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

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.

Index

1. Introduction; 3

2. Pollutants Description; 4

3. Literature review; 83.1 - The relationship between pollution and income; 83.2 - The relationship between pollution and trade; 113.3 - The relationship between trade and environmental regulation; 15

4. Hypotheses formulation; 16

5. The Endogeneity Issue; 185.1 – Description of the problem; 185.2 - The Gravity Model; 195.3 - The income equation; 23

6. Pollutants and Data Description; 24

7. Methodology; 267.1 - Constructing the instrument; 267.2 - Fitting the Data in the model; 277.3 - The instrument’s quality; 287.4 - The environmental damage equation; 307.5 - Robustness checks; 31

8. Results; 33

9. Results Implications; 39

10. Conclusions; 41

11. References; 42 12. Appendix; 45

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

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,

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 sources 3. 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

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

1 United States Environmental Protection Agency2 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

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, N2O, 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, CH 4, 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

management strategies. Regulations for these pollutants have not been enacted yet, but

have been proposed by multiple countries’ governments.

4 http://www.thinkglobalgreen.org/hfc.html5 http://apps.sepa.org.uk/spripa/Pages/SubstanceInformation.aspx?pid=8

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,

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 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

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.

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 site-

specific, 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

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.

(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

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

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.

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

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.

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

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

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.

X ij=Y i E j /d ij2 ( 1)

The specification is the result of the direct application of the assumption. X ij 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:

X ij=G Si M jϕ ij ( 2)

Where Si stands for the capacity of the exporter to supply for the demand of the

other countries. M j 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

7 Only held constant in cross section analyses.8 A change in trade costs between the importer and a third country can influence X ij through changing Si

or M j. 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.

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 trade-

environment 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 (τ ij /GDPiGDP j) will only be specified as bilateral trade share over GDP of the

exporter. In particular, the form of the gravity equation I use is:

ln ( τ ijGDPi

)=α+β1ln Dij+β2ln Pi+β3 lnP j+ β4 ln A i+β5ln A j+β6Lij+ β7Col ij+β8 Langij+β9B ijColij+β10BijLangij+β11BijD ij+β12BijPi+β13BijP j+¿

( 3)

β14Bij A i+ β15B ijA j+β16Bij Lij+lnGDP j+u

9 Ordinary Least Squares, Poisson Pseudo-Maximum Likelihood, and Tobit 10 (Exportsi + Importsi)/GDPi

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:

logGDPi , t=α+β1 log (open )i ,t+β2 popg;i ,t+ β3 log ( pop )i ,t+β4hci , t+β5 school1 ;i ,t+β6 school2 ;i ,t+ β7 school3 ;i ,t+u i ,t

( 4)

11 Landlocki + Landlockj , if both trading partners are landlocked the value will be equal to 2.

where open is a measure of openness, calculated as imports plus exports over GDP of a

country, pop stands for population, popgis 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

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.

VARIABLES N mean sd Min MaxCH4 667 55,659 150,030 0 1.642e+06CO2 cap 4,181 4.656 6.561 0.000580 68.53GHG 919 -37.88 151.6 -1,034 1,329HFC 662 3,519 20,794 0 300,896N2O 667 22,887 58,912 0 550,297PFC 662 702.9 2,656 0 28,056Pm10 3,950 280,106 273,740 0 999,396Pm2.5 1,961 20,585 102,915 0 930,717SF6 662 1,011 4,930 0 57,054SO2 662 1,265 3,794 0 44,625Primary School Enrolment 2,169 86.78 15.57 19.21 100Secondary School Enrolment 1,484 67.09 26.36 2.701 100Tertiary School Enrolment 2,565 29.01 24.00 0 117.9Population 3,564 15.86 66.08 0 784.4Employment 3,564 1.939 1.090 0 3.619Human Capital 3,564 317,386 1.102e+06 -286,252 1.323e+07Cost of Capital 3,564 324,561 1.123e+06 228.8 1.396e+07Real GDP 3,564 980,109 3.496e+06 290.4 4.465e+07Openness 2,727 193,564 176,191 10,824 1.429e+06Developed country 4,356 0.167 0.373 0 1Area 4,312 692,395 1.904e+06 25 1.708e+07Polity 1,601 -0.375 17.63 -88 10

Number of countries 169 169 169 169 169

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.

