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The Policy Studies Journal, Vol. 41, No. 2, 2013
Environmental Regulations and Multinational
Corporations’ Foreign Market Entry Investments
Jorge Rivera and Chang Hoon Oh
In this study, we examine how differences in environmental regulation characteristics are linked to
multinational corporations’ (MNCs) foreign market entry (FME) investments decisions around the
world. We rely on a data set with 29,303 observations from 94 European Fortune Global 500 companies
operating across 77 countries during the period 2001–2007. We found that MNCs are more likely to
enter countries with more certain—i.e., clearer and more stable—environmental regulations than those
of their home countries. Results also suggest that there is a higher level of MNC entry into foreign
countries with environmental regulations that are more stringent than those of their home countries.
This finding challenges the controversial but commonly held view that more stringent environmental
regulations deter MNCs’ FME investments. Notably, the magnitude of the regulatory certainty
relationship with MNCs’ FME investments is larger than that of regulatory stringency. Findings also
indicate that the increased tendency of MNCs to enter countries with more stringent environmental
regulations is higher in more democratic countries and for cleaner industry firms.
KEY WORDS: environmental policy, multinational corporations, foreign investment, regulation certainty, regulation stringency
Introduction
The nature of the relationship between environmental regulations and economic activities, such as foreign direct investment (FDI) and international trade, is
a topic that, for decades, has drawn much attention from influential groups around
the globe. When considering FDIs by multinational corporations (MNCs), much of
the focus of environmental policymaking debates and the scholarly literature have
been on whether environmental regulation stringency negatively affects these
investments. The controversial but popular “race to the bottom” perspective suggests a vicious cycle dynamic in which countries have to steadily relax their environmental protection requirements to attract more FDI (Koniski, 2008; Potoski,
2001; Woods, 2006). Despite its controversial nature, the race to the bottom view
continues to be frequently embraced in heated public policy debates (Koniski,
2008), thus encouraging passionate, and sometimes violent, resistance by environmental activists to the promotion of free-trade and globalization.1 Interestingly,
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Published by Wiley Periodicals, Inc., 350 Main Street, Malden, MA 02148, USA, and 9600 Garsington Road, Oxford, OX4 2DQ.
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major U.S. industry groupsthat have dismissed the race to the bottom perspective
are now opposing the adoption of new environmental regulations because of their
supposed threat to economic growth and business investment.2 Yet, the scholarly
literature examining the link between FDI and environmental regulations’ stringency offers opposing conceptual views and non-conclusive empirical evidence
(Darnall, 2009; Madsen, 2009).3
To be sure, when making FDI decisions, managers may consider other characteristics of environmental regulations, such as their certainty. Given the long-term
nature of most environmental investments, regulations’ certainty is particularly
important for encouraging firms to develop and adopt efficient and/or innovative
ways of compliance. Additionally, a country’s democratic context and the type of
industry may change how business managers perceive the nature of the relationship
between FDI and environmental regulations’ characteristics. This is because the costs
of compliance vary greatly across industries. Democracy levels also vary widely
across countries and they shape the power of business and other interest groups
during the environmental policy process. Accordingly, we address the following
questions: Are differences in the certainty of environmental regulations (between
host and home countries) linked to MNCs’ foreign market entry (FME) investments?
How do host countries’ democracy levels moderate the relationship between MNCs’
FME investments and environmental regulations’ stringency and certainty? How
does the type of industry moderate the relationship between MNCs’ FME investments and environmental regulations’ stringency and certainty?
The term FME investment, a particular kind of FDI, is used in this article to refer
to the initial establishment of a MNC’s wholly owned subsidiary in a foreign
country. It does not include expansions of already existing subsidiaries or subsequent entries by other subsidiaries of a given MNC. Our focus on FME investments
has the advantage of examining those investments for which firms need to dedicate
considerable resources to a foreign country. This is because the initial establishment
of a wholly owned subsidiary brings substantial responsibility, commitment, and
higher risks to a MNC’s headquarters (Anderson & Coughlan, 1987; Hill, Hwang, &
Kim, 1990).
To answer our research questions, we rely on over 29,000 observations of FME
investment decisions from European Fortune Global 500 companies operating across
77 countries during 2001–2007. We focus on these 7 years because for this period,
worldwide cross-country data are publicly available to measure the perceived levels
of environmental regulatory stringency and certainty by top corporate managers.
Our analysis of FME investments by individual companies contributes to previous
empirical studies that have relied on industry-level FDI trends to examine decisions
that are obviously made by the managers of individual firms (Kolk & Pinkse, 2005;
Madsen, 2009).4 Additionally, the wide variety of foreign host countries and European MNCs from multiple industries included in our study provides managers and
policymakers with more generalizable findings than those of previous research.
Earlier research has tended to focus on data from a single or a few heavy manufacturing industries in the United States to examine the relationship between environmental policy and MNCs’ FME investments decisions.
Rivera and Oh: Environmental Regulation and Foreign Entry
245
Literature Review
Trends in Environmental Regulations and Foreign Direct Investment
Since the early 1970s when the first major environmental protection policies and
government agencies were established in Europe and the United States, the stringency and number of environmental regulations have greatly increased, not only in
industrialized countries but also in developing nations around the world. Most
countries now have high-level government agencies or ministries equivalent to the
U.S. Environmental Protection Agency (U.S. EPA). Additionally, given the rising
prevalence of local and global environmental problems, the enactment of more
stringent environmental regulations is expected to continue growing in both developed and developing countries. Regulatory stringency is understood here as the
level of severity of a particular regulation’s targets and requirements.
Estimates of the cost of these regulations suggest that expenditures vary between
0.6 percent and 2 percent of gross domestic product (GDP) among industrialized
countries (OECD, 1999). European MNCs are used to have very strict levels of
environmental regulations and tend to take a proactive approach to environmental
protection (Ramus & Steger, 2000). A small number of European countries, such as
Germany, Denmark, and the Netherlands, have some of the most stringent environmental regulations in the world. Similarly, Finland has some of the most certain
environmental regulations followed by Denmark and Sweden. Regulatory certainty
refers in this article to the degree of clarity and stability of a regulation’s targets and
requirements (Bressers & Rosenbaum, 2000; Marcus, 1981). A few non-European
nations such as the United States, Australia, and New Zealand have environmental
regulation stringency and certainty levels comparable with those in northern
Europe. Yet, it is important to stress that countries in southern Europe (e.g., Spain
and Portugal) have had local environmental regulations that show lower levels of
stringency and certainty than those in the north.
Trends in outward FDI have similarly shown a steady increase since the 1970s
thanks in part to a steady worldwide decrease in countries’ restrictions and tariffs on
foreign ownership and international trade. In fact, for the 2001–2007 period, outward
FDI showed an expansion of more than 200 percent in real terms from US$8.7 to 18
billion (UNCTAD, 2010). During this period outward FDI by the European Union
countries increased about 2.5 times from US$3.5 to 8.9 billion (UNCTAD, 2010). On
average, a large European Union company listed in the Fortune Global 500 had 46
subsidiaries across 14 countries in 2001, and 59 subsidiaries across 18 countries in
2007.
Multinational Corporations’ Foreign Market Entry Investments:
Key Concepts and Empirical Findings
There is a large literature examining the determinants of FME by MNCs. This
literature is influenced by multiple theoretical perspectives highlighting different
foreign country characteristics and firm-level factors that increase the tendency of
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MNCs to establish subsidiaries in specific foreign countries. At the most basic level,
previous work has suggested that MNCs are less likely to enter countries that are
geographically distant, have smaller markets, or that impose stricter limits on foreign
investment (Caves, 1996; Dunning, 1998). Besides geographic proximity, it tends to
be easier for firms to successfully enter foreign countries that have cultures and
institutions that are more similar to those of the firms’ home countries (Fratianni,
2009; Ghemawat, 2007; Kostova, 1997; Madsen, 2009). Firms tend to go abroad
sequentially starting with neighboring geographic countries because international
investments are inherently seen as more risky than domestic ones. This process of
incremental expansion permits the firm to gain knowledge about foreign markets,
and such learning is facilitated through sequential expansion to more and more
distant foreign markets (Johanson & Vahlne, 1977; Kogut & Zander, 1993).
The higher complexity of organizing and managing foreign subsidiaries requires
MNCs to possess and develop resources and capabilities to overcome the increased
market, political, and cultural risks of overseas operations. Previous FME research
suggests that the MNCs better able to overcome these risks are those that are larger,
more profitable, more geographically diversified, own more proprietary technologies, and have more differentiation, trademarks, or brand equities (Dunning, 1998).
