Download The Effect of Trade Networks on Environmental Degradation: A

Final: August 26, 2008
Trade Competition and Domestic Pollution: A Panel Study, 1980-2003
Xun Cao
Lecturer
Department of Government
Universiy of Essex
Wivenhoe Park
Colchester, Essex CO4 3SQ, UK
+44 1206 872506
[email protected]
&
Aseem Prakash
Professor
Department of Political Science
University of Washington
Gowen Hall 39, Box 353530
Seattle, WA 98195
206-543-2399
206-685-2146 (fax)
[email protected]
1
Abstract
This paper examines whether trade competition abets regulatory races in the
environmental area. To measure trade competition and the consequent strategic
interdependence among trading countries, we develop a new measure, structural
equivalence, which assesses competitive threats that a country faces from others
whose firms export the same products to the same importing destinations.
Employing this new measure, we analyze a panel of 140 countries for the period of
1980-2003. The empirical findings indicate that trade competition drives air
pollution (Sulfur dioxide) and water pollution (Biochemical Oxygen demand)
levels among structurally equivalent countries. We then test separately whether
trade competition abets upward and downward environmental races. We find that
trade competition among structurally equivalent countries abets downward races
in the case of air pollution. Our analyses do not lend support to the race to the top
hypothesis for either air or water pollution. This suggests that countries respond
asymmetrically to pressures when their structurally equivalent competitors ratchet
down their regulations as opposed to ratcheting up their regulations.
2
Introduction
Under what conditions might global trade abet environmental races to the
bottom? The key assumption behind most environmental regulations is that
profit-seeking firms are unlikely to voluntarily incur costs to internalize pollution
externalities. Public regulations are necessary to induce firms to curb pollution. 1 From
firms’ perspective, while environmental regulations might compel them to reduce
pollution, they often increase production costs. 2 Given the cost reduction focus of
global markets, these regulations will arguably make domestic firms less competitive
globally, all else equal. Eventually, governments will have to deal with the trade offs
between lowering regulatory costs and lowering pollution levels. While some groups
will favor pollution reduction, globalization critics fear that the structural and
ideational contexts in which most governments function will lead them to prioritize
lowering regulatory costs over lowering pollution levels. In sum, an increasing
exposure to global trade will abet regulatory races to the bottom leading to increased
pollution levels.
This paper challenges the argument that increasing exposure to global trade
abets regulatory races. We believe that the existing research on this subject is
problematic on both conceptual and empirical grounds. While trade competition is the
central driver in the regulatory race hypothesis, the trade-environment literature has
made surprisingly little effort to measure competitive pressures that strategically
interdependent countries face in their trading environments, and then relate these
1
2
Pigou 1960, but see Coase 1960; Ostrom 1990.
For a contrary perspective, see Porter and Linde 1995.
3
pressures to domestic regulatory policies or outcomes. Much of the trade-environment
literature tends to equate trade competition with trade salience (the trade to GDP ratio):
the higher the salience, the more the competitive pressure. 3 The empirical findings it
reports tend to be inconsistent: some studies find higher trade salience to be associated
with lower pollution levels, 4 while others find just the opposite. 5
Conceptually, the existing literature misspecifies the key driver of regulatory
races. We contend that trade salience is an incorrect measure of competitive pressures
that strategically interdependent countries face in their trading environments.
Regulatory races require that governments view themselves in situations of strategic
interdependence which create incentives for them to respond to regulatory changes in
competitor countries. 6 The trade salience does not capture this strategic
interdependence simply because it is not a relational measure. It tells us how
dependent is a given country on trade but does not tell anything about the competitive
threats from other countries. Consider a country with a high trade salience. Assume
this country is also a monopoly exporter of a scarce commodity. Clearly, this country
does not face strategic interdependence in its export markets simply because there is
no competitor country to whose actions it needs to respond. This example suggests
that to uncover the mechanisms of regulatory races, we need to identify the
3
Antweiler, Copeland, and Taylor 2001; de Soysa and Neumayer 2005; Li and Reuveny
2006; Andonova, Mansfield, and Milner 2007; Zeng and Easting 2007.
4
See, for example, Li and Reuvney 2006.
5
See, for example, Lopez 2003.
6
According to Lake and Powell, “A situation is strategic if an actor’s ability to further its
ends depends on the actions others take.” See Lake and Powell 1999, page 7-8. For more
discussion on regulatory races, see Jaffe et al. 1995, Kahler 1998, Spar and Yoffie 2000, and
Drezner 2001.
4
competitor countries and their regulatory policies. Only with this information can one
examine how trade competition might encourage strategic behavior in the regulatory
arena, thereby leading to regulatory races.
To systematically investigate the regulatory race hypothesis, one needs to
construct a measure that takes into account the varying levels of regulatory stringency
of competitor countries and the varying levels of the competitive challenge they pose
across export markets. Taking up this challenge, we introduce a new measure of trade
competition, structural equivalence, to assess strategic interdependence at the level of a
country dyad. Structural equivalence reflects the competitive threats a country faces
from its trade competitors, defined as the ones whose firms export the same products to
the same importing destinations. A regulatory race hypothesis suggests that countries
respond to competitive threats from structurally equivalent competitors. Drawing on a
panel of 140 countries over the period of 1980-2003 we find that structural equivalence
is significantly associated with regulatory races while trade salience ((the measure of
trade competition employed in extant literature) is not associated with these races.
We then examine a second order question: whether countries respond
symmetrically when their structurally equivalent competitors ratchet up or ratchet
down their regulations? Following Kominsky, we employ our structural equivalence
measure to test separately for the race to the bottom and for the race to the top
hypotheses. 7 We find structural equivalence to be associated with a race to the bottom
only. Thus, countries respond asymmetrically to pressures arising from their
7
Kominsky 2007.
5
competitors ratcheting down their regulations as opposed to ratcheting up their
regulations.
How might structural equivalence drive regulatory races? To further global
competitiveness of domestic firms, governments are likely to strategically respond to
the regulatory policies of those countries whose firms compete in the same product
categories in the same export markets. Assume that firms located in country i and
country j compete for the same overseas consumers located in country k. Country i’s
government might seek to confer cost advantage to its firms by lowering regulatory
stringency and/or enforcement. While its domestic pollution levels rise, its firms
might become more competitive in country k. They may grab market share in country
k from competing firms located in country j. Consequently, country j’s government
might come under pressure from its domestic firms (probably labor employed in these
firms as well) to lower regulatory stringency or enforcement simply to remain
competitive against country i’s firms. If country j responds to such pressures, its
pollution levels are likely to increase, all else equal. In effect, competition for market
share in country k has abetted a regulatory race and resulted in increased domestic
pollution levels for both countries i and j. 8
Structural equivalence as a measure of trade competition has another crucial
attribute which strengthens its superiority over other measures. Typically, a country
8
By observing their competitors’ environmental policies, exporters might believe this
constitutes a norm of appropriate corporate behavior. Policies to protect or neglect the
environment might therefore be motivated by the logic of appropriateness. See DiMaggio and
Powell 1983 and March and Olsen 1989.While we recognize that both instrumental and
normative motivations might be at work towards increasing pollution levels, it is difficult to
apportion variance between them.
