Do National Borders Matter?Intranational Trade, International Trade, and the Environment

Carol McAusland�

University of British Columbia

Daniel L. Millimety

Southern Methodist University & IZA

October 4, 2012

Abstract

We develop a theoretical model identifying channels through which trade impacts the environment. First,trade decouples some of regulation�s costs from its bene�ts, prompting demand for stringent environmentalregulations. Second, trade provides consumers with access to new varieties of goods; the associated income(substitution) e¤ect raises (lowers) demand for strict regulation. The model predicts (i) international tradeto be more environmentally bene�cial than intranational trade due to a stronger decoupling e¤ect, and (ii)both intra- and international trade to be pro-environment unless substitution e¤ects are su¢ ciently strong.Using data on intra- and international trade for the US and Canada, along with several environmentaloutcomes, we �nd robust evidence that international trade has a statistically and economically bene�cialcausal e¤ect on environmental quality, while intranational trade has a harmful impact. This pattern isconsistent with a moderate-sized substitution e¤ect along with a stronger decoupling e¤ect of internationaltrade.

JEL: F14, F18, Q56Keywords: Bilateral Trade, Pollution

�Food and Resource Economics, Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall,

Vancouver, BC V6T 2L7, Canada. Phone: 604.822.3350; E-mail: carol.mcausland@ubc.ca.yThe authors are grateful to the International Council for Canadian Studies for grant support, and Jayjit Roy, Ling Yee

Khor and Alice Li for excellent research assistance. The authors also thank Dan Phaneuf, two anonymous referees, Per

Fredriksson, Arik Levinson, and seminar participants at Harvard, Yale, UBC, SMU, the 2008 CEA meetings, UCSB�s 10th

CalWorkshop, and the Rocky Mountain Empirical Trade Workshop for valuable comments. Corresponding author: Daniel L.

Millimet, Department of Economics, Box 0496 Southern Methodist University, Dallas, TX 75275-0496. Phone: 214.768.3269;

Fax: 214.768.1821; E-mail: millimet@smu.edu.

1 Introduction

In 2007, 14 trillion dollars worth of goods crossed international borders.1 Internal trade (i.e., trade between

regions within a country) is also a primary component of economic activity. In this paper, we seek answers

to several questions. What is the impact of this �ow of goods on the environment? Does trade have a

causal impact on emissions? Does it matter whether trade occurs between countries or between regions in

the same country? In other words, do national borders matter in the trade-environment debate?

To answer these questions, we introduce environmental regulation into the canonical Krugman (1980)

model of monopolistic competition with increasing returns to scale and Spence-Dixit-Stiglitz preferences

for di¤erentiated goods. We then test the predictions of our model using data on within- and between

country trade for the U.S. and Canada. Theoretically, the Krugman (1980) model is appealing because it

explains an important stylized fact: for most industrialized countries, intra-industry trade is the dominant

form of trade. That is, for many products, each country is both an importer and an exporter.2 Empirically,

focusing on US-Canadian trade is also appealing. The US-Canadian border is the busiest international

border, with bilateral trade between these two countries averaging CDN$1.7 billion per day. In 2010,

Canada was the primary trading partner for the vast majority of US states. 19% of US exports are to

Canada; 15% of US imports originate in Canada. Internal trade is also plentiful: in 1997, for example, US

intranational shipments were $10.7 billion per day.3

Our simple model reveals new insights about how a region�s trade intensity� de�ned as the sum of its

exports plus imports divided by its national income� impacts local preferences for environmental regula-

tion. Trade in di¤erentiated goods grants consumers access to increased variety, which has income and

substitution e¤ects akin to outright improvements in per capita income and price changes. The income

e¤ects are straightforward: for consumers who love variety, an increase in the range of product varieties

available is a windfall. Broda and Weinstein (2006, p. 582) calculate the income-equivalent of the welfare

gains from increased variety: �US welfare is 2.6 percent higher due to gains accruing from the import of new

varieties.�Notably, these are annual and ongoing, not one time, bene�ts. Feenstra and Weinstein (2010)

estimate that the increase in varieties available in the US due to trade has lowered the merchandise price

level by 3.7%. Klenow and Rodríguez-Clare (1997) �nd that ignoring the bene�ts from increased variety

(i.e., considering only income gains) can underestimate the bene�ts from trade liberalization anywhere

from 33 to 80%.

Intuitively, variety-gains from trade should impact environmental regulation in a manner similar to

1WTO. International Trade Statistics 2008. 2008, p. 185.2Over the 1988-2000 period, intra-industry trade accounted for more than 50% of total manufacturing trade for twenty-one

out of the twenty-nine OECD countries. For Canada and the United States, rates of intra-industry trade over this period

ranged between 73.5% and 76.2%, and 63.5% and 68.5%, respectively (OECD 2002, p. 161).3Authors� own calculation based on data from Statistics Canada, CANSIM, Ta-

ble 228-0003 (summarized at http://www40.statcan.ca/l01/cst01/gblec02a.htm), US Census

(http://www.census.gov/foreign-trade/statistics/state/data/index.html) and Bureau of Transportation (description

available at http://www.bts.gov/programs/commodity_flow_survey/.

1

straight income gains: citizens redistribute their windfall gains so to get more of everything they enjoy,

including environmental quality. However, improved access to variety also changes the relative value of

consumption. The greater the variety of consumer goods, the more valuable is the marginal unit consumed,

with implications for the marginal rate of substitution between private consumption and the environment.

We refer to these as trade�s variety-induced income and substitution e¤ects.4

Trade also changes how the regulatory burden is distributed. Tightening local environmental regulation

raises production costs. Factor owners bear some of these costs via lower factor returns, while remaining

costs are passed on to consumers via higher prices; regulation also reduces the variety of locally-produced

goods. The impact on local consumers of these price hikes and supply contractions is not �xed, but rather

depends on how intensely the region trades. In regions that import actively, the majority of goods in a

household�s consumption basket is produced abroad. Since the price of these imported goods depends on

foreign supply conditions, overall consumption will be relatively una¤ected by changes in local environ-

mental regulation. Moreover, in actively exporting regions, much of consumers�share of the regulatory

burden falls largely on foreigners. In e¤ect, trade partly decouples consumer welfare from the costs of local

regulation, leading to stricter environmental regulation in states that trade intensely.

Importantly, the strength of the decoupling e¤ect depends on who a region trades with. We illustrate

this principle by building a simple two-region model and then examining how changes in each region�s

factor supply a¤ects trade volumes and demand for environmental quality in turn. We consider two

variants of the basic model. In the �rst scenario trade is between two autonomous regions with distinct

environmental policies. In the second scenario the two regions are restricted to have the same policy, as

per states or provinces governed by the same federal environmental policy. Comparing the predictions

of these two models reveals an important channel through which the identity of a region�s trade partner

matters. Suppose, for example, that a state�s trade partner is in another country; supply conditions in

the trade partner are outside the jurisdiction of local regulators. This is not necessarily the case, however,

if the trade partner is another state within the same country; many rules governing pollution in Canada

and the US are set at the national level, often coming as the result of laws passed by federal lawmakers.

As such, the price and variety of goods imported into Alberta from, say, Ontario, are not independent

of the regulations impacting Albertan producers. Thus, the extent to which trade decouples consumption

from regulation is substantially weaker for intranational trade, suggesting that the ceteris paribus e¤ect of

intranational trade on the environment will be smaller than of international trade.

We test our hypotheses using data on interstate commerce, interprovincial commerce, and international

trade between US states and Canadian provinces in 1997 and 2002. We use this data to construct measures

of intra- and international trade intensity for each state and province. We then assess the causal impact of

4Variety induced income and substitution e¤ects have been overlooked in previous research on trade and environment for a

variety of reasons. Many authors simply assume traded goods are homogenous. Meanwhile, models with di¤erentiated goods

tend to assume the number of varieties is �xed exogenously, and/or assume subutility from di¤erentiated goods as a class is

logarithmic, thereby causing income and substitution e¤ects to cancel exactly.

2

each type of trade on several measures of environmental quality. Following Frankel and Rose (2005) and

Chintrakarn and Millimet (2006), we control for the potential endogeneity of trade utilizing an instrumental

variables (IV) approach. The instruments are derived from a �rst-stage gravity model of bilateral trade

�ows. In e¤ect, the technique utilizes across-state/province variation in trade arising from its geographical

determinants (e.g., proximity to other states/provinces, adjacency, etc.) to identify the causal e¤ect of

greater trade on environmental quality. As in Frankel and Rose (2005) and Chintrakarn and Millimet

(2006), we also allow income to be endogenous as well.

Our results are striking. First and foremost, we �nd robust evidence that international trade intensity

lowers toxic releases: in most speci�cations trade has a statistically and economically signi�cant negative

impact on emissions. Moreover, consonant with the mechanism underlying the theoretical model, we also

�nd robust evidence that international trade intensity promotes �green voting�by federal lawmakers in

the US. Second, and also consistent with our theoretical predictions, we �nd that intranational trade is

less bene�cial for the environment than international trade; in fact, in nearly all speci�cations we �nd

intranational trade to be harmful to the environmental. This pattern of results is fully explained by the

theoretical model.

Our research contributes to the trade and environment debate along many dimensions. First, as in

Antweiler et al. (2001), Damania et al. (2003), and others, we develop an explicit theoretical model for the

purpose of generating testable hypotheses concerning the environmental impact of trade. Unlike others,

our empirical analysis is motivated by a model of imperfectly competitive �rms, di¤erentiated products,

and intra-industry trade, all important features of modern trading economies.

Second, while the use of trade intensity as the covariate of interest in empirical tests of trade and

environment relationships is accepted practice, to the best of our knowledge none of the theoretical models

used to motivate these previous empirical analyses have explicitly considered the role of trade intensity.

Instead, trade intensity is generally employed as a proxy for trade frictions. Our theoretical model, on

the other hand, identi�es an explicit functional relationship between trade intensity and environmental

regulation, uncovering speci�c channels through which trade intensity a¤ects incentives to adopt strict

environmental regulation. This turns out to be crucial when estimating the causal e¤ect of intranational

trade.

Our theoretical model builds on insights from the literature on tax exporting. Like Gordon (1983),

Lockwood (2001), P�üger (2001), and Hau�er and P�üger (2004), we �nd that governments in open

economies have an incentive to ignore regulatory costs born by overseas consumers. In our model, this

tax exporting induces governments to tighten emission caps in response to exogenous increases in trade

intensity.5

Finally, our analysis provides important, but previously ignored, evidence vital to the trade and envi-

5Comparing emission taxes in closed and open economies, P�üger (2001) similarly �nds that openness can lead to stricter

regulation via �consumer price spillovers�. Unlike P�üger 2001, in our model global product variety is endogenous, allowing

for spillovers in terms of variety as well as price.

3

ronment debate; a debate that Taylor (2004, p. 1) argues constitutes �one of the most important debates

in trade policy.�The debate has not been resolved in large part due to the inability to identify the causal

impact of trade on the environment; simple associations may mask the causal e¤ect due to problems of

reverse causation or omitted variables. For example, while Antweiler et al. (2001) �nd trade is associ-

ated with lower pollution concentrations, Ederington et al. (2005) and Levinson and Taylor (2008) �nd

abatement costs negatively impact net exports.

Frankel and Rose (2005) o¤ered a signi�cant breakthrough by applying an IV approach based on a

gravity model of trade to a cross-country sample. The authors �nd, as in Antweiler et al. (2001), that

trade is, at worst, environment-neutral and may in fact be pro-environment.

Despite the advance o¤ered in Frankel and Rose (2005), one potential criticism of their analysis is that

the use of cross-sectional data across a diverse set of countries, from a wide array of sources, makes the data

suspect. For example, di¤erences across countries in the measurement of trade levels and/or environmental

degradation, or di¤erences in the level of governmental honesty, may mask the �true�causal e¤ect of trade

on environmental quality. To circumvent this potential shortcoming, Chintrakarn and Millimet (2006)

revisit the analysis in Frankel and Rose (2005) using data on intrastate shipments from the US and state-

level measures of pollution. Using the same IV procedure, the authors seek to replicate the �ndings in

Frankel and Rose (2005) using data from one country (the US) to ensure consistency. The results are

consistent with Frankel and Rose (2005).

In light of this, it might seem as if the trade and environmental debate has been resolved. Unfortunately,

the measure of trade intensity employed in Chintrakarn and Millimet (2006) is not consistent with the

theoretical measure of trade intensity highlighted by the model we present here. Moreover, even if the

�ndings in Chintrakarn and Millimet (2006) prove robust to this change, for their analysis to extend from

intranational to international trade, one must assume that intra- and international trade have the same

environmental impacts; in other words, that state and national borders play the same role in the trade

and environment relationship. However, there is no existing literature exploring this issue. This paper �lls

this gap, o¤ering, to our knowledge, the �rst rigorous analysis of the environmental e¤ects of intranational

versus international trade.

2 Theoretical Model

2.1 Preliminaries

Consider a country, Home, that engages in free and frictionless trade with a Foreign trade partner (Foreign);

values for Foreign are denoted with asterisks. Home is endowed with a generic input H that is used to

produce a di¤erentiated product. We refer to the input H as human capital, although other interpretations

are equally valid. Home has S identical citizens (and so each owns H=S units of human capital). Let w

measure the rent paid per unit of human capital; we often refer to w as the wage. Let e denote emissions

per unit of �nal output. We assume production technologies in Home and Foreign are identical, each

4

exhibiting increasing returns to scale. The total cost for a �rm producing y(i) units of variety i equals

[a(e) + b(e)y(i)]w. a(e) and b(e) measure �xed and variable input requirements; we assume it takes more

inputs to design and use clean production processes, hence a(e) > 0 > a0(e) and b(e) > 0 > b0(e). For

brevity we will regularly suppress arguments, writing a(e) as a and b(e�) as b�, for example. For simplicity,

we assume these functions are isoelastic: �a � �a0ea > 0 and �b � � b0e

b > 0, where �a and �b are constants.