Perspectives on Environmental Regulations and Multinational Corporations’ Foreign
Market Entry Investments
The Pollution Haven Hypothesis. The large differences in the stringency, quantity, and
enforcement of environmental regulations between industrialized and developing
countries have been well established by multiple scholars (Blackman, 2006; Shah &
Rivera, 2007; Wehrmeyer & Mulugetta, 1999). The magnitude of these mismatches
can be illustrated by comparing the number of full-time employees at the U.S. EPA,
almost 18,000 in 2005, with those at the Chinese State EPA, about 400 for the same
year (Balfour, 2005, p. 122). These differences have, for a long time, generated serious
controversies and concerns about the nature of the relationship between environmental regulations and economic activity. The “pollution haven hypothesis” assumes
that valuable manufacturing and natural resource extraction businesses relocate to
developing countries to take advantage of lax and seldom enforced environmental
regulations. This perspective relies on the basic assumption that the costs of environmental protection are high enough to make variations in regulatory stringency a
critical factor in determining business entry and investment in foreign markets
(Ambec, Cohen, Elgie, & Lanoie, 2011; Brunnermeier & Levison, 2004; Jaffe &
Palmer, 1997; Madsen, 2009; Palmer, Oates, & Portney, 1995). Firms find environmental regulations costly because they reduce managerial discretion by forcing investments in specific raw materials, manufacturing technologies and practices, and
byproducts disposal among other things. The reduced pollution and enhanced protection of natural resources that result from all these efforts, although beneficial for
society, do not generate extra income for companies because they are rarely tradable
in the marketplace.
Rivera and Oh: Environmental Regulation and Foreign Entry
247
Awareness that laxer environmental regulations may attract more foreign investment, as the pollution haven hypothesis argument goes, may make policymakers
from different countries relax environmental regulations even more. Competition
among countries for limited foreign investment then results in additional regulatory
stringency reductions to keep attracting business investment. This generates a “race
to the bottom” dynamic involving the decline of valuable industrial activity in
developed countries and an acute increase in pollution and the degradation of
natural resources in developing countries. Additionally, the lenient environmental
protection requirements in developing nations spurs, rarely successfully, calls for
the adoption, in industrialized countries, of protectionist policies that erect barriers
to international trade (Ambec et al., 2011; Brunnermeier & Levison, 2004; Jaffe &
Palmer, 1997; Madsen, 2009). Although the pollution haven logic is now controversial among scholars because of a large body of contradictory empirical studies (see
details on next page), it is still common in public debates involving politicians,
policymakers, industry associations’ lobbyists, and environmentalists. In sum, these
arguments suggest that: MNCs are more likely to enter foreign markets with environmental regulations that are less stringent than those of their home countries.
Win-Win Perspective on Environmental Regulations. An alternative win-win perspective
proposed by Porter (1991) suggests that the traditional view espoused by the pollution haven hypothesis follows from analyzing environmental regulations with a
static approach that assumes away changes in environmental technologies, manufacturing processes, product characteristics, and customer preferences (Porter, 1991;
Porter & Van der Linde, 1995a). Proponents of this alternative view argue that when
taking into consideration the intrinsically dynamic nature of competition, technology
development, and customer desires, more stringent environmental regulations that
are appropriately designed can enhance a country’s competitiveness to promote
business and attract investment even if they are more rigorous and/or implemented
earlier (Palmer et al., 1995; Porter & Van der Linde, 1995a; Seeliger, 1996).
The win-win perspective rejects the assumptions that profit-seeking firms have
perfect access to information, and have already discovered the best and most efficient technologies to comply with environmental regulations (Christmann, 1997;
Porter & van der Linde, 1995a, 1995b). On the contrary, it emphasizes that because
of a reactive approach to environmental management, firms systematically fail to
consider pollution as a waste of resources and as a sign of inefficient production
processes. Thus, firms often neglect opportunities to improve the efficiency of their
manufacturing processes (Hart, 1995; Russo & Fouts, 1997). Implementation of environmental management strategies focused on reducing or eliminating waste before it
is created can, e.g., generate cost savings (Koehler, 2007). Additionally, given the
growing demand for environmentally friendly products and services, stricter environmental regulations can also help enhance competitiveness by allowing first
mover firms to obtain price premiums and/or gain exclusive access to new environmentally sensitive markets (Reinhardt, 1998; Rivera, 2002).5 This reasoning suggests
that: MNCs are more likely to make FME investments in countries with environmental
regulations that are more stringent than those of their home countries.
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Empirical Evidence. There is a large number of empirical studies examining the
contradictory predictions of the pollution haven hypothesis and the win-win perspective. Their findings are inconclusive spanning the whole spectrum from insignificant to small positive or negative effects of environmental regulation stringency
(Domínguez & Grossman, 2007). Even for studies that have found statistically significant effects, the effect size is smaller than the effect of other country factors such
as, e.g., market size, infrastructure, unionization, etc. (Ambec et al., 2011; Levison,
2010). These studies exhibit some key limitations preventing researchers from
drawing conclusions about the contradictory logics advanced by the win-win perspective and the pollution haven hypotheses.
First, most of them have been restricted to examining new plant locations in
different parts of the United States, or on foreign investment decisions by a few U.S.
heavy manufacturing industries. Second, previous research focused on plant location trends at the aggregate industry level, treats all firms as having the same
characteristics ignoring the competitive advantages gained by the most environmentally proactive firms. Third, these studies focus on regulatory stringency, ignoring
other characteristics of well-designed environmental regulations that may affect
MNC tendency to enter foreign countries. Fourth, previous published work has
used country pollution levels and pollution abatement costs as proxies of environmental regulations’ stringency. These proxies are problematic because they can be
both the outcome and the cause of different levels of environmental regulations’
stringency.6 Fifth, almost all previous studies examining these issues have used gross
levels of country environmental regulatory stringency to predict MNCs’ FME investment decisions. Yet, the international business literature suggests that host–home
country differences in regulatory characteristics are better predictors of MNCs’ FME
investment decisions than gross measures of these characteristics (Kostova, 1997;
Madsen, 2009).
Our analytical approach seeks to address these limitations by examining individual MNCs’ FME investment decisions for companies from multiple sectors of the
economy, operating in more than 75 countries around the world. We also avoid using
aggregate pollution levels or pollution abatement costs as proxies for environmental
regulation stringency. Instead, we calculate differences in environmental regulation
stringency between host and home countries using data from the World Economic
Forum’s (WEF’s) Annual Survey. On an annual basis, this survey gathers top corporate executives’ assessment of countries’ environmental regulations stringency (see
details in the Methodology section). Additionally, besides stringency, we also consider how MNCs’ FME investment is affected by host–home country differences in
environmental regulations’ certainty.
Environmental Regulations Certainty and Multinational Corporations’
Foreign Market Entry Investments
When considering foreign investment, a high degree of certainty should also be
considered as a critical characteristic of well-designed environmental regulations.
Regulatory certainty is understood here as the degree of clarity and stability of a
Rivera and Oh: Environmental Regulation and Foreign Entry
249
regulation’s targets and requirements (Bressers & Rosenbaum, 2000; Marcus, 1981).
Given the long-term nature of most environmental investments, stricter requirements need to be certain enough to allow firms to develop and adopt innovative
ways of compliance. Unstable and/or unclear regulations make it very hard for
managers to predict actual environmental protection requirements and thus significantly hinder the potential for win-win environmental innovations to accrue gains
from higher productivity (Rosenbaum & Bressers, 2000). Given the inherently risky
nature of investments in environmental innovations, if environmental requirements
are uncertain, firms will postpone investments in enhanced environmental protection to wait for “final” environmental regulations standards (Johnstone, Hascic, &
Popp, 2010; Marcus, 1981).
Business managers also have a strong preference for regulatory certainty because
it allows them to reduce compliance risks and maintain high levels of legitimacy
with multiple stakeholders. Even the most polluting companies seek to develop and
sustain reputations as good environmental stewards to improve their “green” legitimacy (Rivera, de Leon, & Koerber, 2006). This is, however, difficult to do when
environmental regulations are unstable and/or unclear because regulatory requirements determine the minimum benchmark to attain “green” legitimacy. Hence,
when examining the relationship between environmental regulations and business
investment in different country locations, it is critical to not only consider the
stringency of regulations but also their level of certainty. Overall, these arguments
can be summarized by suggesting that: MNCs are more likely to make FME investments
in countries with environmental regulations that are more certain than those of their home
countries.
Moderating Effect of Political Context and Industry Type
Besides the stringency and certainty of environmental regulations, other country
contextual characteristics such as economic wealth and market size are known to
moderate how firms perceive the attractiveness of a country for investment and how
firms may respond to environmental protection demands. Political context variables,
such as the respect for the rule of law and government stability, are also considered
important factors determining a firm’s market entry decisions (Daude & Stein, 2007).
Political context factors may also change how businesses respond to environmental
regulations, but these effects have not been extensively explored by empirical
research. Most empirical studies examining the moderating effect of political context
on business strategy choices have focused on variations between the more confrontational U.S. style of politics and policymaking and the more cooperative approach
prevalent in Europe (Spencer, Murtha, & Lenway, 2005); thus, assuming very high
levels of democratic rights and freedoms as given.7 Yet, levels of democratization
vary widely around the world. In emerging market countries, democratic traditions
and advocacy channels are more likely to be limited, fragile, and incipient.
Democracy levels are particularly important to consider when examining MNC
responses to environmental regulation, because variations in basic democratic rights
and liberties shape the interaction of different actors during the environmental
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policy process by defining which interest groups and political strategies are legitimate, delineating appropriate advocacy procedures, and establishing rules for government decision making and law enforcement (Ascher, 1999; Payne, 1995; Rivera,
2010). Freedom of the press, speech, association, political participation, and the
unhindered right to vote are core rights taken for granted in democratic systems.