6
faces competitive challenge from a variety of countries some of which might ratchet
up regulations; some might maintain the status quo, while other might ratchet down.
Which pressures will prevail and why? As a measure of trade competition, trade
salience cannot capture such complexities. As we explain subsequently, given that the
unit of analysis for calculating structural equivalence is a dyad, our measure of trade
competition enables us to aggregate pressures arising from different competitors with
varying levels of regulatory stringency in order to estimate the levels and direction of
the “net competitive pressure” that this country needs to respond to. With this
sophisticated measure, we are able to offer a more compelling empirical examination
of the race to the bottom hypothesis.
The contributions of our paper extend beyond the trade-environment
literature. Our paper helps in sharpening the established diffusion and
convergence-divergence debates. 9 There is a well established literature which
examines how trade competition drives the diffusion of rules, norms and practices. 10
Diffusion scholars tend to observe competitive pressures either in terms of bilateral
trade patterns without making any distinction among the types of goods being
traded, 11 or in terms of countries’ industry-level export profiles without making a
distinction in the destination of these exports. 12 These measures of trade competition
can mislead. In the former, two countries exporting to the same overseas market might
9
See, for example, Berger and Dore 1994.
DiMaggio and Powell 1983; Burt 1985; Guler et al. 2002; Simmons and Elkins 2004; Lee
and Strang 2006; Elkins, Guzman, and Simmons 2006.
11
For example, Lee and Strang 2006.
12
For example, Guler et al. 2002.
10
7
export different products and therefore do not compete with each other. In the latter,
two countries exporting similar products might target different overseas markets. Here
again, they cannot be termed as competitors who are likely to respond strategically to
each others’ policies. Thus, a compelling measure of trade competition must reflect
both the bilateral and sectoral dimensions of trade. On this count, structural
equivalence is a superior measure because it captures competitive challenge from
countries whose firms export the same product to the same overseas markets.
Important policy implications emerge from our analysis. Our paper suggests
that domestic pollution levels are influenced by what a country exports and to which
destinations. If so, domestic environmental policy should be viewed as a part and
parcel of a country’s overall developmental strategy, especially if the country is
relying on an export-led model. Typically, environmental regulation is decided in the
environmental ministry whereas development and trade issues are handled by other
ministries. What is required is an integrated system of environment and economic
policy making. Our paper suggests that the choice of product categories in which to
enter global markets will have an important bearing on domestic pollution levels
simply because it will decide the “company a country wants to keep.” Indeed, in as
much as governments encourage specific sectors for exports – through subsidies,
infrastructure investment, foreign exchange provision, etc. --- they bear responsibility
for domestic environmental outcomes. If the health of the domestic environment is
valued by policymakers, then “exports at any cost” is clearly not a desirable policy. In
a similar vein, this paper offer cautionary advice to multilateral organizations and
8
multinational banks that have sought to encourage developing countries to undertake
structural adjustment which favor exports, and at the same time provided loans to the
same countries to deal with environmental issues. Our paper suggests that funding
agencies need to recognize the environmental implications of their economic advice,
not only in terms of purely domestic considerations (which they routinely do via
environmental impact analyses) but in terms of trade implications as well.
Theoretical Perspective
The regulatory race hypothesis can be located within an established
literature that examines jurisdictional competition to attract mobile production
factors. 13 The core idea is that competition to attract and retain mobile capital will
create incentives for jurisdictions to lower the stringency of their regulations. The
so-called Tiebout hypothesis has been widely discussed in the federalism and public
finance literatures. 14 Race to the bottom arguments are similar to the Tiebout
hypothesis and have been examined across issue areas including welfare spending,
economic liberalization, education, and public health. 15
Three literatures examine the relationship between trade and domestic
environmental regulations. The first literature blames the weakening of environmental
13
Tiebout 1955.
For example, Oates 1969; Epple et al. 1976.
15
For empirical analysis in welfare spending literature, see Garrett 1998 as an example. For
economic liberalization, see Simmons and Elkins 2004. For education, see Bailey et al. 2004.
For public health, see Braithwaite and Drahos 1999. Donahaue 1997 attributes the phrase
“race to the bottom” to Justice Brandies who wrote a dissenting opinion in 1933 U.S.
Supreme Court Liggett Company v. Lee case.
14
9
laws not on domestic pressures but on the obligations that the WTO imposes on
national governments. 16 The WTO, an inter-governmental regime, typically does not
allow governments to impose process standards (that is, practices governing how a
product is produced) on imports. Environmentalists argue that this enables exports
from developing countries (with allegedly more lax process standards) to out compete
firms located in developed countries. 17 As a consequence, governments in developed
countries come under political pressure either to establish non-tariff barriers (which
are typically not allowed by the WTO) or to dilute domestic laws. The debates over
NAFTA are emblematic of these concerns. By employing the phrase “giant sucking
sound” across the border, Ross Perot sought to caricaturize the regulatory challenges
that the American government faces from Mexico, which arguably has less stringent
environmental and labor laws.
A second literature examines how trade affects macro environmental
indicators (directly and indirectly via economic growth) such as pollution levels and
deforestation. 18 Because trade can have different types of effects on the environment
--- scale effects (increasing the scale of economic activity and leading to resource
over consumption), substitution effects (encouraging existing firms to substitute one
production technique for another), and composition effects (changing types of firms
that populate an economy) --- trade’s overall impact on the environment depends on
the sum of these effects. 19 There is an on-going debate about the empirical salience of
16
17
18
19
Charnovitz 1993; Lori and Wallach 1999.
Daly 1993.
Grossman and Krueger 1995.
Esty 2001.
10
these different effects and about the validity of the so-called Environmental Kuznets
curve hypothesis.
The third body examines the “pollution haven” and the “industry flight”
hypotheses: whether (and if so, why) “environmentally dirty” industries are migrating
to the pollution havens in the South with an objective of exporting back the “dirty”
products to the home country. Scholars have examined trends in the salience of
“dirty” products in the exports of developing countries to developed countries. 20
Because businesses tend to portray the alleged problem of industrial flight as a
symptom of the broader problem of over-regulation, they demand scaling back of
domestic regulations. 21 Blaming free trade for regulatory races, environmentalists
demand “fair trade” so that domestic firms are not disadvantaged in the world market
by stringent domestic regulations. 22 The problem is that “fair trade” requires
developed countries either to subject their imports to process-based standards (which
the WTO disallows) or to persuade developing countries to strengthen their alleged
lax regulation (which is politically difficult).
This paper contributes to these debates by offering a novel way to assess
the relationship between trade competition and pollution. We investigate how a
country’s embeddedness in global trading networks might subject it to competitive
pressures in different ways, leading to either increases or decreases in pollution levels.
Trade competition among structurally equivalent countries is expected to lead to
20
21
22
Jaffe et al. 1995; Mani and Wheeler 1999.
Walley and Whitehead, 1994.
Charnovitz 1993; Daly 1993.
11
diffusion and mimicry of environmental standards and practices and therefore to lead
to similar environmental outcomes.
