Carbon Dioxide, Methane, Nitrous
Oxide, and Fluorinated gases. These gases all have in common the fact that they are
produced in many different industries at different levels, and the effect they have on the
atmosphere is that they trap heat, thus causing a raise in local and/or global
temperature. These gases mix well in the atmosphere, so that in their effect is not purely
local, in particular in the long run. The main pollutants’ effect is also analyzed singularly,
but the variable is an index including other smaller amounts of greenhouse gases on
United States Environmental Protection Agency
the Global Warming Potential is the capacity of the gas to trap the heat in the earth’s atmosphere.
3 https://www.environment.gov.au/protection/publications/factsheet-sulfur-dioxide-so2
1
2
6
which there is no data available. The variable measures changes in atmospheric levels of
greenhouse gases attributable to forest and land use, for instance: biomass stocks
change due to forests management, logging, wood collection, removal of 𝐶𝑂2 from
abandonment of formerly farmed lands and soils.
HFC, Hydrofluorocarbons, are chemical compounds used in refrigerant
methods and air conditionate. They are also called super greenhouse gases, because
their combined effect could offset the benefits the environment is getting from reducing
other gases such as CO2. They are substituting CFC, Chlorofluorocarbons, which were
Ozone damaging gases previously used for the same purpose of refrigerating, but have
been abolished with the Montreal protocol in 1989. Negative externalities from the use
of these gases are estimated to be rising by 15% each year4, and the greenhouse effect of
them is 3080 times more potent than Carbon Dioxide.
Nitrous Oxide, 𝑵𝟐 𝑶, is produced by numerous human activities such as
agriculture, transportation and industrial processes. Agricultural soil management in
particular is the largest source of emissions, through the use of fertilizers. Other sources
are motor vehicles fuels combustion and the use of fossils fuels in industries, which can
be reduced through technological upgrades. It is 310 times more harmful to the
environment than carbon dioxide, and it also damages the Ozon layer. Measures to
control emissions of this pollutant have first been introduced in 1997, through the Kyoto
protocol. It is estimated that the UK is the country most affected by this gas5.
From the same database Methane, 𝑪𝑯𝟒 , which is the second greenhouse gas for
presence in the atmosphere. It is naturally present in the atmosphere as it is the main
component of natural gas. It is produced by human activities through livestock raising
and leakage from natural gas systems used for producing energy through combustion.
The impact of this pollutant on climate change is 21 times bigger than the one of Carbon
Dioxide, but its lifetime in the atmosphere is shorter. It is estimated that globally around
60% of Methane in the atmosphere comes from human activities. Again, it is possible to
reduce this gas’ emissions through already existing technologies and better livestock
4
5
http://www.thinkglobalgreen.org/hfc.html
http://apps.sepa.org.uk/spripa/Pages/SubstanceInformation.aspx?pid=8
7
management strategies. Regulations for these pollutants have not been enacted yet, but
have been proposed by multiple countries’ governments.
Sulphur hexafluoride (SF6) is a gas produced by human activity not present in
nature, that is colorless, odorless, non-toxic (except when exposed to extreme
temperatures), and non-flammable. It is heavier than air (it is indeed one of the heaviest
gases known) and hence stays close to the ground upon release, so its effect is majorly
local. SF6 is used in the electricity industry as insulating gas for high voltage equipment
and as cover gas in the magnesium industry to prevent combustion of molten
magnesium.
In
smaller
quantities, the
pollutant is
used
in
the
electronic
industry. Excessive exposure to Sulphur hexafluoride may affect the brain. The main
impact of Sulphur hexafluoride on the environment is as a greenhouse gas, due to its
very high heat trapping capacity, and it is Consequently controlled under the Kyoto
Protocol. Of the internationally monitored greenhouse gases it has by far the highest
global warming potential (23,000 times that of carbon dioxide), however it is only
released in small amounts. Due to its stability it has a very long atmospheric lifetime.
I am also analyzing two types of particulate matter, namely pm10 and pm2.5.
pm10 indicates those particles present in the atmosphere which are smaller than 10
micrometers, while pm2.5 is composed of particles smaller than 2.5 micrometers (from
25 to 100 times smaller than a human hair). These particles are the most common kind
of air pollution that can be highly dangerous for human health, in particular the smaller
ones. They are produced by factories (dust), smoke, farming, toxic organic compounds,
and heavy metals, resulting mainly from combustion and in industrial processes of
purification of some materials. Particulate matter can be directly emitted or can be
formed in the atmosphere when gaseous pollutants such as SO2 and NOx react to form
fine particles. Most countries implemented policies establishing the maximum
concentration level of these pollutants in the atmosphere, due to their high riskiness for
human health.
As
for
water,
land
and
materials
pollutants,
I
am
analyzing
PFC,
Perfluorochemicals. PFCs are a family of synthetic chemicals, initially developed by the
3M Company, that have been used for decades to make products that resist heat, oil,
8
stains, grease, and water. Common uses include nonstick cookware, stain-resistant
carpets and fabrics, components of firefighting foam, industrial applications, coatings for
packaging such as milk cartons, cosmetic additives, and other personal products. In the
past, PFCs including perfluorooctane sulfate (PFOS), perfluorooctanic acid (PFOA), and
perfluorobutanoic acid (PFBA) were not regulated. 3M has phased out manufacture of
some PFCs, but there are currently other manufacturers of PFCs around the world. The
chemical structures of PFOS and PFOA make them extremely resistant to breakdown in
the environment. PFOS and PFOA accumulate in humans and animals. Less is known
about PFBA. There is no evidence that this compound is harmful to human health, even
if it is highly likely connected to some liver and thyroid diseases.
3. Literature Review
In the following review of the literature, I will outline the main papers I am basing my
research on. I will start discussing the first studies which linked environmental
degradation to income, and set the basis for further development in the analysis of
pollution. I will then move into the topic of the relation between trade and environment,
with more in depth review of the authors from which I will take the cue for the
methodology of my empirical analysis. I will conclude the section with some
considerations about the relationship between environmental policies and trade, which
I do not analyse empirically but need to be taken into consideration for the study I am
performing.
3.1 - The Relationship Between Pollution and Income
One of the aim of this thesis is to test the truthfulness of the environmental Kuznets
curve. The environmental Kuznets curve has first been theorized by Grossman and
Krueger (1991), who analysed the impact of reduced trade barriers on the
environment. They discuss three different effects, which constitute part of the
theoretical basis for the analysis this research performs. In detail, these are the effects
through which a change in trade policy can affect pollution levels. The first is the scale
effect, which captures the consequence of increased economic activity. If this expansion
is not accompanied by ad hoc shaped policies, the total amount of pollution generated by
that activity will increase, boosting environmental degradation. One such example for
9
expanded trade would be transportation services demand, which with freer trade would
increase and would bring to higher deterioration of environmental quality.
The second effect to be taken in consideration is the composition effect. This is a direct
consequence of trade liberalization, which will cause countries to specialize more in the
production of goods on which they have comparative advantage. The problem would be
now to identify the area in which countries have this advantage: if it derives from a lack
of environmental regulation, this effect will contribute to increase environmental
damage. The other case would be that comparative advantage is driven by differences in
factor abundances and technology; in such case the composition effect on the
environment is not clear. The country would shift more resources to the sector using the
abundant factor more intensively. The effect on the environment will then depend on to
what extent those activities that will expand are pollution intensive. The third effect is
the technique effect. This effect explains that after an increase in openness the
production process will change and be different from that prior liberalization. The effect
should be true in particular for developing countries. The implications being the transfer
of more modern technologies (since the country is more open to trade), which are
usually cleaner, thanks to the fact that in developed countries awareness about
environmental concerns is more spread. A second consequence of the technique effect
would be that since trade liberalization is expected to increase income, the demand for
cleaner technologies would increase, and stricter pollution regulations would be
implemented.
Grossman and Krueger used data from GEMS, which reports air quality data
through measuring sulphur dioxide and particulate concentrations in urban areas all
over the world. The analysis allows for city and site specific effects, in addition they
include in the regression GDP per capita and a time trend. Using a random effect model,
they find that the specification which includes the cubic form of GDP is a good
approximation of the relationship between pollution and income, and, between the
others, they find that 𝑆𝑂2 level is significantly lower for those countries with higher
levels of trade. In a second study in 1995 the authors analyse four other different types
of indicators: urban air pollution, the state of the oxygen in rivers, fecal and heavy metal
contaminations of rivers. Again, they take into consideration the effects discussed above,
which can have an opposite influence on environment with respect to the one caused by
10
the growth in economic activity (composition and technique vs scale effect). The
contribution of the study is involving better data coverage and more pollution measures
investigated, since data started to be gathered with a common methodology over more
countries. The authors use again data from GEMS because they argue that those data
have the crucial qualities of reliability and comparability.
However, they are missing important air pollutants such as Nitrogen Oxides and
Carbon Monoxide, Carbon Dioxide, Methane and Nitrous Oxide. The problem with
oxygen in water is related to the fish’s need of a certain quantity of the element
dissolved in water to metabolize carbon. If that quantity is undermined, fish population
risks to die off. The quantity of oxygen present in water can be influenced by the
presence of fertilizers, used in agricultural areas. Biological Oxygen Demand (BOD) and
Chemical Oxygen Demand (COD) are measures of this, while another used measure to
account for water pollution is Pathogenic contamination. I tried to gather these
measures of water pollution, but the World Bank has discontinued the data availability.
The third set of pollutants they analyse are heavy metals, data are available for water
concentrations of lead, cadmium, arsenic, mercury and nickel.
Grossman and Krueger decide to use a reduced form approach to analyse the
effect of income on pollution, instead of modelling a structural equation. They argue that
the advantages of using this approach are mainly two: first, the reduced form gives the
direct effect of a nation’s income on pollution; secondly they can avoid collecting data on
environmental regulation and on technology. This form has nevertheless a limitation: it
cannot explain why the estimated link between income and pollution exists. In the
estimation equation they include GDP, its square and cube, and the average of GDP over
the previous three years. While pollution is measured at a city level, GDP is only
available at the country level. The cubic term of the average GDP per capita over three
years proxies the effect of permanent income, since past income likely affects
environmental standards. They include a linear time trend to adjust for specific
influences of the year in which the measurement was taken, dummies to indicate the
location of the city (central or rural) and for the kind of land use close to the
measurement station (industrial, commercial, residential). They also use a dummy
indicating the position close to a coastline, and for being within 100 miles from a desert.
11
They use the method of Generalized Least Squares to account for characteristics of the
monitoring site not accounted for by the included variables, with random effects. Even
though current and lagged GDP are highly correlated, the terms are in most cases highly
significant. The results show an inverted U relationship for measures of air quality.
Lagged GDP has in general a lower p-value, indicating that past income is probably more
important in the determination of today’s pollution levels. Concerning water pollution
measures, the inverted U shaped relationship is again to be found, apart from dissolved
oxygen, for which they find a U shaped relationship. Similar results are found for the
third group of pollutants, while for heavy metals the only inverted U shaped relationship
is found for arsenic. Generally, it is possible to conclude that there is no stable increasing
relationship between pollution and income growth.