These rights open information and advocacy channels to multiple grassroots actors
that in authoritarian regimes are traditionally, and almost exclusively, enjoyed by
business, military, and political elites (Ascher, 1999; Grindle & Thomas, 1991). These
freedoms are then used by environmentalists and businesses to limit the discretion
of public policy makers to enact and implement arbitrary environmental regulations
(Rivera, 2010). Hence, in general, business resistance to environmental regulations
may be lower in democratic countries than in authoritarian ones (Rivera, 2010).
It is also important to consider how industry type may moderate the relationship
between FME investments and environmental regulations. Different industries
experience distinct competition dynamics, cost structures, and regulatory requirements that influence companies’ profits and their choice of different strategies
(Powell, 1996; Rumelt, 1991; Schmalensee, 1985; Tashman & Rivera, 2010). In the case
of environmental regulations, the costs of compliance vary greatly across industries,
particularly when comparing firms in “cleaner” manufacturing sectors (e.g., aerospace; computer, office, and electronics; food and drugs; motor vehicle and parts;
and pharmaceutical firms) and those operating in “dirty” industries that produce the
highest levels of pollution (e.g., heavy manufacturing industries such as chemical
and oil and gas refining). Environmentalists and government agencies also tend to
monitor more the environmental practices of companies from heavy polluting industries than those from companies in cleaner industries that produce the lower levels
of pollution (Shah & Rivera, 2007). Accordingly, MNCs from dirty industries, with
higher pollution management costs, may be much more averse to investing in countries with more stringent and certain environmental regulations.
In sum, the previously discussed arguments indicate that the relationships
between MNCs’ FME investments and environmental regulations’ stringency and
certainty are moderated by the democratic nature of the host countries and by the
type of industry. Specifically, we suggest that: MNCs’ tendency to make FME investments in countries with more stringent environmental regulations than those of home
countries is: (i) higher in more democratic countries; and (ii) higher for cleaner industry
firms. We also suggest that: MNCs’ tendency to make FME investments in countries with
more certain environmental regulations than those of home countries is: (i) is higher for
cleaner industry companies; and (ii) higher in more democratic countries.
Research Methodology
Data Collection and Sample
We used MNCs’ annual reports to shareholders and their yearly legal statements
to the U.S. Securities and Exchange Commission (10-K reports) to collect information
about their FME investment decisions. Our final sample consisted of a panel data set
Rivera and Oh: Environmental Regulation and Foreign Entry
251
containing 29,303 company-year observations from 94 MNCs that originated in European Union countries. The 94 MNCs were from 13 countries: Austria, Belgium,
Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, the Netherlands,
Spain, Sweden, and the U.K. These MNCs operated across 77 foreign countries
during the period 2001–2007. This sample was drawn from the population of 217
European Union companies listed as Fortune Global 500 firms during any year
between 2001 and 2007. We excluded three types of companies from this population
of European Fortune Global 500 companies. First, we dropped purely domestic
companies. Second, we discarded companies that did not list their subsidiary locations for multiple years and those that did not provide firm-level information (e.g.,
research and development [R&D] expenditures, sales, general and administrative
expenditures, geographic sales, and current assets and liabilities). Third, we
excluded companies that did not enter a new foreign country during the observation
period. In addition, we excluded countries in which MNCs had established subsidiaries before 2001. The main effect of these exclusions is to focus our analysis on
MNCs engaged in FME investment during the 2001–2007 period.
Variable Measures
Dependent Variable—Multinational Corporations’ Foreign Market Entry Investment.
Given the binary nature of our dependent variable (entering or avoiding a country),
we used a dummy variable equal to one if an MNC invests in a wholly owned
subsidiary in a foreign country for the first time and zero otherwise.
Independent Variables—National Differences in Environmental Regulation Stringency.
We calculated this variable by subtracting the level of a home country’s environmental regulation stringency from that of MNCs’ host country’s environmental
regulation stringency (i.e., host environmental regulation stringency – home environmental regulation stringency). We used a similar procedure to calculate national
differences in environmental regulation certainty. Data on countries’ environmental
regulation stringency and certainty levels were obtained from the WEF’s Annual
Executive Opinion Surveys (WEF, 2000–2007).8 In these surveys, top business executives from over 120 countries rank the overall stringency and certainty of countries’
environmental regulations. Specifically, we use the answers to two questions
included in the WEF Executive Opinion Survey. First, “the stringency of overall
environmental regulation in your country is: (1 = lax compared with most other
countries, 7 = among the world’s most stringent)” for the environmental regulation
stringency dimension. Second, for the environmental regulation certainty dimension: “environmental regulations in your country are: (1 = confusing and frequently
change, 7 = transparent and stable).” These responses about the perceived environmental regulations’ stringency and certainty by top business managers offer the key
advantage that these executives are also the ones making FME investment decisions.
Country Democracy Levels. To measure this variable we used data on democracy
accountability levels from the International Country Risk Guide by Political Risks
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Policy Studies Journal, 41:2
Service (2010). Democratic accountability is a scale variable with a minimum value of
zero for the most authoritarian governments and maximum value of six for highly
democratic countries. It measures how responsive a government is to its people
based on the following factors: free and fair elections for the legislature and executive; the active presence of more than one political party and a viable opposition;
evidence of checks and balances among the executive, legislative and judicial
branches of government; evidence of an independent judiciary; and evidence of the
protection of personal liberties through constitutional or other legal guarantees.
We used dummy variables to indicate four MNC industry type categories: (i) clean
manufacturing for firms in the least polluting production sectors (aerospace, computer, office and electronics, food and drugs, motor vehicle and parts, and pharmaceutical industries); (ii) heavy (“dirty”) manufacturing for firms in the most polluting
production sectors in our sample (chemicals, energy generation utilities, petroleum
refining, and natural resources extraction); (iii) services (entertainment and publishing, merchandiser, telecommunications, food services, and transportation services
firms); and (iv) finance (bank, insurance, and other financial service firms).
Control Variables. We also include in our regression analysis firm-level and countrylevel variables to control for factors that are well known to affect MNCs’ FME
investment decisions (Delios & Henisz, 2003; Holburn & Zelner, 2010; Oh & Oetzel,
2011). The firm-level characteristics included as control variables are: firm size (log of
sales), geographic diversification (entropy measure using geographic sales), R&D
intensity (R&D expenditure divided by sales), advertising intensity (selling, general
and administrative expenditure divided by sales), and financial resources (current
assets divided by liabilities), and managerial capability.9 The data for these variables
were collected from annual reports of sample firms supplemented by Compustat
Global by Standard & Poor’s and OSIRIS by Bureau van Dijk.
Additionally, we included in our models the following country-level variables:
country size (log of GDP), population (log of population), land size (log of squared
kilometers), adult literacy rate (%), unemployment rate (%), openness to trade
(import divided by GDP), and openness to FDI (inward FDI flows divided by GDP).
Data for these variables were collected from the World Development Indicators by
the World Bank (2010).
We also included the following dyadic-level variables (host–home countries):
geographic distance (log of miles), common border (dummy), common language
(dummy), colonial relationship (dummy), and institutional closeness (European
Union membership; dummy). Data for these dyadic-level variables were collected
from the Central Intelligence Agency’s World Factbook (CIA, 2010) and supplemented by various other sources.
Analytical Methodology
Given the dichotomous nature of our dependent variable, MNCs’ FME investment, we used logistic regression for our quantitative analysis. The logit model can
be represented as Equation (1):
Rivera and Oh: Environmental Regulation and Foreign Entry
Pi , j ,t , z ( yi , j ,t , z = 1; X ) = f (1) =
253
exp ( Xi , j ,t , z β )
,
{1 + exp (Xi , j ,t , z β )}
(1)
where yi,j,t,z is the entry (0/1) of firm i in industry j to country z at year t. Xi,j,t,z is a
vector of the independent and control variables, and b is the vector of the coefficients
to be estimated by the econometric modeling. To control for the panel nature of our
data and for unobserved heteroskedasticity, we included two-digit industry and year
fixed effects. The regression analysis also used heteroskedasticity and autocorrelation robust standard errors clustered by firm–host country (Kennedy, 2003). Additionally, in order to reduce possible endogeneity issues, all independent and control
variables were lagged 1 year.
Findings
Table 1 shows summary descriptive statistics for the variables included in the
analysis. FME frequency distributions by different levels of countries’ stringency and
certainty of environmental regulations are presented in Table 2. The FME frequency
distributions provide initial indication of MNCs’ predilection for investing in countries with more stringent regulations than those of their home countries. Of a total of
Table 1. Summary Statistics
Variable
Entry
Dirty manufacturing (dummy)
Clean manufacturing (dummy)
Service industry (dummy)
Financial industry (dummy)
Stringency environmental regulation
Certainty environmental regulation
Democracy accountability
Firm size (log)
Geographic diversification
Financial slack resources
R&D intensity
Advertising intensity
Managerial capability
Host country GDP (log)
Host country population (log)
Host country land size (log)
Host country import openness
Host country FDI openness
Host country unemployment rate
Host country literacy rate
Common border
Common language
Colonial relationship
Geographic distance (log)
Institutional closeness
N = 29,303.
