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Trade and transboundary pollution: quantifying theeffects of trade liberalization on CO2 emissionsTimothy P. Hubbarda

a Department of Economics, Colby College, 04901, Waterville, ME, USAPublished online: 26 Nov 2013.

To cite this article: Timothy P. Hubbard (2014) Trade and transboundary pollution: quantifying the effects of tradeliberalization on CO2 emissions, Applied Economics, 46:5, 483-502, DOI: 10.1080/00036846.2013.857000

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Trade and transboundary pollution:

quantifying the effects of trade

liberalization on CO2 emissions

Timothy P. Hubbard

Department of Economics, Colby College, Waterville, ME 04901, USAE-mail: timothy.hubbard@colby.edu

I consider a multi-country trade model in which a subset of firms emit trans-boundary pollution as a by-product of production. Consumers are harmed bythese emissions, creating a role for government intervention. Theoretically, theeffects of trade liberalization on the level of pollution and aggregate welfare areambiguous – they depend on values of country-specific pollution disutility para-meters. I use real-world data to estimate trade costs and to recover values for thesedisutility parameters that are consistent with the Nash–Walras equilibrium pre-dicted by the model. In counterfactual exercises, I investigate the effects ofchanging trade costs on the aggregate level and distribution of pollution as wellas the welfare of each country. These experiments suggest concern regarding theeffect further trade liberalization has on the level of firm-generated pollution andthat agreements like the Kyoto Protocol can be effective even when governmentsbehave strategically.

Keywords: pollution game; trade; environment; market structure; greenhousegases

JEL Classification: F18; Q56; C72

I. Introduction

Firm-related emissions of carbon dioxide (CO2) increasedby 10.7% from 1980 to 2000. At the American EconomicAssociation’s 2008 ‘Richard T. Ely Lecture’, NicholasStern (2008) stated: ‘Greenhouse gas (GHG) emissionsare externalities and represent the biggest market failurethe world has seen’. Over the same period, the share oftrade in world GDP grew by 28.4%. The Uruguay Roundof the General Agreement on Tariffs and Trade (GATT)led to the creation of the World Trade Organization(WTO), which has promoted international economic inte-gration. Thus, both pollution and trade have becomeincreasingly important to the world economy.

I am interested in quantifying the impact trade has on theenvironment, in terms of firm-related CO2 emissions.

I consider a multi-country, ‘new’ trade model in which asubset of firms emit CO2 pollution as a by-product of theirproduction. Consumers are harmed by the pollution emis-sions, creating a role for government intervention to forcepolluting firms to internalize these negative effects. Mymodel combines both strategic and competitive elements.In particular, each government acts strategically in setting alimit on the (domestic) amount of firm-related emissions itwill allow. Consumers and firms take as given the emis-sions policy of the government and participate in competi-tive markets. In setting its pollution level, each governmentmaximizes its domestic consumers’ welfare, given theemissions choices of all other countries and consideringthe competitive equilibrium that will result under its policy.Thus, I focus on a Nash–Walras equilibrium. I allow gov-ernments to influence the terms of trade as well as domestic

Applied Economics, 2014Vol. 46, No. 5, 483–502, http://dx.doi.org/10.1080/00036846.2013.857000

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and foreign market structures through the endogenousresponses in consumer and firm behaviour.

Copeland and Taylor (1995) have noted that GATTArticle XX explicitly outlaws using environmental policyas disguised trade policy. Nonetheless, Ederington andMinier (2003) have provided empirical evidence that sup-ports treating environmental policy as endogenous, whichsuggests strategic incentives are important. Cole andElliott (2003) also determined that intra- and inter-indus-try trade shares are influenced by differences in environ-mental regulations between countries, something Iaccount for. One might argue that domestic environmentalpolicies have negligible effects for a given country in aninternational setting. However, Levinson and Taylor(2008) found a positive, and statistically significant, rela-tionship between regulatory stringency (pollution abate-ment costs as a fraction of value-added) and net importsfor the United States. Copeland and Taylor (2004) notedthat trade may encourage a relocation of firms from coun-tries with strict environmental policies to those with lessstringent policies. These effects will be realized in mymodel through changes in the number of polluting firmsthat can survive in each country. Thus, environmentalregulations have important consequences in terms ofhow competitive domestic firms are in the world market,and strategic concerns need to be taken seriously.

I find that, theoretically, a movement from autarky tofree trade can either reduce world pollution or lead tofurther degradation of the environment. The effectdepends critically on how pollution emissions affect con-sumers, which is captured in the model by country-speci-fic pollution disutility parameters. It is difficult to assignvalues to these parameters without data concerning thedirect effects CO2 emissions have on consumer welfare.Furthermore, this relationship may depend on other endo-genous factors, such as income and the market structure(composition of output, number of firms, etc.) in eachcountry. Note, too, that real-world data come from theworld economy which is characterized neither by autarkynor by free trade – in reality, the world is somewhere inbetween. Anderson and van Wincoop (2004) argued thattrade costs are large, and important. Thus, to use thepredictions of the model with the observed data, I mustfirst establish the trade costs each country faces. By intro-ducing trade costs, the model is able to generate an empiri-cal relationship describing the trade pattern betweencountries– known to trade economists as the gravity equa-tion –which I estimate using world trade flows to infer thebilateral trade costs.

I then use the structure of the model in conjunctionwith country-level data concerning firm-related CO2

emissions, labour and capital endowments, as well asthe estimated trade costs, to reverse-engineer the coun-try-specific pollution disutility parameters that are con-sistent with the equilibrium predicted by the model.Grossman and Krueger (1993) found there exists animportant relationship between income per capita andpollution – the authors observed what has becomeknown as the environmental Kuznets curve (EKC)which depicts an inverse U-shaped relationshipbetween per capita income and some measure of envir-onmental quality. In addition, Caselli (2005), amongothers, showed that most of the variation in incomeacross countries is explained by differences in totalfactor productivity (TFP). To ensure that the modelgenerates actual differences in income per capita, Iallow TFP to vary across the countries. I solve forthe country-specific TFP terms by requiring the equili-brium income per capita of each country (relative tothe United States) to match that observed in the data.Thus, the equilibrium used to infer the country-specificparameters is also consistent with relative differencesin per capita GDP, an important factor in explainingenvironmental quality in each country.

Copeland and Taylor (2004) reviewed the theoreticaland empirical findings on the environmental conse-quences of international trade and concluded that thevast majority of empirical work has little connection toexplicit theory.1 I help address this weakness: the country-specific parameters are recovered directly from the equili-brium predicted by the model, using observed data.Because my approach is structural, I can use counterfac-tual experiments to provide insight into the effects tradehas on the environment using a method that is linkeddirectly with the theoretical model. For example, I find a15% reduction in the trade costs facing each countrywould increase world emissions by 2.13%. In addition, Itrack how pollution is reallocated across the countries astrade costs change and identify which countries gain, andwhich lose, in terms of GDP as well as welfare.Furthermore, I am able to relate the pollution effectsdirectly to an economic measure by using an equivalentvariation argument: in an example in which trade costswere reduced by 15%, consumers would be willing to giveup 1.15% of their income to avoid lower aggregate welfarethat arises from the increase in world pollution.

Researchers have had difficulty finding empirical evi-dence supporting the notion that trade contributes to envir-onmental degradation; however, the approach that hasbeen adopted requires assumptions which I am able torelax. Specifically, Antweiler et al. (2001) concluded thatfree trade is good for the average country in terms of

1There is also a literature which employs computational general equilibrium (CGE) models with trade and climate change issues. One ofthe main differences is that here pollution policy is endogenous but is usually exogenous in such models. On the other hand, theproduction side of the economy is more stylized in my work.

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sulphur dioxide (SO2) concentrations.2 The authors pro-

vided a theoretical model which was used to link pollutionwith a set of economic factors. Regression analysis wasthen used to control for these factors and relate countrycharacteristics with environmental outcomes. To identifytheir model, the authors had to assume that the pollutiondisutility parameters, which are critical in my model todetermining the effect of trade on the environment, takevalues such that the optimal emissions policy in eachcountry is independent of the level of pollution. Frankeland Rose (2005) were concerned about potential endo-geneity (trade may be determined simultaneously withincome and environmental outcomes) in the Antweileret al. (2001) approach. To investigate this, the authorsused an instrumental variables approach, but presentedno formal theory underlying their empirical model.Nonetheless, they found no evidence that trade is bad forthe environment using a number of different measures ofenvironmental quality. Despite finding no negative effectson the environment from openness to trade, Frankel andRose (2005) concluded: ‘The major example where tradeand growth may have the detrimental effects feared byenvironmentalists is carbon dioxide.’ I find this to be truewithin the confines of my model.

To begin, I present in the subsequent section a modelbased on the new trade approach of Helpman andKrugman (1985). In Section III, I consider two extremescenarios: autarky and free trade. Theoretically, I findtrade may be good or bad for the environment; the effectdepends on the values of key unknown parameters. InSection IV, I introduce trade costs into the model inorder to capture the trade barriers each country faces inthe world economy and then estimate these trade costs. Iuse the estimated trade costs with country-specific dataand observed firm-related emissions to recover the criticalparameters in Section V and I consider counterfactualexperiments in Section VI. These experiments provideinsight into the effects trade has on the environment byconsidering an integrated approach in which the data islinked directly with the theoretical model. Finally, inSection VII, I summarize, conclude and suggest directionsfor future work in this vein.