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

of factors of production, so it is positively

correlated with trade volume. The

Landlocked variable has the expected

negative sign, thus countries with no sea

access trade less due to higher trade

barriers; countries trade more (3% more)

with neighboring states; they trade more

with their colonies and if they have a

common language. Lastly, the importer’s

GDP has a positive sign, this confirms what

has generally been found in previous

literature and research: that there is a

positive association between trade and

income.

7.2 - Fitting the Data in the Model

To be able to fit the gravity model into my

final estimation12, which features the

relationship between trade and pollution

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.

Table 2 – The Gravity Equation

(1)

VARIABLES Exports/GDPi

lnDistance -1.318***(0.0260)

lnPopi -0.944***(0.00501)

lnPopj -0.768***(0.0116)

lnAreai 0.525***(0.00831)

lnAreaj 0.352***(0.00834)

Landlock -1.062***(0.0368)

lnGDPj 0.884***(0.0114)

Contiguity 3.860***(1.239)

Com.Language 0.393***(0.0530)

Colony 2.923***(0.153)

Constant -1.636***(0.284)

R2 0.5878Observations 332,105Number of pairid 22,375

ln ( τ ijGDPi

)=a' X ij+e ij ( 5)

In equation (5), a stands for the vector of coefficients of the variables included in

the gravity equation (α ,β1 , β2 ,…,β16) and X ij is the vector of all the independent

variables used in the specification (Dij ,Pi ,P j ,Col ij , Langij , etc).

The following passage is to aggregate data for each country:

T i=∑j ≠i

e a' X ij

. ( 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

this transformation because the expectation of ln (τ ij

GDP i) conditional on X ijis equal to

e a' X ij 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 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(1) (2) (3)

VARIABLES Open Open Open

lnArea -0.138*** -0.0949***(0.0237) (0.0223)

lnPop -0.0374*** -0.0575***(0.0129) (0.0156)

Open constructed 7.46e-07*** 7.39e-07***(9.55e-08) (9.80e-08)

Constant 0.703*** 0.851*** 1.237***(0.0120) (0.247) (0.294)

Observations 3,422 3,801 3,419R-squared 0.123 0.1051 0.2921

Number of country 167 184 167

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 Explain the test14 literature on this

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+φ1GDPcapi+φ2GDPcapi2+φ3opennessi+¿

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.

15 details in Results section, and for same reasons as in gravity

The term is included to test the environmental Kuznets curve, which predicts that at

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 time-

varying 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

16 In my case, countries characteristics

preferred, as the error terms in the regression fail to meet the hypothesis of

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 Meaning I can use random effects18 Because it is already provided by running the fixed effects command

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.

Table 4 – N2O

(1) (2) (3) (4)OLS OLS IV IV

VARIABLES N2O N2O N2O N2OlnGDP 0.000253*** 0.000260*** 0.000238*** 0.000242***

(1.74e-05) (1.70e-05) (1.93e-05) (1.94e-05)(lnGDP)2 -1.61e-05*** -1.76e-05*** -1.61e-05*** -1.84e-05***

(1.14e-06) (1.12e-06) (1.25e-06) (1.26e-06)lnOpen -1.51e-05** -1.92e-05*** -0.000134*** -0.000174***

(6.15e-06) (6.03e-06) (2.72e-05) (2.45e-05)KL 6.67e-11*** 6.43e-11*** 6.47e-11*** 6.15e-11***

(0) (0) (0) (0)Acap -0.000465*** -0.000559*** -0.000354*** -0.000481***

(0.000114) (0.000119) (0.000137) (0.000152)Constant -0.000750*** -0.000646*** -0.000627*** -0.000508***

(7.61e-05) (6.82e-05) (8.71e-05) (8.34e-05)Fixed effects

Random effectsTime fixed effects

XYes

X

YesX

Yes

X

Yes

Observations 3,116 3,116 2,811 2,811R-squared 0.166Number of countries 181 181 165 165

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 

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.