Mean
SD
Min
Max
0.0554
0.2880
0.1855
0.2671
0.2594
-1.7802
-1.2219
4.4776
9.8339
0.6587
0.8921
0.0192
0.4838
0.4298
24.7890
16.6043
12.3672
46.2242
5.6931
9.1996
88.2158
0.0297
0.1818
0.1618
7.9206
0.1839
0.2287
0.4528
0.3887
0.4425
0.4383
1.3135
1.0626
1.4325
0.9862
0.3921
0.6876
0.0481
0.4545
0.2128
1.7067
1.4380
1.9405
30.0419
23.4701
5.6161
14.8016
0.1696
0.3857
0.3682
0.9961
0.3874
0
0
0
0
0
-4.8
-4.3
1
3.884
0
0
-0.001
0
0.032
20.393
12.896
5.768
9.530
-14.841
0.9
24
0
0
0
1
0
1
1
1
1
1
2.4
2.8
6
12.515
1.323
3.773
0.335
1.663
0.908
30.053
20.827
16.612
216.310
311.900
31.1
100
1
1
1
9.417
1
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Policy Studies Journal, 41:2
Table 2. MNCs’ Foreign Market Entry Investments: Frequency Distributions by Country’s
Environmental Regulation Stringency and Certainty
Country’s Environmental Regulations
Stringency levels*
Top quintile most stringent
Second quintile
Third quintile
Fourth quintile
Bottom quintile
Total
Certainty levels*
Top quintile most certain
Second quintile
Third quintile
Fourth quintile
Bottom quintile
Total
Foreign Market Entry Investments
N
%
2,513
1,766
580
298
184
5,341
47.1
33.1
10.9
5.6
3.4
100.0
2,386
1,763
645
278
269
5,341
44.7
33.0
12.1
5.2
5.0
100.0
*Each quintile represents 20%, or one fifth, of the stringency and certainty scales.
5,341 MNCs’ FME investments observed in our 7-year database, only 3.4 percent
occurred in countries whose environmental regulations’ stringency was in the bottom
20 percent. On the other hand, countries with the top 20 percent most stringent
environmental regulations received 47.1 percent of all the MNCs’ FME investments.
Similarly, countries with environmental regulation certainty in the top 20 percent
received about 45 percent of all the MNCs’ FME investments. Only 5 percent of the
MNCs’ FME investments observed in our database occurred in countries whose levels
of environmental regulation certainty were in the bottom 20 percent.
Table 3 shows the results of the logit regression analysis. In models 1 and 2, we
included environmental regulation stringency and certainty respectively with all
control variables. In models 3 and 4, we added the interaction terms between environmental regulation stringency and certainty and our moderating variables
(national democracy differences and industry categories). As a diagnostic procedure
for our sample and variables, we checked the cross-correlation and variance inflation
factors (average VIF is 3.25 and the highest individual VIF is 6.51) and did not find
a symptom of multicollinearity. Additionally, the Akaike information criterion and
likelihood ratio tests showed that adding interaction terms between industry
dummy variables and democracy variable and interaction term between environmental regulation variables and democracy variable increased the model fit.
The findings from our logistic regression models indicate that the MNCs’ FME is
positively and significantly associated with environmental regulation stringency (see
Table 3, model 1; b = 0.0, p < 0.0) and environmental regulation certainty (see model
2; b = 0.0, p < 0.001). It is also important to note that the environmental regulation
certainty coefficient is larger than the stringency one (p < 0.001) (e.g., Johnstone et al.,
2010; Marcus, 1981).
The nonlinear nature of our logistic models makes it difficult to interpret the
regression coefficients. Hence, we used a simulation-based approach increasingly
Rivera and Oh: Environmental Regulation and Foreign Entry
255
Table 3. Environmental Regulation Characteristics and MNC Foreign Market Entry Investments
Model
Environmental regulation
Dirty manufacturing (dummy)
Clean manufacturing (dummy)
Service industry (dummy)
Environmental regulation
Dirty manufacturing ¥ environmental regulation
(1)
(2)
(3)
(4)
Stringency
Certainty
Stringency
Certainty
-0.9611**
(0.2974)
1.6479***
(0.2498)
-0.3531
(0.2533)
0.0065
(0.0587)
0.0836
(0.0643)
0.2689***
(0.0733)
0.0974
(0.0695)
0.3295***
(0.0493)
0.0559**
(0.0205)
0.2505***
(0.0554)
1.0212***
(0.1274)
0.1078
(0.0963)
-6.0598***
(1.1795)
-1.1611***
(0.1023)
-0.1314
(0.2622)
0.4671***
(0.0513)
0.0696
(0.0579)
0.0960**
(0.0301)
0.0111***
(0.0020)
0.0058***
(0.0011)
0.0206**
(0.0074)
-0.0063
(0.0040)
0.7077***
(0.1487)
-0.2220†
(0.1206)
0.2642*
(0.1329)
-0.3697***
(0.0402)
-0.2835**
(0.1019)
-4,270.4
8,636.8
Against (1)
23.06***
-1.0475***
(0.2926)
1.5757***
(0.2495)
-0.5536*
(0.2460)
0.1691*
(0.0666)
0.0553
(0.0841)
0.2451*
(0.0955)
-0.1486†
(0.0902)
0.2231***
(0.0379)
0.0066
(0.0248)
0.2512***
(0.0548)
1.0577***
(0.1285)
0.0680
(0.0976)
-6.2680***
(1.1791)
-1.2021***
(0.1022)
-0.0419
(0.2579)
0.4210***
(0.0479)
0.0972†
(0.0555)
0.1069***
(0.0301)
0.0097***
(0.0020)
0.0064***
(0.0011)
0.0174*
(0.0072)
-0.0043
(0.0042)
0.7183***
(0.1484)
-0.2049†
(0.1203)
0.2603†
(0.1335)
-0.3752***
(0.0396)
-0.2452*
(0.1007)
-4,265.6
8,627.2
Against (2)
18.03**
-1.0153***
(0.2886)
1.3735***
(0.2302)
-0.4308†
(0.2354)
0.0823†
(0.0429)
-0.9830***
(0.2889)
1.3655***
(0.2322)
-0.4284†
(0.2354)
0.1949***
(0.0454)
0.2260***
(0.0307)
0.2178***
(0.0305)
0.2552***
(0.0565)
0.9920***
(0.1284)
0.0894
(0.0957)
-5.9269***
(1.1715)
-1.1811***
(0.1012)
-0.1060
(0.2608)
0.4628***
(0.0515)
0.0424
(0.0584)
0.1122***
(0.0297)
0.0103***
(0.0019)
0.0059***
(0.0011)
0.0156*
(0.0072)
-0.0069†
(0.0041)
0.7157***
(0.1485)
-0.2127†
(0.1213)
0.2383†
(0.1345)
-0.3766***
(0.0401)
-0.2717**
(0.1010)
-4,282.0
8,651.9
0.2575***
(0.0548)
1.0318***
(0.1290)
0.0847
(0.0964)
-5.9044***
(1.1574)
-1.1710***
(0.1012)
-0.0544
(0.2581)
0.4235***
(0.0467)
0.0930†
(0.0555)
0.1082***
(0.0298)
0.0097***
(0.0019)
0.0065***
(0.0011)
0.0171*
(0.0071)
-0.0046
(0.0042)
0.7201***
(0.1480)
-0.2075†
(0.1209)
0.2515†
(0.1339)
-0.3739***
(0.0400)
-0.2394*
(0.1002)
-4,274.6
8,637.3
Clean manufacturing ¥ environmental regulation
Service industry ¥ environmental regulation
Democracy accountability
Democracy accountability ¥ environmental regulation
Firm size (log)
Geographic diversification
Financial resources
R&D intensity
Advertising intensity
Managerial capability
Host country GDP (log)
Host country population (log)
Host country land size (log)
Host country import openness
Host country FDI openness
Host country unemployment rate
Host country literacy rate
Sharing common border
Sharing common language
Sharing colonial relationship
Geographic distance (log)
Institutional closeness
Log likelihood
Akaike information criterion (AIC)
Likelihood ratio (LR) test (c2)
Note: N = 29,303. †p < 0.10, *p < 0.05; **p < 0.01; ***p < 0.001. We used distance measures for environmental regulation stringency
and certainty and democracy variables. Heteroskedasticity and autocorrelation robust standard errors clustered by firm–host
country are in parentheses. Two-digit industry and year fixed effects are estimated but are not reported here.
