FUNDAÇÃO GETÚLIO VARGAS ESCOLA DE ADMINISTRAÇÃO DE EMPRESAS DE SÃO PAULO
LUCIANA RIBEIRO CHALELA
FIRMS REACTION TO ENVIRONMENTAL REGULATION
SÃO PAULO JULY 2013
LUCIANA RIBEIRO CHALELA
FIRMS REACTION TO ENVIRONMENTAL REGULATION
This thesis is submitted to the EAESP Fundação Getúlio Vargas in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Field: Financial Markets and Corporate Finance
Supervisor: William Eid Junior,Ph.D.
SÃO PAULO JULY 2013
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Chalela, Luciana R. Firms Reaction to Environmental Regulation/ Luciana Ribeiro Chalela - 2013. 100 f.
Orientador: William Eid Junior. Tese (doutorado) - Escola de Administração de Empresas de São Paulo.
1. Controle de poluição. 2. Poluição. 3. Política ambiental. 4. Mudanças climáticas. I. Eid Junior, William. II. Tese (doutorado) - Escola de Administração de Empresas de São Paulo. III. Título.
CDU 504.06
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Undoubtedly this thesis is dedicated to my mom, my most passionate supporter, to whom I always return to find the strength to persevere.
4 ACKNOWLEDGEMENTS
I am grateful to CAPES (Coordination for the Improvement of Higher
Education Personnel) and to GVPesquisa (Fundação Getúlio Vargas Research
Institute) for granting doctorate fee exemptions. I am also thankful to Dr. Ely
Laureano Paiva, Director of the PhD Program at Fundação Getúlio Vargas –
Brazil, and Dr. Angel Diaz, Director of the PhD Program at the Instituto de
Empresa – Spain, for their consideration and patience and for recognizing the unique nature of my case: a dual degree agreement that respects the needs of both institutions. The wisdom of both directors in the decision-making process was crucial to making this research possible, and I express my appreciation to them.
It is difficult to express in a few lines the paramount importance of my advisor, Dr. William Eid Junior, in the doctoral process. I remember my happiness the day that he agreed to be my advisor and how honored I felt to work with such a successful professional in my area. That admiration has grown constantly over the past five years because of his timely and effective interventions. I express my deep gratitude to his extensive support and his decisions that have benefitted my research and my improvement as a researcher. I am also thankful to his assistant, Brielen Madureira, for her readiness to help me on the bureaucratic matters.
I am also indebted to Dr. Ricardo Ratner Rochman and Dr. Rafael
Felipe Schiozer for their valuable suggestions during my proposal defense and my final defense. Their guidelines gave me the necessary direction to successfully complete this work. Indeed, I am truly grateful for the inputs
5 received from Dr. Andrea Minardi and Dr. Mario Monzoni, members of my committee on my final defense.
During the completion of my doctoral course work at the Fundação
Getúlio Vargas - Brazil and the Instituto de Empresas – Spain, I worked with many excellent professors at both institutions who motivated me and with many supportive classmates who became great friends. It s difficult to named all of them here; therefore I want to express my gratitude to all of them through persons that were present friends during all this long process. In Brazil, my ex- classmate and friend, Dr. Cristiane Benetti, has been a constant supporter during my doctoral path. In Spain, I was blessed to meet two future PhDs,
Mudra Mukesh and Michele Esteves Martins, to whom I am especially grateful for helping me persevere through the difficult times and for all the emotional support and care they provided. I would like to express my gratitude to the encouragement words I received from Professor Dilney Gonçalves, my FGV classmate Eduardo Hiramoto and also from Saul de Medeiros, Daniel Karvik,
Leonice Ribeiro and Ingrid Stela,.
I would also like to express my deep gratitude to my family for their love and support during this long and exhaustive process. I am grateful to my mom,
Eunice Ribeiro Chalela, who has been my biggest supporter, offering frequent advice, believing in my success, and providing emotional and financial support during the critical phases of this process. I am thankful for my sister, Simone
Chalela, who encouraged my exchange period abroad and ensured that my needs were met. I am in indebted to my brother-in-law, Tarso Andrade Santos,
6 for his extensive support during the programming of the thesis database; his assistance helped make this thesis possible.
Finally, I am grateful to all the people who understand the complexity of the doctoral process and the need for long periods of exclusive commitment and focus to this work. Their understanding and support helped me find the strength to embrace this long and challenging research journey.
7 LIST OF CONTENTS
ACKNOWLEDGEMENTS ...... 5 LIST OF CONTENTS ...... 8 LIST OF TABLES ...... 9 LIST OF FIGURES ...... 10 ABSTRACT ...... 11 INTRODUCTION ...... 12 CHAPTER I – LITERATURE REVIEW ...... 19 1. Regulation and competitiveness ...... 20 1.1. Pollution Haven Hypothesis ...... 24 1.2. Contrary hypothesis ...... 43 Factor Endowment Hypothesis ...... 44 Footloose Theory ...... 46 Pollution Halo Hypothesis ...... 48 Porter Hypothesis ...... 50 CHAPTER II – DATASET DESCRIPTION ...... 54 CHAPTER III – POLLUTION HAVEN TEST ...... 61 3.1. Method ...... 63 3.2. Model...... 63 Difference-in-difference ...... 63 Difference-in-difference-in-difference ...... 65 3.3. Sample Construction ...... 66 CHAPTER IV – RESULTS ...... 76 Difference-in-difference ...... 76 Difference-in-difference-in-difference ...... 79 Major Results ...... 81 CHAPTER V – CONCLUSIONS ...... 84 Discussion ...... 84 Limitations and Future Research...... 87 Final considerations ...... 88 REFERENCES ...... 90 ANNEX A ...... 97 ANNEX B ...... 98
8 LIST OF TABLES
Table 1: Summary of different studies of PHH ...... 40 Table 2: Summary of different studies of PHH ...... 41 Table 3: Summary of different studies of PHH ...... 42 Table 4: Main hypotheses of the effect of environmental regulation on firm’s competitiveness ...... 53 Table 5: European Pollutant Releases comprising the dataset ...... 54 Table 6: Number of observations and unique companies in the air pollution dataset by sector ...... 58 Table 7: Number of observations and unique companies in the dataset after merging air pollution report with company’s financial information ...... 59 Table 8: Percentage of total population comprised in the final database ...... 60 Table 9: Main regulatory events in the EU ...... 61 Table 10: Diff-in-Diff model specification ...... 64 Table 11: Diff-in-Diff –in-Dif model ...... 66 Table 12: Classification of pollution levels by sectors and pollution amounts statistics ...... 69 Table 13: Main descriptive statistics by year ...... 71 Table 14 Descriptive statistics by treatment and control group ...... 73 Table 15: Frequency of industry sector type and treatment/control groups ...... 74 Table 16: Difference-in-Difference model ...... 77 Table 17: Difference in Difference in Difference (DDD) Model ...... 80
9 LIST OF FIGURES
Figure 1: The 3 mechanisms by which trade and FDI can affect pollution and the rate of depletion of scarce environmental resources. ______36 Figure 2: Sector’s role in percentage of the industrial air pollution emission in Europe ______57 Figure 3: Most representative countries in the dataset of European Pollution Release ______57 Figure 4: Percentage of the total population per sector ______60 Figure 5: Diff-in-Diff graph ______82
10 ABSTRACT
International agreements arising from the need to deal with the global warming promoted by countries decided to embrace a climate change policy bring on the debate of the impacts on firms in a global competitive market.
Facing, therefore, different environmental standards accordingly to firm’s physical location. Once European Union is taking the lead in adopting stringent environmental regulation, this study aims to assess the impact of environmental regulations on firms in Europe. A novel database was constructed providing firm-level air pollution emission information in the European Union. Using difference-in-difference model, the effect of the intervention of EU environmental policy change suggests a negative response in fixed assets among EU firms due to the 2006 EU policy. The evidence to the hypothesis that firms in European Union have been decreasing its firms fixed assets, as a proxy of production capacity, with the change in environmental regulation, provides general support for the PHH, however, it doesn’t remain in robustness checks.
The contribution of this work is bringing a revisited view of the actual effect of environmental regulation based on Kyoto Protocol directives on European firms.
Key words: Carbon Finance, Regulation, Competitiveness, Pollution Haven.
11 INTRODUCTION
To prevent dangerous anthropogenic (i.e., human-induced) climate change, important global commitments have been adopted to decelerate the main causes of global warming. In particular, the Kyoto Protocol, which was signed on Dec. 11, 1997, is an international treaty in which developed countries committed to stabilizing gas emissions and reducing greenhouse gases emissions by 5 percent relative to 1990 levels over the five-year period from
2008 to 2012. However, there is no consensus on the adoption of international environmental agreements. Such agreements would decelerate the rise in global temperatures and require that dirty industries adopt cleaner production processes or control the total pollution that is discharged into the environment.
These requirements may affect industries’ production costs and consequently trade flows; thus, for firms making location decisions, the competitiveness benefits or drawbacks afforded by local regulation become of paramount importance.
An economic impasse arises when firms compete globally but environmental regulations are adopted and enforced locally. The enforcement of a certain green performance level may have two types of implications for firms in a globalized world. Either such a standard may require firm investments to change actual environmental performance levels or firms may need to incur sunk costs when attempting to control the amount of pollution discharged into the environment, even appealing to a cap and trade system, purchasing rights to pollute sold by firms with excess pollution permits, therefore holding emissions levels constant relative to trade (OECD – Organization for Economic
12 Cooperation and Development, 2010). These two distinct consequences are the root of the environmental-economic debate on the effect of environmental regulation on firms. The traditionalist view predicts a negative effect of environmental regulation on firms, whereas the revisionist view expects a positive effect of environmental regulation on firms.
A country’s decision to embrace climate change policy comes at a price; therefore, some countries are not willing to implement regulations to mitigate increases in average temperature because of the costs that such a stringent regulation could impose. The European Union has taken the lead in adopting stringent environmental standards to reach the targets outlined in the
Kyoto Protocol; in particular, the policy calls for monitoring and enforcing emissions disclosure of firms that exceed the pollution threshold imposed by the
European Union, what accounts for approximately 90 percent of the total greenhouse emissions from all industrial emission in the EU territory. Each member state in Europe must provide a complete list of companies that exceed the pollution ceiling resolved by the European Union and the actions that have been taken to reduce those pollution levels. Indeed, the regulation enables companies to trade the rights to pollute to achieve their targets without compromising economic growth. Thus, European Union controls the evolution of member states’ pollution levels over time to achieve the Kyoto Protocol commitment; in turn, each member state is responsible for controlling its companies’ pollution growth. Furthermore, publicly identifying the companies that are surpassing their designated pollution levels is expected to exert pressure on these companies; by harnessing public participation in
13 environmental decision making, these disclosures inform the public as to which companies are contributing to the degradation of air quality in the region.
The evaluation of the impact of these stringent environmental regulations in the European Union remains the main subject of debate in the environmental arena as a result of several concerns. For instance, the increase or decrease in GDP through investments and disinvestments in a country is of particular interest to economists because a regulation that is not harmonized across borders exposes high-pollutant sectors to an elevated risk of carbon leakage; that is, there may be an increase in carbon dioxide emissions in one country as a result of emissions reduction by a second country with a rigorous climate policy.