Table 5 - GHG

(1) (2) (3) (4)OLS OLS IV IV

VARIABLES GHG GHG GHG GHGlnGDP 0.151*** 0.172 0.0827* 0.0813*

(0.0420) (0.110) (0.0436) (0.0427)(lnGDP)2 -1.38e-06*** -1.56e-06* -8.40e-07* -8.31e-07*

(4.33e-07) (9.07e-07) (4.46e-07) (4.37e-07)Acap -18,252** -16,542** -13,822* -14,736*

(7,188) (7,999) (7,418) (7,542)lnKL 88.03* 88.07* 35.57 33.65

(52.31) (47.61) (50.38) (49.32)lnOpen -806.1** -877.4 -5,399*** -3,961***

(358.2) (629.2) (1,428) (1,322)Constant 7,026*** 4,597*** 5,567** 6,171***

(785.1) (1,609) (2,497) (713.4)Fixed effects

Random effects

Time fixed effects

 X

Yes

 X

Yes

 X

Yes

 X

YesObservations 3,116 3,116 2,811 2,811R-squared 0.016Number of countries 181 181 165 165

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 

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.

Table 6 - HFC

(1) (2) (3) (4)OLS OLS IV IV

VARIABLES HFC HFC HFC HFClnGDP -6.08e-05*** -8.02e-05*** -7.10e-05*** -9.03e-05***

(1.36e-05) (1.27e-05) (1.45e-05) (1.37e-05)(lnGDP)2 4.89e-06*** 6.62e-06*** 5.34e-06*** 7.26e-06***

(8.90e-07) (8.13e-07) (9.48e-07) (8.74e-07)Acap -0.000165* 7.21e-05 -0.000191* 0.000108*

(9.49e-05) (4.87e-05) (0.000114) (5.57e-05)lnKL 6.15e-07 6.66e-07 7.98e-07 9.43e-07

(6.87e-07) (6.91e-07) (7.43e-07) (7.65e-07)lnOpen 1.07e-05*** 5.83e-06 -7.46e-06 -4.86e-05***

(4.80e-06) (4.35e-06) (1.84e-05) (1.60e-05)Constant 0.000200*** 0.000219*** 0.000256*** 0.000257***

(5.57e-05) (5.13e-05) (6.30e-05) (5.47e-05)Fixed effects

Random effects

Time fixed effects

X

Yes

X

Yes

X

Yes

X

YesObservations 3,116 3,116 2,811 2,811R-squared 0.023Number of countries 181 181 165 165

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 

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.

Table 7 – PM10

(1) (2) (3) (4)OLS OLS IV IV

VARIABLES PM10 PM10 PM10 PM10

lnGDP 0.382*** 0.390** 0.312*** 0.346***(0.135) (0.153) (0.0774) (0.0796)

(lnGDP)2 -0.0219*** -0.0227** -0.0156*** -0.0114**(0.00821) (0.0103) (0.00496) (0.00519)

Acap -0.0337 -0.00710 0.235 0.291(0.446) (0.876) (0.358) (0.627)

lnKL -0.00321 -0.00285 -0.00659 -0.00577(0.00425) (0.00422) (0.00421) (0.00407)

lnOpen 0.229*** 0.227*** 0.904*** 0.849***(0.0684) (0.0726) (0.0971) (0.101)

Constant -1.548*** -1.189** -1.302*** -1.898***(0.548) (0.551) (0.313) (0.345)

Fixed effects

Random effects

Time fixed effects

X

Yes

X

Yes

X

Yes

X

YesObservations 3,116 3,116 2,811 2,811R-squared 0.092Number of countries 181 181 165 165

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 

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.

Table 8 – SO2

(1) (2) (3) (4)OLS OLS IV IV

VARIABLES SO2 SO2 SO2 SO2

lnGDP 0.911*** 0.872*** 0.720*** 0.368***(0.237) (0.244) (0.111) (0.135)

(lnGDP)2 -0.0502*** -0.0505*** -0.0348*** -0.0360***(0.0156) (0.0164) (0.00699) (0.00834)

Acap -2.438* -5.614*** 0.0595 -2.199**(1.304) (1.869) (0.576) (0.997)

KL 1.52e-07* 1.38e-07 1.81e-08 6.59e-09(8.71e-08) (8.72e-08) (9.59e-08) (1.11e-07)

Open -0.248* -0.255* -0.841*** -1.954***(0.131) (0.136) (0.214) (0.257)

Constant -14.99*** -14.50*** -14.31*** -10.20***(0.992) (0.985) (0.576) (0.757)

Fixed effects

Random effects

Time fixed effects

X

Yes

X

Yes

X

Yes

X

YesObservations 3,099 3,099 2,811 2,811R-squared 0.131Number of countries 180 180 165 165

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 

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.