We observe structural equivalence in the context of network position, a
concept central in social network analysis. Borgatti and Everett note that “actors who
are connected in the same way to the rest of the network are said to be equivalent and
to occupy the same position.” 23 Two countries might be geographically distant and
have little direct contact with each other in the global economy. But if they are
connected to the rest of the world market in a similar fashion, that is, if they export
the same goods to the same foreign markets, they then occupy a similar network
position in international commerce. This is likely to induce competition between them
because from the buyer's perspective, they are substitutable. Of course, it is rare to
observe exact structural equivalence in large networks of binary relationships and
even harder to find it in valued networks (such as trading or communication
networks) where the tie is not simply an indicator of presence or absence of a certain
relationship but also a measurement of the strength of this relationship (such as the
volume of bilateral trade and the number of phone calls). We follow the established
practice in the social network analysis literature and employ the correlation of the two
actors’ profiles of connections to capture the degree of their structural equivalence.24
In our context, structurally equivalent countries have strong incentives to be
more competitive than others in order to secure or maintain access to the buyer’s
market. One way to achieve an advantage, especially for developing country exporters
23
24
See Borgatti and Everett 1992, page 2-3.
Snyder and Kick1979; Nemeth and Smith 1985; Smith and White 1992.
12
that often compete on cost rather than on quality, is to reduce spending on
environmental protection. At the national level, governments with incentives to create
a “business-friendly environment” seek to reduce regulatory costs. They avoid
enacting new laws and even laxly enforce the existing ones --- all with the aim of
reducing firms’ production costs. In competitor countries, these policies and practices
are likely to be watched and mimicked, thereby creating a vicious circle in which all
countries have strong incentives to follow one another’s move towards environmental
neglect. Of course, one could argue that domestic pollution levels simply reflect trade
salience, irrespective of what policies of the trade competitors. Therefore, we propose:
Key Hypothesis:
A country’s domestic pollution levels are correlated with the
pollution levels of its structurally equivalent competitors.
Alternative Hypothesis: A country’s domestic pollution levels are correlated with its
trade salience.
Trade could also influence pollution levels via pressures from importing
markets. This is analogous to the so-called “California Effect.” Vogel’s California
Effect suggests that bilateral trade serves as a mechanism for importing countries’
regulations to exporting countries. 25 The key mechanisms are customer and supplier
pressure emanating from the importing country. Once a trading relationship has been
established between two countries, firms located in the importing country can
25
Vogel 1995.
13
encourage, or even require, firms in exporting countries to adopt stringent practices
which lead to reductions in pollution levels. The exporting firms may, in turn, require
their local suppliers to adopt similar policies. 26 Our paper investigates whether a
similar dynamic can be discerned in terms of a country’s overall pollution levels.
Although trade competition is the primary variable of interest, we also
investigate several other factors that can be expected to influence pollution levels.
By the end of 2000, there were about 62,000 multinational corporations operating
over 820,000 affiliates. 27 This is a dramatic change in the geography of international
production over the last five decades. While globalization critics suggest that foreign
direct investment (FDI) abets environmental races to the bottom, international
business scholars point out that regulatory races to the bottom are rare because
multinational corporations seldom base their FDI location decisions on environmental
costs alone. 28 Some scholars predict that despite considerable variation in the
stringency of domestic regulations, multinational corporations are likely to adopt
stringent practices that are acceptable in both developing and developed countries
because of the high costs of adapting different business models to different contexts. 29
FDI may then serve as a vehicle to transmit environmental norms and practices and,
therefore, eventually affect host countries’ pollution levels.
The economic forces of globalization such as trade and foreign direct
investment are important factors that drive environmental policies. However, any
26
27
28
29
See Prakash and Potoski 2006.
UNCTAD 2002.
Dunning 1993; Henisz 2000; Jensen 2003.
Rugman et al. 1999.
14
environmental policy is eventually made at the domestic level: domestic institutions
matter for environmental policies. In this research, we chose to focus on the regime
type of a country as our first attempt to understand the relationship between domestic
political institutions and pollution levels. It is fair to say that the debate on the
relationship between democracy and pollution levels is still largely unresolved. 30 We
therefore test whether and how political institutions bear upon domestic
environmental regulations and, by extension, domestic pollution levels.
Data and Analytical Methods
Dependent Variable
Ideally, to test the regulatory race hypothesis one would examine how
regulatory stringency on the ground in a given country responds to regulatory changes
in structurally equivalent countries. Two problems arise. First, cross-country data on
regulatory stringency levels for a large enough panel are not available. Second, there is
often a gap between laws on the book (dejure) and laws in practice (de facto).
Environmental laws might be enacted but not enforced. Indeed, in the United States,
there is persistent under-funding of the Environmental Protection Agency, especially
the enforcement wing. 31 Even with stringent laws on the book, governments might cut
enforcement budgets, reduce penalties for enforcement violations, and adopt
administrative policies which undermine the morale of the enforcement staff. In effect,
governments can (and do) diminish regulatory stringency without rewriting the law.
30
31
For a review, see Li and Reuveny 2006.
Fiorino 2006.
15
Given these limitations, we assess levels of regulatory stringency, our dependent
variable, in terms of their behavioral implication: pollution levels. If laws are stringent
and enforced, pollution levels will be low, all else equal. If laws are not stringent and/or
not enforced, pollution levels will be high, all else equal. 32 Thus, we measure
competitive pressures that bear upon domestic regulatory policies in terms of pollution
levels in “structurally equivalent” countries.
Taking guidance from the empirical research in the trade-environment area,
we employ two response variables, one for air pollution (SO2: sulfur dioxide) and one
for water pollution (BOD: biochemical oxygen demand). 33 For SO2, the variable is
reported in (logged) grams of SO2 per unit of gross domestic product (measured in
constant 2000 dollars based on purchasing power parity (PPP)). 34 For water pollution,
the variable is (logged) grams of biochemical oxygen demand (BOD) per unit of gross
domestic product (also measured in constant 2000 dollars based on purchasing power
parity (PPP)). 35
Focusing on these response variables has several merits. SO2 and BOD serve
as excellent variables to test the regulatory race hypothesis. They are very good
proxies for the stringency of environmental regulations. SO2 and BOD are outcomes
of production processes and tend to be regulated pollutants. 36 Air and water quality
32
Furthermore, regulatory measures may not capture the actual environmental practices of
firms that can be expected to be sensitive to the pressures of trade competition.
33
See for example Hilton and Levinson 1998; Selden and Song 1994; Holtz-Eakin and
Selden 1995; Roberts and Grimes 1997.
34
Data are from Stern 2005.
35
Data are from the World Development Indicators. See World Bank 2008.
36
We do not consider carbon dioxide as a response variable primarily because it tends to be
non-regulated in most countries and therefore not a good proxy of regulatory stringency.
16
are important indicators of how economic actors respect or neglect the environment.