The primary objective of this work is to draw on the experience of the
European environmental regulation of polluting industries to contribute to the discussion of whether home and rest-of-world emissions are strategic substitutes or strategic complements (Copeland and Taylor, 2005). In doing so, this thesis aims to accomplish the following objectives: (1) identify the companies that have been affected by this stringent environmental regulation in
Europe; (2) test to determine whether environmental regulation adversely affects international competitiveness; and (3) verify whether this effect is sufficiently strong to affect asset investment pattern in the relevant industry— that is, to determine whether companies are moving to pollution havens to avoid the costs of compliance with environmental regulations.
There is no consensus in the literature regarding the effects of environmental regulation on the competitive advantage of both firms and
14 countries. The contribution of this study relies on an investigation of the effects of the stringent regulation established in Europe. The study will determine whether the regulation is upholding the goals espoused in the Kyoto Protocol or whether the regulation is generating undesirable collateral effects. An example of a collateral effect is captured by the pollution haven hypothesis (PHH), which posits that high-pollutant industries will move from jurisdictions with stringent environmental regulation to those with more lax environmental regulation.
Henderson (1996) stated that a local regulation may lead to improved air quality simply because polluters tend to move. However, the phenomenon of pollution displacement conflicts with the motivation for stringent environmental regulation imposed in Europe; the regulation is intended to help mitigate global warming and reduce global pollution rather than simply to spread it out.
This study provides summaries of how firms respond when confronting free trade and nonhomogeneous environmental regulation across facilities with respect to maintaining firm competitiveness. The study provides three main contributions. First, this work examines the results of monitoring air pollution emissions by collecting the amount of pollution disclosure in Europe by industrial facilities. This analysis provides new insights into the relationship between environmental regulation and competitiveness in a carbon-constrained world. Second, the study tests empirical evidence of the PHH. Few studies have attempted to test the PHH (Eskeland; Harrison, 2003). The empirical studies that do exist have been hampered by difficulties with proxies and data sources, such as the use of indirect pollution measurements and country-level tests, resulting from the limited availability of firm-level data. Third, the study analyzes
15 the differences between companies that confront strict environmental regulation and companies that do not confront such regulation to identify the impact of the lack of environmental standards.
In summary, this thesis explores firms’ reactions to environmental regulation in a scenario in which environmental policies differ across jurisdictions and companies must compete on a global scale. In particular, the central research question is as follows: does the change in environmental policy in Europe cause the reallocation of production to other locations in polluting industries? To address this environmental concern, the fields of environmental economics and environmental finance investigate the financial effects of living in a carbon-constrained world; specifically, such studies fall under the category of carbon finance. The theoretical advancements of this thesis rely on the environmental economics literature.
This thesis is divided into chapters. Chapter 1 provides a review of the literature linking environmental regulation and competitiveness and presents the competing hypotheses that emerged. Describing an inconclusive argument characterized by mixed empirical results. The divergence of results found thus far are related to the impacts of environmental regulation on firms’ and countries’ comparative advantages and to the strategies adopted by a firm in the context of non-harmonized environmental policies.
Chapter 2 describes the construction of the database comprising air pollution data from European Union companies that exceeded their emission thresholds. Ederington et al. (2005) stated that the United States is the only country that has collected pollution abatement cost data for a significant period
16 of time. Therefore, researchers have limited options for exploring the relationship between environmental regulation and competitiveness. The novel database that is constructed in this study covers air pollution information from firms’ production facilities in Europe. This approach contrasts with the use of the
U.S. dataset in previous studies, which tracks only toxic chemical information.
Thus, this work fills a gap in the literature by evaluating the main theories of the effects of environmental regulation on firms in a sample of European companies, which are the most affected by environmental regulation because of their joint participation in environmental treaties, such as the Kyoto Protocol.
The tracking of companies’ reports of their industrial air pollution emissions represents an important contribution to the prior literature. Previous studies concerning similar environmental issues (King and Shaver, 2001; Berrone and
Gomez-Mejia, 2010; Tang, 2010; Smarzynska and Wei, 2001; for instance) have used the public data available from the Toxic Release Inventory (TRI) program of the Environmental Protection Agency (EPA - USA). As Berrone and
Gomez-Mejia (2009) acknowledged, reliable pollution data on a global scale is almost nonexistent and represents both a challenge and an opportunity for future research.
Chapter 3 provides the model for testing the PHH, which states that polluting firms are likely to relocate to countries with lax environmental standards. The sample construction illustrates how this study tests the
European sample at the firm level. This approach eliminates the problem of addressing the PHH using country data such as total foreign direct investment
(FDI), which may mask firm-level effects according to Smarzynska and Wei
17 (2001). Finally, Chapter IV presents the results and Chapter V discusses the conclusions and identifies the study’s limitations and possibilities for future research. The study concludes by discussing final considerations.
18 CHAPTER I – LITERATURE REVIEW
The link between environmental regulation and competitiveness has not yet reached a concise set of conclusions. One of the main hypotheses that attempt to predict the response of firms under environmental regulation is the
PHH, which states that firms would move to jurisdictions with lax environmental standards to avoid the costs that are associated with environmental regulation.
However, this hypothesis has divided researchers into two opposing views: the traditionalists hypothesize a negative effect on firms and attempt to establish empirical evidence for the PHH; conversely, the revisionists base their opinion on the Porter hypothesis, which claims that environmental regulation may have positive effects on firms; these scholars attempt to overcome the lack of robustness in the empirical tests of the PHH.
After a brief review of important points regarding regulation and competitiveness, the PHH section of this chapter will describe the PHH and a weaker version known as the pollution haven effect (PHE). In addition, the literature provides an overview of the main empirical studies of pollution havens and the mixed findings that have been reported thus far. Finally, the literature review groups the studies as focused on (i) direct examinations of location choice, (ii) indirect examinations of input flows and (iii) indirect examinations of output flows (Brunnermeier and Levinson, 2004). Throughout the section, theories competing with the PHH are presented.
19 1. Regulation and competitiveness
Environmental regulations that are implemented at the national level may influence the relative country-specific advantages in the operation of both domestic and foreign multinational enterprises (MNEs). Rugman and Verbeke
(1998) acknowledged that trade could be affected by the impact of environmental regulations on firms’ specific advantages and on country-specific advantages. The authors identified four distinct scenarios: (1) countries in which strict environmental regulations lead to a substantial weakening of national competitiveness for MNEs, which leads environmentally regulated industries to avoid production (increasing outward FDI) and increases the import of pollutant- heavy products into the country (declustering); (2) countries in which weak environmental regulation leads to a substantial strengthening of a nation’s competitiveness from the perspective of MNEs, which increase inward FDI in the pollution haven and increase exports to countries with less favorable regulation (pollution haven); (3) countries in which strict environmental regulations lead to a strengthening of a nation’s competitiveness, which positively affects the performance of firms belonging to a specific innovation cluster (environmental innovation cluster); and (4) countries in which environmental policies are used to shelter domestic companies, generating entry barriers against international competition (strategic environmental policy).
The literature review of this study concentrates on the analysis of the strategies that are employed by firms in reacting to environmental regulation: the PHH and the Porter hypothesis. A firm’s response to new environmental
20 policy depends on the perception of whether the new normative standard will bring advantages or disadvantages to the firm.
Researchers are divided between the traditionalist and revisionist views of neoclassical environmental economics. The traditionalist view (Jaffe, Peterson,
Portney, & Stavins, 1995) claims that the aim of environmental regulation is to internalize the costs of pollution for firms and consequently to correct negative externalities. That is, there is a tradeoff between environmental strategies and profitability. The revisionist view (Porter and van der Linde, 1995) states that improving environmental performance provides a potential source of competitive advantage and is thus beneficial for firms; any loss of competitiveness in the short term will result in rising output over time as a result of enhanced productivity.
Traditionalists state that complying with regulation may reduce the demand for a firm’s output because of higher prices driven by increased production costs. That is, regulation reduces or adversely affects productivity, imposes new cost elements, decreases a firm’s market share and affects perceived product quality. Regulation also may impose transaction costs and reduce the amount of time for management to pursue other tasks. Such effects may decrease expected financial gain, primarily in the short term, and necessitate a long-term vision. Regulation may even result in an unreliable cost- benefit of environmental actions. In summary, traditionalists defend the trade-off between profitability and environmental compliance for firms. However, important works based on the traditionalist view, such as the research of Jaffe et al. (1995), stated that there is relatively little evidence to support the
21 hypothesis that environmental regulations have massive adverse effects on competitiveness.
By contrast, the revisionist view is supported by institutional theory and states that institutional demands induce firms to adopt environmentally friendly policies either because of coercive pressures or because of social expectations regarding appropriate firm behavior. Strong environmental performance can enhance firm legitimacy, which can generate several economic benefits beyond financial performance. Such performance can create demand for a firm’s green products and services, generate product differentiation, assist in obtaining market access and encouraging more efficient processes, and stimulate innovative improvements in productivity. Moreover, environmental performance can enhance a good corporate reputation, promote better stakeholder relations through improved environmental quality (a public good), lower the costs of compliance and reduce the risk of legal or social sanctions or penalties. In addition to the social value for businesses that environmental actions can enhance, such activities can also provide opportunities to improve resource efficiency and increase costs for rivals.
It is important to underline that environmental strategies differ depending on whether environmental adaptation is perceived by a company as pollution control or as pollution prevention (Nerth, 1998). Firms that associate environmental regulation with costs focus on fulfilling their pollution control obligations during the last stage of the production process; such an approach does not require the development of expertise or skill to manage new environmental technologies. Rather, the focus is on the end-of-pipe emissions
22 (that is, removing pollution after it is produced but before it is discharged into the environment). Firms that perceive environmental regulation as a potential advantage will focus on pollution prevention, which requires structural investments in new technologies. Even if pollution prevention can represent a risky investment in the short term, Berrone and Gomez-Mejia (2009) argued that such efforts can provide unique advantages in the long term as a result of the fundamental rethinking of products and processes that leads to improvements and innovations.
It is also important to emphasize that the responses expected for an industry encountering stringent regulation apply only to highly competitive sectors. Firms that sell a homogeneous product in a perfectly competitive market cannot transfer the cost to product prices and will suffer decreases in profitability. On the contrary, industries that do not need to compete to sustain their market share will transfer some part of the costs to the product price, diminishing the competitiveness impacts. One study by Greenstone, List and
Syverson (2011) estimated the effects of environmental regulations as captured by the Clean Air Act amendments’ division of counties into pollutant-specific nonattainment (areas with stringent environmental regulation imposed) and attainment (areas free of environmental regulation restrictions) categories with respect to the total factor productivity (TFP) levels of manufacturing plants.
Examining the period from 1972 to 2000, the authors found that plants increase prices when their county is declared to be in nonattainment; that is, plants transfer much of the cost of regulation to consumers, particularly in monopoly or oligopoly industries lacking a high level of competitiveness. Therefore, it is
23 crucial to account for the level of competition in the sectors being analyzed.