Table 9 – CO2

(1) (2) (3) (4)OLS OLS IV IV

VARIABLES CO2 CO2 CO2 CO2

lnGDP 0.753*** 0.793*** 0.659*** 0.727***(0.132) (0.126) (0.0678) (0.0637)

(lnGDP)2 -0.0265*** -0.0242*** -0.0265*** -0.0216***(0.00883) (0.00805) (0.00457) (0.00405)

logAcap -0.157 -0.0548 -0.102 -0.0618**(0.213) (0.0586) (0.0782) (0.0308)

lnKL 0.00425 0.00438 0.00253 0.00261(0.00292) (0.00295) (0.00341) (0.00330)

lnOpen 0.197*** 0.222*** -0.299*** -0.00597(0.0605) (0.0587) (0.0868) (0.0816)

Constant -4.293*** -4.101*** -3.538*** -3.732***(0.923) (0.577) (0.435) (0.305)

Fixed effects

Random effects

Time fixed effects

X

Yes

X

Yes

X

Yes

X

Yes

Observations 3,051 3,051 2,810 2,810R-squared 0.225Number of countries 180 180 165 165

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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

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 growth-

oriented 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.

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.

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12. Appendix

Appendix 1

Variable Name Definition SourceGDP (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.

World Bank national accounts data, and OECD National Accounts data files.

Population, total 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.

(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.

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.

Food and Agriculture Organization, electronic files and web site.

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.

World Bank national accounts data, and OECD National Accounts data files.

Exports of goods and services (current US$)

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.

World Bank national accounts data, and OECD National Accounts data files.

CO2 emissions (metric tons per capita)

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.

Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States.

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.

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.

HFC gas emissions (thousand metric tons of CO2 equivalent)

Hydrofluorocarbons, used as a replacement for chlorofluorocarbons, are used mainly in refrigeration and semiconductor manufacturing.

European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/

Methane emissions (kt of CO2 equivalent)

Methane emissions are those stemming from human activities such as agriculture and from industrial methane production.

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 (thousand metric tons of CO2 equivalent)

Nitrous oxide emissions are emissions from agricultural biomass burning, industrial activities, and livestock management.

European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/

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 (thousand metric tons of CO2 equivalent)

Perfluorocarbons, used as a replacement for chlorofluorocarbons in manufacturing semiconductors, are a byproduct of aluminum smelting and uranium enrichment.

European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR): http://edgar.jrc.ec.europa.eu/

SF6 gas emissions (thousand metric tons of CO2 equivalent)

Sulfur hexafluoride is used largely to insulate high-voltage electric power equipment.

European Commission, Joint Research Centre (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. 2005US$)

pwt 8.1

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 education (Psacharopoulos, 1994)

pwt 8.1

developed takes value of 1 when a country is classified as developed

United Nations Framework Convention on Climate Change (UNFCCC)

OECD takes value of 1 when the country is part of OECD

United Nations Framework Convention on Climate Change (UNFCCC)

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),World Bank, Development Research Group and Environment Department (2006).

Emissions of Particulates Smaller than 2.5 Microns

Particulate matter contributes significantly to visibility 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

United Nations Framework Convention on Climate Change (UNFCCC)

microns.Emissions of SO2 (National Reports, UNFCCC), Including Land Use, Land-Use Change and Forestry

Emissions of SO2 with LULUCF correspond to total emissions of SO2 and removals from activities relating to land use, land-use change and forestry (from the following categories: forest land, cropland, grassland, wetlands, settlements and other land), measured in Gigagrams CO2 Equivalent

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 . 

Appendix 3

Plot of openness derived from actual trade and the constructed openness variable from bilateral trade flows

Appendix 4Plot showing relationship between openness and distance

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

(1)OLS

(2)OLS

(3)IV

(4)IV

VARIABLES NOx NOx NOx NOx

lnGDP 0.0813 0.0952 0.0152 0.0727(0.210) (0.235) (0.185) (0.194)

(lnGDP)2 0.00493 0.00949 0.00791 0.00876(0.0128) (0.0154) (0.0114) (0.0130)

logAcap 3.741*** 3.511 3.331*** 2.468(0.725) (3.298) (0.691) (1.954)

lnKL 0.0273* 0.0308** 0.0312** 0.0305**(0.0145) (0.0147) (0.0143) (0.0138)

lnOpen -0.130* -0.0257 -0.520*** -0.0459(0.0712) (0.0938) (0.152) (0.197)