Policy Studies Journal, 41:2
Certainty
Stringency
0.08
0.02
0.04
0.06
__
__
0
Foreign Market Entry Investment
0.1
256
-5
-2.5
0
2.5
5
Level of Environmental Regulation (Distance from Home Country)
Figure 1. MNC Foreign Market Entry Investment Tendency and Regulatory Stringency and Certainty.
used in the political science and management literatures to facilitate the interpretation of nonlinear regression results (King, Tomz, & Wittenberg, 2000; Zelner, 2009).10
Figure 1 shows a graph illustrating the average value of these simulation estimations
(with vertical bars representing the 95 percent confidence intervals): as expected, the
probability of MNCs’ FME investments increases with higher levels of environmental regulations’ stringency and certainty. Interestingly, Figure 1 also reveals that at
higher levels of stringency and certainty, regulatory certainty has a more pronounced effect on promoting FME investment than regulatory stringency. The opposite occurs at lower levels of stringency and certainty, where the effect stringency is
higher.
Specific examples of large differences in environmental regulations’ stringency
and certainty may also help illustrate our findings. For instance, other things equal,
model 1 results (see Table 3) indicate that when considering differences in environmental regulations stringency, the likelihood of FME investment of an Italian MNC
into Denmark is about 5 percent higher than the likelihood of entry into Malaysia.
This is because Denmark has environmental regulations that are about two standard
deviations more stringent than those of Malaysia. In the case of environmental
regulations certainty levels, model 2 findings indicate that the likelihood of FME of
an Italian MNC into Denmark is about 7 percent higher than the likelihood of entry
into Brazil, ceteris paribus. Here again, environmental regulation certainty is about
two standard deviations greater in Denmark than in Brazil.
Regarding democracy levels, the findings only suggest statistically significant
support for a moderating effect on environmental regulation stringency (see Table 3,
model 3; b = 0.0559; p < 0.01). This implies that the MNCs’ positive tendency to enter
countries with stricter levels of environmental regulation is higher in more demo-
257
0.03
0.02
__
__
High democracy
Low democracy
0.01
Foreign Market Entry Investment
0.04
Rivera and Oh: Environmental Regulation and Foreign Entry
-4
-2
0
2
Level of Stringency (Distance from Home Country)
Figure 2. Moderating Effect by Democracy Levels.
cratic countries. The moderating effect of democracy level is nonsignificant for
environmental regulation certainty (model 4; b = 0.0066; p < NS). The results confirm
that business resistance to environmental regulations stringency may be lower in
democratic countries than in authoritarian ones (Rivera, 2010). However, environmental regulation certainty remains important by itself irrespective of the democracy
level of host country.
Models 3 and 4 (see Table 3) also indicate statistically significant support for the
moderating effects of clean industry type on regulatory stringency (model 3;
b = 0.2689, p < 0.001) and on regulatory certainty (model 4; b = 0.2451, p < 0.05). The
propensity of European MNCs to enter foreign markets with environmental regulations that are more stringent and/or certain is higher for cleaner industry companies. For example, when the home and host countries have the same level of
environmental regulation certainties the likelihood of FME by a cleaner manufacturing MNC is about 16 percent higher than the likelihood of entry by a dirty manufacturing MNC.
Figures 2 and 3 show simulation estimations illustrating our findings about the
moderating effect of the country’s democracy levels and clean industry type. To
prepare Figures 2 and 3 we followed a similar simulation estimation procedure as
the one used for Figure 1 (see Endnote 10 for additional details).
Robustness Checks
We calculated alternative logistic regression models to verify the robustness of
our findings (see Table 4). First, because the MNCs are more likely to enter wealthier
countries, models 1 and 2 in Table 4 explicitly control for per capita GDP.11 Second,
Policy Studies Journal, 41:2
0.06
0.04
0.02
__
__
Clean industry
Dirty industry
0
Foreign Market Entry Investment
0.08
258
-4
-2
0
2
Level of Environmental Regulation (Distance from Home Country)
Figure 3. Moderating Effect by Clean Industry Type.
previous studies find that host country political safety or risk is an important determinant for the entry decision of MNCs. Thus, we estimated an additional regression
model that controls for host country political safety using data from the World Bank’s
World Governance Indicators (World Bank, 2011; see models 3 and 4; Table 4). Third,
because environmental regulation stringency and certainty may be correlated with
general regulatory quality, we recalculated our models controlling for general regulatory quality using data from the World Bank’s World Governance Indicators (World
Bank, 2011; see models 5 and 6; Table 4). Fourth, our initial regression analysis used
an entropy measure based on geographic sales as the indicator of geographic diversification. As an alternative measure, we used the foreign-to-total number of subsidiaries, which is another common proxy for geographic diversification (see models 7
and 8; Table 4).12 In addition, we tested our model with dyad fixed effects and the
results (shown on Table 5) are consistent with our reported findings.13 All robustness
checks confirm the findings of our original models, showing that: (i) MNCs’ FME
investment remains positively and significantly associated with both environmental
regulatory stringency and certainty; and (ii) similar moderating effects for country
democracy levels and industry type.
Discussion of Results
Foreign Market Entry Investments and Regulatory Stringency
Our logistic regression analyses suggest rather interesting results that challenge the controversial but commonly held view that more stringent environmental regulations deter FME investments by multinational companies. First, when we
Managerial capability
Advertising intensity
R&D intensity
Financial resources
Geographic diversification
Firm size (log)
Democracy accountability ¥ environmental regulation
Democracy accountability
Service industry ¥ environmental regulation
Clean manufacturing ¥ environmental regulation
Dirty manufacturing ¥ environmental regulation
Environmental regulation
Service industry (dummy)
Clean manufacturing (dummy)
Environmental regulation
Dirty manufacturing (dummy)
Model
Stringency
-0.9611**
(0.2974)
1.6479***
(0.2498)
-0.3531
(0.2533)
0.0065
(0.0587)
0.0836
(0.0643)
0.2689***
(0.0733)
0.0974
(0.0695)
0.3295***
(0.0493)
0.0559**
(0.0205)
0.2505***
(0.0554)
1.0212***
(0.1274)
0.1078
(0.0963)
-6.0598***
(1.1795)
-1.1611***
(0.1023)
-0.1314
(0.2622)
(1)
Certainty
-1.0475***
(0.2926)
1.5757***
(0.2495)
-0.5536*
(0.2460)
0.1691*
(0.0666)
0.0553
(0.0841)
0.2451*
(0.0955)
-0.1486†
(0.0902)
0.2231***
(0.0379)
0.0066
(0.0248)
0.2512***
(0.0548)
1.0577***
(0.1285)
0.0680
(0.0976)
-6.2680***
(1.1791)
-1.2021***
(0.1022)
-0.0419
(0.2579)
(2)
Control per Capita GDP
Stringency
-0.9801**
(0.2982)
1.6688***
(0.2501)
-0.3644
(0.2534)
-0.0326
(0.0599)
0.0793
(0.0643)
0.2655***
(0.0733)
0.0947
(0.0695)
0.3206***
(0.0497)
0.0588**
(0.0203)
0.2496***
(0.0559)
1.0134***
(0.1276)
0.1017
(0.0964)
-5.9849***
(1.1810)
-1.1674***
(0.1026)
-0.1582
(0.2624)
(3)
Certainty
-1.0486***
(0.2932)
1.5967***
(0.2495)
-0.5490*
(0.2462)
0.1432*
(0.0675)
0.0514
(0.0844)
0.2430*
(0.0957)
-0.1510†
(0.0902)
0.2139***
(0.0382)
0.0094
(0.0245)
0.2519***
(0.0554)
1.0458***
(0.1288)
0.0661
(0.0976)
-6.3006***
(1.1835)
-1.2091***
(0.1024)
-0.0637
(0.2583)
(4)
Control Political Stability
Table 4. Robustness Check Models
Stringency
-1.0027***
(0.2975)
1.6650***
(0.2500)
-0.3782
(0.2533)
-0.0478
(0.0604)
0.0792
(0.0646)
0.2658***
(0.0737)
0.0938
(0.0698)
0.2857***
(0.0504)
0.0561**
(0.0204)
0.2459***
(0.0560)
1.0096***
(0.1273)
0.1030
(0.0965)
-5.8560***
(1.1762)
-1.1686***
(0.1028)
-0.1817
(0.2624)
(5)
Certainty
-1.0676***
(0.2926)
1.5944***
(0.2493)
-0.5582*
(0.2460)
0.1262†
(0.0680)
0.0483
(0.0848)
0.2402*
(0.0962)
-0.1550†
(0.0906)
0.1894***
(0.0396)
0.010
(0.0248)
0.2490***
(0.0555)
1.0395***
(0.1285)
0.0679
(0.0975)
-6.2063***
(1.1768)
-1.2111***
(0.1026)
-0.0872
(0.2586)
(6)
Control Regulatory Quality
Stringency
-0.8939**
(0.3297)
1.8210***
(0.2539)
-0.1175
(0.2907)
-0.0488
(0.0640)
0.0740
(0.0685)
0.2946***
(0.0771)
0.1046
(0.0765)
0.3192***
(0.0498)
0.0489*
(0.0210)
0.2317***
(0.0537)
1.6523***
(0.1462)
0.0413
(0.0995)
-5.7528***
(1.1769)
-1.0141***
(0.1089)
-0.1238
(0.2682)
(7)
Certainty
-1.0032**
(0.3215)
1.7601***
(0.2511)
-0.3240
(0.2778)
0.1368†
(0.0723)
0.0233
(0.0892)
0.2634**
(0.0981)
-0.1512
(0.0966)
0.2212***
(0.0384)
-0.0013
(0.0252)
0.2308***
(0.0537)
1.6336***
(0.1479)
0.0076
(0.1003)
-6.0324***
(1.1785)
-1.0677***
(0.1088)
-0.0731
(0.2655)
(8)
Alternative Geographic Diversity
Rivera and Oh: Environmental Regulation and Foreign Entry
259
Note: See Table 3.