Competitive pressure is a fundamental assumption for generating the comparative disadvantage that provides incentives for pollution haven movements. Ederington et al. (2005) noted that one of the difficulties of finding robust evidence that environmental regulations influence trade patterns is that most trade occurs among developed countries, which share similarly high levels of environmental stringency. Such patterns appear to violate the PHH.
1.1. Pollution Haven Hypothesis
Xing and Kolstad (2002) confirmed the assumption that environmental regulation has a large adverse effect on competitiveness, affirming that strong environmental regulation can directly increase production costs, decrease waste disposal capacity and prohibit certain factor inputs or outputs. If firms envisage the stringency of environmental regulation as a costly pressure rather than an opportunity to become green efficient firms, then complying with environmental standards by transferring pollution to jurisdictions with lax regulations can be an option to maintain their competitiveness and to gain a comparative advantage relative to competitors in the same sectors. This outcome affects social welfare in a completely different manner than the regulation intended.
The fundamental debate is that more stringent regulations in one country are believed to result in a loss of competitiveness under trade liberalization.
This effect will cause a pollution-intensive industry to concentrate in jurisdictions with lax environmental regulation, according to the PHH (Dean, 1992).
24 According to the logic of the PHH, if it is costly to conform to more stringent environmental requirements in developed countries, then profit-maximizing firms would prefer to relocate their production activities (Smarzynska; Wei,
2001). Copeland (2009) acknowledged that this theory could be divided into two parts to allow for better understanding. First, does more stringent environmental policy adversely affect international competitiveness in polluting industries? This effect is referred to as the competitiveness hypothesis. A test of this hypothesis would assume that the trade regime is constant and evaluate the impacts of tightening the environmental regulation in a given country. Second, is the effect of environmental policy on competitiveness sufficiently strong to determine the pattern of investment flows? To test this hypothesis, one would assume that environmental policy differences across countries are given and simply measure the impacts.
Some authors distinguish between the two questions by defining a
Pollution Haven Effect (PHE) as the scenario in which the competitiveness hypothesis is satisfied and by defining the PHH as the scenario in which the
PHE exists and is sufficiently strong to determine investment flows. Therefore, the PHE states that differences in environmental regulation affect plant location decisions and trade flows; that is, stricter environmental policy decreases net exports of dirty goods. The PHH is a stronger hypothesis that states that under free trade, the production of pollution-intensive goods will move from countries with stringent pollution regulation (typically developed countries) to those with lax regulation (Temurshoev, 2006).
25 Brunnermeier and Levinson (2004) proposed three descriptions of the
PHE: (i) economic activity shifts to jurisdictions with less strict environmental regulation, (ii) trade liberalization encourages an inefficient race to the bottom
(countries have an incentive to lower environmental standards to attract investments and capital) and (iii) trade liberalization shifts polluting economic activity toward countries with lax environmental standards. Mulatu et al. (2010) clarified that even if PHEs occur because asymmetric environmental regulatory stringency influences the inter-jurisdictional distribution of polluting industries, such effects will constitute only one determinant of industry location. To prove the PHH, such effects must be the most important determinant of firm location; at a minimum, the effects must dominate other determinants, such as the availability of capital and skilled labor.
The seminal pollution haven model was developed by Pethig (1976), and this model assesses differences in the pollution taxes of two countries that are assumed to be exogenous. The first study considering endogenous environmental policy was developed by Copeland and Taylor (1994). This study found that environmental quality increases with income and that governments make decisions related to environmental policies and standards based on their expectations of the direction of trade for the polluting good. The negligible results found by earlier studies were believed to occur because environmental regulation was modeled as exogenous. New studies, such as that of Ederington and Minier (2003), have estimated a significantly larger effect of environmental regulation on trade flows, proving that the endogeneity of environmental regulation may have biased previous estimates of the effect of environmental
26 regulation on trade flows downward. Recently, Levinson and Taylor (2008) examined the main problems that hamper researchers attempting to find conclusive results. The authors showed that unobserved heterogeneity plays a large role. That is, many factors affect trade, and if some of these factors are correlated with environmental regulation but are not controlled in the analysis, then the results will be affected by omitted variable bias. Aggregation bias is another limitation: the industry classification system groups at the three-digit level of the industry subsector, which is a heterogeneous mix of four-digit level industry groups. The analysis considers industry subsectors that comprise several related but different industry groups. When examining whether pollution regulation at home raises production costs and causes certain industries to suffer because of foreign competition and shut down, researchers must consider this diversity to ensure a proper analysis.
Apart from the methodological problems, the challenge of obtaining data has resulted in a set of studies that used different proxies to assess environmental regulation. Among those that attempted to quantify the level of environmental stringency, some researchers have used the Clean Air Act in the
U.S. In states that need to improve air quality, plants must reduce emissions; county-level compliance is used as a proxy for environmental stringency. Others studies have used abatement costs. According to Levinson and Taylor (2008), pollution abatement costs and net imports may be negatively correlated in panels of sector-level data. Unobserved changes in foreign costs, regulations or domestic industry attributes can produce a spurious negative correlation between sector-wide pollution abatement costs and net imports. This correlation
27 would bias estimates against finding a PHE. Indeed, it is not surprising that the literature has produced mixed empirical evidence for the PHE as a result of the lack of a sound theoretical and analytical framework and empirical work based on problematic data sources and proxies (Kukenova; Monteiro, 2008), (Kang;
Liu, 2009).
Given the important studies on this topic, some researchers have found no support for the PHH. Some examples are the Kang and Liu (2009) study, which examined the effect of environmental stringency on FDI inflows in China; the Grether and Melo (2003) study on production and international trade flows in five heavily polluting industries for 52 countries over the period from 1981 to
1998, which found support for a pollution halo hypothesis (a contrary hypothesis); and the Kirkpatrick and Shimamoto (2008) study, which also found no support for the PHH when analyzing Japanese inward FDI in five dirty industries.
Other authors found only limited evidence to support the PHH.
Smarzynska and Wei (2001) studied investment projects in 24 transition economies in 1995 and found some support for the PHE when accounting for the effect of host country corruption. The authors used firm-level data on U.S. toxic releases, employed a variety of measures to capture the strength of environmental protection and constructed a measure of pollution intensity at the four-digit industry level. However, the evidence is weak and does not survive numerous robustness checks. Eskeland and Harrison (2003) examined the pattern of foreign investment in four developing countries: Mexico, Morocco,
Côte d’Ivoire and Venezuela. These authors found some evidence that foreign
28 investors are concentrated in sectors with high levels of air pollution, although the evidence is weak at best.
Dam and Scholtens (2008) analyzed the location choice of 2,685 MNEs and found circumstantial evidence in favor of the PHH for firms with weak environmental standards; that is, firms that establish high internal environmental standards will not derive a comparative advantage from locating in countries with poor environmental regulation, whereas firms with little environmental responsibility may have incentives to engage in movement conforming to the
PHH. However, when considering countries with high pollution or high poverty to be “havens”, these authors did not find similar evidence. Mani and Wheeler
(1999) examined international information on industrial production, trade and environmental regulation from 1960 to 1995 and found circumstantial evidence that could be consistent with the PHH. In particular, the authors found that pollution-intensive output as a percentage of total manufacturing has fallen consistently in the OECD and has risen steadily in the developing world.
Moreover, the periods of rapid increase in the net export of pollution-intensive products from developing countries coincided with periods of rapid increase in the cost of pollution abatement in OECD economies. However, these effects were not strongly significant, which suggests a transient effect once economic growth brings countervailing pressure on polluters through increased regulation, technical expertise and clean production.
In another study, Busse (2004) applied a Heckscher-Ohlin model to test whether environmental regulation across 119 countries affects trade patterns in five highly polluting industries. There was no support for the hypothesis except
29 in the iron and steel sectors, which exhibited a considerable decline in the export-import ratio. The Cave and Blomquist (2008) study that determined 1993 to 1999 to be the period of greater environmental stringency in Europe within the period from the 1970s to the 1990s supports the PHH for the EU energy industry. There was an increased amount of EU energy trade with poorer countries during the period of more stringent EU environmental standards.
However, this result is not robust when poorer countries are defined by OECD membership and geographic region: a 1 percent increase in industry energy intensity is associated with a 0.25 percent increase in imports from low-income countries during the period of more stringent environmental standards. In a further study, Dean, Lovely and Wang (2009) found support for the pollution haven theory in high-polluting industries by estimating the determinants of location choice for equity joint ventures (EJVs) from 1993 to 1996 in China.
In addition, Xing and Kolstad (2002) and Kahn and Yoshino (2004) found general support for the PHH. Kellenberg (2009) was the first study to find robust confirmation of a PHE in a cross-country context from 1999 to 2003 by examining U.S. outward multinational affiliate production and accounting for endogenous strategic policy interactions across countries.
The empirical tests of the PHH focus on (a) direct examinations of location choice; (b) indirect examinations of input flows, such as changes in movements of capital (FDI); or (c) indirect examinations of output flows, such as productivity
(Brunnermeier and Levinson, 2004).
The studies focusing on the impact on location decision attempt to empirically establish the fact that environmental policy produces adverse net
30 competitiveness impacts that induce firms to seek alternative jurisdictions for investment or facility locations to avoid losing comparative advantage (OECD,
2010). Levinson (1996) used a conditional logit model of plant location choice and found that environmental regulation does not systematically affect the location choices of most manufacturing plants. However, the Mulatu et al.
(2002) meta-analysis of international trade studies stated that, in contrast to initially weak empirical evidence, recent studies have shown that environmental regulation affects the location behavior of pollution-intensive manufacturing firms. Further, the linkage between environmental regulation and competitiveness should be investigated with industry-level data rather than country-level data.
Some authors found that environmental regulation affects location decisions. Among these studies, Becker and Henderson (2000) found that plant births decreased dramatically in nonattainment counties compared with attainment counties during the advent of air quality regulation in the U.S. from
1963 to 1992. List and Co (2000) used state-level FDI data from the U.S. from
1983 to 1993 and found that environmental regulation is relevant to the new plant location decisions of foreign multinational corporations. Evidence of an inverse relationship between the stringency of environmental regulations and new plant formation is corroborated by List et al. (2003) with county-level data from New York State from 1980 to 1990. Furthermore, Henderson (1996) investigated the effects of air quality regulation in the U.S. for the 1977-1987 period and found that local air quality regulation affects industrial location.
When there is a switch from attainment to nonattainment status, greater local
31 regulatory efforts to improve air quality are induced, which results in the diffusion of polluting industries. This diffusing occurs because firms tend to reduce regulatory scrutiny by relocating over time to attainment areas. Overall, this diffusion has increased the number of polluting plants from 7 percent to 9 percent. Greenstone (2002) found that during the first 15 years of the Clean Air
Act (1972-1987), new nonattainment counties experience employment reduction of approximately 590,000 jobs, investment reductions of $37 billion in capital stock and output reductions of $75 billion (1987 dollars) in pollution- intensive industries. Condliffe and Morgan (2009) also investigated the effects of the 1977 Clean Air Act amendments on the location decision of pollution- intensive manufacturing plants for 1996-1997 and 1997-1998. The authors showed that more stringent county-level environmental regulation affects pollution-intensive capital flows by deterring new plant births. Gray (1997) also found strong negative correlation between plant birth rates in the U.S. and regulatory measures.