In our analysis, we assume Home�s emission intensity is determined by government policy; Foreign emission

intensity, e�, is taken as exogenous throughout our analysis.6

De�ne the mass of varieties produced in Home and Foreign as N and N�. We assume consumers have

Spence-Dixit-Stiglitz (SDS) constant elasticity of substitution preferences over the range of di¤erentiated

products. An individual�s utility from consuming the global range of varieties is a monotonic transformation

of

C =

Zi2N[N�

c(i)� (1)

where c(i) is a consumer�s consumption of variety i and � 2 (0; 1) is a parameter. For any variety i,

Home consumption c(i) equals local production y(i) less exports, all divided by S; in the case of imported

goods, local production is zero while exports are negative. We can interpret C as a Home consumer�s

variety-adjusted consumption.

Because production exhibits increasing returns to scale and consumers love variety, each �rm produces

a single unique variety; thus, N and N� also measure the mass of �rms. Given (1), consumer demands are

as follows:

c(i) =GDP=S

p(i)1

1��P�

��1(2)

where GDP = wH is national income and

P ��Zj2N[N�

p(j)�

��1

� ��1�

(3)

is the price-variety index. P is increasing in the price of any individual variety and decreasing in the

number of varieties available. As is usual in models with SDS preferences, each �rm has zero mass and

faces identical isoelastic demand. Home and Foreign �rms maximize pro�ts by charging respective prices

p(i) =wb

�and p(j�) =

w�b�

�: (4)

Firms are symmetric. We assume free entry, implying zero long-run pro�ts: [a+ by(i)]w = p(i)y(i).

Substituting for p(i) using (4) and collecting terms implies

y(i) =a

b

�

1� � for i = 1; : : : ; N: (5)

6That is, we assume Foreign is policy inactive, even when conducting comparative statics such as changes in Foreign factor

endowments. An analysis of a policy game played between two large, policy-active countries is beyond the scope of this paper

and is left for future work.

5

By symmetry, y�j =a�

b��1�� for j = 1; : : : ; N�: Since equilibrium prices and outputs are identical for each

�rm within a country, we omit arguments (i) and (j�) from here forward.

Full employment requires N [a+ by] = H. Substituting for y using (5) � and similarly for Foreign �

yields

N =1� �aH; N� =

1� �a�

H�: (6)

We assume factor prices equilibrate so as to maintain balanced trade; i.e., the value of Home�s exports,

X = pN w�H�

p1

1�� P� ���1, equals the value of its imports

M = p�N� wH

p� 11��P

���1: (7)

Utilizing P = P �, substituting in for p, p�, N and N� using (4) and (6) and isolating terms indicates that

any pair w and w� satisfyingw

w�=

�a�

a

�1�� �b�b

��(8)

satis�es the balanced trade condition. Without loss of generality, we can normalize

w� =1

a�1��b��(9)

and thus write the factor price in Home as a function of Home variables alone:

w =1

a1��b�: (10)

Substituting for factor prices in (4) gives prices

p =1

�

�b

a

�1��and p� =

1

�

�b�

a�

�1��: (11)

Finally, substituting (2) and (3) into (1) and collecting terms gives variety-adjusted consumption as a

function of aggregate income and the price-variety index:

C =

�wH

SP

��: (12)

We introduce the following measure of Home�s trade intensity, T , de�ned as Home�s total trade as a

fraction of income: T � X+MGDP . Invoking the balanced trade condition, M = X, substituting for M using

(7), and rearranging terms gives

T � X +M

GDP= 2[1� �] (13)

where

� � Np�

��1

P�

��1=

�1 +

H�

H

a1��b�

a�1��b��

��1: (14)

We can interpret � as Home�s share in (price-adjusted) global variety. Note from (13) and (14) that, holding

e constant, � is decreasing (and hence T is increasing) in H�. This is consistent with empirical research

that �nds countries with large neighbors tend to trade more intensely.

6

Rearranging (14) and substituting into (12) yields an expression for Home�s variety-adjusted consump-

tion expressed solely in terms of local variables and trade intensity:

C =

�wH

S

�� hNp

���1i1�� �

1� T2

���1: (15)

2.2 Pollution and Policy Choice

As many pollutants are regulated via direct controls, we assume e is set directly by a government regulator.

Speci�cally, we assume the regulator chooses e to maximize W , the sum of Home citizens�utilities,

W = S[u(C)� �D(Z)]; (16)

where u, � and D are de�ned as follows.

u is a positive, increasing and concave function of variety-adjusted consumption C. The following

de�nition will be useful in subsequent sections: � � ��u00(C)Cu0(C) . � is the elasticity of the marginal rate of

substitution between C and Z with respect to changes in C. We can interpret � as measuring the rate at

which households become sated with private goods (C) relative to the environment.7

D measures pollution damage; D is a positive, increasing, and convex function of emissions Z = eNy:

We assume emissions and pollution are synonymous and that pollution has no transboundary component.

� is a shift-parameter re�ecting sensitivity to pollution and is discussed further in section 3.1. Substituting

in for equilibrium values of y and N using (5) and (6) yields Home emissions as a function of Home�s

emission policy:

Z =e

b�H: (17)

Di¤erentiating (16) with respect to e and converting to percentage changes � denoted by �hat�notation,

whereby e = dee for example � gives

dW

de=S

e[ u0(C)C

C

e| {z }consumption

response

� �D0(Z)Z Ze| {z }

emission

response

]: (18)

The latter term in (18) measures the emission response. Allowing dirtier production techniques raises the

amount of pollution to which households are subjected:

Z

e= 1 + �b > 0: (19)

The other term measures the consumption response: Ce measures how tightly consumption levels depend

on local environmental regulation. Di¤erentiating (12) gives

C

e= �

"w

e� Pe

#(20)

7Concavity dictates the sign of � but not its size. For example, when u(C) = C1��

1�� , � = � and concavity imposes no

restrictions on � other than that � is positive.

7

indicating that regulation impacts households via their factor returns and the price index. Allowing

production to be more emission intensive renders human capital more e¤ective in each of its possible

employments � designing and producing goods � thereby raising nominal factor prices:

w

e= � (21)

where we de�ne � � (1� �)�a + ��b > 0.Changes in P can be decomposed into changes in the price of individual goods and in the range of

varieties available: di¤erentiating P using (3) yields

P

e= �

"�� 1�

N

e+p

e

#: (22)

The e¤ect of regulation on unit prices is ambiguous; although relaxing environmental regulation raises

factor costs, it also reduces the inputs needed to design or produce goods:

p

e= (1� �)[�a � �b]

?? 0: (23)

If unit-input requirements are more environmentally sensitive for production than for design (i.e., �b > �a),

then the productivity increase dominates the wage hike, Home �rms experience a fall in their marginal costs,

and prices fall in response to less stringent standards. However, if �a > �b, then factor prices will rise faster

than productivity along the assembly line, driving prices higher. Although this possibility is theoretically

interesting, we believe a priori that design costs are generally no more sensitive to environmental regulation

than are production costs, and so assume �b � �a without further apology.Relaxing regulation also leads to more varieties being produced. Firms��xed costs include the costs of

developing processes satisfying environmental regulations. To the extent that more design-related inputs

are needed when regulation is strict, loosening regulation frees up productive resources for use by new

entrants, raising the number of varieties produced by Home:

N

e= �a > 0: (24)

We can now calculate the net e¤ect of an increase in e on the price-variety index; substituting into (22)

using (13), (23), and (24) gives

P

e=�� 1��� =

�� 1�

�1� T

2

�� < 0: (25)

Notably, when � is small, the link between Home�s emission cap and the global price-variety index is weak:

if the majority of goods in consumers�baskets are produced abroad, then regulation in Home will have only

a small e¤ect on the overall price and variety of goods available. Home�s trade intensity is also correlated

with the demand elasticity for Home-produced goods as a group. Integrating (2) across the mass of Home�s

varieties and log di¤erentiating with respect to the common price of Home-produced goods reveals the price

elasticity of demand for Home produced goods to be 1���1�� , which is decreasing in � and thus increasing

8

in Home�s trade intensity. This is as one would expect: the more Home trades, the more competition its

producers face and the more responsive quantity demanded is to changes in p.

Combining (19), (21), and (25) in (18) yields

dW

de=S

e

�u0(C)C�

��+ (1� �)

�1� T

2

��� �D0 (Z)Z[1 + �b]

�: (26)

The system formed by (26) (when set equal to zero), (6), (10)-(14), and (17) implicitly de�nes Home�s

optimal pollution policy, eo, and equilibrium emissions.8

3 Theoretical and Empirical Predictions

3.1 International Trade Intensity

Our central question is whether greater Home trade intensity raises pollution and/or pollution intensity.

Because trade intensity is endogenous, we cannot answer this question with a simple comparative static

exercise involving exogenous changes in T . Instead, we must examine the impact of changes in exogenous

factors, such as growth in Foreign via change in H�.

For example, partially di¤erentiating (18) with respect to H�, dividing by �d2Wde2, and invoking the

envelope theorem gives

deo

dH� = �S

eo d2Wde2

[u0(C)C@

@H�C

e| {z }decoupling e�ect

+C

ef u0(C)

dC

dP

@P

@H�| {z }variety � inducedsubstitution e�ect

� �u0(C)dC

dP

@P

@H�| {z }variety � inducedincome e�ect

g]: (27)

Foreign growth impacts Home�s preferred environmental policy through three channels: a decoupling

e¤ect, a variety-induced (VI) income e¤ect and a VI substitution e¤ect. We examine each in turn, beginning

with the decoupling e¤ect.

Foreign growth expands the variety of goods produced abroad, inducing Home consumers � with their

taste for varied goods � to import more; Home consumers �nance their expanded imports by reducing

consumption of Home-produced goods. Both responses raise Home�s trade intensity, which, in turn, weak-

ens the link between local consumption and regulation. To see this mathematically, substitute (21) and

(25) into (20) to obtainC

e= � +

1� ��

�1� T

2

��: (28)

When most of the goods in a Home consumer�s basket are imported, tightening local environmental regu-

lation has only a small deleterious e¤ect on Home consumers because local regulation has no e¤ect on the

8The second order condition for an interior optimum is veri�ed in the appendix.

9

price and variety of goods produced overseas. At the same time, many of the costs of local environmental

regulation are passed-through to consumers overseas, making strict local regulation more attractive.9

Because Foreign-growth raises Home�s trade intensity, this growth magni�es the extent to which con-

sumers are insulated from regulatory costs and to which producers can pass their regulatory costs through

to foreigners. We can see this mathematically by partially di¤erentiating (28) with respect to H� to get

@

@H�C

e= �1� �

�

�

2

@T

@H� = �1� ��

��[1� �]H� (29)

which is negative. As a result, the increase in Home�s trade intensity heightens the extent to which the

costs of Home�s environmental regulation are borne by di¤erent individuals than those bene�tting from

the regulation. We call this trade�s decoupling e¤ect.

The pass-through and decoupling e¤ects are variants of tax exporting. Gordon (1983) and Lockwood

(2001) recognize that, when �rms have market power, some portion of origin taxes will be passed along

to foreign consumers. Ignoring the resulting negative �consumer price spillover,� governments have an

incentive to set ine¢ ciently strict production taxes. Examining emission taxes, P�üger (2001) similarly

�nds consumer price spillovers can induce regulators to set ine¢ ciently strict emission taxes. While our

model does not consider taxes, the principle is the same: when a country�s producers have market power

over the unique goods they produce, some of the costs of complying with strict emission caps will be passed

along to foreign consumers via higher prices and fewer varieties.

Interestingly, consumer price spillovers do not necessarily imply that marginal abatement costs will

exceed the marginal damage from pollution. As is well known from Buchanan (1968) and Barnett (1980),

a Pigouvian tax can exacerbate the allocative ine¢ ciency resulting from market power. Thus, when �rms

have market power, a regulator should set a second-best emission tax that is less than marginal damage;

weakening emission taxes spurs supply, lowering consumer prices in turn. However, governments may

be unwilling to sacri�ce their local environment in an e¤ort to increase consumer surplus if much of that

surplus is enjoyed abroad. Our model suggests governments that trade intensely are less willing to subsidize

consumption via weak environmental regulation.

The second channel through which Foreign growth a¤ects Home�s preferred emission cap is the variety-

induced income e¤ect. An increase in the number of available varieties is a windfall for variety-loving

consumers. A consumer will redistribute this windfall to procure more of everything she values, including

environmental quality. This is akin to conventional income e¤ects: environmental quality is a normal

good, and citizens demand more of it as they get wealthier. Income e¤ects are important in the trade

and environment debate. Grossman and Krueger (1993) and Copeland and Taylor (2003) argue that trade

9As mentioned above, an exogenous increase in Home�s trade intensity also makes demand for Home produced goods

more elastic, suggesting producers bear more regulatory incidence. However, in models with homogeneous monopolistically

competitive �rms such as ours, all producer rents are passed along to factor owners. Consequently, all changes in surplus are

captured via changes in the factor price � which our normalizing assumption (9) renders independent of variables other than

productivity � and the price-variety index P .

10

raises incomes, thereby fueling demand for more environmentally friendly production techniques; this is

one version of the technique e¤ect. In our model, an increase in GDP is unnecessary; holding wH constant,

an increase in variety N + N� lowers P , allowing consumers to purchase the same level of C using less

income, permitting Home to allocate more resources toward environmental quality.

Working in the opposite direction, the emergence of new goods also has a variety-induced (VI) substi-

tution e¤ect. When there are more varieties available, the opportunity cost of foregone factor returns due

to environmental regulation is greater because the goods that could have been purchased are more novel.

The VI substitution e¤ect diminishes the incentive for strict emission controls.

The following proposition identi�es the condition under which the pro-environment decoupling and VI

income e¤ects dominate.