3.2 - The Relationship Between Pollution and Trade
One of the first attempts to theorize and analyse the link between trade and pollution
has been carried out by Antwelier et al. (1998). There existed already studies analysing
the pollution Haven hypothesis and the Environmental Kuznets curve, but evidence has
always been weak. The authors analyse Sulphur Dioxide concentrations, and by using a
panel analysis they can draw conclusions on the scale, composition and technique
effects of trade on pollution. In the research, the authors don’t analyse how a change in
trade flows influence the scale of economic activity or income, since they argue that
these are influenced by additional factors other than trade openness. They focus on the
composition effect controlling for scale and income.
Their data is taken from GEMS, and they use SO2 concentrations by urban areas
over the period 1971-1996. Since the distribution of yearly 𝑆𝑂2 is log normal, they use
the logged variable transformation: this happens probably due to temperature
anomalies or other pollution related episodes that are often reporting too large values
for that observation. Their empirical strategy and estimated equation includes sitespecific, economic and common to world determinants.
Managi et al. (2009), investigate the relationship between trade and environment
exploring the endogeneity problem I will further discuss later in the research. They
argue that the overall effect of trade on emissions induced through scale and technique
12
effect cannot be compared with the composition effect, as these effects have opposite
signs. The study treats trade openness and income as endogenous, since if considered
exogenous the causality cannot be explored. In the study the authors study 𝑆𝑂2 and 𝐶𝑂2
emissions for 88 countries on a time period going from 1973 to 2000, as well as BOD
emissions for 83 countries between 1980 and 2000. The method they use is the
Generalized Method of Moments, and they estimate a set of two equations: the
environmental quality equation and the income equation. Through these the
determinants of emissions are decomposed into scale-technique and composition effect.
The environmental quality equation includes GDP per capita and the square of it, trade
openness as the ratio of aggregate exports and imports on GDP, the country’s capital to
labour ratio, the relative capital to labour ratio and relative GDP per capita, two
dummies for environmental treaties and two terms representing the effects of income
and production on emissions.
They argue that the main factor influencing the composition effect is a country’s
comparative advantage, which is estimated through factor endowment, stringency of
environmental regulation and trade openness. The income equation features as
independent variables population, a proxy for human capital investment, trade
openness, and capital-labour ratio. They subsequently decompose the environmental
quality equation into the composition effect, representing the direct effect of trade, and
scale-technique effect, which represents the indirect effect of trade. The authors use this
method to estimate short and long term overall trade openness on elasticity of
emissions. They find that on the long and short term trade reduces emissions in OECD
countries, while it raises them in non OECD countries, for SO2 and CO2, while for BOD
the effect of trade is positive for all the countries. This can be considered another way to
test the Environmental Kuznets Curve hypothesis.
Cole and Elliot (2003) examine whether a change in pollution following trade
liberalization is originated by differences in factor endowments or in environmental
regulations. They assume that comparative advantage can be driven either by lax of
environmental regulation or by factor endowments of the country taken into account
compared to the ones of the trading partners. The authors investigate which one of
these two effects dominates, and they implement a model on the base of Antweiler et al.
13
(1998). The difference between the two papers is that Cole and Elliot do not separate
between scale and technique effect, dropping the variable GDP by square km and only
estimating national pollution emissions.
To estimate these effects they use lagged per capita income. The authors specify
the difference in using data for pollution concentrations or emissions: concentrations
data require the inclusion of a number of dummy variables to control for site-specific
effects. With concentrations it is possible to separate scale and technique effects, while
using national emissions data the forecast is limited to estimation of the technique effect
and a combination of scale and technique effect. The pollutants they analyse are 𝑆𝑂2,
BOD, 𝑁𝑂2 and 𝑁𝑂𝑥 . They find that for 𝑆𝑂2 and BOD pollution decreases with income per
capita, which would mean that the technique effect is dominant, while for the other two
pollutants emissions increase with income, at a decreasing rate, and this signifies that
scale effects are dominant. Regarding the composition effect, which refers to capital and
labour endowments, they find that increases in K\L ratio increase emissions for all the
pollutants but BOD, for which they don’t get significant results.
Frankel and Rose (2002) use a cross section to investigate whether economic
growth is detrimental for the environment and whether cross border integration helps
or not the process. The focus of the paper is the effect of trade on the environment for a
given level of income per capita. They consider trade as endogenous and make use of
instrumental variables to solve the causality issue: it could be that more open economies
trade more, thus their pollution levels are higher, or it could be that since the
environmental regulation of certain countries is more lax their trade flows are more
concentrated on polluting sectors. I am following their methodology, thus I will further
discuss this issue below.
They determine trade as a function of country size, GDP, population and the
distance between the country and the trading partners. On the other hand, for income,
they use lagged income, size, rate of investment and rates of human capital formation.
They make use of the growth equation and of the environmental quality equation.
Through the first equation they replicate the finding for which there is an association
between trade and income. When they use the IV estimation for solving the problem of
14
endogeneity of openness, they find that the IV and the OLS estimation for trade have a
high correlation of 0.72, meaning that the specification is good enough. When they try to
implement measures of environmental quality, though, they did not find any support for
the positive effect on growth. They generally find that openness to trade reduces
pollution, and when they try to test the pollution haven hypothesis they do not find any
evidence to support it. A limitation of the study is that the authors only analyse a cross
sectional database, for the year 1985.
Chintrakarn and Millimet (2006) study the effect of trade on pollution using data
from US states to analyze within country trade – using subnational data (between
states). In particular, they reproduce the findings of Frankel and Rose utilizing a panel
data at the sub national level, from the united states, gross state product as measure of
income and four types of pollutants. The contribution of their study consists in the fact
that there is no other research on the implications of trade at the sub national level, and
in the fact that using data gathered from a single country ensures that measurements
are consistent and yields a more homogeneous sample. Furthermore, they distinguish
between pollution stocks and flows, for which there is still no generally accepted
theoretical framework, but has been shown to be of crucial importance in those models
examining the environmental Kuznets curve. The main differences between analyzing
inter-state and cross country variation in trade intensities can depend on many
exogenous and endogenous factors. It is considered that trade intensity is determined by
local industrial composition, reflecting natural resources as well as policies, by
population’s preferences for foreign or national goods, and by trade costs. In light of this,
they acknowledge that trade intensity might be determined endogenously by
government policies at both levels. The model specification they use is the same
reported by Frankel and Rose, incorporating GSP per capita in the place of GDP, its
square, land area per capita, and trade intensity. To instrument for the endogeneity of
income and trade, the authors use a generalized method of moments, and the same
instruments as Frankel and Rose (the gravity equation), which provides exogenous
geographical determinants of bilateral shipments. For income endogeneity, they use the
lag of income, population growth, area over population.
Data for pollution are gathered from US EPA’s TRI, which are reliable forasmuch as in
the US any manufacturing facility producing more than a certain quantity must submit a
15
report clarifying the amount of pollutants they used. Data are aggregated at the state
level and into other broad categories (air, water, total releases..) .
With the OLS method, treating trade as exogenous, results show no evidence of a
positive association between trade and pollution. When instead they move to the GMM
analysis, treating trade as endogenous, they reach three main results. Firstly, on average
there is evidence suggesting that higher subnational trade reduces air pollution, both
contemporaneously and two periods ahead. This effect is observed at a slightly lower
extent for water, underground and total releases. The third result is that for what
concerns land pollution, an increase in trade has a negative contemporaneous effect.
They also observe that when pollution is scaled by land area, results are less statistically
significant.
3.3 – The Relationship Between Trade and Environmental Regulation
Another theme to take into consideration when analysing the relationship between
trade and environment is the porter hypothesis, theorized by Porter and Van der Linde
(1995). They form a framework for solving the environment-competitiveness debate
taking in account the fact that the economy and the environmental regulation are
dynamic. They argue that with this framework, properly designed policies can foster
innovation, which may offset the losses caused by the environmental regulation itself. In
the end there is the possibility of having an absolute positive outcome, an advantage,
arising from stricter regulations. First of all, regulation is needed because firms need to
be guided to an environmentally sustainable direction for the type of innovation to
undertake. It signals resource inefficiencies, in particular regarding incomplete
utilization and toxicity of substances. Another advantage is that environmental policies
raise corporate awareness. Furthermore, world demand is moving towards more eco
friendly, resource efficient and energy efficient products, which allows the appliance of
price premiums on “green” products. Of course if policies are not internationally
homogeneous the arguments used in the paper don’t work any more, even though we
can talk about a competitive edge, that could be compared to the “first mover
advantage”, which makes economic gains last more.
16
The concept of pollution prevention is used and discussed in the research, but the
focus must be on resource productivity, which goes beyond pollution reduction: it
concerns the costs companies have to bear because of pollution rather than the
mitigation of pollution’s social costs. Pollution is viewed as an unproductive resource
utilization. Econometric studies relating environmental regulation and competitiveness
costs are biased mainly because innovation benefits are not taken into account, and even
those researches on poorly designed policies have very little effect on competitiveness.
Regulations need to: elaborate environmental goals that can be met in flexible
ways, encourage innovation with the aim to reach those goals, coordinate the system.
The authors suggest in primis to focus on the outcome itself rather than on technologies
to reach those outcomes, then they suggest to foster the use of market incentives
(pollution taxes deposit refund schemes and refund permits).
This would allow
flexibility and would create incentives for ongoing innovation: market incentives, for
instance, could encourage the introduction of technologies exceeding current
standards6. Coordination is fundamental as well; different layers of the government of
one country, and governments of different countries must elaborate consistent
regulations so that companies don’t have to deal with contrasting counterparties with
different requests and laws. The paper is also important because it poses the basis for
the technique and composition effect elaboration.
2. Hypotheses Formulation
In this thesis I am testing a set of hypotheses, derived from the existing literature, of
which I reviewed the most relevant papers above, as well as from theory on trade and
environment. The main objective is to find what is the relationship between trade and
pollution levels, using a panel analysis. Thus, the first hypotheses I am going to test are:
H0: trade negatively influences pollution levels at a country level over time
H1: trade positively influences pollution levels at a country level over time
6
See Blue Angel label in Germany
17
First of all, I consider as established a positive relationship between GDP growth
and trade (Frankel and Romer, 1999). I am furthermore testing the environmental
Kuznets curve, which states that output has a negative effect on pollution levels until a
certain point, and that after this point – after the country has reached a certain level of
GDP, thus development – the relationship changes and further growth has then a
positive effect on pollution levels, bringing them down. This can be explained through
the composition and technique effect, which start to offset the scale effect, that is the one
responsible of higher environmental degradation. This does not mean that if a country
promotes growth, the environmental situation will eventually start getting better itself,
rather that growth is usually associated with more environmentally sustainable policies.