Millimet and List (2004) acknowledged the spatial heterogeneity across countries, suggesting that the impact of environmental standards differs greatly across counties; an important part of this variation can be explained via observable location-specific attributes, such as the level of unemployment, agglomeration externalities and the supply of skilled labor. Mulatu (2008) focused on weighing environmental regulation against other economic determinants (such as factor endowment forces). He analyzed 18 manufacturing industries in 13 European countries, and the results indicated that environmental regulation is a strong factor but that the effect is still smaller
32 than other determinants. This result supports the PHE but not the PHH. A subsequent study by Mulatu et al. (2010) found that firms are not attracted by lax environmental policies, or in other words, firms are not deterred by stricter environmental policy. This result indicates that the magnitude of the environmental regulatory effect is approximately the same as other determinants of industry location. In fact, if an MNE can avoid tariffs or costs by substituting production for exports, then this motivation would be important for location decisions; nevertheless, a firm’s decision to reallocate pollution- intensive goods production depends on the magnitude of the cost of regulation compared with the cost of operating in another country (Dardati; Tekin, 2009).
On average, a market responds positively to firm reallocation or to the expansion of business or production levels (by relocating to a new market or to a new facility) and negatively to a decrease in facilities or production capacity
(Chan, Gau, & Wang, 1995).
Dam et al. (2007) divided the literature exploring on the main determinants of location choice into two strands: new economic geography and the traditional theory of FDI. The first strand focuses on spatial imperfections that affect location choice and performance. Firms want to be located in regions in which competitors operate successful businesses, but insofar as there is an increase in competition, this factor will have a negative effect on the location’s attractiveness. The second strand focuses on the role of local costs, production factors, transportation costs, size and markets, believing that horizontal investments are more likely in the case of large markets and high transportation costs and that vertical investments arise when local costs are relatively low.
33 The second approach empirically tests the PHH using the indirect examinations through input flow proxy approach. In particular, the impact of environmental regulation on international capital movements or FDI is generally analyzed. Many studies focus on investment flows (Smarzynska; Wei, 2001;
Kang; Liu, 2009; Cole et al. 2005); however, a Mulatu et al. (2002) meta- analysis reported that the focus on the impact on trade flows reveals fragile and inconclusive empirical evidence on the PHH. Nevertheless, it seems straightforward to use FDI as a proxy to measure the effect of environmental costs on trade; however, these costs may be outweighed by other factors determining international flows (Dean, Lovely, & Wang, 2009), which introduces a large amount of noise into the results.
Analyzing whether trade liberalization would affect environmental quality in the U.S. through changes in the composition of industries, Ederington,
Levinson and Minier (2004) did not find evidence that the domestic production of pollution-intensive goods (manufacturing) in the U.S. is being replaced by imports from overseas. However, the results of Xing and Kolstad (2002) showed that the laxity of environmental regulations in a host country is a significant determinant of FDI from the U.S. for heavily polluting industries (chemical and primary metals) and is insignificant for less polluting industries. Grether and
Melo (2003) studied the production and trade flows in heavily polluting industries for 52 countries over the 1981-1998 period, and for the southern
U.S., they found a delocalization of iron and steel, industrial chemicals, non- metallic mineral products, and pulp and paper. Kalamova and Johnstone (2011) used a sample of 27 OECD source countries and 99 host countries over the
34 2001-2007 period and found that lax regulation in the host country has a significant effect (between 2.7 percent and 5.5 percent) on incoming FDI flows in both developed and developing countries; furthermore, the relationship appears to be nonlinear. The effects of increased relative environmental policy stringency in the host country and decreasing environmental policy stringency beyond a certain threshold may result from the following combination: above a certain level of policy stringency, increased production costs reduce the attractiveness of the country for foreign investors; meanwhile, excessively lax policy indicates a more uncertain investment environment, which is unattractive.
According to some authors, environmental quality deteriorates during the early stages of economic development and subsequently improves in later stages
(Dinda, 2004); that is, environmental quality appears to deteriorate with economic growth at low income levels but eventually improves with economic growth at higher levels of income.
Grossman and Krueger (1995) stated that economic growth brings an initial phase of deterioration followed by a subsequent phase of improvement; they found the turning point to be a per capita income of $8,000. Developing countries with low income levels are more focused on additional earnings and jobs rather than on health and pollution reduction (Temurshoev, 2006). This finding agrees with the environmental Kuznets curve (EKC), which suggests that as development and industrialization progress, environmental damage increases as a result of the greater use of natural resources, higher pollution emissions and the increased operation of dirty sectors. Grossman and Krueger
(1991) distinguished three separate mechanisms by which trade and FDI can
35 affect pollution and the rate of depletion of scarce environmental resources. The first effect, the scale effect , states that if trade liberalization causes an expansion of economic activity and if the nature of that activity remains unchanged, then the amount of pollution must increase. Second, the composition effect states that trade liberalization will lead countries to shift resources into sectors that make intensive use of their abundant factors; therefore, the net effect on pollution levels in each location will depend on whether pollution-intensive activities expand. Third, the technique effect highlights that the pollution per unit of output could decrease as a result of the transfer of modern (likely cleaner) technologies, either because of the transfer of knowledge among countries or because of political pressures demanding cleaner environmental as an expression of increased national wealth. This last effect presumes that stringent pollution standards and stricter enforcement of existing laws may be natural political responses to economic growth.
Figure 1: The 3 mechanisms by which trade and FDI can affect pollution and the rate of depletion of scarce environmental resources. Source: (Everett, Ishwaran, Ansaloni, & Rubin, 2010, p. 20)
36 Endogeneity of environmental regulation, the idea that government can adjust policy according to their expectations of quality as a common good or economic growth, may have biased previous estimates of the effect of environmental regulations on trade flows downward (Ederington & Minier,
2000). Cole, Elliott and Fredriksson (2006) stated that the FDI of 33 countries for the 1982-1992 period is found to affect environmental policy conditional on the local government’s degree of corruptibility. High corruptibility leads to less stringent environmental policy. The basis argument is that governments pursue trade goals through trade policy; therefore, trade considerations play a role in the establishment of environmental policy.
Levinson and Taylor (2008) acknowledged the endogeneity of pollution abatement cost measures (that is, industries whose abatement costs increase most experience the largest increases in net imports), leading to a spurious correlation related to an opposing theory called the Porter hypothesis. The
Porter hypothesis states that regulation brings cost-reducing innovation and is invoked to explain the positive link between regulatory stringency and exports.
The authors confirm the PHH with a statistically significant finding that accounts for the unobserved heterogeneity across countries and sectors. Adjusting for endogeneity in the U.S. pollution abatement cost data for manufacturing industries from 1978 to 1992, Ederington and Minier (2003) found a negative effect of environmental regulation on trade flows. Wagner and Timmins (2009) analyzed the outward FDI flows of various industries in German manufacturing from 1996 to 2003 and found robust evidence of the PHE for the chemical industry. Brunnermeier and Levinson (2004) concluded that with methodological
37 improvements, studies have found statistically significant pollution haven results of a reasonable magnitude. The link between environmental stringency and FDI has also been supported in studies presented by Kalamova and Johnstone
(2011); Hettige, Lucas and Wheeler (1992); Mani and Wheeler (1999); and
Kukenova and Monteiro (2008).
The third approach to testing the PHH, focusing on the impact on productivity , links environmental policy and firm productivity. The argument here is that the cost of complying with environmental regulation causes declines in productivity (OECD, 2010). The studies of productivity at the plant level in the context of environmental regulation have produced mixed results. Focusing on the oil refineries of the Los Angeles Air Basin, Berman and Bui (2001) found that abatement expenditures did not decrease productivity, but this finding may result from the problematic interpretation of PACE (Pollution Abatement Control
Expenditures) as a net cost of regulation. Abatement costs may overstate the true cost of environmental regulation, a statement also supported by Gray and
Shadbegian (2003) in their study of pulp and paper mills. According to Gray and
Shadbegian, PACE can be underestimated because of unreported costs, such as the time spent by managers addressing regulation issues, or can overestimated if environmental regulation induces plants to install cleaner, more efficient technologies that enhance productivity.
In a study using 1987 data (as the only source of comprehensive data on emissions for a large number of pollutants) covering 10 pollutants, 48 countries and 79 four-digit sectors. Grether et al. (2012) focused on the share of trade and northern and southern imports. The authors found that interregional trade
38 constitutes only approximately 10 percent of total trade for both the northern and southern groups of countries. The authors tested for pollution haven outcomes through the pollution content of imports (PCI). The PCI is then decomposed into three components: (i) a deep component (i.e., traditional variables unrelated to the environmental debate, such as distance, the potential of the market, GDP (gross domestic product), common relation, common language, whether the nation is landlocked), (ii) a factor endowment component
(capital-labor ratio serves as a proxy for endowment differences) and (iii) a pollution haven component reflecting the impact of differences in environmental policies.
This study will test for the presence of the PHH in stringently regulated countries. The study will adopt a different approach to identifying firms attempting to avoid reduced competitiveness by focusing specifically on
European Union data. This region is one containing the developed economies that are most affected by the Kyoto Protocol. Tables 1 to 3 summarize the important papers that have investigated the PHH.
39 Table 1: Summary of different studies of PHH
Author Study Data Method Variables Observations Finding Firm-level data that describes the Looking at participation in international investment decision by 534 major 3 types of measures: degree of participation in four environmental treaties, investment from pollution- Investigates if pollution- multinational firms in 24 countries in Smarzynska Cross-section, different international environmental protection Explicitly takes in account intensive multinational firms as a share of total intensive multinational firms Central/Eastern Europe and the former and Wei probit with treaties, index of the strength of the air and water corruption in a host country as a inward FDI is lower for host countries with a higher flock to countries with weak Soviet Republics. Pollution intensity (2001) clustering ambient and emission standards and actual possible deterrent to FDI. environmental standard, however the support for environmental protection. measure compiled from the Toxics reduction in emissions of carbon dioxide pollution haven is not robust to various sensitivity Release Inventory (TRI) data from EPA checks. (Environmental Protection Agency).