Proposition 1 An increase in Foreign factor supply lowers Home�s emission intensity and emissions if �

is su¢ ciently large; speci�cally, eo

H� < 0 andZH� < 0 if and only if � > �1 �

�[1��]�+�[1��] , where �1 < 1.

Proof of Proposition 1: Substituting (28) and (29) into (27), collecting terms and converting to per-

centage changes gives

eo

H�= �Su

0(C)C�(1� �)(1� �)e2 d

2Wde2

[(1� �)[�+ (1� �)�]� �] : (30)

As d2Wde2

< 0 by the second order condition for an interior optimum, rearranging terms implies eo

H� < 0 if

and only if � > �1 ��(1��)�(1��)+� : Notably,

�(1��)�(1��)+� < 1; hence � � 1 is a su¢ cient condition for

eo

H� < 0: To

arrive at comparable expressions for Z, log di¤erentiate (17) to get

Z = H + [1 + �b]e: (31)

Setting H = 0 and substituting in using (30) gives

Z

H�= �[1 + �b]

Su0(C)C�(1� �)(1� �)e2 d

2Wde2

[(1� �)[�+ (1� �)�]� �]

which is negative if and only if � > �(1��)�+�(1��) .

As is evident from (27), the pro-environment VI income e¤ect of a growth-led increase in varieties dominates

the VI substitution e¤ect if and only if � > 1. However, even if � < 1, Foreign growth may still improve

Home�s environment because the growth raises Home�s trade intensity, which has a pro-environment de-

coupling e¤ect. As indicated in Proposition 1, the VI-income and decoupling e¤ects dominate unless the

VI-substitution e¤ect is strong, i.e. if and only if � < �1. As one would expect, �1 is decreasing in �: when

goods are better substitutes (i.e., � large), Home is less able to pass-through regulatory costs to foreign

consumers, shrinking trade�s decoupling e¤ect and making trade less likely to bene�t Home�s environment.

Growth in Home�s own factor supply also has decoupling, income and substitution e¤ects. For starters,

an increase in H raises the number of varieties produced at home. This has VI income and substitution

11

e¤ects just like those arising from Foreign-growth. Contrary to Foreign-growth, an increase in H intensi�es

the link between consumption and regulation in Home. As the number of varieties produced at home rises,

Home�s consumers �nance purchases of the new goods by curtailing consumption of other goods, including

imports. Home�s trade intensity falls as a consequence, tightening the link between regulation and consumer

welfare in Home.

Finally, local factor accumulation also expands Home�s pollution base, raising the marginal damage

from additional emissions and expanding the scope of Home regulation, both of which argue in favor of

stricter regulation. In our model, the pro-regulation e¤ects dominate unless � is very small, as outlined in

the following proposition.

Proposition 2 An increase in Home�s factor supply lowers Home�s emission intensity as long as variety-

induced income e¤ects are su¢ ciently strong; speci�cally, eo

H< 0 if and only if

� > �2 � 1�1 + D00(Z)Z

D0(Z) �(1��)�(1��)[�+(1��)�]

[�+ (1� �)�]| {z }(+)

where �2 < �1 < 1.

(All proofs from here forward are relegated to the appendix.)

We note that the cuto¤ value, �2, identi�ed in Proposition 2 is decreasing in the elasticity, D00(Z)Z=D0(Z),

of the marginal damage curve: when pollution damage is highly convex, Home is more likely to respond

to local growth with stricter emission caps.

Even though factor growth in Home may foster stricter environmental regulation, pollution levels may

rise nonetheless.

Proposition 3 An increase in Home�s factor supply raises Home�s emissions as long as variety-induced

income e¤ects are not too strong; speci�cally,: ZH> 0 if and only if

� < �3 � 1 +(1� �)�(1� �)[�+ (1� �)�]2| {z }

(+)

where �3 > 1: (32)

Factor accumulation expands the scale of polluting activity. As Proposition 3 implies, this scale e¤ect

dominates the policy response for an intermediate range of �. Only if VI income e¤ects are su¢ ciently

strong � i.e., � > �3 > 1 � will the scale e¤ect not dominate and emissions decline.

Finally, we note that changes in the idiosyncratic damage-sensitivity parameter � also impact regulation

and emissions.

Proposition 4 An increase in Home�s pollution sensitivity lowers Home�s equilibrium emission intensity

and emissions; i.e., deo

d� < 0 anddZd� < 0.

12

What do these theoretical propositions imply about the expected relationship between trade intensity

and emissions? Per the preceding discussion, an increase in Home�s trade intensity partially decouples

regulatory costs and bene�ts, suggesting greater trade intensity is pro-environment unless VI substitution

e¤ects are su¢ ciently strong, as per Figure 1. Thus, we o¤er the following empirical prediction:

Empirical Prediction 1 Conditioning on a state�s/province�s factor supply, an exogenous increase in

own trade intensity lowers local emissions and emission intensity unless variety-induced substitution e¤ects

are su¢ ciently strong (i.e., if and only if � < �1).

Moreover, because changes in Foreign factor supply impact Z and e only via changes in T , we observe that

Home emissions are independent of Foreign attributes conditional on Home trade intensity:

Empirical Prediction 2 Conditioning on a state�s/province�s trade intensity, foreign factor supply is not

associated with local emissions or emission intensity.

However, the same cannot be said for changes in Home�s factor supply and local sensitivity to pollution:

Empirical Prediction 3 Conditioning on a state�s/province�s trade intensity, own factor supply and sen-

sitivity to pollution are associated with local emissions and emission intensity.

3.2 Intranational Trade Intensity

Our model predicts that greater international trade intensity leads to lower emissions, other things being

equal, if � is su¢ ciently large. How does this compare with trade between regions within the same country?

Does intranational trade intensity yield the same environmental bene�ts as does international trade? Do

national borders matter?

Many of the rules governing industrial releases of pollutants are set by federal lawmakers. Many of these

rules apply nationally, but do not extend across national borders. This means, for example, that tightening

federal regulation governing emissions in Texas will simultaneously tighten regulations in Maryland, but

not in Ontario. In terms of our model, it suggests Home�s intranational trade partners will be subject to

the same environmental regulations as Home.

To assess formally the impact of this jurisdictional issue on the relationship between intranational

trade intensity and the environment, we not treat Home as a state or province within a federal system.

We then examine how subnational trade a¤ects the preferences of a federal representative from Home

(e.g., a Canadian Member of Parliament or a US Senator or Congressperson) when voting for federal

environmental regulations. As in the previous international trade scenario, we assume pollution is purely

local: it does not cross state/provincial borders. In contrast to the preceding subsection, the representative

must now account for the fact that regulation impacts both Home and Home�s domestic trade partner. To

distinguish the analysis from the international trade scenario, we shall refer to Home�s trading partner as

Rest of Country (ROC), denoted with daggers (y) and to Home�s policy as �e.

13

Home�s representative chooses �e to solve

max�eS[u(C)� �D(Z)]

subject to the constraint that ey = �e. Di¤erentiating gives

dW

d�e=S

�e

"u0(C)C�

"w

�e� P�e

#� �D0(Z)Z Z

�e

#: (33)

As when trade crosses national borders, Home�s representative balances concerns over factor prices, pollu-

tion, and the price-variety index. The di¤erence, however, is that tightening environmental regulation has

a larger impact on the price-variety index when Home�s trade partner is subject to the same regulatory

standard: P�e= ��1

� �, rendering the link between regulatory stringency and the price-variety index con-

spicuously independent of Home�s trade intensity. Intuitively, if Maryland is governed by the same federal

regulations as Texas, interstate trade between the two does not decouple regulatory costs and bene�ts.

The absence of decoupling means intranational trade only impacts federally mandated policy via the VI

income and substitution e¤ects.

Proposition 5 An increase in ROC factor supply lowers Home�s preferred federally mandated emission

cap if and only if variety-induced income e¤ects are su¢ ciently strong; i.e. �e

Hy < 0 if and only if � > 1.

Of course, when Home is a political subdivision of a federalist state, the emission cap preferred by

Home�s representative is not necessarily the same as that actually governing Home�s producers. In practice,

federal policy will re�ect the preferences of members of Parliament/Congress, related committees, and, in

the case of the US, the Executive branch. However, if we are willing to assume that the policy actually in

force in Home moves in the same direction as Home�s preferred policy, then by the chain rule, as well as the

fact that Home�s trade intensity is increasing in ROC factor supply, we can o¤er the following prediction

as a corollary of Proposition 5.

Empirical Prediction 4 Holding a state�s/province�s factor supply constant, an exogenous increase in

own intranational trade intensity lowers local emissions and emission intensity if and only if variety-induced

income e¤ects dominate variety-induced substitution e¤ects (i.e., if and only if � > 1).

3.2.1 Multiple States/Provinces

So far we have assumed one type of pollution and one regulator. This approach is useful because it

allows us to highlight previously unidenti�ed channels through which trade a¤ects the policy preferences

of an individual province/state. However, in practice there are multiple types of pollutants and multiple

regulators. For example, state-level regulators impact regulatory stringency either directly through state

policies or indirectly through local enforcement of federal rules. For state-level policies, trade of either

type� inter- or subnational� will have a decoupling e¤ect as described in section 3.2.

14

But what about pollutants that are regulated federally? When setting policy, federal regulators must

take into consideration conditions in multiple states/provinces. Below we o¤er a series of thought experi-

ments to explore how federal policy will be a¤ected by inter- and subnational trade when there are multiple

provinces/states in the same federal union.

We start with the case of increased international trade. As per Proposition 2, an exogenous increase in

H� induces Home to prefer stricter policy unless � is very small. If the other states/provinces in the same

union as Home also trade with that same foreign partner, then they should similarly respond to a rise in

H� by preferring stricter federal policy, arguing in favor of stricter federal policy in equilibrium.

What if, instead, it is a domestic trade partner that grows? We do not formally solve the federal policy

making game. However it is entirely plausible that an increase in Hy moves equilibrium policy in the same

directions as Home�s preferences. Consider, for example, a game in which federal emission policy is set via

majority rules, and suppose Home�s trade intensity rises because of factor accumulation in another state

� state A � within the same country. As per Proposition 5, Home�s trade intensity rises and its preferred

emission intensity fall if and only if � > 1; the same would be true of every other state in the union

except state A. State A�s preferred emission intensity would also fall: as per the discussion of Proposition

2, own-growth expands the local pollution base, inducing A to prefer stricter regulation unless � is very

small. Informal analysis suggests that, if � is high, then state A-led growth induces all states to prefer

stricter policy. If instead � takes on an intermediate value less than unity but su¢ ciently larger than zero,

then the preferences of state A and the remaining states move in opposite directions. In a majority rules

system, we would would expect the preferences of the states other than A to prevail, suggesting that a rise

in Home�s trade intensity driven by growth in a domestic trade partner should lead to stricter regulation

in Home if and only if � > 1.

We can summarize these musings as follows. In a world with multiple regulators, greater international

trade will induce stricter state and federal pollution policies unless � is su¢ ciently low. Greater subnational

trade will similarly induce stricter state-level policy, but will only lead to tighter federal policy if � is

su¢ ciently large (i.e. greater than unity). As a consequence, while it is possible that subnational trade

may well engender regulatory stringency, we expect it to be less pro-environment than international trade

because it does not have a decoupling e¤ect on federal policy.

Empirical Prediction 5 International trade intensity is unambiguously more bene�cial in terms of lower

emissions and emissions intensity than intranational trade intensity due to the (stronger) decoupling e¤ect.

Moreover, if greater intranational trade intensity is anti-environment, but greater international trade inten-

sity is pro-environment, our theoretical model posits this arises due to VI substitution e¤ects dominating

VI income e¤ects, but not dominating the combination of VI income e¤ects and the (stronger) decoupling

e¤ect resulting from national borders. This corresponds to � 2 (�1; 1); see Figure 1.

15

3.3 Other Considerations

3.3.1 Per Capita Income

With the exception of Proposition 4, all propositions thus far depend on the magnitude of �. � measures

the rate at which consumers become sated by variety-adjusted consumption, which in turn determines

whether VI income or substitution e¤ects dominate. To the best of our knowledge, there is no empirical

evidence concerning the relative strength of these opposing e¤ects. However, we can draw inferences about

the magnitude of � by examining the relationship between per capita income and emissions.

Proposition 6 Holding H constant, an increase in S raises Home�s equilibrium emission intensity and

emissions if and only if variety-induced income e¤ects are su¢ ciently strong; i.e., deo

dS > 0 anddZdS > 0 if

and only if � > 1.

When Home�s GDP is allocated to fewer people, per capita income and consumption are both higher.

Whether this induces consumers to substitute toward (or away from) more environmental quality depends

once again on the rate at which consumers are sated by variety-adjusted consumption. Speci�cally, our

model suggests the following:

Empirical Prediction 6 Conditioning on a state�s/province�s GDP, an increase in population reduces

per capita income, thereby raising local emission intensity and emissions if and only if � > 1.

As indicated in Figure 1, Prediction 6 provides a test for internal consistency of our model. Speci�cally,

if our empirical analysis reveals a negative e¤ect of population on emissions holding GDP constant, then

based on our theory we would conclude that VI substitution e¤ects dominate VI income e¤ects (i.e., � < 1).

Given Predictions 1 and 4, we would then expect intranational trade to raise emissions, while international

trade may either raise or lower pollution because of the (stronger) decoupling e¤ect. On the other hand,

if we �nd a positive e¤ect of population on emissions holding GDP constant, then based on our theory we

would conclude that VI income e¤ects dominate VI substitution e¤ects (i.e., � > 1), and more of either

type of trade should reduce emissions.

3.3.2 Distance

Throughout our analysis we have assumed trade is free and frictionless, however iceberg transport costs

(not shown) are easily accommodated by our model. With transport costs, consumers in distant regions will

pay more for, and consume less of, imported varieties. However, the set of varieties consumed will still be

identical across all regions. Iceberg transport costs change few of the equilibrium conditions qualitatively.

Equation (8) is an exception; when there are transport costs a closed form solution for w is unavailable.