This is what the hypothesis called “gains from trade” predicts. On the other hand,
another more widely recognized, but still not proven possibility is the one predicted by
the “race to the bottom” hypothesis, which says that those countries more open to
international trade will adopt looser regulations to be able to compete internationally
on the production of dirtier goods.
H0: GDP growth negatively influences pollution levels in a country over time
H1: GDP growth positively influences pollution levels in a country over time
H0: at certain high levels of income GDP positively influences pollution levels over time
H1: at certain high levels of income GDP negatively influences pollution levels over time
Similar to the latter hypothesis is the Pollution haven hypothesis, which has not
been proven right, and predicts that countries where the demand for clean goods is low
and that are trying to compete at a global level in the markets will adopt lax
environmental policies to attract multinationals from richer countries, in countries
where instead regulations are stricter and demand for cleaner goods is predominant,
there will be imports of dirty goods which could not be produced in loco due to
regulations.
18
Another debated topic is the Porter hypothesis, for which tighter environmental
regulation fosters the advancement in technology, producing a positive effect on growth
and environment. This proposition has been further discussed in many papers but has
had controversial conclusions. Furthermore, I will test if higher degrees of openness
bring poor countries to exploit a comparative advantage in pollution intensive goods,
and if openness drives capital intensive countries to exploit a comparative advantage in
pollution intensive goods.
H0: the capital to labor ratio has a positive effect on pollution levels over time
H1: the capital to labor ratio has a positive effect on pollution levels over time
5. The Endogeneity Issue
5.1 – Description of the Problem
In investigating the link between trade and environment, the possibility of encountering
endogeneity issues is high. As argued by Frankel and Rose (2005) studies previously
conducted on trade and environment found that the environmental sustainability score
is higher in economies more open to trade (Eiras and Schaeffer, 2001). This does not
necessarily mean that trade is good for the environment: the result might be deriving
from the fact that, for example, in democracies – most times more open to trade and
integrated in global markets - higher levels of environmental regulation are
implemented. Another possibility is that in those countries where environmental
regulation is stricter, productivity is higher, which is a consequence of the Porter
hypothesis. On the other hand, if trade and pollution were found to have a positive
correlation, it would not automatically mean that trade is bad for the environment. This
result might derive from a negative effect of regulation on a country’s growth, which in
turn has a positive effect on trade.
The problem arises for what concerns the relationship between trade and growth
as well: the causality in this field is unclear too. Rodrik (1995) and Levine and Renelt
(1992) hypothesize this mechanism: an increase in investments in an undeveloped
country which has a comparative disadvantage in the production of capital intensive
19
output will require an increase in imports of these goods. Another possibility is that
trade rises with income as foreign goods are preferred to domestic ones. In addition, the
issue may derive by the fact that domestic free market policies are fostering growth, and
being these correlated with more developed foreign trade policies, a link will be
observed between these last ones and growth even though there is no direct link
between trade and growth. For this reason those studies which tried to estimate a way
to measure trade policies, apart from the fact that they are tricky to quantify, did not
deal with the simultaneity issue arising from the mechanism explained above. For these
reasons, treating trade as exogenous does not allow the investigation of causality.
Harbaugh et al. (2002) explore the effect of trade on pollution controlling for
income; Managi et al. (2009) use a generalized method of moments to solve the
endogeneity problem. There is another way to solve the endogeneity problem: the
gravity model of bilateral trade has the characteristics to be used as an instrument for
trade, as the correlation of aggregated bilateral trade with a country’s overall trade is
high, while geographical variables are definitely exogenous. Furthermore, distance and
the other dummy variables used in the gravity equation are not correlated with
pollution, thus the instrument is implementable in the analysis.
For solving endogeneity of income, following the literature by Frankel and Romer
(1999), Frankel and Rose (2002), Solow (1956), Barro (1991), and Mankiw et al. (1992)
I use another set of instruments: lagged income, size, human capital formation, rate of
investments.
5.2 - The Gravity Model
The Gravity equation for trade has been one of the most successful empirical models in
economics, essentially featuring a positive effect of country size on trade, and a negative
effect of distance on trade. Even though the model is empirically successful and results
are surprisingly good, a unique and commonly accepted theoretical explanation of it has
not been found yet. Alternative trade theories include the Ricardian model, which relies
on differences in technologies between countries to explain trade patterns, and the
Heckscher Ohlin model, which explains trade as deriving from differences in factor
endowments among countries. Neither of these two models can provide any theory
20
foundation for the gravity equation. The first theoretic attempt to explain the gravity
model was produced by Anderson (1979), who assumed that goods were differentiated
by country of origin and consumers are characterized by preferences defined over all
the differentiated products. From this follows that a country will consume a minimum
quantity of at least all the unique goods produced in each country. For this reason it is
assumed that larger countries will import and export more. Another relevant theoretic
base for the gravity model is provided by Bergstrand (1985), who explains how the
model can be derived from the theory of trade based on monopolistic competition
developed by Krugman (1980).
Even if no single specification exists, the gravity model has for long been used for
empirical analyses: Ravenstein (1889) pioneered the method to analyse migration,
while Tinbergen (1962) was the first using the gravity equation for trade flows.
Following the theory on the traditional gravity model (Ravenstein, 1889), the baseline
for building the equation is the assumption for which a mass of goods, or factors,
supplied at the country of origin i, is attracted by a mass of demand for those same good
or factors at the destination country j. The potential flow deriving by the difference
between supply and demand is reduced by the distance between the two countries, dij.
𝑿𝒊𝒋 = 𝒀𝒊 𝑬𝒋 /𝒅𝟐𝒊𝒋
( 1)
The specification is the result of the direct application of the assumption. 𝑋𝑖𝑗 is
the monetary value of exports from country i to j, Y is the mass of factor of production,
such as the exporter’s GDP, E is the mass of demand for the factor, which could be
identified as the importer’s GDP, d is distance. This specification works well in fitting the
data: 80-90% of variation in flows is captured by fitted values (Anderson, 2011). The
traditional model has been then improved by inclusion of other proxies for trade flows
capturing trade costs, such as landlock, common border or relative trade costs
(Anderson and van Wincoop, 2003). A more recent, less specific and generally further
accepted specification of the gravity model is the following:
𝑿𝒊𝒋 = 𝑮𝑺𝒊 𝑴𝒋 𝝓𝒊𝒋
( 2)
21
Where 𝑆𝑖 stands for the capacity of the exporter to supply for the demand of the
other countries. 𝑀𝑗 stands for the importer’s characteristics that make up the importer’s
demand (GDP for instance). G is a variable not depending on either of the trading
partners, also called “gravitational constant”7, which could be the level of world
liberalization, and 𝜙𝑖𝑗 is the inverse of bilateral trade costs, to measure their impact on
trade flows. The important features of this specifications are that each term is included
multiplicatively, and that third country effects must be mediated by importer and
exporter multilateral terms8. The multiplicative form is useful because it allows the
estimation of trade using importer’s and exporter’s fixed effects. To capture trade costs
one of the most common methods is using bilateral distance and dummies to capture an
increase in information costs (common language), and cultural features (colonies).
To improve the model a Remoteness Index was recently developed, constructed
as each country’s average distance from its trading partners. Another attempt to raise
the explanatory power of the model is the inclusion of a multilateral trade resistance
term, calculated as the weighted average of trade costs (Anderson and van Wincoop,
2003). The reason for this would be to control for natural trade impediments such as
oceans, deserts or mountains. If both the exporter and the importer countries’ GDP are
included, the MTR should be included as well to have a robust gravity model. Anderson
and Yotov (2010) argue on the other hand that the MTR is correlated with country size,
so the inclusion of variables such as GDP and population in a model including the index
should be avoided, since it partially gathers the missing explanatory power of the index.
Overall the inclusion of this term has been abandoned and substituted by using importer
and exporter fixed effects (Harrigan, 1996, was the first using this approach; Rose and
van Wincoop, 2001; Feenstra, 2002; Baldwin and Taglioni, 2006).
One of the common problems in dealing with disaggregated bilateral trade flows
data is the frequent presence of zero observations. One interpretation of such cases is
that it is legitimate to drop them, since there is no econometric significance relatively to
the non zero values. The frequent presence of zeroes leads to the issue of
Only held constant in cross section analyses.
A change in trade costs between the importer and a third country can influence 𝑋𝑖𝑗 through changing 𝑆𝑖
or 𝑀𝑗 . It would be impossible to reduce j’s imports from the third country but leave all other imports
unchanged, following a trade agreement between i and j.
7
8
22
heteroskedastic error terms, which would lead to inconsistent estimation when using
the log transformation and an Ordinary Least Squares regression. Santos-Silva and
Tenreyro (2006) propose to use a Poisson Pseudo-Maximum Likelihood estimation, and
this method leads to smaller estimates of trade costs compared to OLS. Another way of
dealing with zeroes is proposed by Martin and Pham (2008), who use Tobit estimators
and obtain better results. I took into consideration the possibility of using the Heckman
method for dealing with zeroes, which accounts for those countries that do not trade
due to too high trade costs, but this method has been implemented only for cross section
analyses, thus I was not able to perform it. Lastly, Anderson and Yotov (2010) argue that
the three different models9 lead to almost identical estimations, since the resulting
gravity coefficients are practically perfectly correlated.
Following the approach adopted by Frankel and Romer (1999) and by Frankel
and Rose (2002), I construct a gravity equation to instrument for trade in the tradeenvironment relationship, since the trade variable is with all probabilities endogenous.