FDI, pollution abatement cost, import penetration (openness in the sector’s product market), Herfindahl index (measure of scale and Testing if there is any tendency for concentration), the interaction of market foreign firms to pollute less or more Eskeland Searching evidences on Data report at the plant level from: Panel regressions concentration and import penetration, the than their local peers, using a proxy Found some evidence that foreign investors locate and whether multinationals are Cote d’Ivoire (1977 to 1987), Venezuela controlling for labor–capital ratio, a measure of regulatory for pollution intensity which is the in sectors with high levels of air pollution, but the Harrison flocking to developing country (1983 to 1988), Morocco (1985 to country-specific barriers against FDI, market size and wages. Used use of energy and dirty fuels. The evidence is weak at best. (2003) as a pollution havens. 1990) and Mexico (1990). factors three different measures of pollution emissions: results show that foreign plants are total particulates, which is a measure of air significantly more energy efficient pollution; biological oxygen demand, which is a and use cleaner types of energy. broad measure of water pollution; and total toxic releases The results showed the importance of take in account these industries' characteristics: the Test several candidate amount of trade with low-income countries, and Dependent variable: net imports/value shipped explanations for the lack of the geographic mobility of the industry. The study Independent variables: environmental costs, tariff, evidence on the PHH. found support for the explanation that most trade human capital and physical capital Demonstrates that pollution takes place among developed countries, which Interactions Explore 3 potential determinants of Ederington, abatement costs are a small share similarity high levels of environmental Regression with Transport costs X environmental cost: significant (- geographic immobility: Levinson component of total costs and PACE (pollution abatement costs stringency, a seeming violation of the PHH. Also time and industry 14.69) transportation costs in product and Minier are unrelated to trade flows. expenditures) from 1978-1992. the study found support that some industries are fixed effects Plant costs X environmental cost: significant (-5.47) markets, plant fixed costs and (2005) The industries with the largest less geographically mobile than others, due to Agglomeration economies X environmental cost: agglomeration economies. pollution abatement costs transportation costs, plant fixed costs, or non significant also happen to be the least agglomeration economies, therefore these less Industry average environmental cost X geographically mobile, mobile industries will be insensitive to differences environmental cost: non significant proving the Footloose Theory. in regulatory stringency between countries, because they are unable to relocate easily, called as Footloose Theory. Simple model to demonstrate how unobserved Data on U.S. regulations and trade with Levinson heterogeneity, endogeneity Simple, multi- PAC and net imports may be negatively correlated Canada and Mexico for 130 Industries characteristics, trade with Mexico, trade and Taylor and aggregation issues bias sector, partial in panels of sector-level data, which can easily bias manufacturing industries from 1977 to with Canada (2008) standard measurements of equilibrium mode estimates against finding a pollution haven effect. 1986. environmental regulation and trade flows.
40 Table 2: Summary of different studies of PHH
Author Study Data Method Variables Observations Finding They found a negative significant interaction effect for both Investigates the relationship Choice of the subsidiary location is the dependent corruption measures, indicating between corporate social variable. Used four indicators of environmental that less responsible firms are less A positive and significant interaction between responsibility and location quality: Environmental Policy, Environmental present in corrupt countries, Dam and Environmental Responsibility and Environmental choice of Multinational Cross-sectional dataset, which covers Binary location Management, Environmental Reporting and indicating that responsible firms are Scholtens Regulation, and Environmental Responsibility and Enterprises (MNEs) and 2685 MNEs. choice model Environmental Performance Impact Improvement, relatively more often located in (2008) Environmental Plans and Treaties, supporting the environmental regulation, applying factor analysis on these four indicators to corrupt countries. Finally, it PHH. governance and wealth of the generate a single factor named Environmental appears that poverty has no country. Responsibility significant effect on location behaviour conditional on corporate environmental responsibility. Found an increased amount of EU energy intensive Used energy and toxicity index as a trade with poorer countries during the period with Examines the impact of Gravity model to measure of industry dirtiness to more stringent EU environmental standards. industry energy intensity and capture the avoid the endogeneity problem. In However, the result is not robust when countries Cave and toxicity, measured by an In total they analyzed 108,057 elasticity between Imports, energy index, toxic index, per capita GDP, order to control for any ‘footloose’ are defined by OECD membership and geographic Blomquist energy index and a Toxic observations of the imports into the EU industry imports low/middle/high income, fixed capital costs, crude behavior they include a measure location. For the energy index there is some (2008) Release Inventory (TRI) index, in 59 industries between 1970 to 1999. and industry energy oil prices, dictatorship, terms of trade, trends to capture fixed costs. Also control evidence of pollution haven behavior, positive and on imports into EU, at the 2- intensity and for trade with other nations that significant. This implies that imports of industries digitit industry level. toxicity have similar environmental with higher energy intensities from low-income standards. countries rise during a period of more stringent environmental standards —a PHH result.
US affiliate value added, GDP, environmental policy This study is the first to find robust It is found that for the top 20th percentile of Empirical examination of index, environmental enforcement, age, confirmation of a Pollution Haven countries in terms of growth in U.S. multinational pollution haven effect manufacturing tariff trade, intellectual property Effect in a cross-country context. It affiliate value added, as much as 8.6 percent of Kellenberg accounting for strategically Covers 50 industries, in 9 sectors, from rights, infrastructure quality, public schools, accounts for the robust evidence OLS/GMM-IV that growth between 1999 to 2003 can be (2009) determined environment, 1999 to 2003. quality, distance, industry capital/ labor ratio, that environmental and other trade attributed to declining relative stringency and trade and intellectual organized crime, institution quality, hidden trade policies (such tariffs and intellectual enforcement policy. It found support to the property right (IPR) policies. barriers, cluster development, hiring and firing property right) are endogenously Footloose Theory also. practices determined. Results show EJVs in highly-polluting industries EJV project data, EJV source classification, EJV funded in Hong Kong, Macao, and Taiwan are industry classification, average levy, average attracted by weak environmental standards. In Tests for pollution haven Dean, Dataset of 2,886 manufacturing equity effluent intensity, industry pollution intensity, contrast, EJVs funded from non-ethnically Chinese behavior by estimating the Conditional logit Lovely and joint venture (EJV) projects in China, skilled/ unskilled/semi-skilled labour, cumulative sources are not significantly attracted by weak determinants of location and nested logit Wang across 28 3-digit ISIC industries during FDI value, number of domestic enterprises, standards, regardless of the pollution intensity of choice for equity joint methods (2009) 1993–1996. telephones, incentive, roads, railroads, the industry. These findings are consistent with ventures (EJVs) in China. consumption, growth rate of real GDP and change pollution haven behaviour, but not by investors in state ownership from high income countries and only in industries that are highly polluting.
41
Table 3: Summary of different studies of PHH
Author Study Data Method Variables Observations Finding The results indicate that the Pollution Haven Effect is present and that the relative strength of such effect is about the same magnitude as other Country characteristics: population, agricultural determinants of industry location. This might be abundance, skilled labor abundance, research and interpreted as finding the Pollution Haven Effect General empirical trade development abundance, environmental standard but failing to support the Pollution Haven model that captures the 16 manufacturing industries from 13 laxity, market potential. Instrumental Variables: Found a significant negative effect Hypothesis. Mulatu et Johnson–Neyman interaction between country European countries. Period average: Corruption, income per capita in log, schooling, on industry location observed only For both measures of pollution intensity, the cut- al. (2010) technique and industry characteristics in 1990-1994. urbanization. Industry characteristics: Log industry at relatively high levels of pollution. off points (0.86 and 2.04) are above the sample determining industry location. shares, agricultural input intensity, skilled labour means (0.73 and 1.67) meaning that, on average, intensity, pollution intensity, intermediate input firms are not attracted by lax environmental use, sales to industry and plant size policies, or stated inversely, are not deterred by stricter environmental policy. This result is a first indication that strict environmental policy does not deter manufacturing industries in general. Most of our empirical specifications use TFP Estimate the effects of measures based on index number methods, where environmental regulations - a plant’s TFP is its logged output minus a weighted captured by the Clean Air Act sum of its logged labor, capital, materials and When applying corrections for two Greenstone Amendments’ division of Empirical energy inputs. Thus, TFP measure is the natural likely sources of positive bias (price Collected information on annual CAAA Among surviving polluting plants, a nonattainment , List and counties into pollutant- specifications with logarithm of a plant’s ratio of output to inputs. The mismeasurement and sample (Clean Air Act Amendments) of 3,141 designation is associated with a roughly 2.6 Syverson specific nonattainment and various sets of fixed paper estimates the expected change in a plant’s selection survival) the total TFP loss U.S counties from 1972 - 2000. percent decline in TFP. (2011) attainment categories—on effects output due to the nonattainment regulation given for polluting plants in nonattaining manufacturing plants’ total a fix set of inputs. Plant’s total factor productivity; counties is 4.8 percent. factor productivity (TFP) the pollutant-specific, county-level levels. attainment/nonattainment designations as its measures of regulation The authors test pollution haven through the pollution content of imports (PCI). The PCI is then Searching for PH evidence, decomposed into three components: (i) a deep They find evidence of traditional PH and FE effect, once in a globalization component (i.e. traditional variables unrelated to although these effects cannot be said to be Year 1987 for an extensive data set Capital-labor ratio serves as a proxy context, which reduces the environmental debate, like distance, potential systematic nor quantitatively important or as Grether et covering 10 pollutants, 48 countries (29 Cross-section for endowment differences, transport costs and/or trade of the market, GDP, common relation, common robust as one would wish across all pollutants. all (2012) developing and 19 developed econometrics capturing the Factor Endowment barrier, would shift language, landlockedness); (ii) a factor endowment They found that for each one of the 10 pollutants countries) and 79 ISIC 4-digit sectors. (FE) effect. investment and production of component (capital-labor ratio serves as proxy for these effects contribute less than 10 percent of the dirty goods to the South. endowment differences) and (iii) a pollution haven overall determinants of emissions. component reflecting the impact of differences in environmental polices
42 1.2. Contrary hypothesis
Another possibility is that there is some underlying relationship between environmental policy and competitiveness but that opposing drivers mask this relationship. In particular, endowments such as capital, skilled labor or natural resources may serve to neutralize the impacts of environmental policies.
According to Tang (2010), the PHH is controversial in part because it is not obvious that poorer countries would have a comparative advantage in producing capital-intensive goods and alter trade patterns, as their factor endowments (the amount of land, labor, capital and entrepreneurship that a country possesses and can exploit for manufacturing) may not be suitable for such an outcome. For these reasons, the factor endowment hypothesis (FEH) opposes the PHH and argues that the factor endowment of a country overwhelms the possible advantage of moving to avoid pollution cost.
Ederington et al. (2005) also presented other alternative hypotheses to explain the gap of robust support in the influence of environmental regulations on trade patterns. The footloose theory states that certain industries are less geographically mobile because of transportation costs, plant fixed costs or agglomeration economies; thus, these less mobile firms will be insensitive to differences in regulatory stringency. Another argument is that firms believe that in the long term, environmental regulation will be standard worldwide; thus, it would be insignificant to take advantage of this benefit in the short term. This rationale is known as the pollution halo hypothesis. Finally, the Porter hypothesis (Porter and van der Linde, 1995) corroborates the revisionist view
43 opposing the traditionalist PHH and posits that environmental regulation stimulates innovation. Therefore, regulation may actually offset the costs of complying with a green policy and generate a competitive advantage for firms.
This section will provide a review of the literature on the main contrasting theories: the factor endowment, footloose, pollution halo and Porter hypotheses.