We have simulated the model numerically and �nd that none of our theoretical predictions are a¤ected

qualitatively by the presence of transport costs. Moreover, variation in transport costs has predictable

e¤ects: states/provinces facing larger trade frictions trade less intensely and set weaker environmental

policy (higher e) and su¤er greater pollution as a result.

16

4 Empirics

4.1 Econometric Model

To test our empirical predictions, we utilize the following estimating equation

ln(Z)ict = �1INTER� TRADEict + �2INTRA� TRADEict (34)

+�3 ln(GDP )ict + �4 ln(POP )ict +Wict� + "ict

where Zict is a measure of emissions in location i (a state or province) in country c (US or Canada) at time

t, INTER�TRADE and INTRA�TRADE are measures of the inter- and intranational trade intensityof location i, respectively, W is a vector of controls, and "ict is the usual error term.10 Variables included

in W follow from the theoretical model:

Home Factor Supply (H): the percent of individuals age 25 and over, by gender, in each state or

province with a high school degree and with a college degree; the local unemployment rate (to proxy for

factor utilization); land area (to proxy other factors of production);

Sensitivity to Emissions (�): log of the total state or provincial area in square kilometers and country-

by-time speci�c dummies.

In addition, in light of Proposition 5, we can view the country-by-time speci�c dummies as capturing ROC

factor supply in addition to pollution sensitivity.

Prior to continuing, two notes concerning the speci�cation in (34) are worth mentioning. First, the

dependent variable is aggregate releases, as opposed to some scaled measure such as releases per unit of

output or per capita. However, such scaling is inconsequential since ln(GDP ) and ln(POP ) are included as

covariates; doing so simply reduces the coe¢ cient on the scaling variable by one and leaves the remaining

estimates unchanged. Second, the trade intensity variables are aggregated across industries. In principle,

one might conjecture that the e¤ects of trade intensity (of any type) may vary depending of the industrial

composition of such trade. Unfortunately, industry-level trade between U.S. states and Canadian provinces

is not available (to our knowledge). However, if the e¤ects of trade intensity vary depending on the

industrial composition of the trade, then one should interpret the instrumental variable estimates (described

below) as representing the local average treatment e¤ects of each type of trade (see, e.g., Angrist et al.

1996). That is, �1 and �2 re�ect the causal e¤ect of each type of trade for locations for which trade

intensity is determined at least in part by the instruments.11

10Many of the predictions of the theoretical model relate to environmental regulation as well as total emissions. However,

de jure regulations are numerous, exhibit little temporal variation, and are di¢ cult to compare across countries. In addition,

such regulations do not account for enforcement and thus do not capture de facto regulation. Thus, we consider emissions as

the dependent variable for most of our analysis; Section 4.3.2 examines regulation as the dependent variable.11 In this case, future work utilizing disaggregate data or relying on alternative identi�cation strategies would be useful to

gauge the generalizability of our �ndings.

17

We can now restate some of our empirical predictions in light of (34). First, Prediction 5 implies

�1 < �2 regardless of the magnitude of �. Second, Prediction 6 implies that �4 > 0 if � > 1 (i.e., VI

income e¤ects dominate VI substitution e¤ects). Third, Prediction 1 implies �1 < 0 and Prediction 4

implies �2 < 0 provided VI substitution e¤ects are su¢ ciently weak, where the latter requires greater

weakness of VI substitution e¤ects. Thus, our test of internal consistency implies that if �4 > 0 (implying

� > 1), then �1 < �2 < 0. On the other hand, if �4 < 0 (implying � < 1), then the theory predicts

�1 < 0 < �2 as long VI substitution e¤ects are not too strong (i.e., � 2 (�1; 1)). If VI substitution e¤ectsare su¢ ciently strong �such that � < �1 �then we expect �4 < 0 and 0 < �1 < �2. Finally, Prediction 3

implies � 6= 0. Prediction 2 is a maintained assumption.Turning to estimation of the model, our theoretical model underscores a point previously articulated

in Frankel and Rose (2005) and Chintrakarn and Millimet (2006); namely, trade intensity and GDP are

endogenously determined. Thus, to obtain consistent estimates of the parameters in (34), we require

valid instruments for these variables. Following Frankel and Rose (2005) and Chintrakarn and Millimet

(2006), two instruments are derived from a �rst-stage �gravity�model of bilateral trade �ows between

states, between provinces, and across states and provinces. Because the unit of observation in the gravity

model is a bilateral pair (of states, provinces, or a combination) in a given year, whereas the second-stage

measures of trade intensity are at a more aggregate level, the �rst-stage estimates are used to create a

predicted level of INTER� TRADE and INTRA� TRADE for each locality, and the predicted values

are used as instruments. This strategy implicitly uses geographical determinants of bilateral trade �ows

from the gravity model as instruments; speci�cally, attributes such as proximity to trading partners and

sharing more common borders with partners yield exogenous increases in trade intensity. In addition, we

include the median age of males and females and the percentage of females in the population as additional

instruments (discussed below), resulting in an overidenti�ed model. The model is estimated using limited

information maximum likelihood (LIML) which has been shown to perform relatively well in small samples

with weaker instruments (e.g., Stock et al. 2002; Flores-Lagunes 2007).12

Before turning to the data and results, the estimation of the �rst-stage gravity model requires additional

explanation. In general, the gravity model stipulates that bilateral trade is determined by the size of

trading partners, trade costs, and additional terms referred to as multilateral resistance (Anderson and

van Wincoop 2003). The multilateral resistance terms are speci�c to each state or province and time

period, but do not vary across trading partners within a time period. Thus, consistent estimates can be

obtained by including the complete set of state/province by time dummies. Here, we specify the �rst-stage

equation as

Xijt = �it�jt exp (Qijt')uijt (35)

where Xijt is shipments from state or province i to state or province j in year t, �it (�jt) are state/province

i (j) by time e¤ects, Qijt is a vector of controls corresponding to trading partners i and j in year t, and uijt12We also estimated the models using General Method of Moments (GMM), Two-Stage Least Squares (TSLS), and Fuller�s

modi�ed LIML estimator. Results are very similar and available upon request.

18

is a possibly heteroskedastic error term with mean one. Q includes the (log) distance between trading pair,

the interaction between distance and a dummy variable indicating whether the exporting region is a US

state, the interaction between distance and a dummy variable indicating whether the importing region is a

US state, a home dummy variable equal to one if i = j (implying intrastate or intraprovincial shipments),

the interaction between the home dummy and a dummy variable indicating whether the exporting region is

a US state, a dummy variable indicating whether i and j are contiguous neighbors, the interaction between

the adjacency dummy and a dummy variable indicating whether the exporting region is a US state, and a

dummy variable equal to one if i and j are located in di¤erent countries (implying international shipments).

Other commonly included gravity controls that do not vary across trading partner (e.g., GDP or population)

are captured in the � terms.

Equation (35) is often estimated using standard �xed e¤ects methods after taking logs of both sides.

However, Santos Silva and Tenreyro (2006) show that if uijt is heteroskedastic and its variance depends on

Qijt, then the expectation of ln(uijt) will in general also depend on Qijt. As a result, �xed e¤ects estimates

of the log-linear model will be biased. Instead, Santos Silva and Tenreyro (2006) propose estimation in

levels using a Poisson pseudo-maximum likelihood (PPML) estimator. Henderson and Millimet (2008) �nd

that the estimation of (35) in levels using PPML outperforms the log-linear model using US intranational

trade data. Thus, we utilize the same estimator for the �rst-stage.

4.2 Data

4.2.1 Trade Intensity

Data on intra- and interstate shipments for the US come from the 1997 and 2002 Commodity Flow Sur-

veys (CFS). The CFS is collected by the Bureau of Transportation Statistics within the US Department of

Transportation. The CFS is designed to provide data on the �ow of goods and materials by mode of trans-

port. The CFS tracks all shipments � measured in nominal dollars and in tons � between establishments

by mode of transportation: rail, truck, air, water, and pipeline. The 1997 survey cover 25 two-digit 1987

Standard Industrial Classi�cation (SIC) industries, spanning mining, manufacturing, wholesale trade, and

selected retail industries (codes 10 (except 108), 12 (except 124), 14 (except 148), 20-26, 27 (except 279),

28-39, 41, and 50) and two three-digit SIC industries (codes 596 and 782). Auxiliary establishments (such

as warehouses) of multi-establishment companies that have nonauxiliary establishments covered under the

CFS or are classi�ed in retail trade are also covered. Total shipments from one state to another (or within

state) are reported.

The 2002 CFS classi�ed establishments using the 1997 North American Industry Classi�cation System

(NAICS). Establishments in mining, manufacturing, wholesale trade, and select retail trade industries

(electronic shopping and mail-order houses) are covered. Auxiliary establishments, such as warehouses and

managing o¢ ces, are covered as in the 1997 CFS, although the coverage of managing o¢ ces was expanded

in the 2002 CFS. Moreover, while the 2002 CFS did attempt to maintain comparability with the 1997 CFS,

19

some di¤erences in coverage arise due to the conversion from SIC to NAICS. The most notable changes

are the fact that the 2002 CFS no longer includes the logging or the publishing industry.

The intra- and inter-provincial trade data come from Statistics Canada, CANSIM, Table 386-0002. The

data are measured in nominal Canadian dollars, and are derived from input-output accounts that cover all

economic activity conducted in each province. The state-province trade data are obtained from Industry

Canada�s Trade Data Online (TDO). These merchandise trade data are in nominal Canadian dollars. Since

imports are tracked more reliably than exports, the Canadian exports are actually collected as US imports

and converted back to Canadian dollars by means of an average monthly rate provided by the Bank of

Canada.

Prior to utilizing the data, several adjustments are made. First, following Anderson and van Wincoop

(2003), the CFS data are adjusted using the ratio of total domestic merchandise trade to total domestic

shipments, where the former is approximated as gross output in agriculture, mining, and manufacturing

less merchandise exports. Data on gross output by sector is obtained from the US Bureau of Economic

Analysis (BEA).13 Data on merchandise exports is obtained from a US Department of State report for

Congress.14 Second, following McCallum (1995), provincial exports (imports) are multiplied by the ratio of

total provincial exports (imports) in input-output data obtained from Statistics Canada to total provincial

exports (imports) in the TDO data. Finally, the data measured in nominal Canadian dollars are converted

to nominal US dollars using exchange rates obtained from the Bank of Canada, and then all �gures are

converted to real 2002 US dollars using the CPI-U.15

With the trade data in hand, we follow the theoretical model and de�ne intranational trade intensity

as

INTRA� TRADEit =Pi;j2c;i 6=j (Xijt +Xjit)

GDPit(36)

where the numerator re�ects the sum of �exports�plus �imports�from other localities in the same country

c and the denominator is GDP.16 We de�ne international trade intensity as

INTER� TRADEit =Pi2c;j2c0;c 6=c0 (Xijt +Xjit)

GDPit(37)

where the numerator re�ects the sum of exports plus imports from localities in the other country c0 and

the denominator is again GDP.

13See http://www.bea.gov/industry/index.htm#annual.14Obtained at http://fpc.state.gov/documents/organization/81946.pdf.15Obtained at http://www.bankofcanada.ca/en/rates/exchange_avg_pdf.html.16We follow Combes et al. (2005) and Chintrakarn and Millimet (2006) and neglect shipments between states/provinces and

the rest of the world; thus, we are explicitly assessing the relative e¤ects of trade that crosses the US-Canada international

border versus trade that only crosses regional boundaries within either country. Since our measure of international trade

intensity is scaled by GDP, incorporating trade between a state/province with the rest of the world only a¤ects the numerator

and would therefore increase the value of international trade intensity. Thus, interpreting our international trade intensity

variable as a measure of aggregate international trade intensity implies a problem of nonclassical (one-sided) measurement

error. This complication is avoided by interpreting our �nds more narrowly, applying only to US-Canadian trade. That said,

as stated in the Introduction, US-Canadian trade constitutes a signi�cant fraction of world trade.

20

Summary statistics are provided in Table 1. On average, US states trade more intensely at the intra-

national level than Canadian provinces, but Canadian provinces trade more intensely with the US than

US states trade with Canadian provinces. In addition, summary statistics are provided for the predicted

values of intra- and international trade intensity. First-stage results of the gravity model are provided in

Table 2 and accord with the literature; namely, an adverse e¤ect of distance on bilateral trade and a large

role for borders (the US-Canadian border as well as state and province borders). Interestingly, the results

indicate that exports originating in US states are less sensitive to distance, but internal shipments (i.e.,

those that do not cross any border) are higher in US states on average than Canadian provinces.

4.2.2 Environmental Quality

We measure environmental quality by industrial releases of toxic chemicals. Data on US toxic releases

are obtained from the US Environmental Protection Agency�s (EPA) Toxic Release Inventory (TRI) using

TRI.net.17 With the passage of the Emergency Planning and Community Right-to-Know Act (EPCRA) in

1986 and subsequent amendments, manufacturing facilities (designated as SIC 20 �39) and selected other

industries are required to release information on the emission of over 650 toxic chemicals and chemical

categories. Any facility which produces or processes more than 25,000 pounds or uses more than 10,000

pounds of any of the listed toxic chemicals must submit a TRI report (US EPA 1992).

Pollution data for Canada come from the Canadian National Pollutant Release Inventory (NPRI),

and are obtained through Environment Canada.18 Under the Canadian Environmental Protection Act,

1999, companies that manufacture, process, or otherwise use one of 341 covered substances beyond the

speci�ed threshold are required to provide facility-speci�c information regarding on-site releases and o¤-

site transfers. The covered substances are grouped into �ve categories, and the thresholds for required

reporting vary by category. For the core substances, facilities must submit a report if 10 tonnes or more

are manufactured, processed, or otherwise used and employees (including contractors) worked more than

20,000 hours at the facility.19

For both the TRI and NPRI, the list of chemicals �rms are required to report, as well as the requirements

for who must �le a report, has evolved over time. For instance, while minor additions and deletions are

made virtually every year to the TRI list of covered chemicals, 286 new chemicals were added beginning in

1995. Similarly, the NPRI included 176 chemicals in 1996, and now includes over 300 substances. Country-

speci�c time dummies included in (34) will capture the di¤erences in pollution that occurs over time and

across countries simply due to di¤erences in covered chemicals and �rms.