Log likelihood
Akaike information criterion (AIC)
Institutional closeness
Geographic distance (log)
Sharing colonial relationship
Sharing common language
Sharing common border
Host country literacy rate
Host country unemployment (rate)
Host country FDI openness
Host country import openness
Host country land size (log)
Host country population (log)
Host country GDP (log)
Host country per capita GDP (log)
Host country regulatory quality (quality)
Host country political safety
Model
Table 4. Continued
0.5368***
(0.0375)
0.0960**
(0.0301)
0.0111***
(0.0020)
0.0058***
(0.0011)
0.0206**
(0.0074)
-0.0063
(0.0040)
0.7077***
(0.1487)
-0.2220†
(0.1206)
0.2642*
(0.1329)
-0.3697***
(0.0402)
-0.2835**
(0.1019)
-4,270.42
8,636.84
0.4671***
(0.0513)
(1)
0.5182***
(0.0372)
0.1069***
(0.0301)
0.0097***
(0.0020)
0.0064***
(0.0011)
0.0174*
(0.0072)
-0.0043
(0.0042)
0.7183***
(0.1484)
-0.2049†
(0.1203)
0.2603†
(0.1335)
-0.3752***
(0.0396)
-0.2452*
(0.1007)
-4,265.62
8,627.24
0.4210***
(0.0479)
(2)
Control per Capita GDP
0.4215***
(0.0529)
0.1504*
(0.0628)
0.0773*
(0.0314)
0.0096***
(0.0021)
0.0062***
(0.0011)
0.0253***
(0.0074)
-0.0063
(0.0039)
0.6970***
(0.1491)
-0.2004†
(0.1206)
0.2722*
(0.1326)
-0.3601***
(0.0399)
-0.2974**
(0.1015)
-4,265.07
8,628.14
0.2205**
(0.0699)
(3)
0.3726***
(0.0511)
0.1727**
(0.0627)
0.0906**
(0.0315)
0.0086***
(0.0020)
0.0066***
(0.0011)
0.0213**
(0.0073)
-0.0042
(0.0041)
0.7189***
(0.1487)
-0.1925
(0.1202)
0.2677*
(0.1333)
-0.3643***
(0.0395)
-0.2652**
(0.1009)
-4,262.01
8,622.02
0.1772**
(0.0684)
(4)
Control Political Stability
0.3255***
(0.0643)
0.2306**
(0.0743)
0.0850**
(0.0305)
0.0080***
(0.0021)
0.0065***
(0.0011)
0.0218**
(0.0072)
-0.0044
(0.0041)
0.7225***
(0.1491)
-0.2423*
(0.1215)
0.2295†
(0.1342)
-0.3818***
(0.0403)
-0.3707***
(0.1044)
-4,261.63
8,621.27
0.4256***
(0.1148)
(5)
0.2935***
(0.0637)
0.2401**
(0.0740)
0.0958**
(0.0305)
0.0073***
(0.0021)
0.0069***
(0.0011)
0.0188**
(0.0071)
-0.0028
(0.0043)
0.7405***
(0.1490)
-0.2294†
(0.1213)
0.2333†
(0.1344)
-0.3818***
(0.0396)
-0.3336**
(0.1043)
-4,259.48
8,616.96
0.3547**
(0.1146)
(6)
Control Regulatory Quality
0.5253***
(0.0530)
0.0303
(0.0602)
0.1176***
(0.0311)
0.0121***
(0.0020)
0.0057***
(0.0012)
0.0201**
(0.0076)
-0.0073†
(0.0041)
0.6779***
(0.1647)
-0.2459†
(0.1279)
0.2359†
(0.1388)
-0.3751***
(0.0415)
-0.2650*
(0.1061)
-4,020.17
8,136.34
(7)
0.4920***
(0.0489)
0.0392
(0.0570)
0.1289***
(0.0310)
0.0108***
(0.0020)
0.0061***
(0.0012)
0.0165*
(0.0075)
-0.0062
(0.0042)
0.6756***
(0.1644)
-0.2284†
(0.1275)
0.2348†
(0.1394)
-0.3824***
(0.0408)
-0.2432*
(0.1047)
-4,020.25
8,136.50
(8)
Alternative Geographic Diversity
260
Policy Studies Journal, 41:2
Rivera and Oh: Environmental Regulation and Foreign Entry
261
Table 5. Dyadic Fixed-Effects Models
Model
Environmental regulation
Dirty manufacturing (dummy)
Clean manufacturing (dummy)
Service industry (dummy)
Environmental regulation
Dirty manufacturing ¥ environmental
regulation
Clean manufacturing ¥ environmental
regulation
Service industry ¥ environmental
regulation
Democracy accountability
Democracy accountability ¥ environmental
regulation
Firm size (log)
Geographic diversification
Financial resources
R&D intensity
Advertising intensity
Managerial capability
Host country GDP (log)
Host country population (log)
Host country land size (log)
Host country import openness
Host country FDI openness
Host country unemployment rate
Host country literacy rate
Institutional closeness
Log likelihood
Akaike information criterion (AIC)
(1)
(2)
(3)
(4)
Stringency
Certainty
Stringency
Certainty
-1.9594***
(0.3356)
0.4105
(0.2588)
-0.5759*
(0.2871)
0.2694*
(0.1370)
0.0232
(0.0758)
0.2218**
(0.0828)
0.0962
(0.0821)
0.0324
(0.1355)
0.1041*
(0.0514)
-0.0829†
(0.0479)
1.8864***
(0.1478)
0.0720
(0.1109)
5.4531***
(1.3953)
-0.9639***
(0.1175)
0.8206**
(0.2914)
3.3984***
(1.0310)
-2.3855
(2.7814)
-19.6089
(20.8728)
-0.0129
(0.0082)
0.0054
(0.0034)
-0.0218
(0.0378)
-0.0803
(0.0772)
-0.2791
(0.3093)
-2,775.56
5,637.11
-2.0597***
(0.3323)
0.3147
(0.2555)
-0.7977**
(0.2788)
0.3520*
(0.1431)
0.0707
(0.1040)
0.1959†
(0.1135)
0.1112
(0.1119)
-0.1314
(0.1066)
0.0283
(0.0581)
-0.0754
(0.0484)
1.9055***
(0.1484)
0.0539
(0.1103)
5.4532***
(1.3876)
-0.9826***
(0.1175)
0.9130**
(0.2906)
4.3507***
(1.0331)
-1.3481
(2.7288)
-26.0892
(21.0282)
-0.0126
(0.0081)
0.0057†
(0.0034)
-0.0157
(0.0379)
-0.0833
(0.0768)
-0.0884
(0.3080)
-2,776.03
5,638.06
-1.9713***
(0.3258)
0.1711
(0.2411)
-0.6629*
(0.2658)
0.2712*
(0.1190)
-1.9962***
(0.3260)
0.1587
(0.2416)
-0.7042**
(0.2657)
0.3239**
(0.1106)
-0.1710†
(0.0882)
-0.1558†
(0.0873)
-0.0869†
(0.0478)
1.8805***
(0.1481)
0.0456
(0.1102)
5.5501***
(1.3897)
-0.9790***
(0.1168)
0.8570**
(0.2883)
3.6215***
(1.0300)
-2.6971
(2.7826)
-17.9291
(21.0076)
-0.0146†
(0.0080)
0.0046
(0.0033)
-0.0215
(0.0377)
-0.0843
(0.0767)
-0.2509
(0.3082)
-2,782.30
5,642.60
-0.0785
(0.0479)
1.8927***
(0.1483)
0.0518
(0.1101)
5.6093***
(1.3863)
-0.9861***
(0.1172)
0.8762**
(0.2882)
4.4396***
(1.0280)
-1.2494
(2.7232)
-26.0509
(20.9167)
-0.0120
(0.0080)
0.0057†
(0.0034)
-0.0138
(0.0378)
-0.0816
(0.0766)
-0.0884
(0.3064)
-2,780.59
5,639.19
Note: N = 18,858. †p < 0.10, *p < 0.05; **p < 0.01; ***p < 0.001. We used distance measures for environmental regulation stringency and certainty and democracy variables. Heteroskedasticity and autocorrelation robust standard
errors clustered by firm–host country are in parentheses. Two-digit industry, dyadic, and year fixed effects are
estimated but are not reported here.