Factor Endowment Hypothesis
The Factor Endowment Hypothesis (FEH) states that even if there is some relationship between environmental policy and competitiveness, then this relationship is masked by the opposing force of endowments—capital, skilled labor or natural resources—that neutralize the impacts of environmental policies. Consequently, empirical support for the PHH is difficult to uncover
(OEDC, 2010). Countries possess different assimilative capacities to absorb pollutants, i.e., different environmental endowments (Dean, 1992). The FEH asserts differences in endowments or technology determine trade rather than differences in pollution policy. Therefore, the effects of trade on the environment both locally and globally depend on the distribution of comparative advantage across countries. It is important to note that comparative advantage is determined jointly by differences in pollution policy and other influences, such as factor endowments (Temurshoev, U. 2006).
The Leontief paradox states that the country with the world’s highest capital per worker has a lower capital/labor ratio in exports than in imports. The
Heckscher-Ohlin model (H-O theory) of international trade empirically tests the
44 Leontief paradox. This model is used to test the FEH showing that countries tend to export goods whose production is intensive in locally abundant factors of production (Brunnermeier, Levinson, 2004). Countries tend to specialize in industries that require the intensive use of resources that are relatively abundant. Therefore, environmental regulation can be considered a drain on endowments resulting in a loss of comparative advantage (Mulatu et al., 2001).
Countries with capital-intensive goods relative to one resource will have comparative advantage over other countries, whereas a labor-abundant country will have a favorable price for labor-intensive goods relative to the prices in other countries.
The Mulatu (2008) study of location decision found support for the traditional H-O theory, indicating that a country’s endowment has a strong influence both individually and jointly. The endowment is likely to dominate the influence of the environmental factor, which demonstrates that cross-country differences play a role in trade patterns. Grether et al. (2012) support this contention with their finding that composition factors related to endowments are favorable to high levels of pollutant production. Eskeland and Harison (2003) stated that the lack of results supporting a pollution haven in Morocco, for instance, could be attributed to the lack of capital endowments that are necessary to attract investment in pollution-intensive industries. According to
Brunnermeier and Levinson (2004), most empirical work has relied on reduced- form regressions of trade flows on factor endowments (countries will export goods whose production is intensive in locally abundant factors of production) and other country characteristics. Cole and Elliot (2005) acknowledged that if a
45 comparative advantage is jointly determined by differences in factor endowments and environmental regulations, FDI will be drawn to countries with a high level of capital endowment relative to the stringency of their environmental regulation. Brunnermeier and Levinson (2004) stated that globalization increases the difficulty of analyzing the competitiveness impacts of environmental policy because labor and capital may be easily mobile; consequently, the two-way link between firm-level competitiveness and national competitiveness is severed.
Footloose Theory
The footloose theory posits that some industries are less geographically mobile; therefore, these firms will be insensitive to changes in regulatory policy.
The effect of stricter environmental regulation is irrelevant compared with the costs of moving a company’s complex structure to a jurisdiction with laxer environmental regulations. Ederington, Levinson and Minier (2005) provided several possible explanations to provide insight into the lack of evidence on the
PHH and to support the footloose hypothesis. High-pollutant industries, which are typically the firms that suffer the most with strict environmental regulation, are less geographically mobile than others as a result of three potential factors: transportation costs, plant fixed costs and agglomeration economies. Therefore, less mobile industries will be insensitive to differences in regulatory stringency between countries because these firms cannot easily relocate. Furthermore,
Levinson (2000) noted that in such a context, environmental authorities find themselves in a favorable position of being able to tax the most pollution-
46 intensive industries at the highest rates without worrying about capital or labor flight to competing jurisdictions.
Kellenberg (2009) acknowledged that the most capital-intensive industries are not necessarily the industries that are most likely to react to changes in environmental policy. Ederington et al. (2005) also found that certain industries are less geographically mobile than others because of transportation costs, plant fixed costs or agglomeration economies; therefore, less mobile industries will be insensitive to differences in regulatory stringency between countries because they are unable to relocate easily. This finding is identical to the aforementioned footloose theory. Even if environmental regulations impose large and significant costs on polluting industries, these costs may represent only a small portion of total production costs, or these vulnerable industries may be less geographically mobile. When reallocation is costly, such industries will be insensitive to differences in regulatory stringency between countries (Jaffe et al. 1995).
Gains from reducing control costs by investing in a “pollution haven” may be counterbalanced by higher costs for other inputs and/or higher risks related to labor availability and quality, a lack of support structure, inadequate market size, transportation costs, political risk or expropriation risk. The flexibility of location for many industries is limited for reasons that include the need for port access, complementary industrial cells, location of resources, logistics and networks.
47 Pollution Halo Hypothesis
The convergence of regulation among national policy climates indicates that all facilities would need to adapt to a new standard of regulation in a window of 15 to 40 years. This shift would be costly and inefficient compared with building preventive anti-pollution measures because pollution-intensive industries also tend to be energy-, capital-, technology-, industrial market- and transport-intensive (Gladwin & Walter, 1976). In a globalized economy, stringent environmental requirements may tend to migrate from strict to lenient jurisdictions; therefore, any competitiveness gains from relocation may be temporary (OECD, 2010). Sorsa (1995) argued that higher environmental standards in industrial countries do not appear to have reduced their international competitiveness. The claim is that these differences appear to have been minor among the countries. Empirical results contradicting the main line of thinking of the traditionalist view shows that multinational firms are cleaner than domestic firms and produce cleaner technologies even when they are not required to do so (Dardati; Tekin, 2009). This finding is consistent with the pollution halo hypothesis, which states that multinational firms that engage in FDI will tend to spread their greener technology to their counterparts in the host country (Birdsall; Wheeler, 1993).
List et al. (2004) found that environmental regulations influence decisions of domestic firms but not of foreign firms that provide an economic stimulus and that are less sensitive to environmental regulation. Corroborating a new perspective on analyzing foreign firms, Dam and Scholtens (2008) found that
MNEs set high internal environmental standards and do not experience the
48 supposed comparative advantage from locating in countries with poor environmental regulation. These researchers find support for the PHH only for firms with weak environmental standards. MNEs, especially larger MNEs, have high standards for socially responsible behavior; therefore, MNEs that establish high internal environmental standards will not experience the supposed comparative advantage from locating in countries with lax environmental regulations.
Furthermore, providing better environmental quality for citizens can be understood as a common good and can thus enhance reputation or economies of scale. These benefits may motivate MNEs and companies that outsource their activities not to exploit differences in environmental policy across jurisdictions; rather, such firms may seek common performance standards corresponding to those in their home countries from their subsidiaries and suppliers. Thus, one result of globalization would be that environmental self- regulation spreads through MNE structures and global supply chains (Porter and Lind (1995); Christmann (2004); Christmann and Taylor (2001)).
Aguilera-Caracuel et al. (2012) analyzed a sample of 135 multinational companies and found that high levels of environmental institutional distance between headquarters and subsidiary countries deters the standardization of environmental practices. However, high-profit headquarters are willing to standardize their environmental practices rather than taking advantage of countries with lax environmental protection to undertake more pollution- intensive activities.
49 Porter Hypothesis
The Porter hypothesis, also known as the revisionist view, states that companies must recognize environmental issues as a competitive opportunity rather than as a cost or a threat. The argument is that properly designed environmental standards can trigger innovation that may partially or more than fully offset the costs of complying with them. This innovation offset can lower the net cost of meeting environmental regulations, can lead to advantages over firms when lower per-unit compliance costs for incumbents can inhibit new firm entry, and can improve productivity and enhance competitiveness. Similar to a traditional learning curve, firms can learn how to effectively handle regulation, how to use technologies efficiently and how to best modify organizational processes to implement these tasks (Porter & Linde, 1995; Heyes, 2009). The stringency of environmental regulations provides incentives for firms to develop new and less costly ways of reducing pollution or potentially to develop entirely new methods of production that can be environmentally friendly and reduce the costs of production. This hypothesis can be identified in three ways: in the weak version in which environmental regulation will stimulate innovation, the benefits of innovation are unclear; in the narrow version, environmental policy provides good incentives for firms to innovate through green technologies; and in the strong version, environmental regulation induces innovation, compensating the compliance costs (Jaffe and Palmer, 1997; Iraldo et al. 2011). Lanoie et al.
(2011) used data from approximately 4,200 facilities in seven OECD countries to test the Porter hypothesis with respect to the four main elements of the causality chain: environmental policies of different types, technological
50 innovation, environmental performance and commercial performance. The authors found strong support for the weak version and qualified support for the narrow version but no support for the strong version.
Findings that appear inconsistent with the PHH are typically explained through the Porter hypothesis, although a strong correlation between environmental costs and industry competitiveness does not necessarily indicate causality. Porter and Linde (1995) claimed that studies that support the finding that environmental regulation raises costs and harms competitiveness are biased because net compliance costs are overestimated by assuming away the innovation benefits. Studies that attempt to link environmental policy and competitiveness based on the Porter hypothesis typically focus on the impacts on innovation, R&D expenditures, patent applications, and the development of new markets and industries.
Lanoie et al. (2008) analyzed the relationship between the stringency of environmental regulation and Total Factor Productivity (TFP) growth in the
Quebec manufacturing sector and found empirical support for the Porter hypothesis. Based on institutional theory, Berrone and Gomez-Mejia (2009) and others have found that green-friendly firms can enhance social legitimacy, corporate reputation and stakeholder satisfaction. In addition, these firms can attract and retain better partners, customers and employees, thus presenting lower unsystematic stock market risk than less legitimate firms. This low risk manifests itself through a lower cost of capital (Berrone and Gomez-Mejia,
2009). Co et al. (2004) tested data on outbound U.S. FDI into several countries from 1982 to 1992 and provided initial evidence that in the long term, FDI may
51 be an increasing function in the level of intellectual property rights and the stringency of environmental standards. Furthermore, Copeland and Taylor
(1995 a, 1995b) stated that tight regulation leads to a comparative advantage.
Porter’s view is based on the assumption that strict domestic environmental regulations can correctly anticipate international regulations, providing a first-mover advantage. However, Rugman and Verbeke (1998) presented several reasons that the Porter hypothesis does not generally hold when the government is assumed to have sufficient power to significantly influence international environmental trends. The reaction patterns of MNEs are assumed to be built on specific bundles of firms’ specific advantages, assuming that innovation diffusion processes in domestic industrial clusters exist and that a shelter against foreign rivals is disguised as an environmental conservation measure.
Table 4 summarizes the main theories described in the literature review and the important points regarding each hypothesis.