Further, while entailing non-trivial costs, abatement technologies are available for
both SO2 and BOD. Because these technologies have non-trivial costs, their adoption
and the consequent reductions in pollution levels are likely to be influenced by
competitiveness concerns. At a practical level, given that a panel design is necessary
to test the regulatory race argument, we need to focus on variables for which data are
available for a relatively long time series. 37 Indeed, longitudinal data for SO2 and
BOD emissions are available for both developed and developing countries. Finally, by
using SO2 and BOD variables, our work remains consistent with the established
practice in two major literatures: trade-environment and Environmental Kuznets
Curve literatures. Given that our work challenges an important finding in these
literatures, namely the key role of overall trade salience in driving pollution levels,
using SO2 and BOD as response variables should enable us to engage with a broad
audience that is spread across social science disciplines.
Independent Variables
We are most interested in examining the role of trade competition in
inducing regulatory races which eventually affect domestic pollution levels. To
capture competition among countries that target same export markets with similar
products, we calculate pair-wise structural equivalence based on sector-level bilateral
trade data. We employ the United Nations' Standard International Trade Classification
37
This is the key reason why we do not employ response variables such as NOx emissions
(Li and Reuveny 2006) and carbon footprint (York et al. 2003).
17
(SITC), Revision 2, to differentiate sectors of trade. 38 SITC classifies 1832 types of
commodities traded in international markets into 10 sections, 63 divisions, 233 groups,
and finally 786 subgroups. We follow the classification at sector level to categorize
ten broad trade sectors in international commerce: Food and live animals directly for
food; Beverages and tobacco; Crude materials, inedible, except fuels; Mineral fuels,
lubricants and related materials; Animal and vegetable oils, fats and waxes; Chemical
and related products; Manufactured goods, classified chiefly by material; Machinery
and transport equipment; Miscellaneous manufactured articles; Commodities and
transactions not classified elsewhere.
Data for dyadic sector-level trade are from the United Nations’ Comtrade
online database. 39 This data set covers international commerce at the dyadic level
from 1962 to 2006. The Comtrade dataset details bilateral trade across different
commodities to the level of five-digits Standard International Trade Classification
(SITC). Aggregating bilateral trade to the one-digit level gives rise to the ten sectors
just described. A correlation matrix of each country's export profile across the 10
different trade sectors and to all other countries in the world is then generated to
capture this structural similarity. 40 Specifically, we calculate the first correlation
between country i's and j's export profiles (that is, the Pearson correlation between
their bilateral exports across ten sectors), and we construct a correlation measure
capturing the structural equivalence between any two countries i and j, that is, sei , j , in
38
United Nations 1975.
United Nations 2008.
40
Similar strategies have been used in Snyder and Kick 1979, Nemeth and Smith 1985, and
Smith and White 1992.
39
18
the network of global trade. The value of the correlation is bounded between −1 and
1 , with 1 representing completely structurally equivalent positions between two
countries, that is, exact profiles of bilateral exports across ten sectors of trade.
Negative one, on the other hand, captures the situation where two countries share the
most dissimilar export profiles.
While countries compete in different export markets, only those exporting
the same products in the same export market are likely to consider one another
competitors. We assume, therefore, that for any country i, export-induced competitive
pressures only come from countries that have a positive score of structural
equivalence with i, that is, sei , j >0. Country i’s decision to set its environmental
standards in response to competitor country j’s is influenced by the overall level of
competition between these two countries. We anticipate that country i’s decision is
more likely to be influenced by its key competitors in the international market. The
level of this pair-wise competition is captured by their level of structural equivalence
in export networks, sei , j . Therefore, for country i, the influence to set its
environmental standards from a fellow exporter j can be summarized
. Equiv .
as wiStruc
=
,j
sei , j
n
∑ se
j ≠i
, where sei , j is the structural equivalence in exports between
i, j
n
country i and j. Note that we standardize sei , j by ∑ sei , j , that is, the total competitive
j ≠i
pressure faced by country i from all its competitors. In other words, j’s influence on i
is a relative term, defined by the relative importance of j’s competitive pressure on i
19
n
( sei , j ) to the total competitive pressure faced by i from all its competitors ( ∑ sei , j ).
j ≠i
Further, we use this relative structural equivalence (
sei , j
n
∑ se
j ≠i
) to weight environment
i, j
degradation indicators (SO2 and BOD per unit of GDP) in country i’s competitor
n
countries:
∑ Emission
j ≠i
j ,t
. Equiv .
is therefore the weighted average of country i’s
× wiStruc
,j
competitor countries’ environment outcome indicators. Note that this is a typical
. Equiv .
spatial lag term in a spatial autoregressive model, only this time the weight ( wiStruc
)
,j
is defined by structural equivalence of trade rather than physical distance. 41
In addition to trade competition, our model includes trade salience (the sum
of imports and exports as a percentage of GDP) which has been used extensively in
the trade-environment research. 42 Finally, our model also allows us to test for the
California Effect argument. If a country’s key export markets have enacted stringent
environment standards, then exporting firms will have incentives to meet these higher
environmental standards. For country i, the pressure via the California Effect to adopt
higher environmental standards and consequently to reduce pollution can be captured
by calculating the weighted average of country i’s export destination countries’
emission levels. For country i, the weight for an export destination country j is
determined by the relative importance of country j to country i as an export market.
Numerically, this relative importance can be measured as the ratio of i’s exports to j
41
Beck, Gleditsch, and Beardsley 2006.
See, for example, Antweiler, Copeland, and Taylor 2001; de Soysa and Neumayer 2005; Li
and Reuveny 2006; Andonova, Mansfield, and Milner 2007; Zeng and Easting 2007.
42
20
( Exportsij ) to i’s total exports ( Exportsi ). Therefore, the weighted average of country
i’s export destination countries’ emission levels can be expressed in the following
way:
n
Bilateral Exports weighted Emission Levels=
∑ Emission
j ≠i
j
⎛ Exportsij ⎞
×⎜
⎟
⎝ Exportsi ⎠
where Emission j is the emission level in country i’s export destination country j,
Exportsij is country i's exports to country j, Exportsi is i's total exports. Note that
⎛ Exportsij ⎞
⎜
⎟ can also be considered as a spatial weight in a spatial model context
⎝ Exportsi ⎠
ornia
( wiCalif
). It is not defined by geographical distance as in a typical spatial model, but
,j
by the strength of export ties in international trade networks. We can therefore use a
simpler notation W California to represent the whole weight matrix to capture the
California Effect of bilateral exports on emission levels. Therefore, the whole term,
n
∑ Emission
j ≠i
j
⎛ Exportsij ⎞
×⎜
⎟ , becomes a typical spatial lag term in a spatial
⎝ Exportsi ⎠
autoregressive model ( W California y ). 43
In addition to trade, foreign direct investment (FDI) is another important
factor that might bear upon domestic pollution levels. We measure a host country’s
overall dependence on FDI (FDI Stock) based on the argument that, irrespective of the
FDI’s source, higher levels of inward FDI influence host countries’ pollution levels.
FDI Stock is calculated as a host country’s total inward FDI stock as a percentage of
its GDP. 44
43
44
Bilateral exports data are from IMF’s Direction of Trade Statistics (CD-ROM).
Data are from UNCTAD 2008.
