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Economic Globalization and the Environment

Vally Koubi,1,2 Tobias Böhmelt,1,3 and Thomas Bernauer1

1 CIS, ETH Zurich

2 VWI, University of Bern 3 Department of Government, University of Essex

Abstract

The authors focus on the standard Factor Endowment Theory (FET) and the Pollution Haven

Hypothesis (PHH) for contributing to our understanding of the relationship between economic

globalization and the environment. One implication of the theoretical framework that

combines both approaches is that inward-investment flows are likely to decrease domestic

environmental quality in developing countries. The theory’s main contribution, however, is

the ability to distinguish between the FET and the PHH with the interactive effects of trade

and inward FDI. Empirically, the authors study the impact of economic globalization in the

form of trade openness and FDI inflows on SO2 emissions in 150 countries between 1971 and

2003. The findings suggest that inward FDI is indeed associated with higher levels of SO2

emissions in developing states, while increases in trade amplify the negative effects of FDI

inflows. This constitutes prima facie evidence in favor of the PHH over the FET.

Keywords: environmental quality; factor endowments; pollution haven; trade; foreign direct

investment; SO2 emissions

Paper to be presented at the annual meeting of the International Political Economy

Society (IPES), Stanford, CA, USA, November 13-14, 2015.

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1. Introduction How do trade liberalization and foreign direct investment (FDI) affect the environment? Put

differently, what is the relationship between economic globalization and environmental

quality? A key development over the last few decades is the significant increase in the degree

of globalization (see, e.g., Dreher et al. 2008). While there are different forms, e.g., political

or social (Dreher 2006), economic globalization has important implications for numerous

issue areas and manifests itself via the following main channels: increased trade in goods and

services and a higher capital mobility. For example, reductions in trade barriers and the

relaxation or elimination of capital controls led to increases in trade and capital flows that

have outpaced the rate of economic growth. In fact, the levels of trade and asset openness1 are

much higher at the present time than, e.g., 25 years ago. To underline these claims, Figures 1

and 2 depict the development of FDI flows and trade openness, respectively, over time.2

_________

Figures 1 and 2 in here

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As indicated, economic globalization influences several policy areas in important ways,

ranging from living standards to the distribution of economic and political power. One such

issue that currently occupies center stage in scientific and political agendas is the

environment: the channels of economic globalization from above are thought to matter for

environmental quality, although different theories and approaches disagree how this actually

occurs and with what empirical implications. Specifically, according to standard trade theory

(i.e., the Heckscher-Ohlin model), trade in goods worsens environmental quality in those

1 We define the former as the sum of imports and exports divided by GDP. The latter is specified by

foreign assets as a percentage of GDP. 2 The graphs are based on the data sources we discuss in the research design section below.

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countries that have a comparative advantage in the production of “polluting” goods. On one

hand, the comparative advantage may derive from the distribution of the world endowments

of the factors of production (i.e., the factor endowment theory, FET), in which case the

developed countries become dirtier with free trade due to their capital abundance. On the

other hand, the comparative advantage may come from policy-related differences in tolerance

of pollution (i.e., the pollution haven hypothesis, PHH), in which case the less developed

countries are expected to have worse environmental quality with more international trade

(Taylor and Copeland 1994; see also Taylor 2004).

Most importantly, however, static-trade theory abstracts from an important determinant of

environmental quality that is affected by international trade, namely income. In a pioneering

study that considers both the direct and indirect effects of trade, Antweiler et al. (2001)

demonstrate that such income (i.e., so-called technique) effects are sufficiently large in the

long run as to overcome the negative impact arising from the scale and the composition of

economic activity. Ultimately, it is found that trade has a positive effect on environmental

quality as measured by lower SO2 concentrations (see also Frankel and Rose 2005).

The relationship between international capital mobility, in particular Foreign Direct

Investment (FDI), and the environment has also received considerable attention. The

predominant theoretical framework underlying this relation is the PHH. In short, it is

postulated that polluting firms will find it profitable to relocate to countries with “lax”

environmental standards. Consequently, FDI flows will worsen the environment in the

receiving country, and improve it in the originating one (e.g., Mani and Wheeler 1998). The

empirical evidence on the relationship between FDI and environmental quality is rather

mixed, though (Copeland and Taylor 2004). For instance, several studies find support, albeit

only weak at times, for the PHH (e.g., Dean et al. 2009; Kellenberg 2009; Jorgenson 2009;

Wagner and Timmins 2009; Acharyya 2009; Cole and Fredriksson 2009; He 2006; Cole and

Elliott 2005; Millimet and List 2004; Eskeland and Harrison 2003; Keller and Levinson 2002;

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Xing and Kolstad 2002). Others, however, provide evidence for the absence of any link (e.g.,

Spilker 2013; Perkins and Neumayer 2009; Elliott and Shimamoto 2008; Javorsik and Wei

2004) or for conditional effects (Lan et al. 2012; Manderson and Kneller 2012).