II. Model

Consider a country having an economy with two sectors: agreen (clean) sector and a brown (dirty) sector. Greenproducts are homogeneous and produced by firms in a

perfectly competitive industry, while brown products aredifferentiated and produced by firms in an industry char-acterized by monopolistic competition. Pollution is gen-erated as a by-product in the production of dirty goods.Pollution adversely affects consumers who experiencedisutility from these emissions. Let the clean product bethe numéraire good and let pi denote the price of variety iof the dirty good. Throughout this section, for notationalparsimony, when describing the domestic structure of arepresentative country I omit country-specific subscripts.

Consumers

Suppose consumers have Dixit–Stiglitz preferences.Specifically, assume the upper-tier utility function, whichtranslates sectoral subutility levels into an overall welfarelevel, takes the following Cobb–Douglas form:

UðB;G;EW Þ ¼ ½vðBÞ�αG1�α � EδW

δ(1)

where G denotes consumption of the green good, B is ann-vector collecting the consumption quantities of the nbrown varieties, vð�Þ maps consumption of each varietyinto a sub-tier utility level obtained from the consumptionof differentiated goods and EW denotes worldwide pollu-tion emissions. Because I am concerned with a GHG,pollution is uniformly mixed and affects consumersthroughout the world, regardless of the emission source.I assume the utility from consumption and the disutilityfrom pollution are additively (strongly) separable.3

Typically, researchers assume δ is weakly greater thanone, so that the marginal disutility from pollution dependson the level of emissions; i.e. marginal disutility (will-ingness to pay for a pollution reduction) is nondecreasingin the level of pollution. I remain agnostic as to the valueof δ for each country. The values of these parameters willbe critical to determining the effect trade has on the envir-onment, which will be clear in Section III. Allowing thepollution disutility parameters to vary across countries issensible for a number of reasons. First, if CO2 emissionslead to climate change, then it is often argued that coun-tries will experience different effects from an increase inglobal temperature. Second, environmental economistsconcerned with economic growth have identified animportant relationship between income per capita and acountry’s tolerance for pollution. Lastly, Copeland andTaylor (2003) considered models in which there are twotypes of consumers, call them environmentalists and

2Note that SO2 is a local pollutant whereas I am interested in CO2, a global pollutant. The effects of trade on the environment differsignificantly depending on the type of pollution considered; see Copeland and Taylor (2003) for an exceptional book-length treatment ofmodels dealing with local pollution.3While it appears that this assumption implies goods consumption can be perfectly substituted for environmental quality, it will be clearin the next subsection that emissions affect both pollution disutility and consumption utility–the two components are interrelated.Furthermore, in a trading equilibrium, an important relationship arises between emissions and income.

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industrialists, who are affected by pollution in differentways. When the share of these types of consumers in acountry’s population varies across countries, so, too, willvaluation of environmental quality.

For the differentiated good, assume that consumershave constant elasticity of substitution (CES) subutility

vðBÞ ¼Xni¼1

Bγi

!1=γ

where Bi denotes the quantity of variety i consumed and γequals ð1� 1

σÞ, where σð>1Þ is the elasticity of substitutionbetween pairs of varieties. In choosing their optimalspending patterns, consumers take prices, income andworld emissions as given. Given the modelling choicesspecified above, in equilibrium consumers will purchase

B�i ðp; n;XbÞ ¼ p�σ

i XbPni¼1

p1�σi

i ¼ 1; . . . ; n (2)

units of variety i, where p collects the prices of the navailable varieties of the dirty good and Xb denotes expen-ditures on brown varieties. Because the upper-tier utility isCobb–Douglas, consumers spend a constant fraction oftheir income on each sector – i.e. X �

b ðIÞ ¼ αI andX �g ðIÞ ¼ ð1� αÞI will be the equilibrium expenditures

on dirty varieties and the green good, respectively, for agiven level of income I.

Firms

Assume the (homogeneous) green good is produced usinga constant returns-to-scale technology gð,; kÞ involvinglabour , and capital k. In particular, I assume the produc-tion technology takes the Cobb–Douglas formgð,; kÞ ¼ A,πk1�π, where A is a country-specific TFPterm. The industry is perfectly competitive and free entryand exit ensures no firm earns a profit in equilibrium. Forsimplicity, assume production of the clean good involvesno pollution emissions.

In contrast, the dirty sector is imperfectly competitive.All firms use a common technology which has a constantmarginal cost and each firmmust incur a fixed cost to enterthe market. The initial fixed cost means that firms canexploit scale economies. Given consumers have CES sub-utility, an individual firm competes equally with all otherfirms and obtains the same profit level for any variety not

supplied by others. Production of each dirty varietyrequires labour and capital, but also involves emitting aGHG as a by-product. Firms have access to a pollutionabatement technology which requires them to dedicatesome of their production factors to emissions reduction.The abatement technology uses factors in the same inten-sity as the production of goods; abatement is modelled as aloss of output. Thus, it is equivalent to consider the firmallocating a fraction θ of its output units to abatement. Theendogenously chosen investment in abatement willdepend on the emissions policy of the government relativeto the abundance of productive inputs in the country. Netproduction of variety i of a dirty good, denoted by Di, isthen

Di ¼ ð1� θiÞf ð,i; kiÞ � �

where � is a fixed cost firms incur to enter the brownindustry.4

I assume the production technology f ð,; kÞ exhibitsconstant returns-to-scale and takes the same form as theclean production technology: f ð,; kÞ ¼ A,πk1�π: Thisassumption is worth further discussion as, typically,researchers concerned with production-related pollutionassume the dirty sector is relatively capital-intensive; seeCopeland and Taylor (2003). Valentinyi and Herrendorf(2008) studied factor shares at the sector level for theUS economy and found, surprisingly, agriculture to bemore than two times as capital-intensive as construction.They reported the capital share of agriculture to be morethan 50% larger than that of the aggregate economy.These findings call into question the assumption that thedirty sector (manufacturing) is more capital-intensive.Note that Valentinyi and Herrendorf (2008) defined capitalas comprising structures, equipment and land. Thus,agriculture appears very capital-intensive because of itslarge land share. The authors found that when land isremoved, the capital share is close to the economy-wideaverage. I consider a two-factor model in which labourand capital are the only productive inputs. In doing so, Iabstract from inputs such as land, energy, raw materialsand so forth.5

Pollution emissions, ei, from the production of (dirty)variety i depend on the amount of inputs the firm uses aswell as how cleanly the firm produces its output

ei ¼ fðθiÞf ð,i; kiÞ: (3)

4Modelling production with a constant marginal cost and a fixed cost in terms of a factor requirement is standard in new trade models; e.g.see Helpman and Krugman (1985).5Alternatively, the model can be interpreted as characterizing only the manufacturing sector of the economy. The manufacturing firmsuse labour and capital in the same way, but production of some manufactures involves emitting a GHG. If firms are partitioned intopolluting and nonpolluting, then little changes in the model outlined above. In such a model, differentiating the green sector isstraightforward.

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As in Copeland and Taylor (2003) and Levinson andTaylor (2008), I assume the abatement technology fð�Þtakes the following form:

fðθÞ ¼ ð1� θÞ1

1�λ (4)

where λ is less than one. If all factors were devoted toabatement, then θwould equal one and no pollution wouldbe generated, but nothing would be produced either. Note,too, that the abatement technology is a decreasing functionof the fraction of factors dedicated to abatement, so thatallocating additional resources to abatement reduces thefirm’s emissions. Solving Equation 3 for fð�Þ and usingthe parameterization in Equation 4 allows net output ofvariety i to be expressed as follows:

Di ¼ ½f ð,i; kiÞ�λe1�λi � �:

Since the abatement technology employs factors in thesame intensity as the output technology, emissions canbe considered an input to production. The interpretationis that pollution emissions (environmental services) arerequired to produce output of the dirty good. Thus, pollu-tion can be treated either as a joint output from productionof dirty goods or as an input required to produce thesevarieties.

Dirty varieties are produced at constant marginal cost

cbðw; r; τÞ ¼ wab, þ rabk þ τabe

where w denotes the wage rate for labour, r denotes therental rate for capital, and τ denotes the cost to emit oneunit of pollution, while abm denotes the unit factor require-ment of input m for production of a brown good; i.e. theamount of factor m firms chose to use in producing oneunit of a brown variety, given equilibrium ‘factor’ prices.

Domestic equilibrium

Consider the equilibrium within a country, for a givenlevel of emissions E. Firms in each sector will maximizeprofits. Thus, firms in the green sector will set price equalto marginal cost pg ¼ 1 ¼ cgðw; rÞ which determines theunit cost of producing the clean good in the economy,provided there is positive output in the green sector.Likewise, the differentiated firms will sell output accord-ing to

p 1� 1

σ

� �¼ cb w; r; τð Þ (5)

which corresponds to the canonical pricing equation for amonopolistically competitive firm. Since all firms produ-cing different varieties have access to the same

technologies and because the elasticity of substitutionbetween pairs of varieties is constant, firms in this sectorwill set the same price p, produce the same quantity ofoutput D and earn the same level of profit in equilibrium.Although price is greater than marginal cost, firms in thebrown sector still earn zero profit in a long-run equilibriumbecause of free entry and exit. Thus, in equilibrium,

p ¼ cbðw; r; τÞ 1þ �

D

� �(6)

where the term on the right-hand side of the equationcorresponds to the average cost of production. Becauseof the fixed costs, only a finite number, n, of differentiatedvarieties will be produced in equilibrium. This zero-profitcondition allows the (endogenous) number of firms in thebrown sector to be determined.