21
Our model controls for a variety of domestic variables that can be expected
to influence pollution levels. The nature and quality of domestic institutions matter for
the environment. We use the annualized polity score, which ranges from -10 for
highly authoritarian states to +10 for highly democratic societies, to gauge the effect
of domestic institutions (particularly, levels of democracy) on pollution levels. We
include both GDP per capita (in purchasing power parity (PPP)) and its squared term
in the baseline model to capture the curvilinear relationship between wealth and
pollution levels (the Environmental Kuznets Curve).We also control for GDP growth
rate. While higher growth rates often require more intensive use of resources and
cause higher levels of pollution, they may also encourage technological advancement
and human capital accumulation that might eventually improve environmental quality.
Thus, the directionality of its effect is not clear. The percentage of industrial
production in GDP is included because this might help us to account for the effect of
the structure of production as industrial production often generates more pollution
than service and agriculture. Moreover, to account for the extent to which countries
rely on oil exports, we add a variable measuring the percentage of fuel exports among
total exports. The conventional wisdom holds that fuel production creates higher
pollution levels, especially in the case of developing countries. 45 We also include two
demographic variables, population density and urban population (as a percentage of
45
For case studies on the relationship between oil export and environmental degradation, see
TED reports at http://www.american.edu/TED/projects/tedcross/xoilpr15.htm#r0.
22
total population) to control for demographic influences on pollution levels. 46
Model and Empirical Findings
We model levels of pollution per unit of GDP in country i in year t as a
function of trade competition, trade salience, California effect, inward FDI stock, and
a battery of country-specific variables. The model can be written as:
W ∗ y
yi,t = β 0 + ϕ yi,t −1 + X i,t β + ρ
− i,t-1 + Ci + Tt + ε i,t
(1)
where β 0 is the population intercept, ϕ yi,t −1 captures the effects of lagged dependent
variable yi,t −1 , and X i,t β the effects of country-specific characteristics such as polity,
W ∗ y
GDP per capita, and population density. ρ
− i,t-1 represents the two temporarily
lagged spatial lag terms of the network diffusion effects: structural equivalence in
trade ( ρ s.e.W Struc. Equiv. y− i,t-1 ) and the California Effect through bilateral exports
represents the two spatial coefficients that we
( ρCaliforniaW California y− i,t-1 ). Here, ρ
estimate to capture the effects of trade competition ( ρ s.e. ) and California Effect of
trade ( ρCalifornia ) on environmental outcomes respectively. Notice that in order to
mitigate the simultaneity bias in the estimation of spatial lag models, the spatial lags,
ρW ∗ y− i,t-1 , are lagged by one year. 47 The assumption here is whatever happened in
countries that are closely connected to country i, it takes a time lag (a year) to
influence the outcome in country i. Lagging the spatial lag has become a common
practice in some of the recent study of policy diffusion and neighborhood effects on
46
Data on GDP per capita, GDP growth rate, industrial production, oil exports, population
density, and urban population are from World Development Indicators. See World Bank
2008.
47
Beck, Gleditsch, and Beardsley 2006.
23
policy choices, 48 mainly because it provides a much simpler way to estimate the
strength of interdependence (by simple OLS regression) than spatial maximum
likelihood approach (spatial ML) and spatial two-stage-least-squares instrumental
variable approach (2SLS). 49 However, this strategy of lagging the spatial lag terms is
based on a strong assumption, that is, the absence of instantaneous effect.
Meanwhile, temporarily lagging the spatial lags and estimating the spatial
lag model by simple OLS can only be a sound solution to the simultaneity bias if the
errors, ε i,t in Equation (1), are serially independent. 50 Including a temporally lagged
dependent variable ( ϕ yi,t −1 ) often helps to mitigate serial correlation in the errors, but
there is no guarantee. Therefore, using a Lagrange Multiplier test, we test the
existence of serial correlation in error terms after estimating Equation (1) by OLS. We
find no serial correlation in the empirical models reported in the following section. 51
Moreover, in the analysis of spatial interdependence, it is of great importance that
48
See, for example, Lee and Strang 2006, Elkins, Guzman and Simmons 2006, and Swank
2006.
49
However, with recent efforts by Robert Franzese and Jude Hays, spatial maximum
likelihood approach (spatial ML) has become much easier to implement for
time-series-cross-sectional data often used in political science settings. Readers interested in
this topic can see any of their papers on spatial econometrics and its application in
international and comparative political economy, for example, Franzese and Hays 2006;
2007; 2008. Other estimators also exist to model spatial effect or interdependence in general.
For a recent application of the Arellano and Bond’s estimator, see Perkins and Neumayer
2008.
50
If ε i,t is not serially independent, for example, when ε i,t follows an AR(1) process, i.e.,
ε i,t = γε i,t −1 + ηi ,t , Equation (1) becomes yi,t = β0 +ϕyi,t−1 + Xi,t β + ρW∗ y−i,t-1 + Ci +Tt +γεi,t−1 +ηi,t .
Notice that ε i,t −1 is the error term for the right-hand-side variable yi,t −1 (they are both at the
right-hand-side of the equation) giving rise to simultaneity bias.
51
We use the bgtest() command of the lmtest package in R.
24
common external shocks (for example, oil crisis) are controlled and distinguished
from interdependence. 52 Equation (1) therefore includes year dummy variables ( Tt ) to
control for potential common shocks. Finally, we allow for cross-sectional
heterogeneity by including country fixed effects ( Ci ).
Insert Table 1 about here
Tables 1 and 2 present the empirical findings regarding the impact of trade
competition on air pollution (SO2) and water pollution (BOD) respectively. In Table 1,
the findings from two model specifications indicate the consistent and significant
effect of trade competition ( ρ s.e. ) on SO2 emission levels: country i’s SO2 emission
level at year t is positively associated with the weighted average of its structurally
equivalent competitors’ emission levels in the previous year t-1. Note that all our
dependent variables are logged to approximate a normal distribution and rescale the
extreme values. The estimated spatial coefficient of Structural equivalence of trade is
0.050 in Model 1 (Table 1). This means that if country i’s trade competitors’ weighted
average SO2 emission levels increase by one unit (in log scale) in year t-1, country i’s
emission level are expected to increase by 0.050 unit of logged grams of SO2
emission per unit of GDP output in the subsequent year. On the other hand, we don’t
observe significant effect of the California Effect of trade ( ρCalifornia ) on SO2 emission
levels as illustrated by Model 2 and 3 in Table 1. This means that exporting countries’
52
See Plümper and Neumayer 2008.
25
SO2 emission levels are not affected by those at their export destination markets. In
other words, regulations, norms, and practices regarding air pollution (SO2 in our
case), no matter good or bad, do not diffuse from importing countries to exporting
countries. Finally and importantly, trade salience, the key variable that previous
research used to test the link between trade and pollution, is not significant across all
model specifications in Table 1. The upshot is that for SO2 emissions, trade induced
structural competition (as opposed to trade salience or the California Effect) is a
strong and consistent driver of domestic emission levels.