In light of this, we believe that the lack of consensus in the empirical results suggests that

the impact of trade and FDI on environmental quality is likely to differ according the

circumstances and, hence, intervening influences. Particularly interesting for this study, Lan et

al. (2012), for example, employ data on Chinese provinces and condition the relationship

between FDI and pollution on human capital levels. They show that the PHH holds only in

provinces with low human capital, whereas FDI is negatively associated with pollution

emissions in provinces with higher human capital due to a “technology effect.” In essence, the

core of our paper is a conditional, interactive effect as well as we seek to provide new insights

into the relationship between economic liberalization and pollution by developing an

argument that simultaneously considers the effects of trade and FDI in an interactive fashion.

Specifically, we pursue a twofold contribution. First, theoretically, we develop an

argument that theorizes on the join impact of free trade and FDI on environmental quality,

while encompassing the PHH and standard-trade theory effects. This argument implies, on

one hand, that FDI inflows are likely to have a negative, i.e., worsening effect on the

environmental quality in developing countries, independent of whether the mechanisms of the

FET or the PHH apply. On the other hand, our argument in fact helps to distinguish between

these two approaches. If the PHH plays the dominant role in production and trade patterns, we

expect a joint effect of FDI and trade to decrease environmental quality in developing

countries. If differences in factor endowments do matter more, the interaction of trade and

FDI should suggest that the joint effect actually improves environmental quality in developed

countries.

Second, we seek to make an empirical contribution by quantitatively examining the join

effect of international trade and FDI on SO2 emissions using time-series cross-section data on

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150 countries between 1971 and 2003. Our analysis, while controlling for a series of

alternative determinants of environmental quality including year and country fixed effects,

highlights that more FDI inflows into less developed states are indeed associated with higher

SO2 emissions. We also obtain evidence that the interaction between trade and inward FDI

results in worse environmental quality. Particularly this last finding provides support for the

PHH over the FET. In fact, we believe that these results and, more accurately, their

underlying interactive specification between trade and FDI might be an explanation for the so

far mixed results of earlier studies on the PHH.

The next section briefly reviews the relevant literature pertaining to the effects of trade and

FDI on the environment and presents our theoretical argument. Afterwards, we discuss the

research design, before presenting the empirical results. The last section concludes with a

discussion of our findings and the implications for future research and policymakers.

2. Development, Trade, and FDI Flows and their Impact on Environmental Quality One of the most debated issues concerns the environmental impact of economic globalization,

i.e., free trade and free capital movement (FDI). However, the effect of economic

liberalization on the environment is both theoretically and empirically ambiguous in the

existing literature. One of the reasons for this ambiguity might stem from the fact that the

impact of economic liberalization on the environment for a given level of income operates at

least via three channels (Frankel 2005): system-wide effects that are either adverse or

beneficial, and effects that vary across countries depending on a local “comparative

advantage”.3

3 Economic liberalization also affects the environment via income and it has been shown that, at least

for local pollutants such as SO2 emissions, the net effect of trade is the reduction of pollution

(Antweiler et al. 2001; Frankel and Rose 2005; Damania et al. 2004).

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Starting with the system-wide effects, several scholars argue that increased competition

between companies induced by economic liberalization causes a “race to the bottom” in

countries’ environmental standards: states will lower their environmental policy standards in

order to protect their industry from international competition (e.g., Esty and Geradin 1998;

Prakash and Potoski 2006b); similarly, governments could seek to attract foreign firms and

FDI with lower environmental protection (e.g., Sheldon 2006: Copeland and Taylor 2004).

While various studies attempt to provide empirical evidence about the nature and impact of

these linkages, there is actually little evidence that environmental regulation is one of the most

important determinants of firms’ ability to compete internationally. It seems that multinational

firms’ location decisions are determined more by issues such as labor costs and market access

rather than the stringency of local environmental regulations (Potoski 2001; Drezner 2001, Jaffe

et al 1995).

In contrast, the “race-to-the-top” argument suggests that economic liberalization induces

an international improvement of environmental standards and, in turn, has a positive effect on

the environment (e.g., Porter and van der Linde 1995). Empirically, Vogel (1995; 1997), for

example, shows that stricter regulations tend to diffuse among industrialized countries,

because companies that produce for multiple markets have an incentive to standardize

production processes for cost minimization. In addition, Porter and van der Linde (1995) state

that a tightening of environmental regulations stimulates technological innovation, which

helps to improve competitiveness. However, there exists only weak evidence that

environmental regulations actually stimulate innovation (Ambec and Barla 2006).