262
Policy Studies Journal, 41:2
consider national differences in environmental regulation stringency (foreign host–
home country) our findings indicate significantly higher levels of company entry
into foreign countries with more stringent environmental regulations than those of
the companies’ home countries.14 This higher tendency to enter foreign countries
with more stringent regulations than those of MNCs’ home countries is observed
above and beyond the effect of more than 20 other factors typically known to influence companies’ foreign investment decisions. These findings suggest that for the
case of large European MNCs (Fortune Global 500 companies), countries with
stricter environmental regulations than those of their home countries may actually
be seen as more attractive for establishing new subsidiaries. Interestingly, this may
be because, contrary to the main suggestion of the pollution haven logic, the costs
of environmental regulations may actually be lower for European MNCs. Large
European MNCs have long been exposed to stricter environmental regulations in
their home countries. Over time, this experience may have allowed these companies to view stricter environmental regulations as opportunities. More stringent
regulations may force top managers to consider environmental protection as a key
business strategy issue requiring increased attention. Thus, triggering the discovery of win-win opportunities to develop new technologies and management
systems that increase environmental protection and simultaneously help them to
become more efficient. The increased environmental performance may also
improve businesses’ “green” reputations resulting in better relations with governments and environmentalist groups, and perhaps higher sales to environmentally
aware customers.
We also believe that these findings cannot only be explained by a logic of cost
and innovation offsets but also by the unique type of business political engagement
prevalent in many European countries. In their home countries, European MNCs
tend to experience a more collaborative and consensus-based political process to
enact and implement environmental regulations. Contrary to the intense adversarial
regulatory processes prevalent in the United States, in Europe regulations tend to be
developed through shared participation with government, business associations, top
environmentalists, and union labor groups (Rivera, 2010). Hence, European MNCs
are less likely to perceive stringent environmental regulations as a threat, and may
actually see them as an opportunity to compete abroad with companies from other
countries.15 Additionally, the increased cooperation and consensus with environmentalists and other groups may allow European MNCs to more easily develop
“green” technologies and management systems.
Foreign Market Entry Investments and Regulatory Certainty
Our analysis does suggest that some environmental regulations may actually be
associated with lower FME investment decisions by MNCs. Yet, as we described
previously, this is not the case for environmental regulations that are more stringent
than those of MNCs’ home countries. We found that multinational companies are
significantly less likely to enter countries with less certain environmental regulations
Rivera and Oh: Environmental Regulation and Foreign Entry
263
than those of their home countries (see Figure 1). That is, environmental regulations
appear to be linked to significantly diminished levels of entry into foreign countries
when they are less clear and less stable than the regulations of companies’ home
countries. Most notably, this finding shows that the magnitude of the regulatory
certainty relationship with MNCs’ FME investments is larger than that of regulatory
stringency.
These findings are consistent with research examining how the certainty of other
types of regulations affects MNCs’ FME investment decisions (Delios & Henisz,
2003). Yet, to the best of our knowledge, previous research examining environmental
regulations has paid little attention to how differences in regulatory certainty may
affect MNCs’ FME investment decisions. FME investments tend to have long-term
payoffs (sometimes over a decade) and are considered risky. Uncertain environmental regulations tend to deter these investments by making them riskier. This is
because unclear and unstable environmental protection requirements make it difficult for companies to develop win-win compliance technologies and management
systems. Also, because research and development horizons for innovative green
efforts are also long, uncertain regulations make it difficult for firms to reap the
benefits of greening.
Moderating Effect of Country Democracy Levels and Industry Type
Our findings suggest that MNCs’ positive tendency to enter countries with
stricter levels of environmental regulation is higher in host nations that are more
democratic (see Figure 2). It is important to stress that the large magnitude of
the moderating effect of host country democracy levels is such that it reverses
the direction of the relationship between MNCs’ FME investments and more stringent environmental regulations. In authoritarian countries, more stringent environmental regulations are linked to a propensity to avoid FME investments by MNCs.
On the other hand, the level of host country democracy does not appear to moderate the relationship between FME investments and environmental regulation
certainty.
Our findings about the moderating effect of countries’ democracy levels stress
the importance of considering not only macroeconomic context factors (such as
country per capita income), but also political context factors when examining the
relationship between environmental regulations’ stringency and MNCs’ FME investments. Higher levels of host country democracy increase substantially the chance
that environmental regulations are perceived as fairer, and thus more legitimate, by
businesses, government officials, environmentalists, and other groups. Hence,
MNCs are actually more likely to enter democratic host countries with more stringent environmental regulations than those of their home countries.
MNC investment is substantially deterred in authoritarian host countries with
stricter environmental regulations because these regulations and their enforcement
are more likely to be perceived as arbitrary and illegitimate. MNCs, in particular,
perceive that when an authoritarian government needs to show symbolic concern for
environmental protection, they are more likely to be made scapegoat targets of
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Policy Studies Journal, 41:2
autocratic enforcement. This may also happen in democratic host countries but there
businesses can take advantage of rights, freedoms, and rule of law traditions to fend
off arbitrary government regulations and enforcement. For example, in democratic
countries freedom of the press and speech allow businesses (and other groups) to be
more informed and to monitor the government’s decisions and the environmental
record of other companies. Free speech traditions also make it easier to convey
concerns and demands to the media and different authorities about the arbitrary
actions of government environmental agencies publicly and in a timely manner.
Also, well-established freedom of association mechanisms, inherent in democratic
nations, expedite the organization of international business associations that are
better able to debate, promote, and sustain fair enactment and enforcement of environmental regulations (Rivera, 2010).
Our results also indicate that belonging to a clean industry moderates the nature
of the relationship between FME investments and environmental regulations’ stringency and certainty. We found that the increased tendency of MNCs to enter countries with more stringent environmental regulations than those of their home
countries is higher for clean industry firms than for other industrial sectors. Similarly, cleaner industry companies are more likely to enter host countries with more
certain regulations than those of their home countries (see Figure 3). Cleaner industry companies may be more attracted to invest in countries with more stringent
environmental regulatory requirements because they are less pollution intensive.
Alternatively, they may also have developed innovative green technologies that allow
them to be more competitive in host countries with more stringent and certain
environmental regulations. That is, cleaner industry companies likely transform
more stringent environmental regulations into business opportunities.
Limitations
Finally, before elaborating on the conclusions, it is important to highlight key
limitations of our study. First, our analysis is restricted to FME investment decisions
by European MNCs during 2001–2007. Although this is an improvement from previous analyses focusing on the United States (see Conclusion section), it preempts
our ability to generalize our findings for MNCs from other countries and for entry
investment decisions made outside this period. To be sure, MNCs from countries
with weaker environmental regulations may show different FME investment patterns in response to host country environmental regulations. Second, our analysis
does not consider variations across the subsidiaries belonging to single MNCs. A
MNC’s response to environmental regulations can differ across its multiple subsidiaries due to the differences in subsidiary roles and characteristics as well as
in-country and industry factors (Birkinshaw, 2008; Birkinshaw, Hood, & Jonsson,
1998; Rugman & Verbeke, 2001). Some foreign subsidiaries can be more proactive
and internally develop unique environmental protection capabilities and a “green”
entrepreneurial culture (Pinkse, Kuss, & Hoffmann, 2010).
Third, our measures of environmental regulation characteristics are from a
survey of top corporate managers’ perceptions. The respondents of the survey may
Rivera and Oh: Environmental Regulation and Foreign Entry
265
not be experts in environmental policy. Future research might include qualitative
interviews with headquarters and subsidiary managers to capture decision mechanisms regarding environmental regulations and FME investments. Another meaningful avenue of future research is the investigation of how subsidiary capabilities
influence entry and expansion decisions within a host country.
Fourth, our sample includes 77 countries where potential MNCs’ FME could
occur. Besides varying in stringency and certainty, the environmental regulations in
these countries may show other differences in incentives, implementation, and/or
other design characteristics (Potoski & Woods, 2002; Rigby, 2007). Our empirical
analysis does not examine the effect of other environmental regulations design
characteristics on MNCs’ FME investments. Future research should consider how
other differences in environmental regulation design may affect MNCs’ FME investments and expansion decisions.
Conclusions
The extensive literature exploring the relationship between environmental regulation characteristics and MNCs’ foreign investment offers contradictory perspectives and inconclusive empirical evidence. Previous research has examined this
relationship by focusing on regulatory stringency and foreign investment into the
United States or by studying the overseas investments of a few heavy U.S. manufacturing industries. Our study offers interesting contributions for managers, scholars,
and policymakers. First, besides considering stringency, we stress the importance
of other regulation characteristics by examining how national differences in the
certainty of environmental regulations affect MNCs’ FME investment decisions.
Second, our findings are more generalizable than previous work because we look
beyond the United States and study worldwide FME investment decisions by MNCs
in a wide variety of industries from over ten European countries.
Our results indicate that European MNCs are more likely to enter countries with
environmental regulations that are more certain than those of their home countries.
The magnitude of the regulatory certainty relationship with MNCs’ FME investments is larger than that of regulatory stringency. Moreover, we found that European
MNCs are more likely to enter countries with environmental regulations that are
more stringent than those of their home countries. This finding about environmental
regulatory stringency challenges the pollution haven hypothesis’ controversial, but
commonly, held wisdom predicting lower levels of entry by MNCs into countries
with more stringent environmental regulations.