Insert Table 2 about here
Table 2 reports findings on BOD discharges per unit of GDP. Here, we find
similar results regarding the effects of trade competition, California Effect, and trade
salience. There is a consistently significant and positive correlation between the
structural equivalence of trade and BOD levels in all three model specifications
(Models 4, 5, and 6), with as well as without the inclusion of the California Effect
variable. Thus, trade competition drives water pollution levels. On the other hand, the
California effect variable does not have a significant effect on BOD levels. These
findings suggest that importing countries’ water pollution standards do not diffuse to
exporting countries through trade. As in the case of air pollution (Table 1), trade
salience is not a significant driver of water pollution.
In Tables 1 and 2, only GDP growth (as a percentage of total GDP) has a
26
consistent and significant effect on both SO2 emissions and BOD discharges: the
higher the GDP growth rate, the lower the levels of SO2 emissions and BOD
discharges. This is good news for countries that are eager to grow their economy,
because, all else equal, higher growth rates eventually reduce pollution levels: we
suspect that this might be a function of technological advancement and human capital
accumulation as a result of economic growth. Other variables, on the other hand, do
not have consistent correlation with these two types of pollutions. Foreign direct
investments are only associated with lower level of BOD discharges (Table 2; Model
4-6). The inverted U-shape Kuznets Curve is evident for SO2 emissions only (Table 1;
Model 1-3). Also, the salience of industry in GDP is significantly correlated with SO2
emission only (Table 1). Population density increases BOD discharges but not SO2
emissions levels. Urban population (as a percentage of total population), on the other
hand, just has the opposite effect: it increases SO2 emission but not BOD discharges.
More democratic countries (polity) are associated with higher levels of SO2 emission
but uncorrelated with water pollution. Finally, the extent to which one country relies
on oil exports has no effect on its air pollution (SO2) and water pollution (BOD).
While not reported in the tables, we also ran our baseline models separately
for cold war and post-cold war periods, for non-EU countries and non-OECD
countries only. The basic findings of a consistent and significant association between
structural equivalence and air and water pollution hold across all these four scenarios.
27
Race to the Bottom or Race to the Top
The findings from Table 1 and Table 2 establish that there are strategic
interactions among structurally equivalent countries regarding their air and water
pollution levels. However, a positive and statistically significant structural
equivalence variable is consistent with both a race to the bottom and a race to the top.
Would a country then respond symmetrically to both ratcheting up and rathcheting
down of regulations in the structurally equivalent competitors? For example, as
Table 1 suggests, if country i’s trade competitors’ weighted average SO2 emissions
increase by one unit (log scale) in year t-1, country i’s emission are expected to
increase in the following year t. However, the converse is also plausible: when
country i’s trade competitors’ weighted average SO2 emission level decreases in year
t-1, country i emission levels are also expected to decrease.
We follow Kominsky, to establish whether the “trade competition affects
pollution” argument holds separately for downward races and upward races. 53 To do
so, we estimate an additional model (Table 3) to see whether the variable “structural
equivalence” retains significance when country i’s trade competitors’ weighted
average emission levels increase from the previous year --- a race to the bottom
scenario, or whether it retains significance when country i’s trade competitors’
weighted average emission level at year t is lower than from that of year t-1 --- a race
to the top scenario. More specifically, we estimate the following model:
yi,t = β0 +ϕ yi,t −1 + Xi,t β + ρbottomDi,tW struct.equiv. y−i,t-1 + ρtop (1− Di,t )W struct.equiv. y−i,t-1 + Ci + Tt + εi,t
Notice this is essentially the same model as in Equation (1) in the previous section
53
Kominsky 2007.
28
except that we delete the spatial lag term for the California Effect of trade,
ρcaliforniaW trade y− i,t-1 , because it has no significant effect on pollution levels. Moreover,
we split ρ s.e.W Struc. Equiv. y− i,t-1 into ρbottomDi,tWstruct.equiv. y−i,t-1 + ρtop (1− Di,t )Wstruct.equiv. y−i,t-1 where
.equiv.
.equiv .
Di,t = 1 if wistruct
y j ,t > wistruct
y j ,t −1 ( j ≠ i ), that is, Di,t = 1 when the weighted
, j ,t
, j ,t
average emission level of country i’s trade competitors’ at year t increases from the
previous year (t-1); Di,t = 0 otherwise. Therefore, the spatial coefficient ρbottom is
only associated with cases in which country i’s trade competitors increase their
emission levels to put country i at a cost disadvantage relative to the previous year. A
positive and significant ρbottom means that country i responds by following up and
increasing its own emission levels --- this is, therefore, evidence supporting the race
to the bottom mechanism. ρtop , on the other hand, tests whether country i responds
when its competitors decrease their emission levels on average. A positive and
significant ρtop therefore supports the race to the top mechanism.
Insert Table 3 about here
Table 3 reports our results on the two mechanisms of strategic interactions
(race to the bottom and race to the top) based on the analysis of SO2 (Model 7) and
BOD (Model 8). We find a positive and significant ρbottom that supports the race to
the bottom mechanism, SO2 emissions only. In other words, with regard to SO2
emission, when structurally equivalent competitor countries lower the stringency of
their regulations as proxied by increased SO2 emissions, the focal country i responds
29
in the subsequent time period by lowering its regulatory standards, as reflected in
increases in SO2 emissions. Importantly, we find no empirical support for the race to
the top mechanism for either SO2 emission or BOD discharges: the positive signs for
ρtop for both SO2 emission and BOD discharges are far from conclusive as indicated
by their p-values in Table 3. 54
Conclusion and Policy Implications
Our paper demonstrates that for the period 1980-2003, trade competition
induces strategic interactions among structurally equivalent countries in
environmental regulations that are eventually reflected in the levels of air and water
pollution. More interestingly, for air pollution (SO2), we find that while countries do
not respond to competitors’ improvements in air pollution levels, they follow when
competitors reduce the stringency of their regulations. Furthermore, unlike the
previous literature, we do not find support for the argument that higher overall
exposure to global trade (trade salience) leads to either environmental protection or
degradation.
Thus, while concerns about races to the bottom have some empirical support,
our paper suggests that the causal story is somewhat different from what globalization
critics as well as some trade-environment scholars portray. If policy implications need
54
We suspect that two reasons might cause the different findings between air pollution (SO2)
and water pollution (BOD) levels. First, economic activities that generate air pollution (such
as electricity generation, manufacturing, etc.) are often more widespread in a typically
exporting economy than activities that contribute to water pollution. Further, for most
exporting industries, air pollution (SO2) abatement costs are typically much higher than water
pollution (BOD) abatement costs and therefore more susceptible to regulatory races. However,
the issue of regulatory races in various pollutant types needs a more careful examination.
30
to cohere with the causal story, our paper suggest that global trade is not the culprit and
abolishing or enfeebling the World Trade Organization may not directly serve to
mitigate incentives for races to the bottom. A more sophisticated understanding of why
countries tend to have different types of trading portfolios and why they are embedded
in different types of trading networks and how both of these might be changed is
required. For example, if trade agreements which tend to privilege “insiders” over
“outsider” tend to divert trade as opposed to create trade (an issue that is debated in the
international trade literature), then this will bear upon the composition of the trade
network and therefore influence competitive pressures a county faces.