With regard to comparative advantage, which constitutes the starting point of our

theoretical argument,4 standard trade theory (i.e., the Hecksher-Ohlin model) asserts that trade

4 Our argument is based on three simple and justifiable assumptions. First, developed countries are

capital abundant and less developed countries are capital poor. Second, manufacturing is polluting

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leads to more production of goods that are intensive in that factor, which is abundant in the

country concerned. The literature then provides two competing approaches to predict the

effect of international trade on the environment: the factor endowment theory (FET) and the

pollution haven hypothesis (PHH). On the one hand, if the comparative advantage derives

from the distribution of world endowments of the factor of production (FET)5, countries

where capital is relatively abundant will export capital-intensive (“dirty”) goods. This

stimulates production, while pollution increases (Antweiler et al. 2001). Given that developed

countries are capital abundant, they will be more polluted with international trade. On the

other hand, if the comparative advantage derives from policy-related differences across

countries in tolerance of pollution (PHH), developing countries are expected to have worse

environmental quality with international trade due to pollution-haven effects (Copeland and

Taylor 1994). Ultimately, this leads us to expect the effect of trade on pollution to be positive

–independent of which theory, the FET or the PHH, is the relevant one.

Hypothesis 1 (Trade-Environment Hypothesis): Environmental quality decreases with

higher levels of trade openness.

Developed and developing countries differ, however, with regard to the levels of income

which is also expected to have an effect on environmental quality. According to the well-

known argument underlying the environmental Kuznets curve (EKC), environmental quality

deteriorates at low levels of income and then improves at higher income levels after a tipping

capital-intensive. Third, manufacturing is more polluting than agriculture and agriculture is more

polluting than service (see also Copeland and Taylor 2003). 5 A comparative advantage could also be determined by endowments of natural resources. In such

case, a country with abundant natural resources, say oil or forests, will most likely export them, and

thus trade is likely to damage the environment.

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point is reached (Grossman and Krueger 1995). The effects of economic scale, technology

and structure, as well as the income elasticity of environmental demand explain this inverted

U-shaped relation between income and environmental pollution. That is, over the course of a

country’s economic development, rising national income increases the scale of economic

activity, which leads to higher pollution levels (scale effect). Then, after a certain threshold of

national income has been reached, the production process of the economy becomes more

technology and information based (technology effect), and the service sector is boosted

(composition effect). This shift in the composition of production, combined with

improvements in technology result in a leveling-off and, eventually, a steady decline of

environmental degradation. In addition, at higher income levels, people pay more attention to

the quality of life and want to enjoy better environmental quality (see also Congleton 1992).

The government will respond to these demands by enacting and enforcing stricter

environmental regulations, which further improve environment quality. As a result, the EKC

argument posits that developed countries are likely to have stricter environmental regulations,

while the developing states are expected to have laxer environmental regulations relative to

the developed ones.

A number of empirical studies (Cole et al. 1997; Shafik and Bandyopadhyay 1997;

Grossman and Krueger 1995; Selden and Song 1994) find this non-monotonic, inverted U-

relationship between several local pollutants and income, suggesting a changing relationship

between environment and growth along the course of a country’s economic development.6

For example Grossman and Krueger (1995) examine data on air pollution levels in 42

6 The literature on the EKC has been criticized that it does not adequately capture all of the factors,

which are considered important for the relationship between income and pollution; and that most

statistical models are not correctly specified (see, e.g., Aklin 2015; Stern 2004; Cole and Elliot 2003;

Dasgupta et al 2002; Millimet et al 2003).

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developed and developing countries and find support for the EKC in that pollution levels first

rise and fall with GDP per capita.7

Based on this discussion, we argue that ultimately the relationship between economic

globalization, that is the combined effect of trade and FDI, and environmental quality might

be conditional on a country’s income.

With higher income, developed countries enact stricter environmental regulations relative

to developing ones, which tend to increase the costs of production and erode some of their

industries’ competitiveness. Under such conditions, multinationals may have an incentive to

shift some or all of their (“dirty”) production to other countries with weaker environmental

regulations. Hence, according to the FET, “dirty” capital will flow out from the developed

countries. Given that developed countries have comparable stringent environmental

regulations, FDI can only go to developing countries that are assumed to have lax

environmental regulations. Cole and Elliott (2005) provide empirical evidence that the level

of pollution abatement costs in a US industry significantly determines that industry’s FDI

decisions. Along the same lines, Eskeland and Harrison (2003) report that industry pollution

abatement costs have a positive, albeit weak, impact on US outbound FDI into four

developing countries. Xing and Kolstad (2002) provide evidence that the laxity of

environmental regulations in a host country is a significant determinant of FDI from the US

for heavy polluting industries.