Third, we advance the literature by analyzing how the relationship of environmental regulations’ stringency to MNCs’ FME investments change when companies
consider entering countries with different levels of democracy. Previous research in
this area has focused on examining how economic contextual factors (e.g., country
income per capita) alter the links between environmental regulations’ stringency and
business investment. However, our results point out the importance of countries’
political context in determining how business FME investment decisions are associated with environmental regulation characteristics. Specifically, we found that
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Policy Studies Journal, 41:2
European MNCs’ positive tendency to enter countries with more stringent environmental regulations than those of their home countries is higher in more democratic
nations. Furthermore, the moderating effect of democracy levels on the MNCs’ FME
investment-environmental regulation stringency relationship is such that in authoritarian countries, more stringent environmental regulations are linked to a propensity
to avoid FME investments by MNCs.
Fourth, our study also contributes to the literature by examining how different
industry types may moderate the relationship between MNCs’ FME investments
and environmental regulations. Previous studies examining this relationship focus
on investment decisions by a single or a few highly polluting industries. Yet, the
compliance requirements and opportunities for green innovation vary greatly across
industries, particularly when comparing firms in “cleaner” industrial sectors and
those operating in highly polluting industries. We found that European MNCs’
positive tendency to enter countries with more stringent environmental regulations
than those of their home countries is higher for cleaner industry companies. Our
findings also indicate that MNCs’ tendency to enter countries with more certain
environmental regulations than those of home countries is higher for cleaner industry companies.
Implications
Our study suggests interesting implications for both policymakers and managers. The fact that countries with more stringent environmental regulations appear to
be more attractive to MNCs’ FME investments suggests that when considering
multiple industries and countries: (i) the worldwide trend to create new and more
stringent environmental regulations may provide competitive advantages to MNCs
with experience operating in countries with very strict environmental standards
(such as those prevailing in Europe), (ii) these competitive advantages may generate
large enough benefits to MNCs that can make up for—or in some case, outweigh—
the cost of complying with stringent environmental regulations.16
Most importantly, our findings about the positive effect of environmental
regulations’ certainty on European MNCs’ FME investments suggest that when
examining the impact of environmental regulations on business investment, policymakers need to look beyond stringency and also pay attention to enacting more
certain regulations. Lax environmental regulations that are uncertain—i.e., unstable
and non-transparent—appear to deter foreign investment by MNCs with global
brand names and access to advanced environmental protection technologies.
Managers also need to consider the authoritarian or democratic nature of the
environmental policymaking process. Democratic rights and freedoms reduce the
discretion of government officials, increasing the ability of investors and other
groups to shape the creation and enforcement of environmental regulations making
them less arbitrary. For policymakers, a higher level of country democracy increases
the legitimacy of environmental regulations allowing the adoption of more stringent
requirements without deterring MNCs’ FME investments. Finally, for managers, our
Rivera and Oh: Environmental Regulation and Foreign Entry
267
findings about clean industries suggest that the pollution haven hypothesis may
have arisen from early past experience with foreign investment by heavy manufacturing and natural resource extraction companies. Clean industry firms that are less
pollution intensive may actually gain cost and differentiations advantages in foreign
countries with more stringent and certain environmental regulations. For policymakers, these findings suggest that countries interested in attracting clean industry
MNCs appear to be better off with environmental regulations that are more certain
and actually more stringent.
Notes
The authors acknowledge helpful comments from the PSJ editors, Peter deLeon and Chris Weible, and
three anonymous reviewers. Financial support was provided by the National Research Foundation of
Korea (NRF-2011-330-B00092).
1. For instance, opposition to the North American Free Trade Agreement in the 1990s and repeated
demonstrations against the annual meetings of the World Bank, World Trade Organization, and the
International Monetary Fund.
2. A recent front page article in the New York Times (Rich & Broder, 2011, p. B1) illustrates this fallback
argument used by business groups:
Do environmental regulations kill jobs? . . . business groups say yes, arguing that environmental
protection is simply too expensive for a battered economy. They were quick to claim victory
Friday after the Obama administration abandoned stricter ozone pollution standards. [Others]
agree that regulation comes with undeniable costs that can affect workers. Factories may close
because of the high cost of cleanup, or owners may relocate to countries with weaker regulations.”
3. According to the International Monetary Fund (2009), “FDI is a category of international investment
in which an investor in one country, generally an enterprise, acquires an ownership interest that
confers [significant] influence over the management of an enterprise in another country (defined as
holding 10 percent or more of voting power).”
4. Examples of studies at the aggregate industry levels are, among others, Jaffe, Peterson, Portney, and
Stavins (1995); Garofalo and Malhotra (1995); Greenstone (2002); and Leiter, Parolini, and Winner
(2011).
5. It is important to stress that the win-win perspective does not suggest that all stricter environmental
regulations enhance competitiveness and promote innovation. Additionally, it does not suggest that
innovation benefits arise quickly and/or always completely offset the cost of environmental protection. The core arguments of the win-win perspective are that stricter environmental regulations must
be well designed and that potential offsets arise over time (Ambec et al., 2011). Characteristics of
well-designed environmental regulations include: (i) using a comprehensive approach that simultaneously protects air, soil, water, and other natural resources; (ii) focusing on continuous performance
improvements rather than requiring the use of specific end-of-pipe technologies; (iii) using regulatory
flexibility and providing either economic or political incentives to proactive environmental firms; (iv)
encouraging participation and coordination of multiple stakeholders (industry, environmentalists,
local governments, and international organizations) in the design and implementation of new standards (Norberg-Bohm, 1999; Porter & van der Linde, 1995a, 1995b; Rondinelli & Berry, 2000); and (v)
they also need to include gradual phase-in periods to allow the development of innovative environmental protection technologies and systems.
6. The nature and direction of causality between regulatory stringency and these proxies (pollution
levels and pollution abatement costs) is not always clear (Wagner & Timmins, 2009). Stringent regulations can result in lower levels of pollution. Alternatively, higher levels of pollution can trigger the
enactment of stringent regulations. Similarly, high abatement costs can be the result of stringent
regulations or lead to laxer regulations. To elucidate the association between environmental regulation
stringency and FME investments, it is necessary to use direct measures of regulatory stringency—
instead of its outcomes and/or causes—available for most countries.
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Policy Studies Journal, 41:2
7. Traditional democratic rights and liberties include: freedom of the press, speech, association, political
participation, and the right to vote.
8. The World Economic Forum has conducted its annual Survey for over 30 years to prepare annual
Global Competitiveness Reports. This survey has also expanded the scope of its sample, achieving in
2007 a record of over 11,000 survey responses from business leaders in 131 economies.
9. The measure of financial resources may not be comparable across different industries with different
capital intensity, but we included industry fixed effects in order to make it comparable within industry.
10. This approach estimates simulated 95 percent confidence intervals for the probability of FME by
randomly drawing a thousand normally distributed values of the coefficient on environmental regulation stringency. Then, a similar simulation estimation is repeated for the coefficient on environmental regulation certainty.
11. In our main model, we controlled for both the log of GDP and log of population. Thus, the model is
the same as model with the log of per capita GDP due to the nature of log transformation.
12. While the results are consistent, we used the sales based entropy measure in our displayed models
because it is a better proxy for geographic diversification in comparison to many different measures
such as foreign-to-total number of subsidiaries, number of foreign subsidiaries, and number of
foreign countries (Oh, 2009).
13. The dyad fixed-effects models add a total of 988 dyadic dummies to our models, in addition to the
industry and year fixed-effects already included in our previous regressions. It is also important to
stress that although estimating additional firm fixed-effects models would reduce unobserved heterogeneity, this regression approach would exclude important time-invariant independent variables
from the analysis. An extensive theoretical and empirical literature examining these investment
decisions stresses that these time-invariant variables have important effects on firms’ FME decision
(see e.g., Berry, Guillen, & Zhou, 2010; Dunning, 1980, 1998; Rugman & Verbeke, 1998, 2001; Selmier
& Oh, 2012). Also, based on extensive previous empirical studies, we do not think that our current
models have an omitted (missing) variable problem. Our models include 22 control variables besides
industry, year, and dyad fixed effects. Moreover, it is likely that when adding many fixed-effects
dummies, the results could be potentially affected by a downward bias and convergence problems.
Simulation studies (Greene, 2002, 2004) show that the estimated asymptotic estimators for fixedeffects estimators uniformly have downward biases in nonlinear models such as probit, logit, and
poisson regression models. We also tested other complex fixed-effects estimators such as firm-year
fixed effects and country-firm fixed effects, but these estimators failed to converge. The problem of
convergence is not uncommon in a logit regression model. In most cases, the frequency distribution
of sample observations across different categories is discontinuous when a model includes dummy
variables (Allison, 2008; Kali & Sarkar, 2011).
14. Our logit regression analysis also shows no significant relationship between gross levels of environmental regulation stringency and MNCs’ FME investments (models not shown).
15. Rugman and Verbeke (1998) note that the level of environmental regulations does not likely discourage European Union MNCs’ FME investment decisions because home country environmental pressures to the MNCs are higher than international environmental pressures.
16. We thank one of the anonymous reviewers for stressing the importance of environmental regulation
offsets.
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