Geographic characteristics are identified as very powerful determinants of trade, and
they are not influenced by other factors such as policies or countries’ income. The
gravity equation has been successfully used to infer the effect of trade flows on exchange
rate mechanisms, customs unions, etc. Logs will be used in order to estimate elasticities,
for instance the dependent variable indicates the percentage change in trade deriving
from a 1 percentage increase in GDP. Since the equation will be used to instrument for
trade share10, the dependent variable which usually enters the model as 𝑙𝑛(𝜏𝑖𝑗 /
𝐺𝐷𝑃𝑖 𝐺𝐷𝑃𝑗 ) will only be specified as bilateral trade share over GDP of the exporter. In
particular, the form of the gravity equation I use is:
𝝉𝒊𝒋
𝐥𝐧 (𝑮𝑫𝑷 ) = 𝜶 + 𝜷𝟏 𝒍𝒏𝑫𝒊𝒋 + 𝜷𝟐 𝒍𝒏𝑷𝒊 + 𝜷𝟑 𝒍𝒏𝑷𝒋 + 𝜷𝟒 𝒍𝒏𝑨𝒊 + 𝜷𝟓 𝒍𝒏𝑨𝒋 + 𝜷𝟔 𝑳𝒊𝒋 + 𝜷𝟕 𝑪𝒐𝒍𝒊𝒋 +
𝒊
𝜷𝟖 𝑳𝒂𝒏𝒈𝒊𝒋 + 𝜷𝟗 𝑩𝒊𝒋 𝑪𝒐𝒍𝒊𝒋 + 𝜷𝟏𝟎 𝑩𝒊𝒋 𝑳𝒂𝒏𝒈𝒊𝒋 + 𝜷𝟏𝟏 𝑩𝒊𝒋 𝑫𝒊𝒋 + 𝜷𝟏𝟐 𝑩𝒊𝒋 𝑷𝒊 + 𝜷𝟏𝟑 𝑩𝒊𝒋 𝑷𝒋 +
( 3)
𝜷𝟏𝟒 𝑩𝒊𝒋 𝑨𝒊 + 𝜷𝟏𝟓 𝑩𝒊𝒋 𝑨𝒋 + 𝜷𝟏𝟔 𝑩𝒊𝒋 𝑳𝒊𝒋 + 𝒍𝒏𝑮𝑫𝑷𝒋 + 𝒖
9
Ordinary Least Squares, Poisson Pseudo-Maximum Likelihood, and Tobit
(Exportsi + Importsi)/GDPi
10
23
Where 𝜏 is the bilateral trade flow, 𝐷 is distance, 𝑃 is population, A is area, L is the sum
of the dummies indicating whether the trading partners are landlocked11, Col is a
dummy indicating whether the trading partners have been one another’s colonies, Lang
is a dummy standing for common language. The variables following these are combined
to form an interaction term with B, which stands for common border. The variable
indicating a common border has not been included alone, since its significance is very
weak and it does not improve the explanatory power of the model. This can be explained
in two ways: on the one hand the variables for sharing a common border and a common
language are highly correlated, and this would alter results. Another interpretation
proposed by Frankel and Romer (1999) is that since a very small number of countries
share a border, the contribution of this variable to the model is very small. The inclusion
of the variable interacted with the other determinants of trade shows how the
coefficient varies if the countries are neighbour, and the terms are significant.
Bilateral trade data are taken from the International Monetary Fund Direction of
Trade Statistics, population is from Penn World Tables 8.1, while the rest of the
variables is taken from CEPII (Centre d'Études Prospectives et d'Informations
Internationales) GeoDist database, which includes 225 countries. The distance measure
is constructed by using latitude and longitude of the most populous city and the method
of the great circle.
5.3 - The Income Equation
To solve income endogeneity, I estimate a second equation which I will use to
instrument for GDP. It is usually assumed that GDP runs from trade to income. The
empirical literature indicates that the causality between trade openness and economic
growth runs in both directions (Harrison 1996; Chow 1987; Hutchinson and Singh
1987). Following research from neoclassical growth equations (Solow, 1956; Barro,
1991; Mankiw et al., 1992), I estimate the following specification:
𝐥𝐨𝐠 𝑮𝑫𝑷𝒊,𝒕 = 𝜶 + 𝜷𝟏 𝐥𝐨𝐠(𝒐𝒑𝒆𝒏)𝒊,𝒕 + 𝜷𝟐 𝒑𝒐𝒑𝒈;𝒊,𝒕 + 𝜷𝟑 𝐥𝐨𝐠(𝒑𝒐𝒑)𝒊,𝒕 + 𝜷𝟒 𝒉𝒄𝒊,𝒕 +
𝜷𝟓 𝒔𝒄𝒉𝒐𝒐𝒍𝟏;𝒊,𝒕 + 𝜷𝟔 𝒔𝒄𝒉𝒐𝒐𝒍𝟐;𝒊,𝒕 + 𝜷𝟕 𝒔𝒄𝒉𝒐𝒐𝒍𝟑;𝒊,𝒕 + 𝒖𝒊,𝒕
( 4)
11
Landlocki + Landlockj , if both trading partners are landlocked the value will be equal to 2.
24
where 𝑜𝑝𝑒𝑛 is a measure of openness, calculated as imports plus exports over GDP of a
country, 𝑝𝑜𝑝 stands for population, 𝑝𝑜𝑝𝑔 is population growth, ℎ𝑐 is human capital, the
three 𝑠𝑐ℎ𝑜𝑜𝑙 terms represent respectively primary, secondary and tertiary school
enrolment rates, and the last term is the error. Lagged income and rates of investments
have not been included due to multicollinearity, which was affecting the model too
strongly for those variables to be incorporated. Concerns about the possibility that these
instruments might in fact be endogenous have been expressed (Bils and Klenow, 1998),
in particular for human capital, but this is by now the best specification I could derive
from the literature to instrument for income growth.
6. Data Description
Data for CO2 are taken from the World Development Indicator database of the World
Bank, and they include gases from the combustion of fossil fuels and cement
manufacture. The data excludes emissions from land use as deforestation.
Concerning GHG, values are collected by the United Nations Framework
Convention on Climate Change (UNFCCC), submitted by each country. Values are in
million metric tons. The data for HFC, N2O, PFC, and SF6 are measured in thousand
metric tons of 𝐶𝑂2 equivalent, and submitted by the European Commission, jointly with
the Joint Research Centre in the Emission Database for Global Atmospheric Research
(EDGAR).
Data for pm 25 and pm 10 are gathered in mean annual exposure of micrograms
per cubic meter, from the World Bank in cooperation with the Institute for Health
Metrics and Evaluation at the University of Washington. Concentrations are measured in
urban and rural areas, weighted by population and aggregated at the national level.
The polity variable is taken from the Polity IV project. It reflects the level of
Democracy or Autocracy in countries over time. Data have been smoothed and adapted
25
due to the limited quality of historical information, in particular for certain countries.
The variable ranges from +10 (Strongly Democratic) to -10 (Strongly Autocratic). It
contains a “fix,” to convert instances of “standardized authority scores” (i.e., -66, -77, and
-88) to conventional polity scores (i.e., within the range, -10 to +10). The values have
been converted according to the following rule set: a value of -66 is given to cases of
foreign “interruption” are treated as “system missing”. -77 identifies cases of
“interregnum,” or anarchy, are converted to a “neutral” Polity score of “0.” -88 Cases of
“transition” are prorated across the span of the transition. Data on Trade and Schooling
rates are taken from the World Bank, while data on GDP, Cost of Capital, Employment,
Population, and Human Capital come from the Penn World Tables 8.1.
Table 1 - Description of Data
VARIABLES
CH4
CO2 cap
GHG
HFC
N2O
PFC
Pm10
Pm2.5
SF6
SO2
Primary School Enrolment
Secondary School Enrolment
Tertiary School Enrolment
Population
Employment
Human Capital
Cost of Capital
Real GDP
Openness
Developed country
Area
Polity
Number of countries
N
667
4,181
919
662
667
662
3,950
1,961
662
662
2,169
1,484
2,565
3,564
3,564
3,564
3,564
3,564
2,727
4,356
4,312
1,601
mean
55,659
4.656
-37.88
3,519
22,887
702.9
280,106
20,585
1,011
1,265
86.78
67.09
29.01
15.86
1.939
317,386
324,561
980,109
193,564
0.167
692,395
-0.375
sd
150,030
6.561
151.6
20,794
58,912
2,656
273,740
102,915
4,930
3,794
15.57
26.36
24.00
66.08
1.090
1.102e+06
1.123e+06
3.496e+06
176,191
0.373
1.904e+06
17.63
Min
0
0.000580
-1,034
0
0
0
0
0
0
0
19.21
2.701
0
0
0
-286,252
228.8
290.4
10,824
0
25
-88
Max
1.642e+06
68.53
1,329
300,896
550,297
28,056
999,396
930,717
57,054
44,625
100
100
117.9
784.4
3.619
1.323e+07
1.396e+07
4.465e+07
1.429e+06
1
1.708e+07
10
169
169
169
169
169
26
7. Methodology
In this section I will explain how I applied what I have theoretically explained in the
previous sections. First I will describe how I constructed the instrument for trade and
how I adapted the data to fit the final model specification. I will then assess the
qualitative power of the instrument, outlining the final equation that features the
relation between environmental quality and trade. Lastly, before explaining the results,
some robustness checks are discussed.
7.1 - Constructing the Instrument
The key assumption in using the gravity equation as an instrument for trade is that
countries’ geographic characteristics are uncorrelated with the residuals of the
environmental quality equation. Size and proximity are not affected by pollution, and at
the same time they are predicting effectively trade flows between countries, making the
gravity equation a perfect way to instrument for trade.
A problem could arise because the estimation through the instrument does not
only take in account distance, but also country size measures (income, population),
which could be correlated with the error term. This happens because smaller countries
could engage in more international trade simply because they engage in less within
country trade, and this aspect should be excluded from the analysis when trying to
predict trade’s impact on pollution (Frankel and Romer, 1999). This is why all the
variables in the model will enter the equation controlled for population, hence there will
be no reason to suspect serial correlation with the residuals from this point of view.
The gravity equation I estimate covers 21 years, from 1990 to 2011 and data are for 185
countries. Since I am using the log transformation for the estimation, bilateral trade
flows recorded with zero value are dropped (Frankel et al., 1995). Results are as
expected, and are shown in the table 2. The interaction terms of the common border
dummy with the other variables are not shown for brevity.
27
First, briefly describing the results, coefficients are of the expected signs. The
value for distance is negative, meaning that two countries far away from each other
trade less, by 1.3 percent for each percentage point increase in distance. The population
of the two countries (importer and exporter) enters the equation with a negative sign.
The rationale behind this is that more populated countries incur in more within country
trade and less international trade. The next term is Area, with a positive coefficient: the
more extended is a country the more it trades. The interpretation of this sign would be
that area is a proxy for size and endowment
Table 2 – The Gravity Equation
of factors of production, so it is positively
correlated
with
trade
Landlocked
variable
has
volume.
the
The
expected
(1)
VARIABLES
negative sign, thus countries with no sea lnDistance
access trade less due to higher trade lnPopi
barriers; countries trade more (3% more)
with neighboring states; they trade more
lnPopj
with their colonies and if they have a lnAreai
common language. Lastly, the importer’s lnAreaj
GDP has a positive sign, this confirms what
has generally been found in previous
Landlock
literature and research: that there is a lnGDPj
positive association between trade and Contiguity
income.
Com.Language
Colony
Constant
7.2 - Fitting the Data in the Model
To be able to fit the gravity model into my R2
final estimation 12 , which features the Observations
Number of pairid
relationship between trade and pollution
Exports/GDPi
-1.318***
(0.0260)
-0.944***
(0.00501)
-0.768***
(0.0116)
0.525***
(0.00831)
0.352***
(0.00834)
-1.062***
(0.0368)
0.884***
(0.0114)
3.860***
(1.239)
0.393***
(0.0530)
2.923***
(0.153)
-1.636***
(0.284)
0.5878
332,105
22,375
levels, I need to transform the bilateral dataset into a normal form panel. To do so, I
aggregate the fitted values from the bilateral trade estimation, by first doing this
transformation:
12
For the use predicted trade as instrument.