According to the PHH, laxer environmental regulations in a developing host country

attract FDI from pollution-intensive sectors that want to avoid costly environmental

7 Empirical evidence shows that global pollutants such as CO2 do not conform to the EKC predictions,

since the impact of global warming is (largely externalized to other countries and future generations.

Hence, these global pollutants are expected to increase monotonically with income, at least within an

observable income range (Cole et al. 1997; Shafik 1994).

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compliance in their developed home country (e.g., Dasgupta et al. 1995).8 Dean et al. (2009)

examine whether weak environmental stringency affects FDI inflows into China. They report

that environmental stringency only influences certain types in highly polluting industries with

investment originating from Hong Kong, Macau, and Taiwan being attracted to provinces

with relatively weak environmental controls.On the other hand, investment from OECD is

attracted to high-regulation regions. Javorcik and Wei (2005) also analyze the relationship

between cross-country FDI flows and environmental stringency for 143 multinational firms in

25 Eastern European countries. They find some evidence for the PHH in regressions

employing treaties as a proxy for environmental standards in a host country, but the overall

evidence is relatively weak.

Given that both the FET and PHH argue that “dirty” FDI will flow from developed to

developing9 countries, the expectation is that the environmental quality will deteriorate in

these countries, and by implication it will improve in developed ones.

Hypothesis 2 (FDI-Environment Hypothesis): Environmental quality in developing countries

decreases with FDI inflows.

8 Opposite to the PHH, there is the pollution halo hypothesis, which states that FDI has a positive

effect on the environment through the use of environmental-friendly techniques of production as well

as the transfer of such technologies from developed countries to developing ones that rely mainly on

environment-damaging production techniques such as less efficient and polluting types of energy (e.g.,

Aklin 2015; Elliott and Shimamoto 2008; Gallagher and Zarsky 2007; Prakash and Potoski 2006a,

2007; Eskeland and Harrison 2003). 9 Given that capital is scarce in developing countries, we should not expect capital outflows from such

countries. However, developing countries rich in natural resources might be an exception. But even in

this case, FDI can flow only to other developing countries and not to developed ones due to the

stringency of their environmental regulations.

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Coming now to the globalization effect on environmental quality, the FET suggests that the

joint effect of FDI outflows and free trade on the environment is positive for only the

developed countries. Recall that developed countries are capital abundant and thus specialize

in the production of manufacturing goods, which are capital-intensive and hence harmful to

their environment. Nevertheless the stringency of their environmental regulations will force

“dirty” industries via FDI to relocate (and it will also not allow any ‘dirty’ capital to enter

independently of the country of origin), and consequently developed countries will specialize

in the production and exports of clean goods. In other words, according to the FET, the

negative effect of trade will be mitigated by FDI outflows and hence their join effect should

lead to improved environmental quality in developed countries.

According to the PHH, countries with laxer environmental regulations will attract FDI

from firms that seek to escape stringent environmental regulations in the developed countries.

Since the developing countries have laxer environmental regulations, because the

environment is considered to be a luxury good at their low level of income, ‘dirty’ FDI is

more likely to flow into developing countries. FDI inflows to developing countries will

increase the scale of economic activity, i.e., more production, which will result in worse

environmental quality. That is, the joint effect of FDI inflows and free trade on the

environment in will be even “worse” than the negative trade-effect per se, since they will

expand production and exports of the dirty goods. In other words, according to PHH, the

negative effect of trade will be amplified by FDI inflows and hence their combined effect

should be detrimental to the environment of less developed countries.

Hypothesis 3 (FDI-Trade- Hypothesis): There is a joint effect of FDI inflows and trade on

environmental quality. Under the FET, the joint

impact improves environmental quality. Under the

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PHH, the joint impact deteriorates environmental

quality.

3. Research Design

Data, Methodology

In order to test the empirical implications of the theoretical framework, we analyze a data set

that has the country-year as the unit of analysis and covers the time period from 1970 to 2003.

This time period is pre-determined by the availability of data for our dependent variable and

the explanatory items; moreover, since all our explanatory variables are lagged by one year,

the “effective” starting date of our analysis is 1971. Given the continuous nature of our

dependent variable (explained in the next paragraph) and the time-series cross-section format

of our data, we use OLS regression models that include country fixed effects, year dummies,

and a lagged dependent variable. The longitudinal nature of our data allows us to consider the

role of countries’ past emission levels on their current emissions.10 While this also captures

any existent time dependencies more generally, year fixed effects control for temporal shocks

that are common for all states in a given year. Finally, country-fixed effects control for

idiosyncratic path dependencies and other forms of cross-sectional heterogeneity.

Dependent Variable

In terms of our dependent variable, we measure environmental quality by sulfur dioxide (SO2)

emissions per capita (SO2 Emissions per capita (ln)).