The supply-side equilibrium conditions also requirefactor inputs to be used completely. The factor market-clearing condition for labour ensures the country’s endow-ment of labour (supply) L equals the firms’ demand forlabour input, so

L ¼ ag,Gþ ab,nðDþ �Þ

where G is the total output from the green sector and n isthe number of firms (varieties) in the brown sector, eachproducing D units of output. Likewise, the capital supplyK in each country must equal the firms’ demand for capitalinput, so

K ¼ agkGþ abknðDþ �Þ

Lastly, since pollution emissions are only required for theproduction of dirty goods, the factor market-clearing con-dition can be written as follows:

E ¼ abenðDþ �Þ

where E represents the country’s level of emissions. Notethat the model outlined above is essentially a specific-factors (Ricardo–Viner) model: labour and capital aremobile across the green and brown sectors, but emissionsare immobile as they are used only in the production ofdirty goods. Typically, in trade models, the endowmentlevels for each country are fixed. The labour and capitalendowments for each country, L and K, respectively, areconsistent with this assumption. In contrast, because pol-lution can be treated as an input to dirty production, in mymodel, this endowment is a choice variable of the govern-ment. Despite the potentially infinite supply of pollution,governments will restrict the amount of pollution firmscan generate.

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Note that the model nests a comparable two-good ver-sion of the model considered by Copeland and Taylor(1995) who were concerned with transboundary pollutionin a factor-proportions model.6 To see this, first considerall dirty varieties to be perfect substitutes; allow the elas-ticity of substitution to go to infinity. Let the productionfunction in the dirty sector exhibit constant returns-to-scale by assuming the fixed costs, �, equal zero. Then theequilibrium conditions in Equations 5 and 6 are redundant– average costs are independent of the scale of production.The factor market-clearing conditions also reduce to thoseof the standard two-sector factor-proportions model. Myfocus on a two-sector model also resembles the Copelandand Taylor (2003) framework, but differs given theirmodel is built around a factor-proportions approach withlocal pollution. The model presented can be extended toallow for additional factors of production, to include other(homogeneous or differentiated) sectors of the economy,or to allow pollution intensities to vary across sectors quiteeasily. I focus on a two-sector model as including addi-tional sectors provides little additional insight and becausethe data I use in the quantitative exercise allow me toidentify pollution emissions only from the aggregate man-ufacturing sector for each country.

Government behaviour

Without government intervention, firms continue usingpollution resources until profits are maximized, ignoringthe adverse effects on consumers. Thus, a role exists forgovernments to impose restrictions on the amount ofpollution firms can generate. Note, too, that the issues Iconsider are international problems in two ways: first,trade between nations is by definition an internationaltransaction; second, I am concerned with a global pollu-tant, so even without international trade, the emissions ofeach nation affect consumers globally. Because countriesare linked in these ways, each government considers theemissions of all other countries in the world when settingits emissions level. Specifically, a typical governmentchooses the emissions level E to maximize consumerwelfare, given in Equation 1, subject to the behaviour ofconsumers and firms, the market-clearing conditions andgiven the pollution choices of other governments as sum-marized by EW.

7

In setting its policy, each government considers thechannels through which emissions affect consumers.Specifically, each government evaluates how manyfirms can survive in equilibrium, the prices thesefirms will charge and how the country’s output com-position will affect the income available to domesticconsumers for a given policy. Consumer income I iscomposed of factor income and the revenue fromemissions permits, which is rebated to consumers; i.e.I ¼ wLþ rK þ τE: Of course, these elements dependcritically on the world economic system, which Idemonstrate in the next section.

III. Autarky and Free Trade

I focus in this section on the equilibrium under thetwo conventional theoretic scenarios: autarky and freetrade. Specifically, for each world economic system, con-sider a noncooperative Nash–Walras equilibrium in whicheach government sets the emissions level E to maximizethe utility of its consumers, taking as given thepollution level of all other countries and considering thecompetitive equilibrium that will result from its policychoice.

Autarky

Under autarky, each country is economically independentof all other countries: each country must be self-sufficient.The government’s objective can be rewritten in terms ofindirect utility as follows:

V ¼ α n1

α�1

p

!α1� αpg

� �1�α

I � EδW

δ(7)

which shows explicitly that each government will con-sider how its emissions level affects the income, I, avail-able to consumers, the number of domestic firms, n, thatoperate, and the price, p, these firms charge in equilibrium.Any consumption gains obtained from increased emis-sions are compared to the additional pollution disutilitydomestic consumers experience. Consider how these vari-ables are affected in an autarkic regime when the govern-ment changes its policy.

6Copeland and Taylor (1995) considered a continuum of goods, each produced using ‘human capital’ and pollution emissions, with theemissions intensity varying across goods. Consider two of these goods, one being a good requiring no emissions, and one being any goodproduced with positive emissions.7 In practice, countries use a range of fiscal policies and measures including carbon/energy taxes, explicit limits on emissions, ‘green’quotas, voluntary agreements between industries and governments, etc., to limit CO2 emissions; cf. The World Bank (2008). In mymodel, setting an emissions level E would be equivalent to a government setting a cost of (tax on) pollution τ, or any of the othermeasures, as there is no uncertainty in the model. If carbon is not taxed in some countries, the price or quantity restriction can be thoughtof as resulting from policies concerning the underlying fossil fuels that generate emissions when burned (for example, coal). The key isthat the government uses some policy-related instrument (direct or indirect as there is no uncertainty in the model) that can be used toimpose a price or cap on emissions such that they are controlled by the governing agency.

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Given the Cobb–Douglas specification for the upper-tier preferences, consumers spend a constant fraction oftheir income (given prices) on each sector. Consumerutility satisfies standard Inada conditions which implyfirms will produce in both sectors of the economy, sothat consumers obtain positive consumption of goods ineach sector. Because of Walras’ law, the goods market-clearing condition needs to be specified in only one of thesectors. Thus, in equilibrium, market clearing for the greensector can be expressed as ð1� αÞI ¼ pgG and the unitcost to produce clean goods, cg , equals pg as the greensector is active. The demand for pollution, given theequilibrium output of the clean good, can be writtenimplicitly as follows;

τ ¼ ð1� λÞcgð�G� GÞλE

(8)

where I define

�G ; ALπK1�π (9)

to be the maximum amount of clean output the countrycould produce if all factors were dedicated to green pro-duction. Going forward, I refer to this measure as the ‘size’of the country as it considers only the tangible endow-ments (and TFP) of the country and is independent of thecountry’s emissions level. Substituting the demand forpollution into the market-clearing condition for the greensector, and recognizing that consumer income derivesonly from factor payments, yields the autarkic equilibriumincome level as follows:

I ¼�G

1� αð1� λÞ :

Each country’s income (GDP) is independent of the emis-sions level imposed by the government under this regime.In the autarkic model, increases in the level of pollutionare offset exactly by decreases in the price of an emissionspermit. Firms follow the standard factor-allocation rule:labour and capital are allocated across the green andbrown industries until the value of the marginal productof each factor is equalized across sectors of the economy.In doing so, firms maximize the revenue (national income)for the economy, for any emissions choice of the govern-ment. Thus, the government is not concerned with income(or composition) effects in an autarkic regime.

The benefits to the country of polluting more underautarky are obtained through a fundamental virtue ofmarket economies: competition. Specifically, the equili-brium cost of pollution can be used to determine thenumber of varieties produced:

n ¼ E1�λð�G� GÞλσ�

where, since income is independent of E, so too is equili-brium output of the green good G. Thus, by increasing theamount of pollution it will allow, the government ensuresits consumers have access to a wider variety of goods.Likewise, the equilibrium price of a differentiated goodcan be solved as follows:

p ¼�

σσ � 1

��cgλ

�� �G� G

E

�1�λ

:

By increasing the number of emissions permits, the gov-ernment not only increases the number of firms that cansurvive in equilibrium, but also reduces the cost of anemissions permit that firms pay in equilibrium. Becauseprice is a constant markup over cost, the price consumersface for dirty products also decreases.

Maximizing the government’s objective in Equation 7allows for emissions to be expressed implicitly as a func-tion of world emissions; i.e. the first-order condition yieldsa country’s best-response function. In determining theoptimal pollution policy, the government equalizes themarginal disutility consumers receive from additional pol-lution with the marginal increase in consumption utilitywhich is obtained through the effects discussed above. InFig. 1, I depict, using solid lines, typical best-responsefunctions for two countries – home and foreign – eachoperating under autarkic governments. As the emissionslevel for one of the countries increases, world pollutionincreases, and the best-response function of the othercountry decreases. The Nash–Walras equilibrium of theautarkic pollution game occurs when no country wants toalter its pollution policy, given the emissions choices of all

Emissions level for the home country

Em

issi

ons

leve

l for

the

fore

ign

coun

try

Best−response functionfor the home country

Best−response functionfor the foreign country

Fig. 1. Example of Nash–Walras equilibrium underautarky

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other countries. In Fig. 1, this corresponds to the (unique)point where the best-response functions intersect.

The best-response functions will, of course, depend onthe parameter choices. For example, in Fig. 1, I plot multi-ple best-response functions for the foreign country (usingdifferent line styles) which correspond to differentassumptions concerning the value of the foreign pollutiondisutility parameter, δf . Higher values of the pollutiondisutility parameter decrease the best-response functionof the foreign country because foreign consumers aremore sensitive to pollution for higher values of δf . Allbest-response functions in the figure involve cases wherethe marginal disutility of consumers is increasing in thepollution level; i.e. δ is larger than one for each country. Incontrast, when the marginal disutility is constant (δ equalsone), the optimal pollution choice of the country is inde-pendent of foreign emissions. Graphically, the best-response functions of two countries, each having pollutiondisutility parameter equal to one, would be represented byorthogonal lines. Intuitively, when δ equals one, no com-plementarity exists between consumption utility and pol-lution disutility (world pollution becomes a constant in theutility function), so strategic elements become irrelevantunder such a parameterization.