Because not all substances reported to the TRI and NPRI are regulated, early research into the environ-

mental impacts of mandatory reporting requirements assumed subsequent emission reductions were largely

voluntary. However, emissions of many of the substances covered by the TRI and NPRI are restricted

17See http://www.epa.gov/tri/tridotnet/index.html.18See http://www.ec.gc.ca/inrp-npri/.19For other reporting thresholds, see http://www.ec.gc.ca/pdb/npri/2006Guidance/brochure2006/brochure2006_e.cfm.

21

by a web of state/provincial and federal rules which target speci�c substances, media, and/or sectors.

Subsequent research has found actual or threatened state and federal regulation signi�cantly in�uences the

pattern of reductions. For example, examining releases reported to the NPRI from 1993 to 1999, Harrison

and Antweiler (2003) �nd that facilities highly regulated by the Canadian Environmental Protection Act

reduced their on-site releases more quickly than other �rms. Studying releases by participants in the 33/50

program, Khanna and Damon (1999, p. 19) �nd that �[l]arge and increasing potential liabilities under the

Superfund Act and costs of compliance with the proposed [National Emissions Standards for Hazardous

Air Pollutants] standards have statistically signi�cant deterrent e¤ect�on releases. See also Santos et al.

(1996) and O�Toole et al. (1997).

For the analysis, chemical releases are aggregated to the state or provincial level using three di¤erent

categories: on-site releases to the air, on-site releases to the water, and total on-site releases (the sum

of the previous categories plus on-site land and underground releases). Aggregating toxic releases of

numerous chemicals, however, raises concerns over heterogeneity in the environmental consequences of

di¤erent substances. To address this issue, the EPA provides aggregate releases weighted by their toxicity

score. The NPRI, on the other hand, does not. To proceed, then, we examine the weighted and unweighted

TRI data at the chemical level in order to back out the EPA�s toxicity score for each chemical. We then

use these toxicity scores to form weighted aggregates of the NPRI data, thus ensuring as much uniformity

across the two data sources.20

As an alternative method of circumventing the toxicity issue, we also use total on-site releases of just

carcinogenic chemicals. The list of chemicals deemed to be carcinogenic do di¤er, however, at least to

some extent in the TRI and NPRI. The TRI uses a list of carcinogenic chemicals as determined by the

US Occupational Safety and Health Administration (OSHA), whereas the NPRI uses the chemical list

compiled by the State of California under Proposition 65. Inclusion of country-speci�c time e¤ects in the

empirical model controls for these measurement di¤erences.

Finally, we also examine the impact of trade intensity on releases of speci�c chemicals. By focusing

on speci�c chemicals, we need not worry about the precise toxicity of the chemical. When deciding which

chemicals to analyze in isolation, we chose chemicals that received fairly high toxicity scores and had high

levels of aggregate releases. We settled on cadmium, chlorine, chromium, formaldehyde, lead, manganese,

and nickel. In the interest of brevity, we only report the results for chlorine, chromium, and lead (the

remainder are available upon request).21

20Speci�cally, we obtain aggregate releases, both toxicity-weighted and unweighted, in the US by chemical in 1988, 1996,

2001, and 2007. Because some chemicals have been dropped from the TRI over time, the choice of these years ensures maximum

coverage. We then back out the implicit toxicity score for each chemical and use the score from the most recent year if multiple

scores are available. As not all chemicals have toxicity scores, this process yields toxicity scores for 337 chemicals. Thus, our

�nal measures are aggregates of these 337 chemicals.21Whereas the average toxicity score of the 337 chemicals is roughly 10,000 (with a standard deviation of roughly

69,000), chromium has a score of almost 22,000 and represents 1.6% of unweighted total releases in the sample. Chlo-

rine and lead have toxicity scores of approximately 8,800, with chlorine (lead) comprising almost 12% (5%) of unweighted

22

In viewing the summary statistics in Table 1, the two countries look very di¤erent. While there are

indeed some di¤erences, upon deeper inspection the two countries are actually more similar than they

appear at �rst glance. Speci�cally, US states on average release fewer emissions in the air and water, but

more overall. Average total toxic releases are very di¤erent across the two countries, as are releases of

carcinogenic chemicals. However, much of this di¤erence is attributable to the State of Nevada in 2002,

where a single mine was responsible for the release of over 274 million pounds of arsenic compounds (with

a toxicity score of almost 60,000). We assess sensitivity to this observation below. Finally, Canada releases

more chlorine on average than in the US, but the reverse is true for chromium and lead.

4.2.3 Remaining Variables

In terms of the remaining variables, GDP for US states is obtained from the US BEA. State population

is obtained from the US Census Bureau. Corresponding provincial data come from Statistics Canada,

CANSIM.22 The percent of individuals age 25 and over, by gender, in each state or province with a high

school degree and with a college degree are obtained from the US Census Bureau and Statistics Canada,

CANSIM.23 In the �rst-stage gravity model, distance between states and provinces is measured as the

minimum driving distance between the most populated city in each locality. For the US, the data come

from Wolf (2000). For interprovincial and state-province distances, the data are obtained online.24 Finally,

within state or province distance (i.e., internal distance) is calculated as in Wolf (2000) and Millimet and

Osang (2007); namely, one-half the distance to the closest neighbor.

Finally, additional instruments included in the estimation are the median age of males and females in

the state or province, and the fraction of females in the population. These variables � obtained from the

same sources as the population data � are hypothesized to a¤ect GDP, given by wH in the theoretical

model, through wages as age and gender are staples in the empirical literature on wage determinants.25

total releases in the sample. Chromium is used primarily for making steel and other alloys; chromium compounds are

used for chrome plating, making dyes and pigments, leather and wood preservation, and treatment of cooling tower water

(http://www.epa.gov/ttn/atw/hlthef/chromium.html). Chromium targets the respiratory tract and is a known carcinogen.

Chlorine is commonly used as an oxidizing agent in water treatment and chemical processes, as well as in the bleaching process

of wood pulp (http://www.epa.gov/ttn/atw/hlthef/chlorine.html). Chronic exposure leads to respiratory problems. The

main use of lead is in the production of batteries, but it is also used in the production of metal products and in ceramic glazes,

paint, ammunition, cable covering, and others; airborne sources include combustion of solid waste, coal, and oils and emissions

from iron and steel production and lead smelters (http://www.epa.gov/ttn/atw/hlthef/lead.html). Chronic exposure to

lead may lead to neurological symptons, particularly in children, and an array of harmful e¤ects on human fetuses.22Provincial GDP data are obtained from Table 384-0002; population data come from Table 051-0001.23Provincial level data are obtained from Table 282-0004. State-level data are only available from the 1990 and 2000 Census.

Thus, we linearly interpolate the data to obtain values for 1997 and 2002.24See http://www.craigmarlatt.com/canada/geography&maps/distances_between_cities.html. for the interprovincial

data. State-to-province distances are obtained at http://www.randmcnally.com/rmc/directions/dirGetMileage.jsp?cmty=0.25Because there is some empirical evidence that females may be more pro-environment than males, we do assess the sensitivity

of our �ndings to the omission of this instrument. The results are qualitatively similar and available upon request.

23

4.3 Results

4.3.1 Baseline Speci�cation

Table 3 presents the baseline results, with Panel A containing the results treating trade intensity and per

capita GDP as exogenous and Panel B displaying the LIML results treating these variables as endogenous.

In addition to reporting the estimated coe¢ cients of interest, we also present the results of several diagnostic

tests. First, we test whether the impact of intra- and international trade are equal. Second, we test the joint

signi�cance of the trade and GDP variables, as well as the controls designed to re�ect Home factor supply

(H) and pollution sensitivity (�). In the LIML models, the joint signi�cance of the endogenous variables is

accomplished using the Anderson-Rubin (1949) Wald test, which is robust to weak instruments. Third, we

test for homoskedasticity of the errors using White�s (1980) test in Panel A and Pagan and Hall�s (1983)

test in Panel B.26 Fourth, we perform Ramsey�s regression speci�cation error test (RESET) in Panel A,

and Pesaran and Taylor�s (1999) version of this test adapted to the case of endogenous regressors in Panel

B. These tests are sometimes interpreted as a test for omitted variables, but are more properly interpreted

as a test for neglected nonlinearities.27 Finally, in the LIML models, we also provide results from the

Anderson (1951) canonical correlations test for underidenti�cation and Sargan�s overidenti�cation test.

Turning to the OLS results (Panel A), we obtain three main �ndings. First, we �nd that greater

intranational trade intensity is statistically associated with greater toxic air and water releases, as well

as chromium releases; the trade variables are individually statistically insigni�cant at usual con�dence

levels in the remaining models. However, for these outcomes, the relationship between intranational trade

intensity and toxic releases is economically meaningful. For example, a one standard deviation ceteris

paribus increase in intranational trade intensity for a US state (Canadian province) is associated with a

128% (59%) increase in toxic water releases.28 Moreover, in line with Prediction 5, we �nd that b�1 < b�2 forall seven outcomes, although we only reject equality at the p < 0:10 level for toxic air releases. Second, the

e¤ect of population is statistically insigni�cant at p < 0:10 con�dence level in six of the seven speci�cations.

Thus, there is little meaningful evidence regarding the true value of �. Finally, consonant with Prediction

3, controls re�ecting Home factor supply (H) and pollution sensitivity (�) are jointly statistically signi�cant

at the p < 0:01 con�dence level in six speci�cations; signi�cant at the p < 0:06 level for toxic water releases.

While interesting, the potential endogeneity of trade and GDP make the causal interpretation of the

OLS results suspect. This is borne out by our rejection of the null hypothesis of no mis-speci�cation

of the functional form according the RESET test at p < 0:10 level in the speci�cations for toxic air

and total releases, carcinogens, chlorine, and chromium. Moreover, we also reject the null hypothesis of

26Given the small sample size, we do not wish to use robust (or clustered) standard errors unless necessary. Thus, we report

conventional standard errors and test for heteroskedasticity.27Both versions are implemented using a fourth order polynomial of the (optimal) �tted values of the dependent variable in

the auxilary regressions.28The e¤ect is given by [exp(�j�cj) � 1], where �j , j = 1; 2, refers to the coe¢ cient on inter- and intranational trade

intensity, respectively, and �cj is the standard deviation of trade intensity measure j in country c.

24

homoskedasticity in six of the seven outcomes. Thus, we turn to Panel B.

Prior to examining the LIML estimates, note that the models now fare extremely well in terms of

the diagnostic tests. Speci�cally, we reject the null that the model is underidenti�ed in all cases at the

p < 0:02 con�dence level and the overidenti�cation tests do not suggest any problems with the validity of

the instruments, consonant with prior studies. Furthermore, the RESET test now fails to �nd any evidence

of mis-speci�cation at the p < 0:10 level for all outcomes except toxic water releases. Finally, we fail to

reject the null hypothesis of homoskedasticity at conventional levels for all outcomes except toxic water

releases.

In terms of the results, we again point to three main �ndings. First, greater international trade intensity

has a statistically signi�cant, negative e¤ect on emissions in all seven speci�cations. Moreover, we �nd a

statistically signi�cant, positive causal e¤ect of intranational trade intensity on all outcomes except toxic

water releases. In addition, we reject equality of the two trade variables at at least the p < 0:05 level in all

cases, consistent with Prediction 5, and the Anderson-Rubin test rejects the null hypothesis of no impact

of the three endogenous variables. Thus, there is strong evidence that international trade intensity is

bene�cial, consonant with Prediction 1, while intranational trade intensity is harmful to the environment.

The impacts are also quite substantial. A one percent increase in international trade intensity for the

average US state (Canadian province) causes a 1.2% (10.1%) reduction in total toxic releases. A one

percent increase in intranational trade intensity for the average US state (Canadian province) causes an

increase in total toxic releases of over 5.3% (3.3%).

Second, in all speci�cations except for toxic water releases, the coe¢ cient on population is negative; the

estimate is statistically signi�cant at conventional levels for total releases. Consonant with Prediction 6,

this marks fairly suggestive evidence that � < 1 (i.e., VI substitution e¤ects dominate). In combination, the

results are consistent with VI income e¤ects being su¢ ciently weak to render intranational trade intensity

harmful for the environment, but strong enough such that international trade intensity is bene�cial. This

follows from the fact that the restriction on � for international trade intensity to be pro-environment

(� > �1g, see Proposition 1) is less stringent than that for intranational intensity (� > 1, see Proposition5), combined with the fact that the point estimates on population are predominantly negative.29 Thus,

our results are not only internally consistent, but also suggest that � 2 (�1; 1). Thus, the results are alsoconsistent with Predictions 1 and 4.29While the coe¢ cient estimates on populaton are consistently negative, they are admittedly not precisely estimated. There

are two possible explanations for why we do not �nd statistically stronger evidence of a negative e¤ect of population despite

�nding statistically strong evidence of a positive (negative) e¤ect of intranational (international) trade intensity. First, given

the small sample size, we are taxing the data. Second, population is highly related to the theoretical measure of input, H

(which includes total human capital supply). Thus, the coe¢ cient on population will also partially capture the impact of H

on emissions. As indicated in Proposition 3, we expect H and emissions to positively related unless � > �3 > 1. As such,

if the true value of � is less than one, then while we expect the coe¢ cient on population conditional on H to be negative,

the coe¢ cient will be pushed toward zero if population is also capturing some of the positive e¤ect of H on emissions. This

appears to be the case.

25

Finally, consonant with Prediction 3, controls capturing Home factor supply (H) and pollution sensi-

tivity (�) are jointly statistically signi�cant at the p < 0:05 con�dence level in all seven speci�cations.