52 Table 4: Main hypotheses of the effect of environmental regulation on firm’s competitiveness Theory Descriptions Justification Variants Expected Effect Actions Pollution Haven Effects: environmental regulation is one determinant among others of firm's Pollution Haven Industries will relocate to avoid strict Trade-off between profitability and location decision. Pollution Negative Hypothesis environmental regulations green actions Pollution Haven Hypothesis: environmental control regulation is the most important determinant of firm's location decision. The amount of land, labor, capital and Factor endowments is an Factor Endowment entrepreneurship that a country possesses It s the differences in endowments oppose driver that mask Hypothesis overwhelms the particular environmental that determine trade. Pollution Haven standard a country adopts. High pollutant firms are Some industries are less geographically Transportation costs, plant fixed costs insensitive to differences Footloose Theory mobile, and therefore, insensitive to or agglomeration economies turns in environmental differences in regulatory stringency firms on less mobile regulation once they can't easily relocate. MNEs set high internal Expectation that in a near future (15-40 environmental standards Pollution Halo Competitiveness gains might be Pollution years) countries will have a global standard and don’t exploit Hypothesis temporary (only in the short term) Prevention of environmental regulation. advantage of local environmental regulation The weak version: environmental regulation will stimulate innovation, but the benefits of Studies that support that innovation are unclear. Environmental regulation stimulates environmental regulation raises costs The narrow version: environmental policy provide innovation and can therefore offset the and harms competitiveness are bias Pollution Porter Hypothesis good incentive for firms innovate through green Positive costs of complying green policy, bringing a once net compliance costs are Prevention technologies. competitive advantage to the firm overestimated by assuming away The strong version: environmental regulation innovation benefits induce innovation, compensating the compliance costs
53 CHAPTER II – DATASET DESCRIPTION
In this thesis, we combine two datasets: (1) the European Pollutant
Release with information on the air pollution emissions of European firms and
(2) ORBIS, which contains financial information on European companies. The
European Pollutant Release provides information from the EPER (European
Pollutant Emission Register) and E-PRTR (European Pollutant Release and
Transfer Register). The EPER, which was established in 2000, was the first
European-wide register of industrial emissions in which member states reported data from 2001 to 2004. E-PRTR supersedes and improves upon EPER by increasing the number of pollutants tracked from 50 to 91; monitoring pollution to land, air and water; and expanding the coverage area and member states reporting data annually.
Table 5: European Pollutant Releases comprising the dataset European Number of Pollutant Year Coverage area facilities Release Austria, Belgium, Denmark, Finland, France, Germany, 1st Greece, Hungary, Ireland, Italy, Luxembourg, EPER 2001 9,400 coverage Netherlands, Norway, Portugal, Spain, Sweden, area United Kingdom. 2nd 1° coverage area + Cyprus, Czech Republic, Estonia , EPER 2004 12,000 coverage Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia. area 3rd 2° coverage area + Bulgaria, Iceland, Romania, Serbia, E-PRTR 2007 to 2010 28,000 coverage Switzerland. area
Table 5 shows the increase in the coverage area and the new countries that began reporting in each of the years. The ORBIS database contains
54 comprehensive information on companies worldwide and is a well-known database that provides comprehensive information on more than 50 million
European companies.
To proceed with the analysis, a new database was constructed to provide an integrated dataset containing both pollution information and financial information for the relevant companies. Thus, the construction of our dataset begins with the list of the companies reporting air pollution information. Because the motivation of this study is to analyze the effect of environmental regulations in the EU under the commitment to control emissions in the Kyoto Protocol climate change treaty, the study will be restricted to reports involving air pollution emissions. Thus, land and water pollution reports are excluded, and only companies with available financial information in Orbis Database are retained.
The first step involves obtaining pollution emission information for all of the companies that were required to release air pollution information, which is the subset of companies that exceeded the threshold deemed acceptable by EU regulation. These companies are those appearing in the air pollution reports in
2001 and 2004 through the EPER and the air pollution reports in 2007, 2008,
2009 and 2010 through the E-PRTR.
For the years 2001 and 2004, the first years in which European industries had to report whether they exceeded a regulatory threshold of emissions, information was obtained through the EPER. In 2006, this register was replaced by the E-PRTR , when companies had to begin reporting their annual surplus of pollution. These reports were required in 2007, 2008, 2009 and 2010. It is
55 important to note that there was a delay in making this information public because the process of collecting the data from all facilities, transferring the information to the country level and then reporting it to the European
Environmental Agency requires some time. As a consequence, the next data release will include 2011 reporting but will only be available as of the second half of 2013. Therefore, the analysis represents the status of air pollution emission reporting through March 2013.
The parental companies that exceeded a threshold level determined by the European Union must report their emissions. These companies represent
90 percent of the industrial greenhouse gas emissions of all European Union industries. Of all years available in the emissions data, only 2004 onwards were retained because the first year of the Orbis accounting information is 2003, representing the last 10 years of financial reporting. The 15,530 observations related to the 2001 air pollution report were deleted because it was not possible to match these data to accounting information for the respective companies.
Therefore, the dataset comprises 111,708 observations with non-missing information for parent companies at the four-digit industry level and 18,642 unique parent companies’ names, covering 31 countries. The countries are not equally represented because some countries are more developed in the manufacturing and industrial sector than others, such as Germany’s automobile industry. In addition, some countries joint the EU member state later; therefore, they needed a longer period of time to adapt to the common union policies.
56 2% Animal and vegetable products from the food and beverage sector 10% 9% Chemical industry
Energy sector 10% Intensive livestock production and aquaculture
3% Mineral industry 28% 6% Other activities
Paper and wood production processing
13% Production and processing of metals
Waste management 19%
Figure 2: Sector’s role in percentage of the industrial air pollution emission in Europe
Ireland Greece Denmark Norway Hungary Romania Finland Sweden Portugal Netherlands Czech Republic Belgium Poland Italy France United Kingdom Germany Spain
0,00% 2,00% 4,00% 6,00% 8,00% 10,00% 12,00% 14,00% 16,00%
Figure 3: Most representative countries in the dataset of European Pollution Release
57 Figure 2 shows the contribution of each industrial sector to the 90 percent of total greenhouse emissions from facilities in Europe represented in the sample. Figure 3 presents some representative countries from the dataset.
Austria, Bulgaria, Cyprus, Estonia, Iceland, Latvia, Lithuania, Luxemburg, Malta,
Serbia, Slovakia, Slovenia and Switzerland are excluded from the graph because they account for less than 1 percent of emissions in the dataset. Given the graphs, it is possible to analyze how much of the total industrial European pollution emission is concentrated among the countries and sectors.
Table 6: Number of observations and unique companies in the air pollution dataset by sector Number of observation Number of unique companies Sector 2004 2007 2008 2009 2010 Total 2004 2007 2008 2009 2010 Total Animal and vegetable products from the food and beverage sector 550 538 549 533 2170 230 87 65 72 454 Chemical industry 1769 2075 1970 1891 1832 9537 663 490 100 92 92 1437 Energy sector 5595 6302 6367 6313 6329 30906 912 676 150 136 144 2018 Intensive livestock production and aquaculture 5194 5406 5550 5534 21684 2931 1006 711 547 5195 Mineral industry 2540 3365 3135 2652 2604 14296 717 570 129 107 82 1605 Other activities 4820 467 443 390 392 6512 3260 211 79 58 52 3660 Paper and wood production processing 927 913 912 939 3691 155 53 25 30 263 Production and processing of metals 2196 2485 2341 2016 2176 11214 619 466 140 123 118 1466 Waste management 1796 2351 2459 2571 2521 11698 1028 827 281 234 174 2544 Total 18716 23716 23572 22844 22860 111708 7199 6556 2025 1551 1311 18642
The next step was to adapt the industry classification from EPER to that from E-PRTR organized in nine industries (ANNEX A). Information on the number of observations and unique companies in each sector is presented in
Table 6.
The pollution database does not provide any identification of companies to conduct the matching with Orbis. Thus, the matching was performed using company names, resulting in a careful process to avoid mistakes caused by the databases using different names for the same company. This match combined financial and accounting information of the companies with their emissions.
58 The two datasets were merged into a single dataset including only the companies that reported air pollution emissions because they exceeded the EU threshold and provided financial information to Orbis. Thus, all of the companies that provided financial information for at least one year of the sample (2004,
2007, 2008, 2009 and 2010) were downloaded.
Furthermore, programming was used to match the two datasets because there was not a common identification useful for merging both datasets. The country, sector and name of each company were used to verify that the match was correct; furthermore, a manual check was performed to avoid any mistakes.
Table 7: Number of observations and unique companies in the dataset after merging air pollution report with company’s financial information
Number of observations Number of unique companies Sector 2004 2007 2008 2009 2010 Total 2004 2007 2008 2009 2010 Total Animal and vegetable products from the food and beverage sector 147 140 152 154 593 48 17 14 19 98 Chemical industry 455 772 747 742 752 3468 154 144 31 34 34 397 Energy sector 768 1362 1494 1502 1602 6728 103 98 30 28 33 292 Intensive livestock production and aquaculture 307 309 298 316 1230 115 35 30 38 218 Mineral industry 396 539 560 544 511 2550 79 59 24 14 10 186 Other activities 439 138 142 110 106 935 228 50 16 15 9 318 Paper and wood production processing 139148162207 656 32 13 7 9 61 Production and processing of metals 384 621 579 564 627 2775 111 119 30 29 33 322 Waste management 268 663 663 698 709 3001 117 104 33 33 26 313 Grand Total 2710 4688 4782 4772 4984 21936 792 769 229 204 211 2205
After matching, the constructed database comprised 21,936 observations and 2,205 unique companies (Table 7). These companies were those that reported to the European Environmental Agency (EEA) because they exceeded the air pollution emission threshold and provided public data to Orbis.
59 Table 8: Percentage of total population comprised in the final database
% of total observations % of total unique companies Sector 2004 2007 2008 2009 2010 Total 2004 2007 2008 2009 2010 Total Animal and vegetable products from the food and beverage sector - 26.73% 26.02% 27.69% 28.89% 27.33% - 20.87% 19.54% 21.54% 26.39% 21.59% Chemical industry 25.72% 37.20% 37.92% 39.24% 41.05% 36.36% 23.23% 29.39% 31.00% 36.96% 36.96% 27.63% Energy sector 13.73% 21.61% 23.46% 23.79% 25.31% 21.77% 11.29% 14.50% 20.00% 20.59% 22.92% 14.47% Intensive livestock production and aquaculture - 5.91% 5.72% 5.37% 5.71% 5.67% - 3.92% 3.48% 4.22% 6.95% 4.20% Mineral industry 15.59% 16.02% 17.86% 20.51% 19.62% 17.84% 11.02% 10.35% 18.60% 13.08% 12.20% 11.59% Other activities 9.11% 29.55% 32.05% 28.21% 27.04% 14.36% 6.99% 23.70% 20.25% 25.86% 17.31% 8.69% Paper and wood production processing - 14.99% 16.21% 17.76% 22.04% 17.77% - 20.65% 24.53% 28.00% 30.00% 23.19% Production and processing of metals 17.49% 24.99% 24.73% 27.98% 28.81% 24.75% 17.93% 25.54% 21.43% 23.58% 27.97% 21.96% Waste management 14.92% 28.20% 26.96% 27.15% 28.12% 25.65% 11.38% 12.58% 11.74% 14.10% 14.94% 12.30% Total 14.48% 19.77% 20.29% 20.89% 21.80% 19.64% 11.00% 11.73% 11.31% 13.15% 16.09% 11.83%
Waste management
Production and processing of metals
Paper and wood production processing
Other activities
Mineral industry
Intensive livestock production and aquaculture
Energy sector
Chemical industry
Animal and vegetable products from the food and …
0,00% 5,00% 10,00% 15,00% 20,00% 25,00% 30,00% 35,00% 40,00%
Figure 4: Percentage of the total population per sector
In Table 8 and Figure 4 , it is possible to analyze how closely the database created reflects the total pollution of firms that reported to EEA once they exceeded the air pollution threshold. For the chemical sector, the sample includes nearly 30 percent of the companies that repo rted pollution; however, the intensive livestock production and aquaculture sector constitutes only 4.20 percent of the total pollution reports. This small share of total polluting intensive livestock production and aquaculture companies is driven by a low public information financial accounts disclosure rate among small producers and farmers.