Our paper brings both good and bad news to environmentalists and activist
groups that are worried about the environmental consequences of global trade. Their
task is to manipulate the trade context in which countries operate in order to
encourage upward races or discourage downward races. Indeed, environmental groups
can harness the trading system to their advantage if they can identify the key trade
competitors of a given country and then work on improving environmental laws and
practices in these competitor countries. The challenge is to identify the pressure points
and to subject them to political and economic pressure to improve levels of
environmental protection.
Indeed, activists are already adopting such strategies via public politics and
private politics. 55 In addition to lobbying the government, activists have created a
variety of voluntary programs which encourage, and sometimes coerce, firms to adopt
55
See Baron 2001.
31
beyond compliance environmental policies. Thus, in response to the pressures from
global markets to reduce costs, pressure from activist groups might actually encourage
firms to incur new costs in order to get rewards in the form of reputation and other
benefits that membership in such programs and certification codes confers. The
burgeoning literature on voluntary programs provides initial evidence that such
dynamics might be at work in several industries. 56 In sum, activist pressure via
voluntary programs could mitigate the pressure from global markets to neglect the
environment in order to reduce production costs. This is an exciting area for future
research.
56
Haufler 2001; Bartley 2003; Cashore et al. 2004; Prakash and Potoski 2006.
32
References
Antweiler, Werner, Brian R. Copeland and M. Scott Taylor. 2001. “Is Free Trade
Good for the Environment?” The American Economic Review, 91( 4): 877-908.
Bailey, Michael, Mark Rom, and Matthew Taylor. 2004. “State Competition in
Higher Education: A Race to the Top, or a Race to the Bottom?” Economics of
Governance, 2004, vol. 5, issue 1, pages 53-75.
Baron, David. P. 2001. “Private Politics,” Journal of Economics & Management
Strategy 12: 31-66.
Bartley, Tim. 2003. “Certifying Forests and Factories,” Politics & Society 31(3):
433-464.
Beck, Gleditsch and Beardsley. 2006. “Space is more than Geography: Using Spatial
Econometrics in the Study of Political Economy.” International Studies Quarterly
50(1).
Beckfield, Jason. 2003. “Inequality in the World Polity: The Structure of International
Organization.” American Sociological Review 68(3):401-424.
Bernauer, Thomas and Vally Koubi. ND. “Political Determinants of Environmental
Quality.” Center for Comparative and International Studies (CIS), Swiss Federal
Institute of Technology (ETH).
Borgatti, Stephen P. & Martin G. Everett. 1992. “Notions of Position in Social
Network Analysis.” Sociological Methodology 22:1–35.
Braithwaite, John and Peter Drahos. 1999. "Ratcheting Up and Driving Down Global
Regulatory Standards" 42(4) Development 109-114.
33
Burt, Ronald S. 1976. “Positions in Networks.” Social Forces 55:93–122.
Cashore, B. G. Auld, and D. Newsom. 2004. Governing Through Markets. New
Haven: Yale University Press.
Charnovitz, S., 1993. “Environmentalism confronts GATT rules.” Journal of World
Trade 27, pp. 37–53.
Christmann, Petra and Glenn Taylor. 2001. “Globalization and the Environment:
Determinants of firm-Self Regulation in China.” Journal of International Business
Studies 32(3): 439-458.
Coase, Ronald.1960. “The Problem of Social Cost.” Journal of Law and Economics 3,
1-44.
Daly, Herman. 1993. “The Perils of Free Trade.” Scientific American 269(5): 51-55.
de Soysa, Indra & Eric Neumayer. 2005. “False Prophet or Genuine Savior?
Assessing the Effects of Economic Openness on Sustainable Development,
1980-99.” International Organization 59(3):731–772.
Dean, Judith M. 2002. “Does Trade Liberalization Harm the Environment? A New
Test.” Canadian Journal of Economics, vol. 35, no. 4, pp 819-842.
DiMaggio, Paul J. & Walter W. Powell. 1983. “The Iron Cage Revisited: Institutional
Isomorphism and Collective Rationality in Organizational Fields.” American
Sociological Review 48(2):147–160.
Donahue, John. 1997. Disunited States. New York: Basic Books.
Drezner, Daniel 2001. “Globalization and Policy Convergence.” International Studies
Review 3(1): 53–78.
34
Dunning, J. H. 1993. Multinational Enterprises and the Global Economy. New York:
Addison-Wesley.
Elkins, Zachary, Andrew T. Guzman & Beth A. Simmons. 2006. “Competing for
Capital: The Diffusion of Bilateral Investment Treaties, 1960-2000.”
International Organization 60(4):811-846.
Epple, Dennis, Allan Zelenitz, and Michael Visscher. 1978. “A Search for Testable
Implications of the Tiebout Hypothesis.” The Journal of Political Economy,
86(3): 405-425.
Esty, Daniel. 2001. “Bridging the Trade-Environment Divide,” Journal of Economic
Perspectives, 15(3).
Everitt, Brian S. & Graham Dunn. 2001. Applied Multivariate Data Analysis. New
York, NY: Oxford University Press.
Fiorino, Daniel J. 2006. The New Environmental Regulation. MIT Press.
Franzses, Robert and Jude C. Hays. 2006. “Spatial-Econometric Models of
Cross-Sectional Interdependence in Political-Science Panel and
Time-Series-Cross-Section Data.” Political Analysis 15(2):140-64.
Franzses, Robert and Jude C. Hays. 2006. “Strategic Interaction among EU
Governments in Active-Labor-Market Policymaking: Subsidiarity and Policy
Coordination under the European Employment Strategy.” European Union
Politics 7(2):167-89.
35
Franzses, Robert and Jude C. Hays. 2008. “Interdependence in Comparative Politics:
Substance, Theory, Empirics, Substance.” Comparative Political Studies, 40th
Anniversary Issue: “Frontiers of Comparative Politics,” 41(4/5):742-80.
Garrett, Geoffrey. 1998. Partisan Politics in the Global Economy. New York:
Cambridge University Press.
Grossman, Gene M. and Alan B. Krueger. 1995. “Economic Growth and the
Environment,” The Quarterly Journal of Economics, May 1995, 110(2), pp.
353-77.
Hanneman, Robert A. & Mark Riddle. 2005. Introduction to social network methods.
Riverside, CA: University of California, Riverside: published in digital form at
http://faculty.ucr.edu/~hanneman/.
Haufler, Virginia. 2001. A Public Role for the Private Sector. Washington: Carnegie
Endowment for International Peace.
Henisz, W. 2000. “Institutional Environments for Multi-National Investment.”
Journal of Law, Economics and Organizations 16(2): 334-364.
Heritage Foundation. 2003. Index of Economic Freedom. Heritage Foundation,
accessed on July 22, 2004:
http://www.heritage.org/research/features/index/2003/IndexHistory95-03.xls.
Hilton, F. G. Hank and Arik Levinson. 1998. “Factoring the Environmental Kuznets
Curve: Evidence from Automotive Lead Emissions.” Journal of Environmental
Economics and Management, 35(2): 126-141.