In sum, the empirical evidence accords well with our empirical predictions, highlights an important

role of national borders, provides some empirical evidence on the magnitude of �, and strengthens the

previous �ndings in Antweiler et al. (2001) and Frankel and Rose (2005), among others. The intranational

trade results, on the other hand, di¤er from Chintrakarn and Millimet (2006) who �nd little impact of

intranational trade intensity. Additional analysis (available upon request) reveals that the di¤erence arises

from a change in the measurement of trade intensity. Whereas Chintrakarn and Millimet (2006) scale

intranational imports and exports by total exports, we scale trade by GDP as indicated by our theoretical

model. However, before reaching too strong of a conclusion regarding the causal impacts of the two types

of trade, we undertake several sensitivity analyses.

4.3.2 Robustness Checks

Regional E¤ects The theoretical model posits an important role for �, a location-speci�c parameter

re�ecting pollution sensitivity. To better control for �, we add region dummies to the empirical model.30

Region dummies also control for the fact that southern states tend to have higher toxic releases as well as

be further from Canada (e.g., Dutzik et al. 2003); they also control for regional di¤erences in industrial

composition. Before turning to the results, it is worth mentioning why we do not include state and province

�xed e¤ects. Although technically identi�ed, inclusion of state and province �xed e¤ects eliminates most

of the variation in predicted trade since the geographical determinants of trade are time invariant. Within

variation in predicted trade arises solely due to the fact that (35) includes state/province by time e¤ects,

rather than just state/province e¤ects. Only roughly 2% (1%) of the variation in predicted intranational

(international) trade intensity is within variation.

Nonetheless, we are not overly concerned about our short panel for two reasons. First, when not

including �xed e¤ects, any time invariant, state- or province-speci�c unobservables are relegated to the error

term. This has the e¤ect of making it more di¢ cult to �nd valid exclusion restrictions for the endogenous

variables, as well as �pass�the RESET test. However, since we test the validity of the instruments in Table

3, and look for evidence of model mis-speci�cation via the RESET test, and the models fair well, this

does not appear to be a concern. Second, regional dummies should control for at least some of the time

invariant, geographic-speci�c unobservables about which one worries. However, even in this case we do

urge caution in interpreting the results given the small sample size.

The results are presented in Table 4. The OLS estimates in Panel A di¤er little from the OLS estimates

in Table 3. The most notable discrepancy is the fact that the impact of intranational trade intensity on

chlorine releases is now statistically signi�cant; the coe¢ cient on GDP is also now statistically signi�cant

30We divide states into four US Census regions: northeast, midwest, south, and west. Canadian provinces are grouped into

three regions: west (Alberta, British Columbia, and Saskatchewan), central (Manitoba, Ontario, and Quebec), and east (New

Brunswick, Newfoundland, Nova Scotia, and Prince Edward Island).

26

for chromium releases.

The LIML results in Panel B are less precise, but are also qualitatively similar to the results presented

in Table 3. First, the models continue to fare well in terms of the diagnostic tests. Speci�cally, we reject

the null that the model is underidenti�ed in all cases at the p < 0:06 level and the overidenti�cation tests

do not suggest any problems with the instruments except for chlorine. Moreover, we �nd no statistically

meaningful evidence of heteroskedasticity, and the RESET test does not suggest any mis-speci�cation

except for toxic water releases. Second, the Anderson-Rubin test strongly rejects the null hypothesis that

the three endogenous variables are jointly insigni�cant in all models; we also reject the null that the controls

re�ecting factor supply and emissions sensitivity are jointly insigni�cant in �ve of the seven models. Third,

international trade intensity is found to have a negative and statistically signi�cant impact on all seven

pollution measures. In contrast, intranational trade intensity has a positive e¤ect on emissions in all seven

speci�cation; the impact is statistically signi�cant at the p < 0:10 level for �ve outcomes (chlorine and lead

are the exceptions). Furthermore, we reject the null hypothesis of equal e¤ects of intra- and international

trade intensity for all outcomes at the p < 0:10 level. Finally, the point estimate for population is negative

in six of seven cases, albeit never statistically signi�cant at conventional levels (see footnote 29). In short,

our results continue to be internally consistent, consistent with � 2 (�1; 1), as well as consistent with theother empirical predictions of the theoretical model.

Variable De�nitions, Industrial Composition, Border E¤ects, and Sample Composition Our

next set of robustness alter the de�nition of trade intensity, allow for a direct e¤ect of the US-Canadian

border on environmental quality, and alter the composition of the sample. The results are condensed and

presented in Table 5. For the sake of brevity, we only present the LIML results using total toxic releases to

measure emissions, where Panel A (B) omits (includes) region dummies.31 Column 1 in Table 5 replicates

the baseline results from Panel B in Tables 3 and 4 for ease of comparison.

In Column 2 we measure inter- and intranational trade density using only exports in the numerator.

The theoretical model emphasizes an important role of both exports and imports. On the one hand, higher

exports imply more of the regulatory burden is borne outside the local regulator�s jurisdiction. On the

other hand, higher imports lead to a weaker link between the local price index and local regulation. To

examine whether the latter channel is important empirically, we test the model using exports as the only

value in the numerator. The results show that this model fares poorly, predominantly due to a loss in

precision once imports are neglected. Nonetheless, the pattern of results is comparable to the baseline

speci�cation.

Column 3 incorporates additional controls re�ecting industrial composition within the state. Specif-

ically, we control for the share of GDP attributable to agriculture, mining, utilities, construction, man-

ufacturing, and government.32 The �ndings regarding the impacts of trade intensity are very similar to

31The full set of results are available upon request.32Data are obtained from http://www.bea.gov/regional/ and Table 379-0028 from

27

the baseline model, and the model continues to fare well according to the speci�cation tests. The primary

a¤ect of the inclusion of the new controls is that the magnitude of the coe¢ cients on GDP and population

fall (in absolute value) and are no longer statistically signi�cant.

Column 4 is identical to the baseline model with the exception of an additional, binary control variable

indicating states and provinces located on the US-Canadian border. Because some pollution in practice

may be transboundary, this may have a particularly salient impact on environmental policy for areas along

the international border (by a¤ecting sensitivity to emissions, �). The �ndings are again qualitatively

similar to the baseline speci�cation. However, the standard errors are much larger, particularly in Panel

B, and in neither case do we reject the null that the model is underidenti�ed.

In Column 5, we omit Michigan and Ontario from the analysis. Because these two areas are dominated

by the auto industry, the results may be sensitive to their inclusion. However, the results turn out to

be quite robust. In fact, the point estimates are more precise, although we fail to reject the null of

underidenti�cation in both panels. Column 6 omits Nevada in 2002 for reasons discussed above. The

results are nearly unchanged.

Finally, Column 7 measures pollution using total releases of the same 337 chemicals except now we do

not weight the releases by toxicity. The results in Panel B are qualitatively similar and the model fares

well diagnostically. However, in Panel A the coe¢ cient on population switches from negative to positive,

and it is no longer statistically signi�cant. Moreover, the RESET test indicates some mis-speci�cation in

the model. Thus, the data appear to �t the theoretical model much better when weighting releases by

toxicity.

Regulatory Preferences Because the mechanism by which trade intensity a¤ects emissions in the

theoretical model is through regulatory stringency, we use a measure of regulatory demand by policymakers

as the dependent variable (see footnote 10). Speci�cally, we use the League of Conservation Voters (LCV)

annual environmental scorecard for each of the US states.33 The scores range from zero to 100, with a

higher score indicating more �green�votes by politicians from that state. As such, our theory now predicts

�1 > �2 > 0 if � > 1 and �1 > 0 > �2 if � 2 (�1; 1). In addition, �4 > 0 is consistent with � < 1. For

brevity, we only present the LIML results in Table 6.

The estimates are partially consistent with our empirical predictions. First, regardless of whether we

examine votes by members of the House of Representatives or the Senate or pool the observation together,

regardless of whether we model the LCV scores in levels or logs, and regardless of whether we include region

dummies, we obtain a statistically signi�cant, positive e¤ect of international trade intensity on green voting

behavior in nearly all cases. The estimated e¤ects of intranational trade intensity are negative in all cases,

but only statistically signi�cant in one speci�cation (the pooled model in levels); we reject equality of the

http://www5.statcan.gc.ca/cansim/a01?lang=eng.33The data are obtained at http://lcv.org/scorecard/. Since there is no analagous measure to our knowledge, Canadian

provinces are excluded from the (�second-stage�) analysis (although this does not impede our ability to estimate the e¤ect of

inter- and intranational trade intensity).

28

two trade e¤ects at conventional levels in all cases.

Second, the e¤ect of population is negative and statistically signi�cant in four of six cases; this is

consistent with � > 1. In this case, we would then expect intranational trade intensity to also have a

positive impact on �green�voting. While we do not �nd this, note two things. First, we do �nd much

weaker evidence of a detrimental e¤ect of intranational trade intensity than in the prior models examining

emissions. Second, the only model yielding a negative, statistically signi�cant e¤ect of intranational trade

intensity �the pooled model in levels �fails the RESET test. In short, the only speci�cations that fairs

well in terms of the under- and overidenti�cation tests and the homoskedasticity and RESET tests are the

models for House scores in levels. Here, neither the e¤ect of population nor intranational trade intensity

are statistically signi�cant.

Finally, in the majority of cases, the controls designed to re�ect Home factor supply (H) and pollution

sensitivity (�) are jointly statistically signi�cant at the p < 0:05 level, consistent with our empirical

predictions.

The Role of Distance Our �nal undertaking explores the role of distance in our �ndings. One possible

interpretation of our �ndings is that it is not the decoupling e¤ect of international trade that is environ-

mentally bene�cial, but rather it is simply trade that occurs over longer distances that is pro-environment

regardless of whether that trade crosses the US-Canada border. This explanation may have some merit

if goods that are traded over longer distances tend to be �cleaner�than goods trade over short distances.

In particular, if pollution-intensive goods tend to be heavier, transport costs may be positively correlated

with the �dirtiness�of goods. As a result, one might expect trade in pollution-intensive goods to occur more

frequently with close trading partners, and thus to �nd that intranational trade intensity, which is more

likely to involve close trading partners, to be worse for the environment than international trade intensity,

which is more likely to involve trading partners that are further away.

Although certainly plausible, we do not subscribe to this interpretation. First, there is no data to

our knowledge to support the contention that the pollution-intensity of goods is positively correlated

with weight and, more importantly, transport costs. Second, this interpretation amounts to an omitted

variable story where states or provinces with a comparative advantage in clean goods engage in more

international � relative to intranational � trade due to the (assumed) lower transport costs associated

with clean goods. Thus, the pollution-intensity of goods constitutes an omitted variable that is positively

(negatively) correlated with intranational (international) trade intensity. However, estimation of the causal

e¤ects of intra- and international trade intensity via instrumental variables is designed to overcome exactly

this type of situation. We obtain consistent estimates of the e¤ects of trade intensity utilizing exogenous

variation in trade arising from geographical determinants of bilateral trade.

Nonetheless, we do perform some additional analysis. In Table 7 we display results where our measures

of intra- and international trade are replaced with measures of �near�and �far�trade intensity, where the

former (latter) is de�ned as trade occurring between contiguous (non-contiguous) states and/or provinces

29

regardless of whether the trade crosses the US-Canadian border. Formally,

CONTIGUOUS � TRADEit =

Pj2i;i6=j (Xijt +Xjit)

GDPit(38)

NON � CONTIGUOUS � TRADEit =

Pj =2i;i6=j (Xijt +Xjit)

GDPit(39)

where i denotes the set of states and/or provinces that are contiguous to location i.34 Predicted near

and far trade intensity, utilized as instruments, are generated in similar fashion. Table 7 contains results

for total toxic releases and House and Senate LCV scores (in levels).

The results are not consistent with the notion that the bene�cial e¤ects of international trade intensity

are simply capturing the cleanliness of lighter goods that are more likely to be traded over longer distances.

For total toxic releases, we �nd a bene�cial e¤ect of trade with contiguous neighbors and a harmful e¤ect

of non-contiguous trade. This is the opposite of what one would expect if the cleanliness of goods were

positively correlated with distance over which they are traded. Neither trade intensity variable has a

statistically signi�cant e¤ect on LCV scores. However, it is important to note that no model passes all the

relevant diagnostic tests. In sum, the models based on near and far trade do not �t the data, nor conform

to the predictions of the theoretical model, as well as those using intra- and international trade.

4.4 Summation

Our analysis leads to three main conclusions. First, our results are consistent with a meaningful role of the

decoupling e¤ect of international trade, as well as VI income and substitution e¤ects of su¢ cient strength

to place � in an intermediate range less than unity, but above �[1��]=f�+�[1��)]g. As a result, nationalborders do matter when it comes to understanding the relationship between trade and the environment.

Second, the combination of the decoupling e¤ect and su¢ ciently strong VI income e¤ects leads to a robust,

pro-environment causal impact of international trade intensity. Third, the weaker decoupling e¤ect of

intranational trade in conjunction with the magnitude of the VI substitution e¤ects leads to deleterious

environmental e¤ects of trade that does not cross national borders.

Our �ndings on the pro-environment e¤ects of international trade strengthen the results in previous

studies such as Antweiler et al. (2001) and Frankel and Rose (2005). As a result, while Copeland and

Taylor (2004, p. 7) state that �for the last ten years environmentalists and the trade policy community

have engaged in a heated debate over the environmental consequences of liberalized trade,�we are hopeful

that this additional evidence may help bring the debate to a resolution.

34For Prince Edward Island, we de�ne contiguous neighbors as other provinces that are immediately accessible by water

(New Brunswick, Nova Scotia, and Newfoundland).

30

5 Conclusion

This paper devises a model of trade and environment interactions that highlights the intra-industry na-

ture of trade between and within most industrialized nations. Trade grants consumers access to foreign

goods. The range and price of goods produced abroad is independent of the regulatory climate at home;

consequently, much of a consumer�s consumption basket is insulated when her country trades intensely.