10 The inclusion of a temporally lagged dependent variable also has implications for inference. Given

the structure of the data, serially correlated errors within countries might be possible. The lagged

dependent variable addresses this possibility (Beck 2001). We are aware of the arguments against the

inclusion of a temporally lagged dependent variable in fixed effects models (Plümper, Troeger, and

Manow 2005), but we opt to include it, since it yields more conservative estimates. Note, however,

that the substantive results we report below are identical (or even stronger) when the temporally

lagged dependent variable is omitted.

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While there are several pollutants or emission types that could serve as indicators of

environmental quality, for the purposes of our study, a pollutant should fulfill the following

requirements. First, it must be produced by human activity. Second, it must have harmful

effects on humans, ecosystems, and the economy. Finally, for statistical purposes, there must

be available data for both developed and developing countries. Air pollution, and in particular

SO2 emissions fulfill all these requirements.

First, SO2 is one of the so-called criteria pollutants,11 used by the World Bank, the OECD,

and numerous other national and international authorities to measure air quality. Second, SO2

is arguably the most prominent form of air pollution worldwide, since it has direct and visible

effects on human health, ecosystems, and the economy. Third, SO2 emissions can be directly

affected (i.e., decreased or increased), if governments wish to, by altering the techniques of

production. For example, SO2 emissions can be curtailed by reducing consumption of fossil

fuels (especially high-sulfur coal), by using smoke-scrubbing equipment in power plants and

smokestacks, by reducing the sulfur content of fossil fuel, or by increasing energy

efficiency.12 Finally, emissions are more closely linked to economic activity than

concentrations (Stern et al., 1996).

The data for countries’ annual SO2 emissions are taken from Stern (2005; 2006), which

cover the time period until 2003. We divided this information by a state’s population to

standardize it, and consider its natural log as the final variable in order to scale down the

variance and reduce the effect of outliers.

Core Explanatory Variables: Trade and Foreign Direct Investment

We operationalize our first core explanatory variable, trade, with a country’s degree of

openness, i.e., the share of imports plus exports divided by GDP (Trade Openness (ln)). This 11 Carbon monoxide (CO), nitrogen oxide (NO2), ozone (O3), particulate matter (PM10 and PM2.5), and

lead (Pb) are other criteria pollutants. 12 Although these emission-reduction measures are readily available and effective, they are also costly.

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item is based on Gleditsch’s (2002) trade and population data, and we lag and log it as well.

Imports and exports of this variable are measured in current US dollars.

The data for our second set of core variables are taken from Lane and Milesi-Ferretti

(2001; 2007) who recently updated their data on foreign direct investment to 188 states in

1970-2011. Specifically, it is distinguished between FDI inflows and outflows, with “capital

inflows measuring net purchases or sales by nonresidents of domestic assets, while outflows

measure net purchases or sales of foreign assets by residents” (Lane and Milesi-Ferretti 2007:

225). More generally, though, according to these authors, “the FDI category includes

controlling stakes in acquired foreign firms (at least 10% of an entity’s equity – in practice,

however, most FDI holdings reflect majority control), as well as green-field investments. In

addition, at least for some countries, an increasingly important component of FDI is foreign

property investment” (Lane and Milesi-Ferretti 2007: 227). In line with our theoretical

argument, FDI flows are disaggregated into inflows and outflows as we argued above that the

effect of these should go in opposite directions. The former is measured by FDI liabilities,

while we take FDI assets for the latter. Both variables are measured in current US dollars,

expressed as a percentage of a country’s GDP, and lagged by one year and logged.

Finally, in order to test our expectations regarding the intervening influence of GDP per

capita and trade, respectively, some of our models include multiplicative terms between FDI

Inflows / GDP (ln) and GDP per capita (defined below) and between FDI Inflows / GDP (ln)

and Trade Openness (ln).13 Since we cannot directly interpret the size, signs, and t-statistics of

the components of a multiplicative specification in an OLS regression, we calculated average

marginal effects (Brambor et al. 2006), which are displayed in graphical form below.

13 As argued above, the impact of trade does not vary across developed and developing states, and we

do find evidence for this. Hence, specifying a three-way interaction between income, trade openness,

and FDI inflows is not necessary to test our third hypothesis; a two-way specification between trade

and inward FDI is sufficient.

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Control Variables

We also consider a series of control variables, which account for alternative determinants of a

country’s SO2 emission level. Hence, our control variables include standard domestic-level

economic and political variables and international-level influences such as the participation in

international organizations or the number of international environmental treaties a country has

ratified.