28
𝝉𝒊𝒋
( 5)
𝐥𝐧 (𝑮𝑫𝑷 ) = 𝒂′ 𝑿𝒊𝒋 + 𝒆𝒊𝒋
𝒊
In equation (5), a stands for the vector of coefficients of the variables included in
the gravity equation (𝛼, 𝛽1 , 𝛽2 , … , 𝛽16) and 𝑋𝑖𝑗 is the vector of all the independent
variables used in the specification (𝐷𝑖𝑗 , 𝑃𝑖 , 𝑃𝑗 , 𝐶𝑜𝑙𝑖𝑗 , 𝐿𝑎𝑛𝑔𝑖𝑗 , 𝑒𝑡𝑐).
The following passage is to aggregate data for each country:
̂ 𝒊 = ∑𝒋≠𝒊 𝒆𝒂̂′ 𝑿𝒊𝒋 .
𝑻
( 6)
What this transformation (6) is doing is to estimate the geographic components
of country i’s trade as the sum of the coefficients of the variables predicting bilateral
trade with all the counterparty countries for which I have data. It is possible to perform
𝜏
𝑖𝑗
this transformation because the expectation of ln(𝐺𝐷𝑃
) conditional on 𝑋𝑖𝑗 is equal to
𝑖
𝑒
𝑎̂′ 𝑿𝒊𝒋
multiplied by the expected value of the error term. I am modeling the error as
homoskedastic, so the expectation on the residual is the same over all observations, for
this reason the transformation illustrated above can be performed.
7.3 - The Instrument’s Quality
To assess the quality of the instrument, I compute the correlation between actual and
constructed trade share, which is 0.43. Geographic variables account for a sizeable part
of the variation in international trade. Table 3 displays the relation between the actual
trade share and the one coming from aggregating bilateral trade flows from the gravity
model.
The values of population and area are included in natural logs to normalize the
distribution. The relation between constructed and actual trade shows an increase of 0.7
to any unity increase in actual trade. This value goes down by roughly 0.05 when the
controls, area and population, are added. Running the regression without constructed
trade (2), the two terms remain significant and their value has a bigger impact on actual
trade: as the physical size and the population of a country decrease, its trade share is
increased. The same happens with population in model (3), as expected. The table
29
shows that constructed trade contains enough information about actual trade to use it as
an instrument and not produce excessively big standard errors in the final estimation.
Table 3 - The Relation Between Actual and Constructed Trade Share
VARIABLES
(1)
Open
lnArea
Constant
Observations
R-squared
Number of country
(3)
Open
-0.138***
(0.0237)
-0.0374***
(0.0129)
7.46e-07***
(9.55e-08)
0.703***
(0.0120)
0.851***
(0.247)
-0.0949***
(0.0223)
-0.0575***
(0.0156)
7.39e-07***
(9.80e-08)
1.237***
(0.294)
3,422
0.123
167
3,801
0.1051
184
3,419
0.2921
167
lnPop
Open constructed
(2)
Open
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
For what concerns the instrument for income, which features as independent
variables trade, human capital, years of schooling and other controls (as specified in the
endogeneity section), I resolved not to include it. After having run the regression
including this instrument, the Sargan-Hansen test resulted significant, indicating an over
identification error. Thus, my conclusion is that this instrument is not efficient for my
analysis. I argue that since I am trying to instrument for income with trade, and at the
same time I am using distance to instrument for trade, I cannot include in the exogenous
group of variables I am using as instruments, one of the variables I’m instrumenting for.
Furthermore, excluding this set of instruments from the modeling leads to an exactly
identified regression13. I do not argue that Income is exogenous, since there is evidence
proving the contrary14, but that a better instrument should be constructed to fit this
particular specification I am exploring.
13
14
Explain the test
literature on this
30
7.4 - The Environmental Damage Equation
My empirical analysis assesses the environmental consequences of international trade
at a country level. To do this, I construct an equation relating environmental quality
measures to the term of primary interest, trade, and other determinants of pollution
derived from the literature.
𝑒𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑑𝑎𝑚𝑎𝑔𝑒𝑖 = 𝛼0 + 𝜑1 𝐺𝐷𝑃𝑐𝑎𝑝𝑖 + 𝜑2 𝐺𝐷𝑃𝑐𝑎𝑝𝑖2 + 𝜑3 𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠𝑖 +
𝜑4 𝐾𝐿𝑖 + 𝜑5 𝐴𝑟𝑒𝑎𝑐𝑎𝑝𝑖 + 𝑒𝑖
( 7)
I estimated separate equations for each measure of environmental damage, for all
the pollutants I listed in the data description section. I will illustrate only the regressions
for pollutants on which I obtained significant results. For almost all environmental
quality specifications, the measure of environmental damage is in per capita form, to
control for the size of the country15. I considered including other explanatory variables
which I did not include, such as the polity variable, which I excluded because of too
many missing data, a dummy for developed countries, and a dummy for OECD countries.
These two latter terms were not included because in the OLS specification, as I will
explain in the Results, the fixed effects estimation accounts for the same part of variation
in pollution levels. The specification does not furthermore include additional variables
that could play a role in the determination of pollution levels. I choose to not add them
to the model because I am not trying to investigate which are the determinants of
pollution levels in different countries over time; instead I want to focus on the effects
that trade has on pollution. In addition, doing so I do not leave out trade’s impact on
environmental quality that might operate through other channels. I perform a panel
analysis, which is one of the main contributions of this thesis, as this method gives some
advantages. The large number of data, increasing the degrees of freedom, reduces
collinearity between the explanatory variables; by doing so the efficiency of estimations
is improved. Furthermore, it can isolate specific case effects and exclude them from the
final results, which is a way to control for omitted variable bias. The first two variables
are income per capita and its square, which enter the specification in current US dollars.
The term is included to test the environmental Kuznets curve, which predicts that at
15
details in Results section, and for same reasons as in gravity
31
certain levels of income per capita the pollution curve eventually will start turning
down. The capital labor ratio is included to control for countries’ relative factor
endowments, as well as to catch the composition effect, to capture factor endowment
and analyze if capital abundant countries exploit a comparative advantage in pollution
intensive goods production. It is computed as capital stock level in current US dollars
over the number of people employed. The logarithm of per capita income is used to
capture both scale and technique effects, so a positive association between pollution and
income is interpreted as a dominant scale over the technique effect.
7.5 - Robustness Checks
First of all, I identified outliers as those countries with a trade flow too substantial
compared to their size: Luxemburg, El Salvador, Hong Kong and Equatorial Guinea.
These countries are dropped from the dataset.
My analysis procedure is to start from estimating the OLS regression for each
pollutant, and then proceed with the inclusion of the instrument. I first run an Hausman
test to decide whether I should use fixed effects or random effects estimation in the OLS.
Fixed effects models are used when the interest is to analyze the impact of the timevarying dimension on the dependent variable. This method controls for individual
characteristics of each entity16 that may influence the regression and produce biased
results. This is the interpretative translation of the assumption I tested, for which there
is correlation between the error term and the predictors. Fixed effects remove time
invariant aspects, so that the net effect can be analyzed.
On the other hand, the random effects model assumes that variation between countries
is random, and uncorrelated with the independent variables included in the regression.
The assumption held to use random effects is stronger, so I test my model to understand
whether my estimation allows the use of this method.
The Hausman method tests the null hypothesis for which the unique errors are
not correlated with the regressors, in which case the random effects model should be
used. On the other hand, if the test is significant, the fixed effects model should be
preferred, as the error terms in the regression fail to meet the hypothesis of
16
In my case, countries characteristics
32
orthogonality. In my case, when I run the test on the OLS estimation it is significant,
meaning I should use fixed effects. When instead I perform the IV estimation and run the
same test, there is no evidence to reject the null hypothesis17. For this reason, in the
results, I provided the estimates for both random and fixed effects for each method of
estimation(IV and OLS), for the sake of comparison. Since the fixed effects method
accounts for differences and individual characteristics across entities over time, I cannot
include each country’s fixed effects in the regression18, while I include the Time
dummies. The inclusion of one dummy variable for each year controls for specific effects
that might have happened during a particular time period, and might have influenced
the dependent variable (pollution levels) in ways that are not linked with trade or the
other controls I’m including in the specification. Examples for the case of my analysis
could be yearly weather anomalies, or years of particularly high pollution levels due to
industrial accidents.
To test whether it is actually necessary to instrument for trade, I perform a
Durbin-Wu-Hausman test (augmented regression test), which tests for endogeneity of
the variable of choice starting from an OLS regression. This test is performed running
the regression including the suspect endogenous variable among other independent
variables included in the model, and subsequently running the same regression
augmented with the inclusion of the fitted values of the residuals from the variable I
want to test. The third step consist in running the test which assesses if the two
specifications differ significantly. If they do, the OLS is not consistent. When I perform
this test on the variable for openness, the outcome suggests that I should proceed and
use an instrumental variable estimation to correct for endogeneity. The first stage F test
on excluded instrument, which tests the power of the variables I am using to correct for
endogeneity, is always very high (39 or more), meaning that the instruments I am using
are powerful enough to avoid an estimation bias.
17
18
Meaning I can use random effects
Because it is already provided by running the fixed effects command
33
8. Results
I will now talk about the main results I obtained through the empirical analysis. I will
describe in detail only those regressions that had a meaningful interpretation, as not all
the pollutants I tested gave the expected results, or were significant at all.
Concerning Nitrous Dioxide, results are shown in Table 4 and they are generally
significant, but their magnitude is very small. The dependent variable is not in the
logarithmic form, since I had no log-normal distribution and hence no particular
evidence suggesting the need to transform it. There is evidence in support of the
environmental Kuznets curve, since the log of GDP per capita has a positive sign,
indicating a positive correlation with the quantity of N2O per capita present in the
atmosphere, while the squared term has a negative sign. The coefficient, albeit with the
expected sign, is very close to zero. The reason for this might be that I am trying to
predict the effect on the level of nitrous oxide per capita, since controlling for population
leads to more consistent results. The openness coefficient displays a similar result: the
sign is as expected and it is significant, but, in particular in the OLS regression, the
impact of the variable is close to zero. When instrumenting for trade, the value increases
by two decimal positions, but the effect is still negligible. The capital to labor ratio has a
positive sign, giving evidence of the hypothesis for which capital abundant countries are
also producing more pollution, while area per capita features a negative sign, supporting
the hypothesis for which highly populated countries suffer more from environmental
deterioration.
The second set of pollutants I analysed is Greenhouse Gases, and results are very
similar to the ones from Nitrous Dioxide, although with increased magnitude (Table 5).
Income enters the equation positively, while the quadratic term is negative, supporting
again an inverted U-shaped relationship with the level of greenhouse gases in the
atmosphere.
