Environmental regulations and MNC foreign market entry

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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, 243 0190-292X © 2013 Policy Studies Organization 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

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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.


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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


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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


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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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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):


Pi , j ,t , z ( yi , j ,t , z = 1; X ) = f (1) =

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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


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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.


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


0.04


-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,


0.06 0.04 0.02

__ __

Clean industry

Dirty industry

0


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


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


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


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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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


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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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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


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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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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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