In determining its optimal pollution level, each countryis concerned only with the adverse effects pollution has onits (domestic) citizens. Consider, instead, a worldwidesocial planner who allocates pollution to each country soas to maximize world utility, defined as the sum of utilitiesover all countries, subject to markets clearing within eachcountry. The social planner’s optimal pollution level forthe countries with best-response functions depicted assolid lines is denoted by � in Fig. 1: relative to the socialplanner’s efficient allocation, each individual countryoveruses the environment. Thus, in pursuing its own inter-est, each country allows too much pollution and a tragedyof the commons obtains. Each country realizes the fullbenefit in terms of increased consumption utility fromallowing additional pollution, but incurs only a fractionof the associated cost as consumers throughout the worldexperience disutility.

Free trade

The effects of trade can be introduced into the model quiteeasily. Consider now a world in which trade is completelycostless. Two effects arise: first, consumers gain access togoods from other countries and, second, firms now havedemand for their products from consumers of foreign coun-tries. Let pj denote the price of a dirty product produced incountry j. The derived demand for a given variety whenprices differ across varieties was provided in Equation 2.Adapting the notation and interpretation of this equationyields the demand from consumers in country i for a repre-sentative variety produced in country j:

B�ijðp; n;XbiÞ ¼

p�σj XbiPJ

k¼1nkp1�σ

k

where Xbi denotes country i’s expenditures on browngoods and nk is the number of varieties produced incountry k. The vectors p and n collect the prices of dirtyvarieties and the number of varieties produced in eachcountry, respectively. This derivation represents the quan-tity demanded for one representative variety produced incountry j, where a total of nj varieties are produced andsold at price pj.

In a trading equilibrium, governments no longer need toworry about markets for goods clearing within theirrespective countries. All countries will now be involvedin world markets, which will help discipline the amount ofpollution each government allows its firms to use. Inparticular, world demand for the green good must equalthe world supply of the green good:

ð1� αÞIW ¼ pgXJk¼1

Gk

where Gk denotes country k’s supply of clean product and

IW;PJ

k¼1 Ik is defined to be world income. Likewise, thesupply of an individual dirty variety produced in eachcountry must equal the world’s demand for that variety:

D ¼XJi¼1

Bij for all j ¼ 1; . . . ; J (10)

where I use the fact that output per firm is constant for alldirty varieties. These conditions, one of which can bedropped by Walras’ law, serve as equilibrium constraintseach government must consider in setting its optimalpollution policy. Before proceeding, it is useful to notethe following:

Proposition 1 (common world price): All firms produ-cing dirty varieties, regardless of the country in which theymanufacture the goods, will charge the same price in freetrade.

To see this, note that Equation 10 can be rewritten asfollows:

D ¼XJi¼1

p�σj X biPJ

k¼1 nkp1�σk

¼ αIWp�σjPJ

K¼1 nkp1�σk

for all j ¼ 1; . . . ; J :

Thus, the market-clearing condition for each varietyrestricts prices to be equal across countries, as all other

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variables appear in each of these conditions. Let p denotethe common world price for dirty varieties. In light of this,the following result holds.

Proposition 2 (equal cost of pollution): In an equilibriumwith free trade, the cost of pollution will be equalizedacross all countries, provided each country is active inboth sectors of the economy.

From Proposition 1, prices are equalized across countriesunder free trade. Given this, consider the domestic equili-brium in each economy. The brown sector uses labour andcapital in the same factor shares as the clean sector. Thus,green goods can be treated as an input, along with pollu-tion emissions, to produce dirty goods.8 Recall that theclean good is the numéraire. Hence, if the clean sector isactive in each country, then its unit cost, cg, is identicalacross all nations. An implication of the CES sub-tierutility is that prices are proportional to the unit cost, asdescribed by the condition in Equation 5. Since the priceof each dirty variety is fixed at a common world level andunit costs to produce the green good are constant, the costof pollution, τ, is also constant across countries inequilibrium.

Consider, again, a government that sets the emissionslevel to maximize the welfare of domestic consumers,taking as given the policies of all other countries, theworld market conditions and the endogenous responsesfrom consumers and firms given its choice. Indirect con-sumer utility for consumers in country i under free tradecan be written using the result from Proposition 1 asfollows:

V �i ðp; n; IiÞ ¼

αn1

σ�1W

p

!α1� α

pg

� �1�α

Ii � EδiW

δi

where nW represents the number of varieties availablethroughout the world, defined as nW;

PJk¼1 nk : As in

autarky, consider the implications that changing the emis-sions level in country i has on income Ii, the number ofvarieties produced domestically ni and the (world) price p.

While consumer utility under free trade and autarkylook quite similar, new considerations appear that wereabsent from the autarkic setting. The cost of pollution, fora given emissions level, takes the same form as in autarky,given in Equation 8. However, output of the clean good isno longer required to take place domestically – consumerscan import the clean good from other countries, constitut-ing inter-industry trade. Specifically, country i’s output ofthe green good, Gi, can be written as a function of theemissions level and the world price of dirty goods

Gi ¼ �Gi � λσ � 1

σ

� �p

� � 1

1� λEi if Ei � �Ei

0 if Ei > �Ei

8>><>>:

where �Ei denotes a country-specific emissions threshold.If a country’s pollution emissions exceed its threshold,then all productive factors are allocated to the dirty sector– the country is completely specialized in the productionof brown goods; otherwise, the country’s economic com-position comprises output from both sectors provided theemissions level is positive. The threshold is country-spe-cific because countries have different labour and capitalendowments as well as TFP terms and because its con-sumers experience potentially different effects fromincreased pollution. This expression makes clear thatincreases in either the level of emissions or the worldprice for dirty goods reduce a country’s output of theclean good.

Assuming emissions do not exceed country i’s thresh-old and using this equation with the cost of pollution,given in Equation 8, allows income to be written asIi ¼ 1

λ�Gi � ð1� λÞGi½ � which shows that a country’s

income decreases as production of the clean goodincreases. By increasing emissions, a government allo-cates productive factors to the dirty sector of its economy,where the marginal product of labour and capital increase,and reduces output from the clean sector. In doing so, agovernment can increase GDP (income) because the dirtysector has economic rent associated with it; i.e. the dirtyfirms generate revenue from using environmentalresources as an input. Thus, in a trading equilibrium,governments have incentive to increase the level of emis-sions to encourage the production of dirty goods (and,thus, increase consumer income), while pushing cleanproduction towards other countries.

Market structure effects are apparent when consideringthe number of brown firms in country i for a given policy.The number of firms in country i takes the same form asunder autarky,

ni ¼ E1�λi

�Gi � Gið Þλσ�

:

However, a comparable increase in the emissions levelunder a trading regime has a larger impact on the numberof domestic firms than under autarky. Specifically, inaddition to the direct effect (an increase in Ei increasesni), there is also an indirect effect: as emissions areincreased, output of the clean good (which is fixed in

8With essentially two factors, this proposition can be thought of as a factor-price equalization result. However, if labour and capital sharesare not the same in both sectors, factor-price equalization becomes unlikely as the number of factors exceeds the number of sectors in theeconomy; see, for example, Feenstra (2004) for a discussion of such results.

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autarky) decreases because productive factors shift to thedirty sector.

The other channel the government must consider whensetting its pollution level is the effect it will have on theprice of dirty goods. From Proposition 1, there will be acommon world price for dirty goods, which can be rewrit-ten as p ¼ ðαIW Þ=ðnWDÞ: By increasing the level of emis-sions, a government can potentially influence the price fordirty goods through the number of varieties available inthe world. Thus, terms of trade effects are introduced in aworld economy with trade.

Comparing autarky and free trade

While I have emphasized the beneficial aspects of increas-ing emissions in a given country, it is important to remem-ber that consumers are directly harmed by emissions.Note, too, that compared to autarky, trade allows dirtygoods to be imported and this reduces the amount ofemissions a government wants to allow – importedgoods help satisfy the consumers’ love of varietypreferences.

It is easy to construct examples where free trade caneither increase or decrease world pollution: the effectdepends critically on the pollution disutility para-meters, δj

� �Jj¼1

. Consider Fig. 2 which depicts worldpollution under autarky and free trade when all coun-tries have the same pollution disutility parameter. Thefigure highlights two important properties of these

parameters: first, a higher pollution disutility parameterimplies governments will allow less pollution, regard-less of the economic regime, and so the equilibriumlevel of world pollution will decrease; second, a higher(lower) value of the pollution disutility parameter, δ,will lead to less (more) environmental degradation oran improvement in environmental quality under amovement to free trade, relative to the correspondingautarkic equilibrium.

In the case where the parameter is the same acrosscountries, as in Fig. 2, there exists a threshold value�δ: world pollution will be lower (higher) under freetrade than under autarky for δ > �δ (δ < �δ). When δ isallowed to vary across countries, the threshold prop-erty still holds, but it is now conditional on thevalues of the other countries. Nevertheless, the intui-tion and interpretation remain the same. In order toidentify whether free trade is good or bad for theenvironment, Antweiler et al. (2001) assumed thatthe marginal disutility from pollution was constantin their reduced-form analysis. This assumptionrestricts the pollution disutility parameters to equalone for all countries, which has strong implications:the pollution policy of each country is independent ofthe level of world pollution.9 I avoid this assumptionand remain agnostic about the values of theseparameters.10

IV. Trade Costs

Researchers concerned with trade and the environmenttypically consider only the extreme cases consideredabove: autarky and free trade. In reality, trade costs aresubstantial; see Anderson and van Wincoop (2004).Fortunately, admitting trade costs to the model is relativelystraightforward and extremely fruitful: the model is able togenerate the gravity equation – the central empirical rela-tionship used by trade economists to measure how difficultit is for countries to trade with each other. Most researchersconcerned with trade and the environment have consid-ered a factor-proportions (Heckscher–Ohlin) framework.This may explain why it has been difficult to link theore-tical and empirical work – the factor-proportions modelsays nothing about bilateral trade flows, so the gravityequation does not result in multi-country models basedon this framework.