34
Table 4 – N2O
VARIABLES
lnGDP
(lnGDP)2
lnOpen
KL
Acap
Constant
(1)
OLS
N2O
0.000253***
(1.74e-05)
-1.61e-05***
(1.14e-06)
-1.51e-05**
(6.15e-06)
6.67e-11***
(0)
-0.000465***
(0.000114)
-0.000750***
(7.61e-05)
Fixed effects
Random effects
Time fixed effects
Observations
R-squared
Number of countries
X
Yes
(2)
OLS
N2O
0.000260***
(1.70e-05)
-1.76e-05***
(1.12e-06)
-1.92e-05***
(6.03e-06)
6.43e-11***
(0)
-0.000559***
(0.000119)
-0.000646***
(6.82e-05)
X
Yes
3,116
(3)
IV
N2O
0.000238***
(1.93e-05)
-1.61e-05***
(1.25e-06)
-0.000134***
(2.72e-05)
6.47e-11***
(0)
-0.000354***
(0.000137)
-0.000627***
(8.71e-05)
X
Yes
3,116
2,811
0.166
181
181
165
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(4)
IV
N2O
0.000242***
(1.94e-05)
-1.84e-05***
(1.26e-06)
-0.000174***
(2.45e-05)
6.15e-11***
(0)
-0.000481***
(0.000152)
-0.000508***
(8.34e-05)
X
Yes
2,811
165
When using the IV estimation the variable loses some significance, and the value
drops from 0.1 to 0.08. Land area per capita holds the negative sign, indicating that
higher population densities are correlated with higher pollution, with a coefficient of 14
(IV). The capital labor ratio coefficient is positive, but significant only at the 10 percent
level in the OLS estimation, and not significant when using the IV. On the contrary,
openness gathers significance when using the instrumental variables, suggesting a
negative relation with pollution. The dependent variable is not logged, so the
interpretation is that for each percentage point increase in trade, greenhouse gases will
decrease by around 5 thousand metric tons of carbon dioxide equivalent, versus the 0.8
predicted by the OLS. The coefficient is not in per capita terms, so the value is intended
at a country level. This coefficient suggests that in this case the OLS method understates,
rather than overstates, the effect of trade on pollution.
35
Table 5 - GHG
VARIABLES
lnGDP
(lnGDP)2
Acap
lnKL
lnOpen
Constant
Fixed effects
Random effects
Time fixed effects
Observations
R-squared
Number of countries
(1)
OLS
GHG
0.151***
(0.0420)
-1.38e-06***
(4.33e-07)
-18,252**
(7,188)
88.03*
(52.31)
-806.1**
(358.2)
7,026***
(785.1)
X
(2)
OLS
GHG
0.172
(0.110)
-1.56e-06*
(9.07e-07)
-16,542**
(7,999)
88.07*
(47.61)
-877.4
(629.2)
4,597***
(1,609)
(3)
IV
GHG
0.0827*
(0.0436)
-8.40e-07*
(4.46e-07)
-13,822*
(7,418)
35.57
(50.38)
-5,399***
(1,428)
5,567**
(2,497)
X
X
Yes
Yes
3,116
3,116
0.016
181
181
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(4)
IV
GHG
0.0813*
(0.0427)
-8.31e-07*
(4.37e-07)
-14,736*
(7,542)
33.65
(49.32)
-3,961***
(1,322)
6,171***
(713.4)
X
Yes
2,811
Yes
2,811
165
165
HFC is the third pollutant I am analyzing. Referring to table 6, the variable is
scaled by population, and not in the log form. For this pollutant, coefficients do not show
the expected results. Income and its squared show evidence of a U-shaped relationship
(not inverted) with pollution in all the regressions, and they are always significant. The
capital-labor ratio has the expected sign, but it is not significant, while the sign for
openness is ambiguous. The variable features a positive and significant relationship with
hydrofluorocarbons in the OLS regression, while in the instrumental regression (with
random effects) it turns to a negative coefficient, of almost the same magnitude as in the
OLS (but inverted sign). The F-test on excluded instrument takes the value of 44,
meaning that the instrumental specification is correct. The interpretation of this
coefficient is not straightforward and I cannot explain this sudden change in the sign.
36
Table 6 - HFC
VARIABLES
lnGDP
(lnGDP)2
Acap
lnKL
lnOpen
Constant
Fixed effects
(1)
OLS
HFC
-6.08e-05***
(1.36e-05)
4.89e-06***
(8.90e-07)
-0.000165*
(9.49e-05)
6.15e-07
(6.87e-07)
1.07e-05***
(4.80e-06)
0.000200***
(5.57e-05)
X
Random effects
Time fixed effects
Observations
R-squared
Number of countries
(2)
OLS
HFC
-8.02e-05***
(1.27e-05)
6.62e-06***
(8.13e-07)
7.21e-05
(4.87e-05)
6.66e-07
(6.91e-07)
5.83e-06
(4.35e-06)
0.000219***
(5.13e-05)
(3)
IV
HFC
-7.10e-05***
(1.45e-05)
5.34e-06***
(9.48e-07)
-0.000191*
(0.000114)
7.98e-07
(7.43e-07)
-7.46e-06
(1.84e-05)
0.000256***
(6.30e-05)
X
(4)
IV
HFC
-9.03e-05***
(1.37e-05)
7.26e-06***
(8.74e-07)
0.000108*
(5.57e-05)
9.43e-07
(7.65e-07)
-4.86e-05***
(1.60e-05)
0.000257***
(5.47e-05)
X
X
Yes
Yes
Yes
3,116
3,116
2,811
0.023
181
181
165
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Yes
2,811
165
Table 7 shows results for particulate matter 10; the variables of interest are
significant and consistent with expectations. The value is measured in per capita terms,
and not logged. The coefficient for income does not change massively when moving on
the instrumental estimation, although it decreases a little. The square of income is
negative, showing again an inverted U-shape Kuznets curve. Area per capita and the KL
ratio are not significant, even if for what concerns area the coefficient has the consistent
sign. Openness is unfortunately positive and significant in this model, and in the
instrumental variable regression the value for trade more than doubles its magnitude,
suggesting that trade has, in the case of this pollutant, a detrimental effect on the
environment and on human health.
37
Table 7 – PM10
VARIABLES
lnGDP
(lnGDP)2
Acap
lnKL
lnOpen
Constant
(1)
OLS
PM10
0.382***
(0.135)
-0.0219***
(0.00821)
-0.0337
(0.446)
-0.00321
(0.00425)
0.229***
(0.0684)
-1.548***
(0.548)
Fixed effects
Random effects
Time fixed effects
Observations
R-squared
Number of countries
(2)
OLS
PM10
0.390**
(0.153)
-0.0227**
(0.0103)
-0.00710
(0.876)
-0.00285
(0.00422)
0.227***
(0.0726)
-1.189**
(0.551)
X
X
Yes
3,116
(3)
IV
PM10
0.312***
(0.0774)
-0.0156***
(0.00496)
0.235
(0.358)
-0.00659
(0.00421)
0.904***
(0.0971)
-1.302***
(0.313)
(4)
IV
PM10
0.346***
(0.0796)
-0.0114**
(0.00519)
0.291
(0.627)
-0.00577
(0.00407)
0.849***
(0.101)
-1.898***
(0.345)
X
X
Yes
Yes
3,116
2,811
0.092
181
181
165
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Yes
2,811
165
Sulphur dioxide is one of the most analyzed pollutants in the literature. The
variable is expressed in per capita terms, and the specification is consistent with
expectations. GDP and its squared form again show evidence for an inverted U
relationship, suggesting that at certain levels of development technique effects prevail
on scale effects. Area per capita is negative, apart from the case of IV with random
effects, for which the term is not significant and takes the opposite sign. The openness
coefficient is significant at the 10 percent level in the OLS, while it gathers explanatory
power when running when IV regression. It is negative, supporting again the hypothesis
for which trade is not detrimental for the environment.
The last environmental quality indicator I analyzed is CO2 per capita, which is
measured in logarithm terms since the distribution of the pollutant is log-normal.
Results are shown in Table 9.
38
Table 8 – SO2
(1)
OLS
SO2
0.911***
(0.237)
-0.0502***
(0.0156)
-2.438*
(1.304)
1.52e-07*
(8.71e-08)
-0.248*
(0.131)
-14.99***
(0.992)
VARIABLES
lnGDP
(lnGDP)2
Acap
KL
Open
Constant
Fixed effects
Random effects
Time fixed effects
Observations
R-squared
Number of countries
X
Yes
3,099
(2)
OLS
SO2
0.872***
(0.244)
-0.0505***
(0.0164)
-5.614***
(1.869)
1.38e-07
(8.72e-08)
-0.255*
(0.136)
-14.50***
(0.985)
X
(3)
IV
SO2
0.720***
(0.111)
-0.0348***
(0.00699)
0.0595
(0.576)
1.81e-08
(9.59e-08)
-0.841***
(0.214)
-14.31***
(0.576)
(4)
IV
SO2
0.368***
(0.135)
-0.0360***
(0.00834)
-2.199**
(0.997)
6.59e-09
(1.11e-07)
-1.954***
(0.257)
-10.20***
(0.757)
X
X
Yes
Yes
3,099
2,811
0.131
180
180
165
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Yes
2,811
165
The coefficients are generally significant for the variables of interest: income and
trade. Even for those other non-significant variables, the signs do not experience big
variations from what observed until now, area per capita is negative, and the capital
labor ratio is positive. Area per capita features the expected sign and a significant term
in the Instrumental Variable regression, with random effects, although in the same
specification the variable for openness loses its explanatory power. As for HFC, the
coefficient for openness goes from being positive to negative when the instrumental
variables regression is run.
39
Table 9 – CO2
VARIABLES
lnGDP
(lnGDP)2
logAcap
lnKL
lnOpen
Constant
Fixed effects
(1)
OLS
CO2
0.753***
(0.132)
-0.0265***
(0.00883)
-0.157
(0.213)
0.00425
(0.00292)
0.197***
(0.0605)
-4.293***
(0.923)
X
(3)
IV
CO2
0.659***
(0.0678)
-0.0265***
(0.00457)
-0.102
(0.0782)
0.00253
(0.00341)
-0.299***
(0.0868)
-3.538***
(0.435)
X
X
Random effects
Time fixed effects
Observations
R-squared
Number of countries
(2)
OLS
CO2
0.793***
(0.126)
-0.0242***
(0.00805)
-0.0548
(0.0586)
0.00438
(0.00295)
0.222***
(0.0587)
-4.101***
(0.577)
Yes
Yes
(4)
IV
CO2
0.727***
(0.0637)
-0.0216***
(0.00405)
-0.0618**
(0.0308)
0.00261
(0.00330)
-0.00597
(0.0816)
-3.732***
(0.305)
X
Yes
3,051
3,051
2,810
0.225
180
180
165
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Yes
2,810
165
Furthermore, in model (4), with random effects, which should be the preferred
specification for the IV estimation, the variable for trade loses its significance. As argued
in Frankel and Rose (1999), this pollutant is not the most meaningful for this kind of
analysis, since its spillovers are purely global, and countries cannot expect to be able to
control Carbon Dioxide only by national regulation.
9. Results Implications
From the obtained results, it is possible to draw some conclusions on how new
environmental and trade policies could be designed to be consistent among each other,
and enhance a sustainable kind of growth and liberalization process.