Similarly, trade intensity determines who bears the costs of local regulation. Firms with market power

pass some of their regulatory costs to consumers in the form of higher prices and fewer varieties. When a

country trades intensely, most of the loss in consumer welfare falls on foreigners, and is therefore outside

the regulator�s concern. Thus, trade partially decouples the bene�ts and costs of environmental regula-

tion, providing an incentive for policymakers to favor more stringent regulation and generating a positive

relationship between trade intensity and environmental quality.

The strength of this decoupling e¤ect depends on whether one�s trading partner resides outside the

regulator�s jurisdiction. As a result, the decoupling e¤ect should be stronger for trade between US states

and Canadian provinces than for within-US or within-Canada trade since at least some environmental

regulations are set federally, rather than at the state or local level. Consequently, our theoretical model

suggests that international trade is more bene�cial to the environment than intranational trade. Whether

or not either is bene�cial for the environment, however, depends also on the relative size of variety-induced

income and substitution e¤ects.

We use data on provincial and state toxic releases in Canada and the US, as well as intra- and inter-

national trade intensity, to test these hypotheses. Our results indicate that international trade generates

statistically, and economically, signi�cant bene�ts for the environment. On the other hand, intranational

trade has a statistically and economically signi�cant, adverse e¤ect on the environment. Given the de-

mands placed on the data and the limited sample size, future work corroborating these �ndings based on

di¤erent samples and/or identi�cation strategies would prove useful.

Even though our theoretical and empirical analysis focuses on industrialized countries in general, and

the US and Canada in particular, we believe our results also shed light on the global debate over the

environmental impacts of trade. By comparing the environmental e¤ects of intra- and international trade

intensity in a common framework, we are able to answer a heretofore unexplored question in the trade

and environment debate: do national borders matter? At least with regard to the US-Canada border,

the answer is a resounding yes, suggesting that empirical assessments of the environmental e¤ects of

intranational trade should be viewed as a lower bound on the impact of international trade. The data

necessary to replicate our analysis for additional countries does not yet exist. But as more data become

available on state/province-level trade with the rest of the world, it would be worthwhile to examine how

state-level trade with multiple trading partners impacts emissions, particularly when the trade is with

countries at di¤erent development levels.

31

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35

Appendix� Proofs

Proof of Second Order Condition for an Interior Optimum

Con�rm the second order conditions are met (locally) by di¤erentiating (26) with respect to e, evaluating

at eo, and collecting terms to get

d2W

de2

����e=eo

=Su0(C)C�2[�+ (1� �)�]

e2

�(1� �)[�+ (1� �)�] + (1� �)�(1� �)

[�+ (1� �)�] �B1 + �b�

�where B � 1 + D00(Z)Z

D0(Z) � 1. As � � 0, B � 1, �b � �, then [� + (1 � �)�] + (1��)�(1��)[�+(1��)�] < 1 is a su¢ cient

condition for d2Wde2

���e=eo

< 0. Rearranging gives [� + (1 � �)�] + (1��)�(1��)[�+(1��)�] = 1 �

(1��)(1��)2�[�+(1��)�] < 1, hence

the second order condition for an interior optimum is met.

Proof of claim that dTdH�=H� > 0.

To verify this claim mathematically, di¤erentiate T with respect to H� using (13) and (14)� recognizing

that e also varies with H� as per Proposition 1� and rearrange to get

dT

dH�=H� = 2�2

"H�

H

a(1��)b�

a�(1��)b��

#�+ (1� �)� �B 1+�b

� � �[�+ (1� �)�]26664[�+ (1� �)�]�(1� �)[�+ (1� �)�] + (1� �)�(1� �)

[�+ (1� �)�] �B[1 + �b]

�

�| {z }

(�) by S:O:C:

37775:

Because �+ (1� �)� < 1 < B 1+�b� , while � � 0, then dT

dH�=H� > 0.

Proof of Proposition 2

Log di¤erentiating the system de�ning eo with respect to e and H, converting to hat notation and rear-

ranging gives

eo

H= �

(1� �)[�+ (1� �)�] + (1��)�(1��)[�+(1��)�] �B

�h(1� �)[�+ (1� �)�] + (1��)�(1��)

[�+(1��)�]

i�B[1 + �b]

(40)

where B � 1+ D00(Z)ZD0(Z) � 1. As �

h(1� �)[�+ (1� �)�] + (1��)�(1��)

[�+(1��)�]

i�B[1+�b] is negative by the second

order condition for an interior optimum, sgnheo

H

i= sgn

h(1� �)[�+ (1� �)�] + (1��)�(1��)

[�+(1��)�] �Biwhich is

negative if and only if � < 1�B� (1��)�(1��)

[�+(1��)�][�+(1��)�] : Finally, rearranging the de�nitions of �1 and �2 and canceling

like terms reveals �2 < �1 if and only if�

�+�(1��) < B which is always true.

36

Proof of Proposition 3

Dividing (31) through by H, substituting using (40), and collecting terms yields

Z

H=

(�)z }| {[� � [1 + �b]]

h(1� �)[�+ (1� �)�] + (1��)�(1��)

[�+(1��)�]

i26664��(1� �)[�+ (1� �)�] + (1� �)�(1� �)

[�+ (1� �)�]

��B[1 + �b]| {z }

(�)

37775;

rearranging, the unsigned termh(1� �)[�+ (1� �)�] + (1��)�(1��)

[�+(1��)�]

iis positive if and only if condition (32)

holds.

Proof of Proposition 4

By the envelope theorem, sgn�deo

d�

�= sgn

�@@�dWde

�= sgn

h�D0(Z)Z[1+�b]

e

i= (�): Di¤erentiating (17) with

respect to e gives dZ=de = Z[1+�b]e > 0, hence sgn

�dZd�

�= sgn

�dZdeded�

�= (�):

Proof of Proposition 5

Substituting in for P =�e = �, w=�e = � and Z=�e = 1+�b into (33) and partially di¤erentiating with respect

to H� gives@

@H�dW

d�e=Su0(C)C

H��e[1� �]�(1� �)[1� �]

which is negative if and only if � > 1. Assuming the second order condition for an interior maximum holds,

then by the envelope theorem, d�edH� is negative if and only if � > 1.

Proof of Proposition 6

By the envelope theorem, sgn�d~eo

dS

�= sgn

�@@S

dWde

�= sgn

2664�u0(C)Ce2| {z }(�)

[1� �] ��[(1� �)� + �]| {z }(+)

3775 : Thus, sgn �deodS � =sgn [�� 1] : As dZdS =

dZdedeo

dS anddZde =

Ze [1 + �b] > 0, then sgn

�dZdS

�= sgn

hde0

dS

i= sgn [�� 1].

37

Figure 1. Empirical Predictions Contingent on Strength of Variety-Induced Income and Substitution Effects

VI-Income Effects Dominate;Increase in Population or Intra-N i l T d I i l

VI-Substitution Effects Dominate;Increase in Population or Intra-National Trade Intensity raises National Trade Intensity lowers

PollutionNational Trade Intensity raises

Pollution

Increase in i l

Increase in i l

Increase in InternationalInternational

Trade Intensity Raises Pollution

International Trade Intensity

Lowers Pollution

International Trade Intensity

Lowers Pollution

1 μ3μ1μ2VI‐SE Strong VI‐IE Strong

Table 1. Summary Statistics

Variable

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Trade Measures

Intranational Trade Intensity 0.504 0.163 0.314 0.092 0.471 0.169

Predicted Intranational Trade Intensity 0.573 0.192 0.321 0.111 0.530 0.205

International Trade Intensity 0.039 0.047 0.334 0.130 0.090 0.131

Predicted International Trade Intensity 0.043 0.029 0.517 0.113 0.125 0.187

Pollution Measures

Toxicity-Weighted Releases (lbs., billions)

Air 17.079 56.190 24.824 30.321 18.415 52.618

Water 0.041 0.055 0.850 1.909 0.181 0.836

Total 493.606 2447.731 26.306 32.058 413.037 2231.817

Non-Weighted Releases (lbs., millions)

Carcinogens 11.143 42.147 4.679 6.674 10.029 38.481

Chlorine 0.858 6.194 20.288 41.574 4.208 19.277

Chromium 0.692 1.856 0.102 0.216 0.590 1.704

Lead 1.938 6.388 0.186 0.337 1.636 5.846

Controls

Per Capita GDP (thousands, 2002 US$) 33.707 5.741 21.358 4.224 31.578 7.220

Unemployment Rate 5.218 1.065 9.682 3.885 5.987 2.510

Area (Thousands km2) 159.689 120.622 606.293 482.025 236.689 281.273

Population (Millions) 5.740 6.147 3.054 3.647 5.277 5.870

Female Percent of Population 50.969 0.673 50.501 0.432 50.888 0.661

Median Age, Females 36.751 2.054 37.365 1.688 36.857 2.002

Median Age, Males 34.434 1.869 35.680 1.614 34.649 1.881

Percent of Population with High School 81.896 4.734 71.781 6.062 80.152 6.270

Degree, Age 25 +, Females

Percent of Population with College 22.081 4.258 13.945 2.779 20.678 5.078

Degree, Age 25 +, Females

Percent of Population with High School 81.039 4.566 70.542 6.995 79.229 6.416

Degree, Age 25 +, Males

Percent of Population with College 25.043 4.711 15.841 3.396 23.457 5.695

Degree, Age 25 +, Males

NOTES: Data are from the 48 contiguous U.S. states and ten Canadian provinces for 1997 and 2002 (N = 116).

U.S. States Canadian Provinces Full Sample

Table 2. Gravity Equation Results (PPML Estimates)

Variable Coefficient

(Std Error)

ln(Distance) -0.913*

(0.096)

ln(Distance)*I(US Exporter) 0.280*

(0.091)

ln(Distance)*I(US Importer) 0.068

(0.103)

Home (1 = Yes) 0.991*

(0.160)

Home*I(US Exporter) 0.641*

(0.164)

Adjacent (1 = Yes) 0.677*

(0.209)

Adjacent*I(US Exporter) -0.043

(0.214)

International (1 = Yes) -0.779*

(0.106)

NOTES: ‡ p<0.10, † p<0.05, * p<0.01. Data represents bilateral trade between 48 contiguous

U.S. states and 10 Canadian provinces in 1997 and 2002 (N = 6234). Estimation by Poisson

Pseudo Maximum Likelihood. Other regressors include the complete set of state/province by

time dummies. Robust standard errors in parentheses.

Table 3. Impact of Trade Intensity on Pollution

Air Water Total Carcinogens Chlorine Chromium Lead

A. OLS

Intranational Trade 3.281† 5.071† 0.421 0.511 2.775 4.006‡ 2.999 Intensity (1.301) (2.182) (1.496) (0.883) (2.147) (2.350) (2.123)International Trade -1.706 3.245 -1.914 0.171 -4.079 2.331 -0.207 Intensity (2.165) (3.633) (2.491) (1.470) (3.574) (3.912) (3.534)ln(GDP) 0.514 -1.536 0.952 0.523 1.529 1.564 -2.867

(1.187) (1.992) (1.366) (0.806) (1.959) (2.145) (1.937)ln(pop) 0.331 3.108 -0.226 0.307 0.025 -0.207 3.611‡

(1.278) (2.144) (1.470) (0.867) (2.109) (2.308) (2.085)

Ho: Equal Trade Effects p = 0.059 p = 0.677 p = 0.438 p = 0.848 p = 0.114 p = 0.723 p = 0.453

Ho: Joint Significance of p = 0.086 p = 0.037 p = 0.774 p = 0.893 p = 0.392 p = 0.327 p = 0.132

Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.052 p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000

Remaining Controls

Ho: Homoskedasticity p = 0.000 p = 0.084 p = 0.000 p = 0.810 p = 0.001 p = 0.000 p = 0.002

RESET Test p = 0.000 p = 0.351 p = 0.013 p = 0.095 p = 0.039 p = 0.046 p = 0.526

B. LIML

Intranational Trade 9.397† 8.327 10.555† 5.038† 11.714‡ 12.955‡ 10.359† Intensity (4.137) (5.968) (4.715) (2.441) (6.739) (6.688) (5.271)International Trade -27.704* -33.117† -30.116† -14.503† -47.619* -46.470* -31.671† Intensity (10.657) (15.278) (12.031) (6.213) (17.365) (16.981) (13.447)ln(GDP) 5.382 -10.408 11.360‡ 5.090 6.077 5.701 1.603

(5.406) (7.808) (6.169) (3.194) (8.805) (8.748) (6.897)ln(pop) -4.752 12.694 -11.283‡ -4.522 -4.646 -4.383 -1.048

(5.770) (8.336) (6.587) (3.410) (9.397) (9.342) (7.364)

Ho: Test of Equality p = 0.007 p = 0.035 p = 0.008 p = 0.014 p = 0.008 p = 0.006 p = 0.015

Anderson-Rubin Test p = 0.000 p = 0.000 p = 0.000 p = 0.003 p = 0.000 p = 0.000 p = 0.005

of Joint Significance

of Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.024 p = 0.000 p = 0.000 p = 0.000 p = 0.026 p = 0.000

Remaining Controls

Anderson LM Statistic p = 0.015 p = 0.015 p = 0.015 p = 0.015 p = 0.015 p = 0.015 p = 0.015

Underidentification Test

Sargan Overident. Test p = 0.248 p = 0.445 p = 0.595 p = 0.749 p = 0.239 p = 0.947 p = 0.611

Ho: Homoskedasticity p = 0.422 p = 0.042 p = 0.278 p = 0.649 p = 0.287 p = 0.168 p = 0.226

RESET Test p = 0.120 p = 0.001 p = 0.615 p = 0.940 p = 0.772 p = 0.370 p = 0.999

NOTES: ‡ p<0.10, † p<0.05, * p<0.01. Data are from the 48 contiguous U.S. states and ten Canadian provinces for 1997 and 2002

(N = 116). Dependent variable in logs. Other covariates include: a constant, a time dummy, a dummy for Canadian provinces, the

interaction between the time and Canada dummies, ln(area), ln(population), unemployment rate, percent of females over age 25 with

a high school diploma, percent of males over age 25 with a high school diploma, percent of females over age 25 with a college

diploma, and percent of males over age 25 with a college diploma. Predicted intra- and international trade intensity, percentage

females in the population, median age of females, and median age of males used as exclusion restrictions.