_δ

Pollution disutility parameter δ

Wor

ld p

ollu

tion

Free tradepollution levels

Autarkypollution levels

Fig. 2. World pollution under autarky and free trade

9 This assumption may be a reasonable one in their research which concerned SO2, which is a local pollutant, whereas CO2 is atransboundary pollutant.10While my primary concern is to determine whether trade is good (or bad) for the environment, my approach also provides some insightinto whether assuming δ equals one is a reasonable assumption. To my knowledge, no one has estimated or recovered these parameters,meaning the shape of the pollution disutility remains as yet unknown.

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Incorporating trade costs

I model trade costs using the standard iceberg assumption:if one unit of a good is shipped from country j to country i,only 1=tij units arrive, where tij is greater than or equal toone (tii equals one for every country). The trade costinvolves ðtij � 1Þ units of the good melting away duringtransport, which is meant to capture all costs involved ingetting the good from a firm in country j to a consumer incountry i, other than the marginal cost of production.These costs include physical barriers (distance, borders,whether the countries are islands or landlocked and soforth), relational barriers (whether the countries share acommon language or use the same currency, whether onecountry is a former colonizer and so forth), policy-orientedbarriers (tariffs, quotas and so forth) and any other effectsthat limit trade between country i and country j.

The iceberg assumption is convenient as it does notdistort firm pricing within each country: all firms withina given country charge the same free-on-board (FOB)price in all markets, given by Equation 5. Thus, tradecosts change nothing on the supply-side of the economy.Of course, the prices consumers in each foreign countrypay will vary due to the presence of trade costs whichdiffer across countries. Consumer demand for a represen-tative variety must be adjusted to account for the higherprices foreign consumers face under the presence of tradecosts. In particular, the cost, insurance, and freight (CIF)price a consumer in country i must pay to obtain one unitof a good produced in country j is now tijpj; i.e. pj is theprice received by producers in country j and the consumerneeds to buy tij units to obtain one consumption good.Specifically, the demand for a representative variety pro-duced in country j, from consumers in country i, can beexpressed as follows:

B�ijðp; n;Xbi ; tiÞ ¼

ðtijpjÞ�σXbi

P1�σi

(11)

where P1�σi ¼PJ

k¼1 nkðtikpkÞ1�σ is what Anderson andvan Wincoop (2003) refer to as a price index of inwardmultilateral resistance as it depends on all trade costs thatcountry i faces. Given the demand for a representativegood, the market-clearing conditions for dirty varietiescan be written as follows:

D ¼XJi¼1

tijPi

� �1�σ

Xbip�σj for all j ¼ 1; :::::; J : (12)

These market-clearing conditions ensure the (gross) sup-ply of each variety equals the quantity consumed byindividuals in each country, as well as those units thatare used up in transit.

Derivation of the gravity equation

An attractive feature of the model presented above is itsability to generate the gravity equation. In deriving therelationship, I adapt the approach of Anderson and vanWincoop (2004).11 First, use the demand given inEquation 11 to define

Mij ; njðtijpjÞBij ¼ njtijpjPi

� �1�σ

Xbi (13)

as the CIF value of total (gross) exports of all varietiesproduced by firms in country j to consumers in country i.The goods market-clearing conditions, given by Equation12, can also be written in terms of the value of dirtyvarieties produced in each country

Yj ; njpjD ¼XJi¼1

tijPi

� �1�σ

Xbinjp1�σj for all j ¼ 1; ::::; J :

Solving this equation for the supply price, pj, and substi-tuting the expression into Equation 13, yields

Mij ¼ XbiYjYW

tijPi�j

� �1�σ

(14)

where YW ¼PJk¼1 Yk is the value of the world’s output of

dirty goods and

�1�σj ¼

XJi¼1

tijPi

� �1�σ Xbi

YW

is what Anderson and van Wincoop (2004) refer to asoutward multilateral resistance. This gravity equationexpresses trade flows as a function of trade barriers andthe set Yi;Xbif g. The relationship can be used to inferunobservable trade costs for a given parameterization ofthe trade barriers and after making an econometricassumption on how the theoretical relationship is linkedwith observed trade.

Thus, before estimating the relationship, I need tomake an assumption about how trade costs relate toobservables. I assume tij ¼ hCijZρ

ij; where Zij is the dis-tance between country i and country j and Cij is a dummyvariable denoting whether country i and country j share aborder (are contiguous). This simple parameterization isquite common and assumes that trade costs are sym-metric (tij equals tji), which was also required byAnderson and van Wincoop (2003). Henderson andMillimet (2008) considered a detailed study of the statis-tical assumptions, empirical specification, and estimation

11 In footnote 5, I noted an alternative interpretation in which the model characterizes only the manufacturing sector of each economy.Under such an assumption, this derivation would follow Anderson and van Wincoop (2003).

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strategy of the gravity model in which they found evi-dence that symmetric costs are a reasonable approxima-tion, and that distance enters in this way. To account fordeviations from the theory, empirical researchers con-sider the stochastic version of trade coststij ¼ hCijZρ

ijexpðηijÞ where ηij reflects unobserved vari-ables that determine bilateral trade costs. Substitutingthis into the gravity expression in Equation 14 yields

Mij ¼ XbiYjYW

hCijZρij

Pi�j

!1�σ

εij (15)

where εij ¼ exp½ð1� σÞηij�n o

is assumed to be indepen-

dent of the regressors and the exogeneity conditionEðεijjXbi ; Yj;Cij; Zij;Pi;�jÞ ¼ 1 holds.

Estimation of the gravity equation

Two issues regarding estimation of this equation arerelevant: first, treatment of zero-value observations and,second, consideration of the multilateral resistanceterms. Typically, estimating the gravity equationinvolves a log-linearization of Equation 15 which yields

mij ¼ xbi þ yj þ ½ð1� σÞlog h�Cij þ ½ð1� σÞρ�log Zij

� ð1� σÞlogPi � ð1� σÞlog�j þ ð1� σÞηij (16)

where I omit the constant term and where mij, xbi and yjdenote the logarithm of exports from country j to country i,the logarithm of country i’s total spending on dirty goodsand the logarithm of country j’s total output of differen-tiated goods, respectively. However, this logarithmictransformation, while convenient, introduces a concernabout how to treat zero-value observations. Most empiri-cal researchers choose to drop the country-pairs whichhave zero trade and to estimate the log-linear model viathe method of least squares (LS). Also, as noted by SantosSilva and Tenreyro (2006), the gravity equation is a multi-plicative model, so if the error term is heteroscedastic, log-linearization will lead to biased estimates because ofJensen’s inequality. To deal with these issues, the authorsinstead advocated using a Poisson pseudo-maximum like-lihood (PPML) estimator with a robust covariance matrix.The PPML estimator is a consistent estimator provided theconditional mean is correctly specified; the data do notneed to be Poisson distributed. Under this approach, thestochastic gravity model, Equation 15, can be written asthe exponential function

Mij ¼ exp xbi þ yj þ ½ð1� σÞlog h�Cij þ ½ð1� σÞρ�log Zij��ð1� σÞlogPi � ð1� σÞlog�j

�εij (17)

which can be estimated via PPML, with any inferencebeing based on an Eicker–White robust covariance matrix.

To handle the multilateral resistance terms, Andersonand vanWincoop (2003) adopted a nonlinear least squaresestimator and suggested, as an alternative, includingregion-specific dummy variables. Both approaches yieldconsistent coefficient estimates. I employed the latterapproach and introduced country-specific fixed effects totake into account the unobserved price indexes. This tech-nique is also adopted by Rose and van Wincoop (2001)and is discussed in detail by Feenstra (2004). The intuitionof the fixed-effects approach is that, because the multi-lateral resistance terms are unobserved, they can be mea-sured as the coefficients of importer- and exporter-countrydummy variables (rather than calculating them). Note,however, that the country-specific variables Xbi and Yjare perfectly correlated with the fixed-effect dummy vari-ables. Consequently, they must be excluded. Thus, thefixed effects capture the terms ½xbi � ð1� σÞlogPi� and½yj � ð1� σÞlog�j�.

To estimate the gravitymodel, I used bilateral trade flowsdata from Feenstra et al. (2005). Specifically, I focused ontrade in polluting industries, which I defined to be trade inmanufactured goods, as well as trade in the other industrieshighlighted as pollution-intensive by Tobey (1990) orManiand Wheeler (1998).12 I estimated the model using tradedata for the entire sample of 152 countries for which I havecomplete data, as well as trade data among only the coun-tries in the restricted sample I used in the forthcominganalysis. The restricted sample comprises the 20 countrieswith the highest firm-related CO2 emissions, in absoluteterms, for the year 2000. I refer to this group of countriescollectively as the ‘high-pollution’ sample, and the entire152 countries as the ‘full’ sample.