On the whole, concerning the second hypothesis, there is evidence that for
countries with lower income levels, growth is detrimental for the environment. This is
40
shown by the positive sign that the term for GDP features in almost all the regressions.
In these regards, it is not feasible to suggest to craft policies that slow down economic
growth just for the sake of environmental quality. However, results also show that for
higher levels of GDP, growth starts being beneficial for pollution levels on the average
country. This fact highlights that as income rises, the scale effect is dominated by the
technique effect, since those are the channels reflected by GDP level. Consequently,
concerning the second and third sets of hypotheses, it is advisable to enact growthoriented policies and not harm the environment in the long term. In particular, the focus
should be on clean technologies, since it is through this channel that eventually a turning
point is reached and further growth stops damaging the environment.
Concerning trade, it is found that the gains from trade effect overcomes the race
to the bottom hypothesis. This meaning that trade has a positive effect on pollution,
reducing emissions for a given level of income. More advanced technologies together
with higher demand for environmentally friendly processes are boosted with openness,
and the process should be assisted by liberalization enhancing policies. To note that the
magnitude of the positive effect of trade overcomes the negative one of GDP for all the
pollutants but CO2 and HFC. The majority of environmental quality measures taken into
consideration are greenhouse gases, thus responsible for global warming. On the basis
of the results, there is no evidence to assess that trade is worsening the issue or
contributing in the raise of the world temperature. Further conclusions on the issue
cannot be drawn as this research does not investigate empirically the link between trade
and environmental policies
Concerning the capital labour ratio, when significant, it is positive. This leads to
conclude that in capital endowed countries, a comparative advantage in the production
of polluting goods is exploited. This evidence should raise some concerns regarding
policies implementation. Whether the comparative advantage is lead by lacking
regulation on methods of production is not clear. Usually, capital endowed countries are
the developed ones, though this is not always the case. Consequently, policies should
move in the direction of optimizing the capital utilization, adapting technologies and
upgrading to cleaner ways to exploit the endowment of this factor of production.
41
10. Conclusions
This study attempts to identify the channels through which trade affects pollution.
Starting by differentiating between scale technique and composition effects, the paper
analyses the relationship between openness and pollution at a country level. Using an
instrumental variable for trade solves its endogeneity, and allows drawing conclusions
on the causal relationship with measures of environmental quality. The use of panel data
widens the analysis and gives more consistent results. On the whole, results indicate
that an economy that opens up to trade will make pollution levels decrease, even if in
small amounts. This is true for all pollutants listed in the results section, apart from CO2.
This discrepancy is probably due to the fact that carbon dioxide is more of a global
externality, rather than local. Furthermore, the Environmental Kuznets Curve is
evidenced for all the environmental equations, but for HFC, for which instead a U-shaped
relationship is found. Generally, for a given level of income, trade appears to be
beneficial for the environment. One limitation of the study is the inability to solve
income endogeneity, as a good way to instrument for it could not be found in the
existing literature. In this sense, further research is needed to be able to draw proper
conclusions on this other important determinant of environmental quality.
42
11. References
Anderson, James E. (1979), A theoretical foundation for the gravity equation. The
American Economic Review: 106-116.
Anderson, James E. (2010), The gravity model. Annual Review of Economics,
Annual Reviews, vol. 3(1): 133-160.
Anderson, James E., and Eric Van Wincoop. (2001), Gravity with gravitas: a
solution to the border puzzle. American Economic Review, American Economic
Association, 93(1): 170-192.
Anderson, James E., and Yoto V. Yotov (2010), Specialization: pro-and antiglobalizing, 1990-2002. CAGE Online Working Paper Series 15, Competitive Advantage in
the Global Economy (CAGE).
Antweiler, Werner, Brian R. Copeland, and M. Scott Taylor (2001), Is free trade
good for the environment?. American Economic Review, American Economic Association,
91(4): 877-908.
Baldwin, Richard, and Daria Taglioni (2006), Gravity for dummies and dummies
for gravity equations. CEPR Discussion Papers 5850, C.E.P.R. Discussion Papers.
Bergstrand, Jeffrey H (1985), The gravity equation in international trade: some
microeconomic foundations and empirical evidence. The Review of Economics and
Statistics : 474-481.
Brauer, M. et al. (2015), Ambient Air Pollution Exposure Estimation for the Global
Burden of Disease. Paper submitted for publication. Institute for Health Metrics and
Evaluation, University of Washington, Seattle.
Chintrakarn, Pandej, and Daniel L. Millimet, (2006), The environmental
consequences of trade: Evidence from subnational trade flows. Journal of Environmental
Economics and Management, 52(1): 430-453.
Cole, Matthew A., and Robert JR Elliott, (2003), Determining the trade–
environment composition effect: the role of capital, labor and environmental
regulations. Journal of Environmental Economics and Management, 46(3): 363-383.
E.G. Ravenstein (1889), The laws of migration. Journal of the Statistical Society,
52.
Eiras, Ana I., and Brett D. Schaefer (2001), Argentina’s Economic Crisis: An
‘Absence of Capitalism. Heritage Foundation Backgrounder 1432: 5-6.
Feenstra, Robert C. (2002), Border effects and the gravity equation: consistent
methods for estimation. Scottish Journal of Political Economy, 49: 491-506.
43
Frankel, Jeffrey A., and Andrew K. Rose. (2005), Is trade good or bad for the
environment? Sorting out the causality. Review of Economics and Statistics, 87(1): 85-91.
Frankel, Jeffrey, and Andrew Rose. (2002), An estimate of the effect of common
currencies on trade and income. Quarterly Journal of Economics: 437-466.
Fratianni, Michele U. (2007), The gravity equation in international trade. Working
Papers 307, Universita' Politecnica delle Marche, Dipartimento di Scienze Economiche e
Sociali.
Grossman, G., and A. Krueger. (1995), Economic environment and the economic
growth. Quarterly Journal of Economics, 110(2): 353-377.
Grossman, Gene M., and Alan B. Krueger. (1991), Environmental impacts of a
North American free trade agreement. Papers 158, Princeton, Woodrow Wilson School Public and International Affairs.
Harbaugh, William T., Arik Levinson, and David Molloy Wilson. (2002),
Reexamining the empirical evidence for an environmental Kuznets curve. Review of
Economics and Statistics, 84(3): 541-551.
Harrigan, James. (1996), Technology, factor supplies and international
specialization: estimating the neoclassical model. American Economic Review, American
Economic Association, 87(4): 475-94.
Kerr, William Alexander, and James D. Gaisford, (eds. 2007), Handbook on
International Trade Policy. Edward Elgar Publishing.
Krugman, Paul. (1980), Scale economies, product differentiation, and the pattern
of trade. The American Economic Review: 950-959.
Levine, Ross, and David Renelt. (1992), A sensitivity analysis of cross-country
growth regressions. The American Economic Review: 942-963.
Managi, Shunsuke, Akira Hibiki, and Tetsuya Tsurumi. (2009), Does trade
openness improve environmental quality?. Journal of Environmental Economics and
Managemen,t 58(3): 346-363.
Martin, Will, and Cong S. Pham. (2008), Estimating the gravity equation when
zero trade flows are frequent. School of Accounting, Economics and Finance, Deakin
University.
Porter, Michael E., and Claas Van der Linde. (1995), Toward a new conception of
the environment-competitiveness relationship. The Journal of Economic Perspectives: 97118.
Rodrik, Dani. (1995), Political economy of trade policy. Handbook of
International Economics, 3(3): 1457-1494.
44
Rose, Andrew K., and Eric Van Wincoop. (2001), National money as a barrier to
international trade: The real case for currency union. American Economic Review: 386390.
Silva, JMC Santos, and Silvana Tenreyro. (2006), The log of gravity. The Review of
Economics and Statistics, 88(4): 641-658.
Tinbergen, Jan. (1962), Shaping the world economy; suggestions for an
international economic policy. Books (Jan Tinbergen).
45
12. Appendix
Appendix 1
Variable Name
Definition
Source
GDP (current US$)
GDP at purchaser's prices is the sum of gross value
added by all resident producers in the economy plus
any product taxes and minus any subsidies not
included in the value of the products. It is calculated
without making deductions for depreciation of
fabricated assets or for depletion and degradation of
natural resources. Data are in current U.S. dollars.
Dollar figures for GDP are converted from domestic
currencies using single year official exchange rates. For
a few countries where the official exchange rate does
not reflect the rate effectively applied to actual foreign
exchange transactions, an alternative conversion
factor is used.
Total population is based on the de facto definition of
population, which counts all residents regardless of
legal status or citizenship--except for refugees not
permanently settled in the country of asylum, who are
generally considered part of the population of their
country of origin. The values shown are midyear
estimates.
World Bank national accounts
data, and OECD National
Accounts data files.
Population, total
Land area (sq. km)
Land area is a country's total area, excluding area
under inland water bodies, national claims to
continental shelf, and exclusive economic zones. In
most cases the definition of inland water bodies
includes major rivers and lakes.
Imports of goods and
services (current US$)
Imports of goods and services represent the value of
all goods and other market services received from the
rest of the world. They include the value of
merchandise, freight, insurance, transport, travel,
royalties, license fees, and other services, such as
communication, construction, financial, information,
business, personal, and government services. They
exclude compensation of employees and investment
income (formerly called factor services) and transfer
payments. Data are in current U.S. dollars.
(1) United Nations Population
Division. World Population
Prospects, (2) United Nations
Statistical Division. Population
and Vital Statistics Report
(various years), (3) Census
reports and other statistical
publications from national
statistical offices, (4) Eurostat:
Demographic Statistics, (5)
Secretariat of the Pacific
Community: Statistics and
Demography Programme, and (6)
U.S. Census Bureau:
International Database.
Food and Agriculture
Organization, electronic files and
web site.
World Bank national accounts
data, and OECD National
Accounts data files.
46
Exports of goods and
services (current US$)
CO2 emissions (metric
tons per capita)
Exports of goods and services represent the value of all
goods and other market services provided to the rest
of the world. They include the value of merchandise,
freight, insurance, transport, travel, royalties, license
fees, and other services, such as communication,
construction, financial, information, business,
personal, and government services. They exclude
compensation of employees and investment income
(formerly called factor services) and transfer
payments. Data are in current U.S. dollars.
Carbon dioxide emissions are those stemming from the
burning of fossil fuels and the manufacture of cement.
They include carbon dioxide produced during
consumption of solid, liquid, and gas fuels and gas
flaring.
GHG net
emissions/removals by
LUCF (Mt of CO2
equivalent)
GHG net emissions/removals by LUCF refers to
changes in atmospheric levels of all greenhouse gases
attributable to forest and land-use change activities,
including but not limited to (1) emissions and removals
of CO2 from decreases or increases in biomass stocks
due to forest management, logging, fuelwood
collection, etc.; (2) conversion of existing forests and
natural grasslands to other land uses; (3) removal of
CO2 from the abandonment of formerly managed
lands (e.g. croplands and pastures); and (4) emissions
and removals of CO2 in soil associated with land-use
change and management. Data are in million metric
tons.