First, regarding the economic determinants, some forms of environmental degradation,

including SO2 emissions, follow a Kuznets curve pattern (e.g., Grossman and Kruger 1995;

Selden and Song 1994): pollution first deteriorates and then improves as economic

development in the form of GDP per capita increases. In light of this, we include GDP per

capita (ln) and its square term in our estimations. We also employ this variable to distinguish

between developed and developing states and, hence, to model the interaction between GDP

per capita (ln) and FDI Inflows / GDP (ln).The data are taken from Gleditsch (2002) and are

measured in current US dollars (million).

Second, environmental degradation may be the result of high rates of economic growth and

a larger scale of economic activity more generally (e.g., Grossman and Kruger 1995; Selden

and Song 1994). Moreover, a larger population usually consumes more natural resources and,

hence, produces greater environmental degradation (e.g. Spilker 2012). In order to address

these concerns, we include a state’s lagged yearly average percentage growth in GDP and

measure the scale of economic activity by the lagged and log level of a country’s GDP.

Moreover, we consider Population Density (ln), which is the lagged and logged midyear

population divided by land area in square kilometers. All of these three variables are taken

from the World Bank Development Indicators, while GDP (ln) and GDP Growth in % are

measured in current million US dollars.

Third, coming to a domestic-level political influence, it is frequently claimed that non-

democratic regimes are likely to underprovide public goods, including environmental quality

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(Congelton 1992; Bueno de Mesquita et al. 2003; Fredriksson et al. 2005; Li and Reuveny

2006; Bernauer and Koubi 2009). The underlying rationale for this contends that non-

democratic regimes are typically ruled by small elites that use the resources of their respective

country to create personal wealth and to redistribute income from their populations towards

themselves. If the costs of stricter environmental policies are born disproportionately by the

elites (as it would be the case with restrictions on polluting industrial activities) while the

benefits are uniformly dispersed throughout the population, these elites would have little

incentive to implement such policies.

In contrast, in democracies, the median voter who decides on public policy faces a lower

cost from environmental policies relative to the economic and political elite. This makes the

adoption and implementation of stricter environmental policies more likely in democratic

regimes. In order to control for this mechanism, we rely on the polity2 item from the POLITY

IV data set (Marshall and Jaggers 2002). This variable is a composite index that includes the

following elements: presence of competitive political participation, guarantee of openness and

competitiveness of executive recruitment, and existence of institutionalized constraints on the

exercise of executive power. Our final variable, Democracy, ranges from -10 (highly

autocratic) to +10 (highly democratic).

Finally, we incorporate international determinants in the form of states’ membership in

intergovernmental organizations (IGOs) and their participation in international environmental

treaties. International policy cooperation is an important component of political globalization

(Dreher 2006). International policy cooperation can produce a globally cleaner environment

by limiting free riding/externality problems in the production of pollution, i.e., by reducing

the cross-country spillover effects of poor, national environmental policies. In addition, states

more strongly involved in the IGO network are also more likely to cooperate on

environmental issues, due to socialization, information exchange, and enhanced possibilities

for issue linkages across policy areas. Recent studies have shown that indeed membership in

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intergovernmental organizations (IGOs) is robustly associated with a cleaner environment

(Böhmelt and Vollenweider 2015; Spilker 2012; Ward 2006). We thus include the number

IGOs a state is a member of (see, e.g., Ward, 2006; Bernauer et al., 2010; Spilker, 2012). The

data for this count variable are taken from Pevehouse et al. (2004).

Moreover, international environmental treaties oblige member countries more directly than

IGO Membership to cooperate on environmental problems such as air or water pollution,

climate change, trade in toxic waste, and endangered species (Ward, 2006). We employ the

degree of participation in international environmental treaties, i.e., the cumulative number of

ratified environmental treaties in a given year, in order to capture this mechanism. We use the

data from Spilker and Koubi (2014).

Table 1 summarizes the descriptive statistics of the variables included in our analysis. We

also present the variation inflation factors (VIFs) for each explanatory variable to see whether

multicollinearity constitutes a major problem in our setup. As demonstrated in the table,

however, all independent variables have VIFs well below the threshold level of 5 – the only

exception being, not surprisingly, GDP per capita (ln) and its square term due to the

construction of these variables. Furthermore, there seems to be not much overlap between,

e.g., IGO Membership and Env. Treaties Rat.

__________

Table 1 in here

__________

Empirical Results

Table 2 presents the results for our baseline models that focus on the impact of FDI flows and

trade on environmental quality either with or without the control variables. These

specifications seem important in light of Clarke (2005; 2009) who argues that control

variables may actually increase the bias in our estimates instead of decreasing it. Hence, while

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Model 1 focuses on our core variables of interest only (although country fixed effects, year

dummies, and the lagged dependent variable are included), Model 2 also incorporates the

control variables we discussed above. Finally, Model 3 drops the GDP related variables (GDP

(ln) and GDP Growth in %) as well as the variable capturing the ratification of environmental

treaties as these might be overlapping with or are in fact very much related to the items that

are standardized by GDP (e.g., FDI Inflows / GDP (ln)) or are likely to be included in IGO

Membership, respectively. Either way, while the model-fit statistics demonstrate that the

control variables lead to an improvement of the model, there is not much difference between

Model 2 and Model 3.