I estimated both the log-linearized gravity model inEquation 16, using LS, as well as the multiplicativemodel in Equation 17, using PPML. The estimationresults, using both the full sample and the restrictedhigh-pollution sample, are presented in Table 1. The coef-ficient estimates vary quite dramatically depending on thesample and estimation strategy considered. The distancecoefficient is significant at size 0.01 in all cases, but theelasticity is substantially larger under LS than underPPML, which is consistent with the findings of SantosSilva and Tenreyro (2006). The differences in the coeffi-cient estimates across the samples are not surprising giventhe restricted size of the high-pollution sample, whichcontains data from 132 fewer countries and hence has far

12Details on the specific industries that fall under this definition can be found in the Data Appendix. There, I also provide details on thedata used for distances and the contiguity relationship between countries.

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fewer observations from countries that share a border.Nonetheless, the continuity dummy is also significant atsize 0.01 under all estimations.

In the last row of the table, I report results from aRegression Specification Error Test (RESET) using thesquare of the predicted values from each of the esti-mated models. The p-values correspond to a signifi-cance test on the coefficient of the squared of thefitted values. The models in log world trade flowsestimated by LS are both rejected by the RESETtest, suggesting the models may be misspecified andcan be improved. So, too, is the full sample PPMLmodel. However, I am unable to reject the nullhypothesis for the model estimated by PPML usingthe high-pollution sample at any standard size test.Thus, the RESET test is unable to detect any misspe-cification for this restricted sample. I use these esti-mates to infer trade costs facing the 20 highestpolluting countries, which are used in the exerciseconsidered next.

V. Recovering the Country-SpecificParameters

The estimated bilateral trade costs provide me with areasonable benchmark for where, in reality, the worldeconomy is on the autarky-free trade spectrum. Takingthese trade costs as given, I can use the model, togetherwith data concerning the firm-related emissions, endow-ment characteristics and GDP of each country, to recoverthe country-specific parameters. However, before pro-ceeding, I must first assign reasonable values for para-meters that are not identified in the model.

Parameter choices and data

In Table 2, I specify values for parameters that cannot besolved for from the model. I used data from the ConsumerExpenditure Survey from the year 2000 to determine the

share of income consumers spend on differentiated goods.The study reports annual expenditures in various con-sumption categories. I computed the share of expenditureson all categories except for food, alcoholic beverages andcash contributions, which I considered a proxy for theshare of consumer spending on dirty goods. I found thisvalue to be 82.36%. Anderson and van Wincoop (2004)reported many estimates of the elasticity of substitutionparameter ranging from 5 to 20. Eaton and Kortum (2002)estimated this parameter to be 9.28, which is what I used.Note that the elasticity of substitution for differentiatedgoods is important both as a parameter of the model and inderiving estimates for the trade costs between countries;the exponent (coefficient) on distance (the contiguity vari-able) in the trade costs is identified from estimating thegravity equation only for a given choice of σ. In general,the higher the elasticity of substitution, the lower theestimated trade costs.

For the firm-related parameters, Jaffe et al. (1995) useddata concerning the total costs of pollution control to inferpollution costs as a share of gross national product. Theyestimated this value, which corresponds to ð1� λÞ in mymodel, to be 2.61% for the year 2000. This seems accep-table as Tobey (1990) reported the share of pollutionabatement costs in total costs is between 1.92% and2.89% for his pollution-intensive industries. Gollin(2002) found that labour and capital factor shares areapproximately constant over time as well as across

Table 1. Estimates of the gravity equation

Estimator: LS LS PPML PPML

Dependent variable: logMij logMij Mij Mij

Sample: High pollution Full High pollution FullLog distance ‒0.920*** ‒1.324*** ‒0.602*** ‒0.701***

(0.089) (0.026) (0.042) (0.029)Contiguity dummy 0.939*** 0.657*** 0.891*** 0.615***

(0.264) (0.101) (0.131) (0.088)Fixed effects Yes Yes Yes YesObservations 355 9120 380 22 952RESET test p-values 0.000 0.003 0.161 0.006

Notes: SEs of the coefficients are given in parentheses.���Indicates significance at size 0.01.

Table 2. Parameters: values and foundation for choice

Parameter Value Source

Consumer-related:α 0.8236 Consumer Expenditure Survey (2000)σ 9.2800 Eaton and Kortum (2002)

Firm-related:λ 0.9739 Jaffe et al. (1995)π 2/3 Gollin (2002) and Valentinyi and

Herrendorf (2008)� 200 N/A

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countries. His results indicate that the aggregate labourshare is about two-thirds and is uncorrelated with incomeper capita. As discussed earlier, Valentinyi and Herrendorf(2008) found consistent evidence for the aggregate econ-omy. Thus, I assume π equals two-thirds. There is littleguidance concerning the fixed costs firms incur in order toproduce. Furthermore, as specified in the model, the fixedcost corresponds to the number of units a firm must‘invest’ to enter the industry. Because prices are deter-mined endogenously, it is extremely difficult to assign amonetary value to this cost. In the results I present, Iassumed this cost corresponds to 200 units. I conducteda sensitivity analysis in which I found this choice has anegligible effect on the disutility parameters of interest: a50% increase (decrease) in the fixed cost decreases(increases) the parameters of interest by only 0.20%(0.34%), on average. Thus, the elasticity of a given pollu-tion disutility parameter, with respect to the fixed cost, isextremely small. Changes in the fixed cost affect thequantity each dirty firm produces and the number of activefirms in equilibrium for each country but have no effect onthe GDP or aggregate composition of output for thecountries.

I used these parameter choices in conjunction with dataconcerning firm-related CO2 emissions, labour and capitalendowments and income per capita for each country forthe year 2000. In particular, I obtained firm-related CO2

emissions from the World Resources Institute (2008). Theremaining country-specific data come from the PennWorld Table (PWT) of Heston et al. (2006). All data arePPP converted to take into consideration the cost of livingin each of the countries. The PWT provides data concern-ing real GDP per capita directly and I used data regardingother variables to derive the number of workers and capitalstock in each country. Specifically, I inferred the numberof workers in each country using the population, real GDPper capita and real GDP per worker variables. I con-structed capital stock data using the perpetual inventorymethod with population, real GDP per capita and invest-ment data.13

Recovering country-specific parameters

Suppose that the world is in a Nash–Walras equilibriumand that the emissions I observe in the data correspond tothe optimal levels for each country. Because I observeemissions for each country, I can use the governments’best-response functions to recover the country-specificpollution disutility parameters δj

� �Jj¼1

. These best-response functions are determined by each governmentmaximizing its consumers’ (indirect) utility which, usingthe consumer demand for a representative variety given inEquation 11, can be written as

V �i ðp; n; IiÞ ¼

αPi

� �α 1� αpg

� �1�α

Ii � EδiW

δi:

Each government takes as given the bilateral trade costs,the labour and capital endowments as well as the TFP ofits country, and the emissions choices of the other coun-tries. In solving this maximization problem, governmentsconsider the competitive equilibrium that will arise giventheir emissions choices. Thus, the first-order conditionsdepend on how the price of differentiated goods in each ofthe countries, determined implicitly by the market-clear-ing conditions, respond to changes in the emissions levelof each country.

Thus, for a world with J countries, the pollution dis-utility parameters are derived from the J best-responsefunctions, one for each government; the J prices for thedirty varieties are determined by the J market-clearingconditions. In addition, there are ðJ � JÞ derivativeterms of the form @pi=@Ej which are pinned down by theimplicit function theorem. Finally, I assume the UnitedStates has TFP (AUS) equal to one and determine theremaining ðJ � 1Þ TFP terms Aj

� �j�US by requiring that

relative income per capita predicted by the model matchexactly those observed in the data for each country. This isimportant as, since Grossman and Krueger (1993), manyresearchers have shown an important relationship existsbetween a country’s income per capita and its pollutionlevel. Thus, by allowing TFP to vary across countries, themodel replicates (in equilibrium) the observed variation inincome per capita, an important factor in explaining dif-ferences in pollution across countries. Calculating theNash–Walras equilibrium of a game with J players isintensive computationally if J is at all large. I solved thissystem of ðJ 2 þ 3J � 1Þ nonlinear equations using thenonlinear optimization solver KNITRO. Unfortunately,memory storage became an issue when J exceeded 20countries. Thus, I limited my analysis to the 20 highestpolluting countries, in terms of absolute firm-related CO2

emissions. Despite this restriction, these countries accountfor nearly 80% of both the world’s firm-related emissionsand aggregate GDP.

In Table 3, I present the country-specific parameters ofinterest along with data on the emissions per GDP and ameasure of the relative size of each country – defined byEquation 9, both of which are important in understandingthe results. The table is sorted in descending order by thepollution disutility parameter value for each country. Theparameters are sensible in that they accord closely withdata concerning emissions per GDP. For example, rankingcountries based on these two columns of the table andcomputing Spearman’s rank correlation coefficient yieldsa value of 0.98. Disparities between the rankings are

13Details concerning the data and these methods are discussed in the Data Appendix.

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explained by differences in the size of the countries – largecountries are ‘big’ players in the pollution game in thesense that their pollution choices account for a substantialshare of the world’s emissions. Given this potential influ-ence, these countries have higher disutility parameters ifthey have relatively large output of the clean good inequilibrium. In the last column of the table, I present thecountry-specific TFP terms. These values are the same asresearchers concerned with growth accounting wouldobtain using the Solow-residual approach; see, for exam-ple, Caselli (2005).

VI. Counterfactual Experiments

Having reverse-engineered the country-specific parametervalues, I can consider the effect trade has on the environ-ment by changing trade costs and solving for the emis-sions levels of each of the countries under the hypotheticaltrade structure. My approach also allows me to track howpollution is reallocated across the countries and to deter-mine which countries gain or lose, in terms of income aswell as welfare, from changes to the trade costs.Additionally, I can evaluate the effects of internationalenvironmental agreements, such as the Kyoto Protocol.