HFC gas emissions
Hydrofluorocarbons, used as a replacement for
(thousand metric tons of chlorofluorocarbons, are used mainly in refrigeration
CO2 equivalent)
and semiconductor manufacturing.
Methane emissions (kt of Methane emissions are those stemming from human
CO2 equivalent)
activities such as agriculture and from industrial
methane production.
World Bank national accounts
data, and OECD National
Accounts data files.
Carbon Dioxide Information
Analysis Center, Environmental
Sciences Division, Oak Ridge
National Laboratory, Tennessee,
United States.
United Nations Framework
Convention on Climate Change.
For Annex-I countries under the
UNFCCC, these data are drawn
from the annual GHG inventories
submitted to the UNFCCC by
each country; for non-Annex-I
countries, data are drawn from
the most recently submitted
National Communication where
available.
European Commission, Joint
Research Centre
(JRC)/Netherlands Environmental
Assessment Agency (PBL).
Emission Database for Global
Atmospheric Research (EDGAR):
http://edgar.jrc.ec.europa.eu/
European Commission, Joint
Research Centre
(JRC)/Netherlands Environmental
Assessment Agency (PBL).
Emission Database for Global
Atmospheric Research (EDGAR):
http://edgar.jrc.ec.europa.eu/
Nitrous oxide emissions Nitrous oxide emissions are emissions from agricultural European Commission, Joint
(thousand metric tons of biomass burning, industrial activities, and livestock
Research Centre
CO2 equivalent)
management.
(JRC)/Netherlands Environmental
Assessment Agency (PBL).
Emission Database for Global
Atmospheric Research (EDGAR):
http://edgar.jrc.ec.europa.eu/
47
Other greenhouse gas
emissions, HFC, PFC and
SF6 (thousand metric
tons of CO2 equivalent)
Other greenhouse gas emissions are by-product
emissions of hydrofluorocarbons, perfluorocarbons,
and sulfur hexafluoride.
European Commission, Joint
Research Centre
(JRC)/Netherlands Environmental
Assessment Agency (PBL).
Emission Database for Global
Atmospheric Research (EDGAR):
http://edgar.jrc.ec.europa.eu/
PFC gas emissions
Perfluorocarbons, used as a replacement for
European Commission, Joint
(thousand metric tons of chlorofluorocarbons in manufacturing semiconductors, Research Centre
CO2 equivalent)
are a byproduct of aluminum smelting and uranium
(JRC)/Netherlands Environmental
enrichment.
Assessment Agency (PBL).
Emission Database for Global
Atmospheric Research (EDGAR):
http://edgar.jrc.ec.europa.eu/
SF6 gas emissions
Sulfur hexafluoride is used largely to insulate highEuropean Commission, Joint
(thousand metric tons of voltage electric power equipment.
Research Centre
CO2 equivalent)
(JRC)/Netherlands Environmental
Assessment Agency (PBL).
Emission Database for Global
Atmospheric Research (EDGAR):
http://edgar.jrc.ec.europa.eu/
rkna
Capital stock at constant 2005 national prices (in mil.
pwt 8.1
2005US$)
emp
Number of persons engaged (in millions)
pwt8.1
hc
Index of human capital per person, based on years of
schooling (Barro/Lee, 2012) and returns to
pwt 8.1
education (Psacharopoulos, 1994)
developed
takes value of 1 when a country is classified as
developed
OECD
takes value of 1 when the country is part of OECD
Concentrations of
Particulate Matter less
than 10 microns (PM10)
Data for countries and aggregates for regions and
income groups are urban-population weighted PM10
levels in residential areas of cities with more than
100,000 residents. The estimates represent the
average annual exposure level of the average urban
resident to outdoor particulate matter. The state of a
country’s technology and pollution controls is an
important determinant of particulate matter
concentrations. Source: Kiren Dev Pandey, David
Wheeler, Bart Ostro, Uwe Deichmann, Kirk Hamilton,
and Katherine Bolt. "Ambient Particulate Matter
Concentrations in Residential and Pollution Hotspot
Areas of World Cities: New Estimates Based on the
Global Model of Ambient Particulates (GMAPS),"
United Nations Framework
Convention on Climate
Change (UNFCCC)
United Nations Framework
Convention on Climate
Change (UNFCCC)
United Nations Framework
Convention on Climate
Change (UNFCCC),
World Bank, Development
Research Group and
Environment Department (2006).
48
Emissions of Particulates Particulate matter contributes significantly to visibility
Smaller than 2.5 Microns reduction and, as a carrier of toxic metals and other
toxic substances, exerts pressures on human health,
especially fine particulates. An effort has been made to
present data on particulates smaller than 2.5 microns.
Emissions of SO2
Emissions of SO2 with LULUCF correspond to total
(National Reports,
emissions of SO2 and removals from activities relating
UNFCCC), Including Land to land use, land-use change and forestry (from the
Use, Land-Use Change
following categories: forest land, cropland, grassland,
and Forestry
wetlands, settlements and other land), measured in
Gigagrams CO2 Equivalent
United Nations Framework
Convention on Climate
Change (UNFCCC)
United Nations Framework
Convention on Climate
Change (UNFCCC)
Appendix 2
Source: IPCC (2007); based on global emissions from 2004. Details about the sources
included in these estimates can be found in the Contribution of Working Group I to the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change .
49
Appendix 3
Plot of openness derived from actual trade and the constructed openness variable from bilateral
trade flows
50
Appendix 4
Plot showing relationship between openness and distance
51
The next four appendices show the pollutants i analysed but did not include in the results,
because not significant and of little relevance for the analysis.
Appendix 5. NOx regression
VARIABLES
lnGDP
(lnGDP)2
logAcap
lnKL
lnOpen
Constant
(1)
OLS
NOx
0.0813
(0.210)
0.00493
(0.0128)
3.741***
(0.725)
0.0273*
(0.0145)
-0.130*
(0.0712)
-9.241***
(0.753)
Fixed effects
Random effects
Time fixed effects
Observations
R-squared
Number of exporter
(2)
OLS
NOx
0.0952
(0.235)
0.00949
(0.0154)
3.511
(3.298)
0.0308**
(0.0147)
-0.0257
(0.0938)
-9.363***
(0.817)
X
(3)
IV
NOx
0.0152
(0.185)
0.00791
(0.0114)
3.331***
(0.691)
0.0312**
(0.0143)
-0.520***
(0.152)
-8.972***
(0.728)
X
Yes
422
129
(4)
IV
NOx
0.0727
(0.194)
0.00876
(0.0130)
2.468
(1.954)
0.0305**
(0.0138)
-0.0459
(0.197)
-9.433***
(0.832)
X
X
Yes
422
0.186
129
Yes
399
Yes
399
126
126
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
52
Appendix 6. PFC regression
VARIABLES
lnGDP
(lnGDP)2
logAcap
lnKL
lnOpen
Constant
Fixed effects
(1)
OLS
PFC
0.000276
(0.000172)
-1.73e-05
(1.10e-05)
-0.000928
(0.000884)
-6.89e-06
(4.51e-06)
0.000173
(0.000165)
-0.000756
(0.000490)
X
Random effects
Time fixed effects
Observations
R-squared
Number of exporter
(2)
OLS
PFC
-1.86e-05
(8.83e-05)
2.54e-06
(5.64e-06)
0.000146
(0.000166)
4.75e-06
(5.22e-06)
6.62e-05
(7.22e-05)
-3.78e-05
(0.000333)
(3)
IV
PFC
0.000241
(0.000176)
-1.82e-05
(1.18e-05)
-0.00134
(0.00177)
-5.64e-06
(1.26e-05)
-2.51e-05
(0.000179)
-0.000536
(0.000755)
X
X
Yes
422
0.052
129
(4)
IV
PFC
-1.51e-05
(0.000105)
2.27e-06
(6.38e-06)
0.000230
(0.000214)
5.28e-06
(1.12e-05)
8.84e-05*
(4.99e-05)
-5.12e-05
(0.000417)
X
Yes
422
Yes
399
Yes
399
129
126
126
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
53
Appendix 7. SF6 regression
VARIABLES
lnGDP
(lnGDP)2
logAcap
lnKL
lnOpen
Constant
Fixed effects
(1)
OLS
SF6
5.14e-05*
(2.94e-05)
-3.49e-06*
(2.10e-06)
4.67e-05
(8.27e-05)
-1.95e-07
(5.74e-07)
1.01e-05*
(5.62e-06)
-0.000154*
(9.19e-05)
X
Random effects
Time fixed effects
Observations
R-squared
Number of exporter
(2)
OLS
SF6
1.21e-05
(1.36e-05)
-1.32e-07
(8.12e-07)
1.20e-05
(3.06e-05)
6.28e-07
(7.22e-07)
2.77e-06
(4.50e-06)
-8.57e-05
(5.75e-05)
(3)
IV
SF6
5.29e-05***
(1.63e-05)
-3.58e-06***
(1.09e-06)
4.48e-05
(0.000164)
-1.31e-07
(1.16e-06)
2.31e-06
(1.65e-05)
-0.000164**
(6.97e-05)
X
X
Yes
422
0.080
129
(4)
IV
SF6
1.45e-05
(1.41e-05)
-2.06e-07
(8.60e-07)
1.53e-05
(3.94e-05)
1.18e-06
(1.18e-06)
-1.87e-05**
(9.14e-06)
-0.000108*
(5.52e-05)
X
Yes
422
Yes
399
Yes
399
129
126
126
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
54
Appendix 8. PM25 regression
VARIABLES
lnGDP
(lnGDP)2
logAcap
lnKL
lnOpen
Constant
Fixed effects
(1)
OLS
PM25
0.0172*
(0.00907)
-0.00118*
(0.000644)
-0.00102
(0.0108)
-8.37e-05
(6.18e-05)
0.00102
(0.000828)
-0.0562*
(0.0293)
X
Random effects
Time fixed effects
Observations
R-squared
Number of country
(2)
OLS
PM25
0.0105*
(0.00554)
-0.000614*
(0.000333)
0.000507
(0.00314)
-6.66e-05
(5.36e-05)
0.000719
(0.000665)
-0.0418*
(0.0214)
(3)
IV
PM25
0.0176***
(0.00248)
-0.00130***
(0.000161)
0.000211
(0.0195)
-7.05e-05
(0.000126)
-0.00608*
(0.00314)
-0.0523***
(0.0107)
X
X
Yes
3,116
0.038
181
(4)
IV
PM25
0.0115***
(0.00213)
-0.000672***
(0.000136)
0.00178
(0.00707)
-4.70e-05
(0.000126)
-0.00317
(0.00217)
-0.0469***
(0.00850)
X
Yes
3,116
Yes
2,811
Yes
2,811
181
165
165
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
55