_________

Table 2 in here

__________

Coming to our core variables of interest in Table 2, we see that our results are generally very

robust across specifications. More specifically, Trade Openness (ln) is positively signed

throughout Models 1-3 and statistically significant at conventional levels. Substantially, Table

2 shows that a 10% increase in Trade Openness (ln) induces on average a 0.4% rise in SO2

emissions per capita. Hence, our findings actually suggest that the stronger a country is

involved in the global trade, the more it will pollute. This means that we obtain support for

both the FET and PHH and our first hypothesis, which implies that higher economic activity

(scale effect) as captured by imports and exports comes with worse environmental quality – in

this unconditional model and only when focusing on Trade Openness (ln).

Contrary to the trade item, FDI Inflows / GDP (ln) is in fact negatively signed and

statistically significant at least at the 5% level regardless of model specifications. Therefore,

we obtain support for the pollution hallo argument, which states that environmental quality

19

improves with more inflows of FDI into a country14 (e.g., Aklin 2015; Elliott and Shimamoto

2008; Gallagher and Zarsky 2007; Prakash and Potoski 2006a; 2007; Eskeland and Harrison

2003). Substantively, when increasing FDI Inflows / GDP (ln) by 10%, SO2 emissions per

capita go down by about 5% on average. Somewhat unexpectedly, however, FDI Outflows /

GDP (ln) shows inconclusive results in Table 2: the variable’s coefficient is either

insignificant (and also changes signs) in Models 1-2 or positively signed and significant in

Model 3, which actually suggests that a state’s environmental quality as measured by SO2

emissions actually worsens with more outflows of FDI.

That said, the estimations presented in Table 2 rely on “unconditional” specifications and,

thus, do not allow for a direct test of our second and third hypotheses. Specifically, we need to

estimate the effect of FDI inflows conditional on a country’s income level and, in a second

step, conditional on its level of trade activity. Hence, we calculated multiplicative terms along

these lines and estimated “conditional” or interactive models, which rely on the specifications

used for Model 3.15 Table 3 summarizes our findings here, while we present the marginal

effects of the interactive relationships in Figures 3 and 4.

14 Since this variable captures total FDI inflows, i.e., FDI inflows to both developed and developing

countries, it seems that multinational companies (MNCs) not only export greener technologies, but

also conduct business in an environmentally friendly manner in host countries. The main reasons for

doing so might be that (1) MNCs use the same type of (advanced) technologies across countries to

minimize production costs, (2) MNCs possess newer and less polluting production methods as

compared to local firms, (3) MNCs use less polluting technologies in order to avoid criticism from

consumers in their home countries, and (4) host countries might require the same as local and/or

stricter environmental standards for foreign companies. 15 While Model 3 does not include all controls that are considered for Model 2, the model fit of the

former is better than for the latter. Hence, due to parsimony, we focus on the specification of Model 3

in the following, although our interaction results remain unchanged when using Model 2’s

specification.

20

_________

Table 3 and Figure 3 in here

__________

As demonstrated in Figure 3, FDI inflows into the developed countries do not matter for the

environment. However, FDI inflows play a major role for (highly) underdeveloped countries:

the marginal effect of FDI Inflows / GDP (ln) becomes statistically insignificant at a logged

GDP per capita level of about 6.5. As soon as we cross this threshold level, FDI Inflows /

GDP (ln) has a statistically insignificant effect. Before that threshold, however, FDI Inflows /

GDP (ln) is actually positively signed. Substantively, a 10% increase in FDI Inflows / GDP

(ln) leads to 1.5% increase in SO2 emissions per capita in the most underdeveloped countries

(i.e., GDP per capita (ln) level of 4; according to our data, Bhutan had such an income level

between 1971 and 1978). Overall, these findings are consistent with the PHH and the FET.

Specifically, according to the FET, FDI inflows into developed countries must go into non-

polluting activities, because the rate of return in those activities is higher than in the rest of the

world. The PHH has the same implication: clean capital will flow into those states that have

higher environmental standards, while dirty capital will go into the countries with the less

stringent environmental regulation. More generally, however, we not only find support for our

second hypothesis, but also show that the effect of FDI Inflows / GDP (ln) can change when

taking a conditional relationship into account. We do so in Models 4-5, but have estimated

unconditional models in Table 2 above.