Across-the-board changes in trade frictions

Many academic researchers and policy-makers are inter-ested in the effect trade has on the environment. Doesfurther trade liberalization lead to environmental

degradation or an improvement in the quality of the envir-onment? Alternatively, policy-makers have historicallyresorted to protectionist strategies in times of economicrecession. What would such policies mean for the level ofworld pollution and welfare of a nation? I can use thepredictions of my model to provide insight into thesequestions and, in fact, to say more. Specifically, I holdconstant the parameter values as well as the country-spe-cific endowments, discussed in the previous section, andconsider across-the-board changes in trade costs.Typically, multilateral trade agreements involve such anondiscriminatory reduction in trade barriers. Bilateralagreements often violate the most-favoured nation (nor-mal trade relations) principle which establishes equaltreatment of all WTO member states, in effect makingbilateral agreements multilateral. After changing thetrade costs, I solve for the Nash–Walras equilibrium emis-sion choices each government would make when facingthe new structure of trade costs. I can then evaluate howpollution, income and welfare change under alternativetrading scenarios.

In Figs 3 and 4, I present my central findings: from thecurrent state of the world, marginal decreases (increases)in trade costs lead to an increase (decrease) in the level ofthe world’s firm-related CO2 emissions and a decrease(increase) in aggregate welfare. Specifically, Figs 3 and 4depict how world emissions and aggregate welfare,respectively, would change given across-the-boardchanges in trade costs ranging from a 15% decrease to a15% increase. For example, a 15% across-the-boarddecrease in all trade costs would lead to an increase inworld emissions of about 2%. To put this in context, from

Table 3. Derived pollution disutilities

Country Pollution disutility Emissions per GDP ð�1000Þ Country size TFP (US = 1)

United Kingdom 2.5622 0.0501 0.1488 0.64France 2.5555 0.0583 0.1525 0.72United States of America 2.5408 0.0722 1.0000 1.00Germany 2.5341 0.0699 0.2116 0.59Italy 2.5125 0.0772 0.1333 0.65Brazil 2.4847 0.0899 0.1297 0.43Spain 2.4838 0.0926 0.0819 0.64Japan 2.4831 0.1019 0.3124 0.60India 2.4823 0.1029 0.2720 0.27Indonesia 2.4700 0.0981 0.0871 0.40Mexico 2.4619 0.1043 0.0835 0.50Canada 2.4377 0.1219 0.0854 0.82Republic of Korea 2.4230 0.1473 0.0757 0.73Russian Federation 2.4207 0.1727 0.1388 0.34Islamic Republic of Iran 2.4002 0.1709 0.0412 0.49China 2.3962 0.2376 0.5173 0.27South Africa 2.3927 0.1742 0.0394 0.51Turkey 2.3904 0.1890 0.0403 0.35Saudi Arabia 2.3666 0.2218 0.0360 1.60Ukraine 2.3143 0.3290 0.0255 0.19

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1980 to 2000, firm-related CO2 emissions throughout theworld increased by 10.7%. Thus, a 2% increase wouldcorrespond to the average increase in emissions over a 5-year period. A reduction in trade barriers by 15% wouldalso decrease aggregate welfare by nearly one-half of 1%,which corresponds to a reduction in world GDP of 1.15%in terms of equivalent variation. Thus, world consumerswould be indifferent between losing over 1% of theirincome as opposed to reducing trade barriers by 15%and obtaining lower welfare due to the increase in theassociated pollution externality (which dominates the con-sumption gains from further trade liberalization).

In Table 4, I present other results of interest for the casewhere trade costs between all pairs of countries arereduced by 15%. In particular, I document how worldpollution gets reallocated among the countries and indi-cate which countries gain from lower trade barriers, interms of income as well as welfare. Driving the resultsare two factors: the pollution disutility parameters andstrategic effects. All else equal, countries with low pollu-tion disutility parameters will increase emissions as they

are less environmentally sensitive. However, these effectsare often dominated by strategic effects: in maximizing itswelfare, each country wants to produce dirty goods andimport the green good. This composition effect occursbecause of the nature of the pollutant: consumers areindifferent concerning the source of emissions becausethe pollutant is global, but income for each country isincreasing (decreasing) in output of the dirty goods(clean good). As a result, governments would like dirtyproduction to take place domestically and try to outsourceproduction of the clean good to other countries. Becausebigger countries account for a larger share of the world’spollution, proportional changes in their policies have moresignificant effects on the level of world pollution relativeto those of small countries. Thus, bigger countries have astrategic advantage.

By far, China is calculated to realize the largest increasein its share of world emissions. China is a large countrywith a low pollution disutility parameter, as shown inTable 3. In contrast, the United States is a country nearlytwice the size of China (according to the TFP-adjustedmetric discussed earlier), but has a much higher pollutiondisutility parameter, which tempers its desire to increasepollution. In fact, the United States is calculated toincrease its emissions, but by less than the increase inworld emissions, resulting in a slight decrease in itsshare of world pollution. The strategic effects of large

−15 −10 −5 0 5 10 15−1.5

−1

−0.5

0

0.5

1

1.5

2

Percent change in trade costs

Per

cent

cha

nge

in w

orld

em

issi

ons

Fig. 3. World pollution due to changes in trade frictions

−15 −10 −5 0 5 10 15

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

Percent change in trade costs

Per

cent

cha

nge

in w

orld

wel

fare

Fig. 4. World welfare due to changes in trade frictions

Table 4. Changes due to reduced trade costs

Percentage change in

CountryShare of worldemissions

Countryincome

Countrywelfare

China 1.21 0.17 0.86RussianFederation

0.19 ‒0.01 0.07

Germany 0.11 0.03 ‒1.01France 0.07 ‒0.03 ‒3.55India 0.07 0.01 ‒0.16Italy 0.04 ‒0.08 ‒2.58Japan 0.02 0.03 ‒0.17United Kingdom 0.00 ‒0.12 ‒5.85Spain ‒0.02 ‒0.21 ‒5.42Brazil ‒0.04 ‒0.09 ‒2.38Indonesia ‒0.04 ‒0.19 ‒4.32United States ofAmerica

‒0.05 0.12 0.31

South Africa ‒0.08 ‒0.34 ‒7.36Republic ofKorea

‒0.08 ‒0.28 ‒2.03

Turkey ‒0.11 ‒0.34 ‒4.67Islamic Republicof Iran

‒0.11 ‒0.36 ‒6.20

Mexico ‒0.11 ‒0.32 ‒3.73Saudi Arabia ‒0.13 ‒0.38 ‒4.20Ukraine ‒0.44 ‒0.43 ‒1.67Canada ‒0.50 ‒0.38 ‒1.29

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countries are most prevalent in the percentage change incountry incomes reported in the third column of Table 4.The countries that gain from reduced trade costs, in termsof increased GDP, are the five biggest countries in thesample. These countries are successful at moving produc-tion of the clean good to foreign countries and using theirfactors in the brown sector. However, of these countries,only the United States and China alter their output com-position sufficiently well enough to realize an increase inoverall welfare, reported in the last column of the table.While Russia’s income remains essentially constant, itsconsumers obtain higher consumption welfare because ofan increase in the number of varieties available and areduction in the prices they pay for foreign goods due tothe lower trade costs. Russia has a low pollution disutilityparameter, allowing these consumption-related benefits todominate the increase in pollution disutility and result in anet welfare gain. All other countries also attain higherconsumption utility because of trade-related effects, butexperience a decrease in overall welfare because of theincreased emissions and a loss, or insufficient gain, inincome given their country-specific characteristics.

The Kyoto Protocol

The Kyoto Protocol is an international environmentalagreement to the United Nations Framework Conventionon Climate Change (UNFCCC) which specifies GHGemissions targets for many industrialized countries. TheProtocol took effect on 16 February 2005 but the UnitedStates has not ratified the agreement. However, even if theUnited States were to approve the guidelines, it is unclearwhether the agreement would lead to a reduction in globalemissions: while emissions targets are set for industria-lized countries, developing countries are required only toreport their emissions – no restrictions are imposed ontheir activity. A concern is that large developing countriescan substantially increase their emissions and that theseincreases could dominate the reductions of industrializedcountries, yielding a net increase in world pollution.

I can use my framework to provide insight into thisconcern and to help evaluate the Kyoto Protocol’s poten-tial for limiting pollution. Specifically, because I haverecovered the country-specific parameters, I can considera Kyoto-like counterfactual experiment. The Protocol spe-cifies an 8% reduction in emissions for European Unioncountries, a 7% reduction for the United States, a 6%reduction for Japan and a 0% change for the RussianFederation and Ukraine. All recommendations are relativeto a baseline level of emissions, which typically corre-spond with emissions in 1990. Assume the United Statesratifies the Kyoto Protocol and that all countries satisfysuccessfully their emissions targets which are based on1990 levels. I constrain these industrialized countries to

meet their Kyoto mandates, but leave free the emissionspolicies of all other countries.

I find that if industrialized countries followed the Kyotoguidelines, but the emissions levels of other countrieswere left unconstrained, world CO2 pollution resultingfrom firm-related activities would decrease by 0.36%.All countries not constrained by the Kyoto agreementwould increase their emissions relative to the year 2000data used to recover the country-specific parameters. Note,too, that in this Kyoto-like exercise, the level of worldwelfare increased by 0.25%.