36
Holtz-Eakin, Douglas and Thomas M. Selden. 1995. “Stoking the fires? CO2
emissions and economic growth.” Journal of Public Economics, 57(1): 85-101.
Jaffe, Adam, Steven Peterson, Paul Portney and Robert Stavins. 1995.
“Environmental Regulation and the Competitiveness of U.S. Manufacturing.”
Journal of Economic Literature 33(1): 132-163.
Jensen, Nathan M. 2003. “Democratic Governance and Multinational Corporations:
Political Regimes and Inflows of Foreign Direct Investment.” International
Organization 57 (3): 587-616.
Kahler, Miles. 1998. “Modeling Races to the Bottom.” Unpublished manuscript.
http://irpshome.ucsd.edu/faculty/mkahler/RaceBott.pdf; accessed 02/22/2004
Konisky, David M. 2007. “Regulatory Competition and Environmental Enforcement:
Is There a Race to the Bottom?” American Journal of Political Science
51(4):853–872.
Lake, David A. and Robert Powell. 1999. Strategic Choice and International
Relations. Princeton University Press, Princeton, NJ.
Lee, Chang Kil and David Strang. 2006. “The International Diffusion of Public-Sector
Downsizing: Network Emulation and Theory-Driven Learning.” International
Organization 60(4):883-909.
Li, Quan & Rafael Reuveny. 2006. “Democracy and Environmental Degradation.”
International Studies Quarterly 50(4):935–956.
Liliana Andonova, Edward D. Mansfield, and Helen V. Milner. 2007. “International
Trade and Environmental Policy in the Postcommunist World.” Comparative
37
Political Studies 40: 782-807.
Lopez, Ramon. 2003. “The Policy Roots of Socioeconomic Stagnation and
Environmental Imposition: Latin America 1950-2000.” World Development, vol.
31, no. 2, pp 259-280.
Nemeth, Roger and David A. Smith. 1985. “International Trade and world-System
Structure: A Multiple Network Analysis.” Review (Fernand Braudel Center)
8:517-60.
Mani, Muthukumara. and David Wheeler. 1998. “In Search of Pollution Havens?”
Journal of Environment and Development. 7(3): 215–47.
March, J. & J. Olsen. 1989. Rediscovering Institutions. New York, NY: The Free
Press.
McCubbins, Mathew D.1985. “The Legislative Design of Regulatory Structure.”
American Journal of Political Science, 29:721-48.
Oates, Wallace. 1969. “The Effects of Property Taxes and Local Public Spending on
Property Values: An Empirical Study of Tax Capitalization and the Tiebout
Hypothesis.” The Journal of Political Economy, 77(6): 957-971.
Ostrom, Elinor. 1990. Governing the Commons. Cambridge University Press.
Panayotou, Theodore. 1997. “Demystifying the environmental Kuznets curve: turning
a black box into a policy tool.” Environment and Development Economics 2:
465-484.
38
Perkins, Richard and Eric Neumayer. 2008. Fostering Environment-efficiency
through Transnational Linkages? Trajectories of CO2 and SO2, 1980-2000.
Environment and Planning A (forthcoming).
Pigou, A. C. 1960[1920]. The Economics of Welfare. 4th ed. London: McMillan.
Plümper, Thomas and Eric Neumayer. 2008. Model Specification in the Analysis of
Spatial Dependence. http://papers.ssrn.com/sol3/papers/cfm?abstract_id=1092113.
Porter, M. and van der Linde, C. 1995. “Toward a New Conception of the
Environment-Competitiveness Relationship.” Journal of Economic Perspectives.
9:97-118.
Prakash, A. and M. Potoski. 2006. “Racing to the Bottom? Globalization,
Environmental Governance, and ISO 14001.” American Journal of Political
Science, 50(2): 347-361.
Timmons, Roberts J. and Peter E. Grimes. 1997. “Carbon intensity and economic
development 1962–1991: A brief exploration of the environmental Kuznets
curve.” World Development. 25(2): 191-198.
Rugman, Alan, Julie Soloway and John Kirton. 1999. Environmental Regulations and
Corporate Strategy: A NAFTA Perspective. Oxford, UK: Oxford University
Press.
Scholz, John T. 1984. “Cooperation, Deterrence, and the Ecology of Regulatory
Enforcement.” Law & Society Review, 18(2): 179-224.
Scholz, John T. 1986. Regulatory Enforcement in a Federalist System. The American
Political Science Review, 80(Dec): 1249-1270
39
Selden, Thomas M. and Daqing Song. 1994. “Environmental Quality and
Development: Is There a Kuznets Curve for Air Pollution Emissions?” Journal of
Environmental Economics and Management, Volume 27, Issue 2, Pages 147-162.
Simmons, Beth and Zachary Elkins. 2004. “The Globalization of Liberalization.”
American Political Science Review, 98(1): 171-189.
Smith, David and Douglas R. White. 1992. “Structure and Dynamics of the Global
Economy: Network Analysis of International Trade 1965-1980.” Social Froces
70(4):857-93.
Snyder, David & Edward L. Kick. 1979. “Structural Position in the World System and
Economic Growth, 1955-1970: A Multiple-Network Analysis of Transnational
Interactions.” American Journal of Sociology 84(5):1096-1126.
Spar, Debora and David Yoffie 2000. A Race to the Bottom or Governance from the
Top? In Coping with Globalization, edited by Aseem Prakash and Jeffrey A.
Hart, pp. 31-51, London: Routledge.
Stern, David I. 2005. “Global sulfur emissions from 1850 to 2000.” Chemosphere, 58:
163-175.
Swank, Duane. 2006. “Tax Policy in an Era of Internationalization: Explaining the
Spread of Neoliberalism.” International Organization 60(4):847-882.
Tiebout, C.M. 1956. “A Pure Theory of Public Expenditure.” Journal of Political
Economy. 64: 416-424.
Vogel, David. 1995. Trading Up. Cambridge, MA: Harvard University Press.
40
United Nations. 1975. Standard International Trade Classification, Revision 2. United
Nations.
United Nations, 2008. Comtrade database. Accessed 20 February 2008.
UNCTAD. 2002. World Investment Report, Geneva: UNCTAD.
UNCTAD. 2008. Foreign Direct Investment database online. Accessed on
02/20/2008.
Wallach, Lori, and Michelle Sforza. 1999. Whose Trade Organisation? Washington:
Public Citizen.
Walley, Noah and Bradley Whitehead. 1994. “It’s Not Easy Being Green.” Harvard
Business Review, 72(3), 46-51.
Wapner, Paul. 1996. Environmental Activism and World Civic Politics. Albany: State
University of New York Press.
Weingast, Barry and Mark J. Moran. 1983. “Bureaucratic Discretion or Congressional
Control? The Journal of Political Economy, 91(5): 765-800.
World Bank. 2008. World Development Indicator online. Accessed on 11 February,
2008.
York, Richard, Eugene Rosa and Thomas Dietz. 2003. “Footprints on the Earth: The
Environmental Consequences of Modernity.” American Sociological Review,
68(2): 279-300
41
42
43