__________

Figure 4 in here

__________

Turning to the third hypothesis and, hence, our second interactive effect, the combined

influence of trade openness and FDI inflows is also positive (Figure 4) – and thus “reverses”

the unconditional findings we obtained in Table 2. In more detail, FDI Inflows / GDP (ln) is

21

statistically insignificant for trade-openness levels of about 1.0 (logged) or less. For higher

values of Trade Openness (ln), however, FDI Inflows / GDP (ln) exerts a strongly positive

and significant impact on SO2 emissions: at the maximum of 4.0 of Trade Openness (ln) in

Figure 5, a 10% increase in FDI Inflows / GDP (ln) leads to 1.9% increase in SO2 emissions.

Most importantly, it is this finding that has discriminating power across the two theories of

the PHH and FET the positive impact of the interactive term favors the PHH, while a negative

one would have supported the FET.

Finally, coming to our control variables, we focus on the statistically significant items only

due to space limitations. First, the lagged dependent variable is, not surprisingly, positively

signed and highly significant. A higher level of SO2 emissions in the previous year leads to

more emissions in the current year. On average and across Models 1-5, our calculations

suggest that a 1% increase in the lagged dependent variable increases SO2 Emissions per

capita (ln) by about 0.8%. Second, we only find weak (at best) support for the environmental

Kuznets effect. Regardless of model specifications, the estimated coefficient of GDP per

capita on pollution is positive, while its square term is negatively signed. Note, however, that

the square term reaches conventional levels of statistical significance only in Model 5. Hence,

although the signs of the GDP per capita variables are consistent with the expectations of the

Kuznets curve literature (e.g., Grossman and Kruger 1995; Selden and Song 1994), the (in-)

significance levels of the square term actually point to a linear effect of income on SO2

Emissions per capita (ln). Third, the only remaining control item that is statistically

significant is population density. The variable’s coefficient estimate is consistent both in

terms of substance and significance across Tables 2 and 3. On average, a 10 % rise in

Population Density (ln) induces an increase of about 5.3% in SO2 Emissions per capita (ln).

All other control items have statistically insignificant coefficient estimates. For example,

participation in international organizations, either in terms of participating in IGOs more

generally or ratifying international environmental agreements, does not make it more or less

22

likely that a country will have a higher environmental quality. The poor performance of the

control variables in Tables 2 and 3 might be explained by the fact that fixed effects models

lack the ability to make inferences about time-invariant or slow-moving variables, because

those covariates are highly collinear with fixed effects and their coefficients are either not

identified or difficult to estimate with precision (Plümper and Troeger 2007). Thus, fixed

effects soak up most of the explanatory power of slowly changing variables (Beck 2001: 285).

We also changed a series of model specifications in order to examine the robustness of our

findings. We briefly describe these additional checks here, but omit tables or graphs as these

can be replicated with our replication material. First, and as demonstrated above, including or

excluding control variables does not affect the substance of our findings (Clarke 2005; 2009).

Second, we also estimated models that replaced fixed effects with random effects, substituted

the year dummies by a time (yearly) trend variable, or relied on a Prais-Winsten specification

with panel-corrected standard errors. Again, none of these modifications in our quantitative

setup altered our core findings. Third, SO2 emissions are just one type of pollutant that is

frequently used as a proxy for environmental quality. Other work also focuses on CO2

emissions as another, major determinant of climate change and, hence, environmental quality.

We thus re-estimated all models with the variable CO2 Emissions per capita (ln). While FDI

Inflows / GDP (ln) becomes insignificant in some models that are based on Table 2, the

substantive interpretation of our interactive relationships remains unchanged. Hence, our

findings are robust across two related, but different pollutants.

4. Conclusion

In this paper, we have developed a theoretical framework that allows us to discriminate

empirically between two competing theories, namely the FET and the PHH. This is due to two

innovations in our theoretical argument relative to the existing literature. The first is the joint

study of international trade and FDI. And the second is the inclusion of both PHH and FET

23

effects. The key discriminating implication is that the interaction between trade with inward

FDI is expected to be positive under the PHH, but negative under the FET. Our empirical

findings from a large set of countries indicate that the former is true.

Furthermore, we believe that the discrimination between the two theories is of particular

interest to policymakers and the scholarly community. While the FET and the PHH do not

have different implications about which type of countries (developed vs. less developed) will

become “dirtier” with higher levels of trade, both theories do have different implications

about the effects of the harmonization of environmental standards on pollution levels, though.

In particular, under the PHH, harmonization will eliminate the “comparative” advantage in

pollution enjoyed by the less-developed countries and, thus, lead to lower pollution there.

Under the FET, on the other hand, the harmonization of environmental standards will have no

such effects, as it is the developed countries (which already have high standards) that become

dirtier with globalization.

Consequently, a key policy implication of our analysis is that the globalization of

environmental standards (international harmonization) has the potential to reverse the increase

in pollution in less developed countries that derives from the globalization of trade and FDI.

24

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