Table 4. Impact of Trade Intensity on Pollution: Regional Fixed Effects

Air Water Total Carcinogens Chlorine Chromium Lead

A. OLS

Intranational Trade 3.808* 5.625† 0.932 0.607 4.215‡ 4.472‡ 2.009 Intensity (1.306) (2.375) (1.525) (0.948) (2.318) (2.394) (2.292)International Trade 0.837 4.840 0.364 0.740 -4.218 2.440 1.225 Intensity (2.234) (4.062) (2.608) (1.621) (3.965) (4.094) (3.919)ln(GDP) 1.156 -1.516 1.333 0.800 2.156 4.562† -2.693

(1.168) (2.123) (1.363) (0.848) (2.072) (2.140) (2.049)ln(pop) -0.253 2.886 -0.366 0.121 -0.325 -3.221 3.605

(1.252) (2.276) (1.461) (0.909) (2.222) (2.294) (2.196)

Ho: Equal Trade Effects p = 0.281 p = 0.875 p = 0.860 p = 0.947 p = 0.086 p = 0.687 p = 0.871

Ho: Joint Significance of p = 0.032 p = 0.022 p = 0.770 p = 0.721 p = 0.236 p = 0.077 p = 0.316

Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.093 p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000

Remaining Controls

Ho: Homoskedasticity p = 0.000 p = 0.044 p = 0.047 p = 0.987 p = 0.000 p = 0.004 p = 0.011

RESET Test p = 0.000 p = 0.201 p = 0.019 p = 0.121 p = 0.008 p = 0.000 p = 0.330

B. LIML

Intranational Trade 12.874† 16.441‡ 15.114† 7.558‡ 28.674 26.856† 13.252 Intensity (5.988) (9.072) (7.264) (4.116) (20.624) (13.410) (9.021)International Trade -29.439† -48.410† -34.148† -17.204‡ -84.783‡ -64.198† -39.200‡ Intensity (14.146) (21.411) (17.158) (9.722) (48.541) (31.677) (21.309)ln(GDP) 7.416 -1.904 13.158‡ 6.838 18.153 22.212 2.847

(6.555) (10.068) (7.988) (4.501) (21.634) (14.621) (9.821)ln(pop) -6.549 3.923 -12.577 -6.100 -16.395 -21.213 -1.819

(6.888) (10.598) (8.399) (4.729) (22.617) (15.356) (10.313)

Ho: Test of Equality p = 0.028 p = 0.025 p = 0.035 p = 0.062 p = 0.093 p = 0.035 p = 0.072

Anderson-Rubin Test p = 0.000 p = 0.000 p = 0.000 p = 0.012 p = 0.000 p = 0.000 p = 0.005

of Joint Significance

of Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.267 p = 0.000 p = 0.000 p = 0.093 p = 0.264 p = 0.000

Remaining Controls

Anderson LM Statistic p = 0.051 p = 0.051 p = 0.051 p = 0.051 p = 0.051 p = 0.051 p = 0.051

Underidentification Test

Sargan Overident. Test p = 0.271 p = 0.594 p = 0.342 p = 0.260 p = 0.066 p = 0.227 p = 0.213

Ho: Homoskedasticity p = 0.672 p = 0.142 p = 0.583 p = 0.723 p = 0.423 p = 0.377 p = 0.258

RESET Test p = 0.807 p = 0.005 p = 1.000 p = 0.842 p = 0.978 p = 0.997 p = 0.983

NOTES: Regional dummies included. For other details, see Table 3.

Table 5. Impact of Trade Intensity on Pollution: Sensitivity Analyses

Baseline Trade Control Control Omit Omit Unweighted

Based On for Industry for Border Michigan Nevada ToxicX Only Composition States & Ontario in 2002 Releases

A. No Region Dummies

Intranational Trade 10.555† 9.538 8.324† 15.454‡ 10.426† 10.367† 5.353† Intensity (4.715) (6.414) (3.820) (8.454) (4.322) (4.635) (2.239)International Trade -30.116† -56.293‡ -23.315* -56.419† -31.304* -30.950* -17.391* Intensity (12.031) (29.168) (8.219) (27.324) (11.694) (11.747) (5.726)ln(GDP) 11.360‡ 13.031‡ 6.418 13.865 12.957† 9.298 0.133

(6.169) (7.658) (7.766) (9.338) (5.940) (6.566) (2.930)ln(pop) -11.283‡ -12.943 -5.633 -13.697 -13.423† -9.067 1.050

(6.587) (8.110) (7.831) (9.867) (6.422) (7.013) (3.127)

Ho: Test of Equality p = 0.008 p = 0.055 p = 0.002 p = 0.038 p = 0.005 p = 0.006 p = 0.002

Anderson-Rubin Test p = 0.000 p = 0.004 p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000 of Joint Significance

of Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.000 p = 0.000 p = 0.004 p = 0.000 p = 0.000 p = 0.000 Remaining Controls

Anderson LM Statistic p = 0.015 p = 0.026 p = 0.007 p = 0.191 p = 0.011 p = 0.018 p = 0.015 Underidentification Test

Sargan Overident. Test p = 0.595 p = 0.159 p = 0.336 p = 0.984 p = 0.929 p = 0.597 p = 0.484

Ho: Homoskedasticity p = 0.278 p = 0.238 p = 0.315 p = 0.562 p = 0.235 p = 0.282 p = 0.519

RESET Test p = 0.615 p = 0.103 p = 0.242 p = 0.188 p = 0.004 p = 0.676 p = 0.022

B. Region Dummies

Intranational Trade 15.114† 35.577 9.350† 21.401 11.558† 15.755† 8.335† Intensity (7.264) (59.230) (4.675) (13.652) (4.639) (7.886) (3.714)International Trade -34.148† -155.861 -21.870† -62.023 -29.212† -37.009† -21.031† Intensity (17.158) (253.303) (9.543) (39.068) (11.382) (18.359) (8.773)ln(GDP) 13.158‡ 42.537 9.313 17.898 9.362‡ 12.432 2.900

(7.988) (73.820) (8.656) (13.717) (5.594) (9.088) (4.066)ln(pop) -12.577 -43.148 -8.227 -17.237 -9.128 -11.789 -1.671

(8.399) (76.841) (8.798) (14.224) (5.971) (9.547) (4.272)

Ho: Test of Equality p = 0.035 p = 0.539 p = 0.014 p = 0.107 p = 0.006 p = 0.036 p = 0.014

Anderson-Rubin Test p = 0.000 p = 0.012 p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000 of Joint Significance

of Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.801 p = 0.000 p = 0.057 p = 0.000 p = 0.001 p = 0.030 Remaining Controls

Anderson LM Statistic p = 0.051 p = 0.081 p = 0.027 p = 0.184 p = 0.009 p = 0.059 p = 0.051 Underidentification Test

Sargan Overident. Test p = 0.342 p = 0.041 p = 0.159 p = 0.351 p = 0.379 p = 0.284 p = 0.272

Ho: Homoskedasticity p = 0.583 p = 0.528 p = 0.569 p = 0.625 p = 0.489 p = 0.562 p = 0.658

RESET Test p = 1.000 p = 0.039 p = 0.080 p = 0.999 p = 0.144 p = 0.999 p = 0.761

NOTES: ‡ p<0.10, † p<0.05, * p<0.01. Dependent variable is the log of total toxic releases. X = exports; the numerator in the

trade openness measure now only contains exports, not imports. Control set and instruments are as in Tables 3 and 4 except in

Column 3 which adds controls for the share of GDP due to agriculture, mining, utilities, construction, manufacturing, and

government and Column 4 which adds a dummy variable for states and provinces along the US-Canadian border.

Table 6. Impact of Trade Intensity on LCV Scores: LIML

House Senate ln(House) ln(Senate) Pooled Pooled(levels) (logs)

A. No Region Dummies

Intranational Trade -21.691 -47.461 -0.150 -2.327 -34.731‡ -1.277 Intensity (19.144) (30.115) (0.815) (1.477) (17.997) (0.860)International Trade 194.513* 200.489† 5.125† 7.419 196.602* 5.998† Intensity (57.628) (90.606) (2.447) (4.532) (54.116) (2.600)ln(GDP) 54.740‡ 109.498† 2.626† 3.670 81.546* 2.998†

(28.481) (44.737) (1.205) (2.312) (26.691) (1.296)ln(pop) -48.960 -103.628† -2.330‡ -3.167 -75.673* -2.586‡

(30.935) (48.593) (1.309) (2.511) (28.992) (1.408)

Ho: Test of Equality p = 0.000 p = 0.008 p = 0.035 p = 0.034 p = 0.000 p = 0.006

Anderson-Rubin Test p = 0.001 p = 0.001 p = 0.062 p = 0.002 p = 0.000 p = 0.000 of Joint Significance

of Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.000 p = 0.002 p = 0.010 p = 0.000 p = 0.000 Remaining Controls

Anderson LM Statistic p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000 Underidentification Test

Sargan Overident. Test p = 0.312 p = 0.335 p = 0.406 p = 0.035 p = 0.132 p = 0.028

Ho: Homoskedasticity p = 0.528 p = 0.766 p = 0.000 p = 0.031 p = 0.770 p = 0.000

RESET Test p = 0.443 p = 0.053 p = 0.115 p = 0.438 p = 0.008 p = 0.134

B. Region Dummies

Intranational Trade -18.716 -55.989 0.156 -1.628 -39.531‡ -1.165 Intensity (21.464) (35.987) (0.975) (2.255) (20.639) (1.064)International Trade 185.761* 232.710† 6.552† 13.873‡ 200.419* 8.259† Intensity (63.108) (108.720) (2.872) (7.299) (61.367) (3.228)ln(GDP) 57.225‡ 127.756† 3.649† 7.779‡ 86.305† 4.387†

(34.510) (62.022) (1.576) (4.531) (34.177) (1.854)ln(pop) -52.083 -126.620‡ -3.548† -7.806 -82.581† -4.226†

(37.813) (67.932) (1.727) (4.959) (37.442) (2.031)

Ho: Test of Equality p = 0.001 p = 0.006 p = 0.025 p = 0.020 p = 0.000 p = 0.003

Anderson-Rubin Test p = 0.005 p = 0.000 p = 0.017 p = 0.000 p = 0.000 p = 0.000 of Joint Significance

of Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.001 p = 0.012 p = 0.128 p = 0.000 p = 0.000 Remaining Controls

Anderson LM Statistic p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000 Underidentification Test

Sargan Overident. Test p = 0.324 p = 0.034 p = 0.264 p = 0.001 p = 0.013 p = 0.001

Ho: Homoskedasticity p = 0.546 p = 0.843 p = 0.001 p = 0.209 p = 0.887 p = 0.000

RESET Test p = 0.456 p = 0.010 p = 0.016 p = 0.754 p = 0.002 p = 0.365

NOTES: ‡ p<0.10, † p<0.05, * p<0.01. Data are from the 48 contiguous U.S. states for 1997 and 2002 (N = 96); in pooled models,

N = 192. Control set and instruments are as in Tables 3 and 4 except for the terms involving the dummy variable for Canadian

provinces.

Table 7. Impact of Contiguous and Non-Contiguous Trade Intensity: LIML

ln(Total House Senate

Toxic Releases) LCV Score LCV Score

A. No Region Dummies

Contiguous Trade -6.109‡ -52.547 -106.981 Intensity (3.198) (53.017) (75.704)Non-Contiguous Trade 8.251† 110.877 123.312 Intensity (4.083) (90.605) (127.755)ln(GDP) 9.406† 117.147 182.450‡

(4.088) (73.774) (103.957)ln(pop) -9.540† -120.993 -188.491

(4.462) (82.441) (116.150)

Ho: Test of Equality p = 0.022 p = 0.221 p = 0.222

Anderson-Rubin Test p = 0.019 p = 0.017 p = 0.001 of Joint Significance

of Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.000 p = 0.002 Remaining Controls

Anderson LM Statistic p = 0.002 p = 0.008 p = 0.008 Underidentification Test

Sargan Overident. Test p = 0.297 p = 0.033 p = 0.040

Ho: Homoskedasticity p = 0.039 p = 0.734 p = 0.989

RESET Test p = 0.009 p = 0.259 p = 0.142

B. Region Dummies

Contiguous Trade -3.292 -88.906 -392.239 Intensity (2.914) (89.458) (630.123)Non-Contiguous Trade 8.583‡ 223.018 908.142 Intensity (4.822) (203.998) (1631.417)ln(GDP) 7.429 195.801 804.862

(4.695) (159.325) (1275.069)ln(pop) -7.112 -207.605 -882.151

(5.116) (177.213) (1418.812)

Ho: Test of Equality p = 0.082 p = 0.270 p = 0.562

Anderson-Rubin Test p = 0.036 p = 0.046 p = 0.000 of Joint Significance

of Trade Intensity, GDP

Ho: Joint Significance of p = 0.000 p = 0.006 p = 0.989 Remaining Controls

Anderson LM Statistic p = 0.003 p = 0.110 p = 0.110 Underidentification Test

Sargan Overident. Test p = 0.031 p = 0.147 p = 0.060

Ho: Homoskedasticity p = 0.766 p = 0.554 p = 0.593

RESET Test p = 0.075 p = 0.307 p = 0.256

NOTES: ‡ p<0.10, † p<0.05, * p<0.01. N = 116 (96) in Column 2 (3 and 4). Control set and instruments are as in

Tables 3, 4, and 6 except predicted trade reflects the contiguous/non-contiguous definition. See text for further

details.