VII. Conclusions

I proposed a new, integrated approach to quantifying theeffects of trade on firm-related pollution emissions.Specifically, I presented a theoretical model in which theeffect of trade on the level of worldwide emissions wasambiguous. The sign as well as the magnitude of the effectdepended critically on how consumers in each countrywere harmed by pollution emissions. I recovered thisinformation by estimating the bilateral trade costs eachcountry faces and using data on country-specific endow-ments, income per capita and firm-related emissions, inconjunction with the equilibrium conditions of the model.I then fixed these structural parameters and consideredcounterfactual exercises in which I determined how emis-sions levels changed when countries faced alternativetrade costs. These experiments suggest concern regardingthe effect further trade liberalization has on the level offirm-generated CO2 pollution.

At least for domestic market failures, trade economistshave argued that tariffs, quotas and other trade-relatedpolicies are second-best solutions – domestic policiesshould be used to target directly the problem’s source.Applying this rationale, the best way to control pollutionwill not be through trade-related barriers. However, myresearch suggests that to exploit the benefits associatedwith trade liberalization, policy-makers need to focus oncontrolling emissions. This can be achieved through moredirect means than trade-protectionist policies; for exam-ple, my research may provide motivation for a globalemissions trading (cap-and-trade) system, eco-labellingof products or the creation of an international environ-mental organization concerned with regulating GHGs.Unfortunately, given the inherent international marketfailure that transpires from global pollution, domesticpolicies may be insufficient to resolve the problem.Thus, a global organization responsible for controllingGHG emissions may require assistance from the WTO toimpose (credible and incentive-compatible) trade sanc-tions on states that do not meet GHG standards.

While I see my work as highlighting a potentialdirection for other researchers to build off the core

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Copeland–Taylor framework, there are clear caveatswhich limit how literally we can interpret the quantitativeresults. The model is very stylized and there are a numberof limitations which suggest avenues for future research.For example, the parameterization of the utility functiongiven in Equations 1 and 7 implies income enters linearlywhich restricts the ability of the model to allow for pollu-tion policy to vary endogenously across countries withincome. As such, the pollution disutility parameters cap-ture some of these effects, but are fixed through the coun-terfactual exercises. An alternative model might consider aspecification in which the marginal damage is not inde-pendent of income. Another extension might consideralternative technologies which relax the equal factor inten-sities. In my model, trade is driven by product differentia-tion, endowments and differences in pollution policy butalternative assumptions that keep the model tractablemight introduce other motives for trade. Analogously,the model could be extended to consider other sources ofpollution, such as consumption.

Acknowledgements

I am grateful to Harry J. Paarsch and Srihari Govindan fortheir helpful suggestions throughout this project. I wish tothank Daniel J. Henderson, Raymond G. Riezman, JoaoM.C. Santos Silva, John L. Solow, GuillaumeVandenbroucke and Michael E. Waugh for helpful discus-sions. I also received valuable feedback from seminarparticipants at the University of Oregon and the USEnvironmental Protection Agency’s National Center forEnvironmental Economics. Lastly, I thank two anon-ymous referees for their suggestions.

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Data Appendix

The data I used come from a variety of different sources. Inthis appendix, I document the data sources and providefurther details concerning the construction of somevariables.

Pollution emissions data

Data on country-level CO2 emissions are from the WRI’sCAIT Version 5.0.14 I defined firm-related emissions asemissions from the CAIT sector ‘Industrial Processes’ aswell as energy use-related emissions from the‘Manufacturing/Construction’ sector. CO2 emissions areprovided inMtCO2 for nearly all countries that are party tothe UNFCCC, as well as for Brunei and Iraq. To standar-dize country definitions across all data sources, I com-bined some of the countries listed in CAIT, as the countrydefinitions are more disaggregated here than in othersources (in particular, the world trade flow data detailedbelow). Specifically, I combined emissions from Lesotho,Namibia, Swaziland, Botswana and South Africa, as thesecountries are grouped and collectively referred to as‘South Africa’ in the NBER-UN world trade flow data.This standardization has little effect on the results as SouthAfrica accounts for 98.34% of these five countries’ com-bined emissions. Likewise, trade flow data for Indonesiaand Maldives are aggregated. To make data compatibleacross sources, I combined data from Indonesia andMaldives (Maldives has 0 MtCO2 of firm-related emis-sions, but is combined for all data sources forconsistency).

I focused on the 20 highest polluting countries whichaccount for 78.27% of firm-related emissions worldwide.These countries also account for 71.74% of total world-wide CO2 emissions, which includes emissions from con-sumption-related activities (e.g. electricity, heat andtransportation), as well as emissions from land-use changeand international bunkers.

Population and gross domestic product

Data on each country’s population and GDP come fromthe PWT of Heston et al. (2006).15 The database providesdata concerning a number of variables which have beenPPP-converted for 188 countries. I used the populationand real GDP per capita variables. As with the CAITemissions data, I combined the GDP and population totalsfor Lesotho, Namibia, Swaziland, Botswana and SouthAfrica, as well as for Indonesia and Maldives, all ofwhich are given independently in the PWT data, but

aggregated in the bilateral trade flow data. The 20 coun-tries I considered made up 65.31% of the world’s popula-tion and accounted for 76.97% of the world’s GDP in2000.

Labour and capital stocks

I derived labour and capital endowments for each countryusing data from the PWT as well. The PWT provides dataconcerning population, real GDP per capita in constantprices using a chain series and real GDP per worker inconstant prices using a chain series. I infered the numberof workers in each country by first computing real GDP inconstant prices and then dividing this by the real GDP perworker in constant prices.

I constructed capital stock data using the the perpe-tual inventory method. Specifically, I used the perpe-tual inventory equation Kt;i ¼ It;i þ ð1þ dÞKtþ1;i;where Kt;i and It;i are the capital stock and aggregateinvestment for country i at time t, respectively. Togenerate these estimates, I used an initial year of1970 and computed the initial capital stock K1970;i as½I1970;i=ðgi þ dÞ�, where gi is the average geometricgrowth rate in investment in country i between 1970and 1980 and d is the depreciation rate which Iassumed to be 0.06. The initial capital stock corre-sponds with the expression for the steady state capitalstock in the Solow growth model. Caselli (2005)describes the approach in detail. Investment data isprovided in the PWT but is not available for theformer Soviet states during the years they were apart of the Union of Soviet Socialist Republics(USSR). These countries are major contributors tothe world’s firm-related CO2 emissions; e.g. RussianFederation is the fifth highest polluting country interms of firm-related emissions. In order to considerthese significant contributors in the analysis, I neededto make an additional assumption: I allocated invest-ment data from USSR. across the former Soviet statesaccording to the fraction of the USSR population thatresided in each current republic during each year.While this assumption is crude, there is no alternativeas the only data available for these countries duringthe years they belonged to USSR is population; cf.Heston et al. (2006). Changes in this assumption willhave little effect on the pollution disutility parametersas any residual effects related to country endowmentsare captured by the TFP terms which ensure the rela-tive income per capita predicted by the model areconsistent with the data.

14 The data can be accessed at http://cait.wri.org/cait.php15The PWT data can be accessed at http://pwt.econ.upenn.edu/php_site/pwt_index.php

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Distances

Distances between country-pairs were obtained fromthe Centre d’Études Prospectives et d’Infor-mationsInternationales (CEPII).16 Distance calculation requiresinformation on geographical coordinates (latitude andlongitude) of at least one city in each country.Typically, this city is the capital of the country, unlessthe capital was not populated enough, in which casethe economic center was used. The distances werecalculated using the great circle formula which con-siders the shortest distance between two points along apath on the surface of a sphere. In addition to provid-ing data on distance, the CEPII also provides country-level data such as the country’s land area (km2), inter-nal distance, continent, a dummy variable denoting ifthe country is landlocked, as well as many dummyvariables describing the bilateral relationship betweencountries such as if the countries are contiguous, sharea common language, or if the countries have ever hada colonial link. While use of these variables is notstated explicitly in the paper, many of the variableswere used in robustness exercises I considered. For theaggregation of Lesotho, Namibia, Swaziland,Botswana and South Africa, I consider data for SouthAfrica, which is the largest country in terms of eco-nomic activity, population and pollution. Likewise,when aggregating Indonesia and Maldives, I considerdata for Indonesia.

World trade flow data

Feenstra et al. (2005) provided a set of bilateral trade databy commodity, as defined by four-digit SITC (revision 2)sectors.17 Because the data derive from United Nationstrade data, data from the World Trade Database, UnitedStates trade data and other country-specific sources, theauthors often combined data to standardize country defini-tions so that they were compatible across the different datasources. In addition, the primary data sources for variousparts of their data differ, as do the SITC definitions (revi-sions to the SITC definitions are required as productschange and new products are added). In creating a unifieddata set, the authors defined a new primary key (countryidentifier) which they referred to as an ‘NBER-UN’ countrycode. Trade among the 20 countries I considered accountfor 43.21% of total trade in the year 2000. I was interestedin trade in ‘dirty’ industries so I restricted trade to what aretypically considered semi-finished and finished manufac-tured goods (SITC sections 5–8). In addition, I includedtrade within a division or group that either Tobey (1990) orMani and Wheeler (1998) highlighted as a pollution-inten-sive industry. Most of the sectors specified by these authorsfall under the ‘manufacturing’ definition of SITC sections5–8; however, I also included trade flows from four-digitsubgroups within the following SITC divisions: 23, 24, 25,26, 27, 28, 32, 33 and 34. By restricting the data to tradeflows within these sectors, I captured 91.63% of theobserved trade among the 20 countries in my sample.

16Data on bilateral distances between countries, as well as other country-specific and bilateral data, can be accessed at http://www.cepii.fr/anglaisgraph/bdd/distances17The data can be accessed from the Center for International Data’s website at http://cid.econ.ucdavis.edu/

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