Constant -9.241*** -9.363*** -8.972*** -9.433***(0.753) (0.817) (0.728) (0.832)

Fixed effects

Random effects

Time fixed effects

X

Yes

X

Yes

X

Yes

X

YesObservations 422 422 399 399R-squared 0.186Number of exporter 129 129 126 126

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Appendix 6. PFC regression

(1) (2) (3) (4)

OLS OLS IV IVVARIABLES PFC PFC PFC PFClnGDP 0.000276 -1.86e-05 0.000241 -1.51e-05

(0.000172) (8.83e-05) (0.000176) (0.000105)(lnGDP)2 -1.73e-05 2.54e-06 -1.82e-05 2.27e-06

(1.10e-05) (5.64e-06) (1.18e-05) (6.38e-06)logAcap -0.000928 0.000146 -0.00134 0.000230

(0.000884) (0.000166) (0.00177) (0.000214)lnKL -6.89e-06 4.75e-06 -5.64e-06 5.28e-06

(4.51e-06) (5.22e-06) (1.26e-05) (1.12e-05)lnOpen 0.000173 6.62e-05 -2.51e-05 8.84e-05*

(0.000165) (7.22e-05) (0.000179) (4.99e-05)Constant -0.000756 -3.78e-05 -0.000536 -5.12e-05

(0.000490) (0.000333) (0.000755) (0.000417)Fixed effects

Random effects

Time fixed effects

X

Yes

X

Yes

X

Yes

X

YesObservations 422 422 399 399R-squared 0.052Number of exporter 129 129 126 126

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Appendix 7. SF6 regression

(1) (2) (3) (4)OLS OLS IV IV

VARIABLES SF6 SF6 SF6 SF6lnGDP 5.14e-05* 1.21e-05 5.29e-05*** 1.45e-05

(2.94e-05) (1.36e-05) (1.63e-05) (1.41e-05)(lnGDP)2 -3.49e-06* -1.32e-07 -3.58e-06*** -2.06e-07

(2.10e-06) (8.12e-07) (1.09e-06) (8.60e-07)logAcap 4.67e-05 1.20e-05 4.48e-05 1.53e-05

(8.27e-05) (3.06e-05) (0.000164) (3.94e-05)lnKL -1.95e-07 6.28e-07 -1.31e-07 1.18e-06

(5.74e-07) (7.22e-07) (1.16e-06) (1.18e-06)lnOpen 1.01e-05* 2.77e-06 2.31e-06 -1.87e-05**

(5.62e-06) (4.50e-06) (1.65e-05) (9.14e-06)Constant -0.000154* -8.57e-05 -0.000164** -0.000108*

(9.19e-05) (5.75e-05) (6.97e-05) (5.52e-05)Fixed effects

Random effects

Time fixed effects

X

Yes

X

Yes

X

Yes

X

YesObservations 422 422 399 399R-squared 0.080Number of exporter 129 129 126 126

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Appendix 8. PM25 regression

(1) (2) (3) (4)OLS OLS IV IV

VARIABLES PM25 PM25 PM25 PM25

lnGDP 0.0172* 0.0105* 0.0176*** 0.0115***(0.00907) (0.00554) (0.00248) (0.00213)

(lnGDP)2 -0.00118* -0.000614* -0.00130*** -0.000672***(0.000644) (0.000333) (0.000161) (0.000136)

logAcap -0.00102 0.000507 0.000211 0.00178(0.0108) (0.00314) (0.0195) (0.00707)

lnKL -8.37e-05 -6.66e-05 -7.05e-05 -4.70e-05(6.18e-05) (5.36e-05) (0.000126) (0.000126)

lnOpen 0.00102 0.000719 -0.00608* -0.00317(0.000828) (0.000665) (0.00314) (0.00217)

Constant -0.0562* -0.0418* -0.0523*** -0.0469***(0.0293) (0.0214) (0.0107) (0.00850)

Fixed effects

Random effects

Time fixed effects

X

Yes

X

Yes

X

Yes

X

YesObservationsR-squared

3,1160.038

3,116 2,811 2,811

Number of country 181 181 165 165

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1