60 CHAPTER III – POLLUTION HAVEN TEST
In European Union (EU) law, there are more than 350 statutes related to the environment. However, since the adoption of the Kyoto Protocol in 1997, which entered into force in 2005, the EU has moved from specific legislation to a comprehensive effort involving all member states with an aim to reduce the emission of harmful gases into the environment.
Table 9: Main regulatory events in the EU Regulatory event Description Implementation Related Acts Implementation of the European EPER has been integrated into the E- 2000/479/EC 28-Jul-00 pollutant emission register (EPER) PRTR (Regulation (EC) No. 166/2006) National emission ceilings for certain Council Decision 2003/507/EC, Directive 2001/81/EC 27-Nov-01 atmospheric pollutants Regulation (EC) No. 219/2009 States that were members of the EU Commission Decision 2006/944/EC and before 2004 must collectively reduce their Commission Decision 2010/778/EU Decision 2002/358/EC 2-May-02 greenhouse gas emissions by 8 % from determining the new targets for 1990 levels between 2008 and 2012 pollution control Directive 2004/101/EC, Commission Decision 2006/780/EC, Commission Establishing a scheme for greenhouse gas Decision 2007/589/EC, Directive emission allowance trading within the EU, Directive 2003/87/EC 25-Oct-03 2008/101/EC, Directive 2009/29/EC, in compliance with the Kyoto Protocol’s Regulation (EC) No. 219/2009, project mechanism Commission Regulation (EU) No. 1031/2010 Concerning a mechanism for monitoring European Union community greenhouse Decision 280/2004/EC 10-Mar-04 gas emissions and for implementing the Kyoto Protocol. Companies are obliged to take into consideration – and reduce – the impact of Directive 2004/35/EC 30-Apr-04 their activities on the environment (according to the “polluter pays” principle). Kyoto Protocol Kyoto Protocol enters into force 16-Feb-05 Regulation (EC) No. Establishment of a European Pollutant Council Decision 2006/61/EC, 24-Feb-06 166/2006 Release and Transfer Register (E-PRTR) Regulation (EC) No. 596/2009
The EU’s formal commitment to reducing greenhouse gas emissions from 1990 levels by an average of five percent demonstrated that it would work to ensure the success of the Kyoto Protocol. After 2000, a wave of legislation
61 began to enforce pollution disclosures, to monitor pollution emitters (see Table
9) and to encourage the concerned member states to achieve their targets under the Protocol. By taking a lead role in reducing the impact of global warming, the EU has become a heavily environmentally regulated region.
These new circumstances raise a significant question about firms’ reactions to environmental regulation:
Has the change in environmental policy in the European Union generated a reallocation of industrial production to other locations?
Ho: Firms react negatively to the change in regulation, reducing fixed assets investments in strict regulated areas, suggesting the existence of pollution haven in non-EU areas.
This chapter aims to test the pollution haven hypothesis, which posits that companies located in jurisdictions with strict environmental regulations are transferring their production to other jurisdictions with less stringent regulations.
This chapter investigates whether European firms that belong to a global parent company operate at different production levels to their parent company in response to such stringent regional policies. In other words, this study tests whether there is a mismatch in production activities at the local and global levels as a result of the changes in EU policy. To assess production capacity, this study uses the proxy of changes in fixed assets and fixed assets during the period an industrial firm has to report to the European Environmental Agency
62 and the changes in fixed assets and fixed assets of the global parent company as a control group.
3.1. Method
A difference-in-difference model will be used to measure the treatment effect of the EU’s environmental policy standards. Additionally, difference-in- difference-in-difference will be used to measure the effect on sectors that pollute more and are, therefore, the most affected by regulation.
3.2. Model
Difference-in-difference
Difference-in-difference (DD) is a panel data model of two groups
(treatment/control) and two time periods (pre/post) that uses a “natural experiment” to measure the pure treatment effect, which is the impact of the
EU’s environmental policy change. The structure of the experiment implies that
EU firms would have operated in the same way as non-EU firms if the policy intervention had not taken place. In other words, the control and treatment groups have the same characteristics and trend in the same direction over time.
In this study, the treatment is the 2006 EU policy that placed new restrictions on firms. The reinforcement in the law implemented in 2000, it was made in 2006.
Therefore, 2004-2005 represents the pre-policy period, and 2006 represents the change in the law. The years 2007-2010, in turn, are the post-policy period.
63 The DD model will estimate the treatment effect of this policy on fixed asset levels between 2004 and 2010. EU firms are the treatment group, and non-EU firms are the control group, subject to the following equation:
= + ∗ _ + ∗ + _ ∗
where
= ln fixed asset or fixed asset investment = (fixed asset 2 – fixed asset 1+ depreciation)/ fixed asset 1 _ = 1 for EU firms, 0 for non-EU firms = 0 for pre-policy period (2004 and 2005), 1 for post-policy period (2007-2010)
Table 10: Diff-in-Diff model specification
Before EU Policy After EU Policy Difference Treated – (EU Firms) + + + + + Control – (Non-EU Firms) + Net Treatment Effect
The variable is the outcome of interest; _ captures differences between the treatment and control groups; and captures differences between pre-policy and post-policy years for both EU and non-EU firms. The difference-in-difference estimator, interacts with treatment status and the post-policy period dummy variables. It gives an estimate of the treatment effect of the policy for EU firms, as shown in the following:
= − − −
64 Estimating the above model revealed an Ordinary Least Squares (OLS) violation. The Breusch-Pagan / Cook-Weisberg test revealed heteroskedasticity, and Wooldridge’s test revealed autocorrelation. In correction, this study applied the White covariance matrix structure with Huber-White robust standard errors.
This practice is established in the literature and is convenient because it does not change OLS coefficients and standard errors are often bigger. Additionally, to correct for autocorrelation, this study used cluster bootstrapped standard errors, which gather the error terms among groups into clusters (in this study, firms across the years), to give a more refined, resampled estimation of the standard errors.
Difference-in-difference-in-difference
In order to refine the previous model, a difference-in-difference-in- difference (DDD) was performed to include the analysis of different groups, that is, apart of analyzing the effect of regulated (European firms) and non-regulated
(rest of the world), to examine furthermore the effect of the most affected sector by regulation (that is, the sectors that report higher quantities of pollution against other sectors). Therefore, apart from controlling the variation across regulated and non-regulated area, the DDD was implemented to study how the policy has affected high pollutant sectors compared to others. The following equation describes the model:
fixedassets it / fa_investments it = β 0 + β 1 eu_group it + β 2 affectedsector it +
β3 eu_group*affectedsector it + δ0 policyperiod it +
δ1 eu_group*policyperiod it + δ 2 affectedsector*policyperiod it +
δ3 eu_group*affectedsector*policyperiod it
65
It gives an estimate of the treatment effect of the policy for EU firms, as shown in the following:
3 = [ eu , aff, post – eu , aff, post ] – [ eu , nonaff, post - eu , nonaff, pre ] –
[ noneu , aff, post - noneu , aff, pre ] – [ noneu , nonaff, post - noneu , nonaff, post ]
Table 11: Diff-in-Diff –in-Dif model
Before EU Policy After EU Policy Difference Affected Non-Affected Affected Non-Affected
Sectors Sectors Sectors Sectors
Treated – (EU Firms) 0+ 1+ 2+ 3 0+ 2 0+ 1+ 2+ 3+ 0+ 2+ 0+ 2 1+ 3 0+ 1+ 2+ 3
Control – (Non-EU 0+ 1 0 0+ 1+ 0+ 1 0+ 0 Firms) 1
Net Treatment Effect 2+ 3 2 2+ 3+ 2+ 2 3 2+ 3
As the previous DD model, heteroskedasticity and serial correlation indeed were a problem in the DDD model corrected with cluster-bootstrapped standard errors to give a more refined, resampled estimation of the standard errors.
3.3. Sample Construction
This study’s sample includes companies that meet the following conditions: (1) Companies located in the EU that have subsidiaries located in other countries, or companies located in the EU that belong to a global corporate group with headquarters in a distinct country. (2) The global ultimate owner cut-off point was based on the zero percentile of the regulated EU group,
66 which exceeds the pollution threshold. Leveling off both dataset, the one related to the local regulated group and the other related to the global (non European) group. The global owner is found by analyzing a company’s shareholding structure and is the independent shareholder with the highest direct or total percentage of ownership in the same main activity sector of the local company.
(3) Companies that published financial information in Orbis for both the local and global groups.
Based on an evaluation using these criteria, the following 31 regulated countries reporting pollution to European Union members states are included in the sample: Austria, Belgium, Bulgaria, Cyprus, the Czech Republic, Denmark,
Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy,
Latvia, Lithuania, Luxemburg, Malta, the Netherlands, Norway, Poland,
Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland and the United Kingdom. It s important to notice that all these countries adopted stringency policies after European Union sign and ratify the Kyoto Protocol, committing to reduce emissions on 8 percent comparing with 1990 levels. Apart for European Union, among developed economies, just Japan sign and ratify the commitment to reduce emission levels in a lower rate than EU, e.g., 6 percent comparing with 1990 levels. United States didn’t ratify and Canada withdrew from Kyoto Protocol. The developing economies that sign the Kyoto
Protocol, doesn’t have the commitment to reduce emissions, therefore, categorizing countries between the local (European countries) and global (rest of the world) can depict the actual scenario of a stringency regulation to mitigate climate change against countries that even if it agrees and are part of Kyoto
67 Protocol, doesn’t have the obligation to reduce emissions and the respective economic impact of doing so.
Another concerned of this study was to avoid capture the fluctuations of the economic crisis that happened in 2008. Therefore, real estate sector was excluded of the sample. Also, tests in the post period were perform considering it 2007 to 2010 and only 2007 to exclude the problematic year of 2008. Both tests, using or excluding 2008, turn into similar results controlling for GDP growth.
To construct the sample, different local companies from the same global company were aggregated, and the account of each local company was subtracted from the accounts of the global group. Thus, it was possible to have two distinct groups: a European group and a non-European group, which was the global group after the subtraction of the European accounts.
To analyze the pollutant information, the toxicity level of each pollutant was weighted. This procedure transforms the analysis from one of absolute numbers to one of relative values, which permits the consideration of the magnitude of environmental damage caused by a company. Because the
European Environmental Agency does not have a pollutant toxicity index like the Environmental Protection Agency in the U.S., it was not possible to weigh each pollutant by toxicity using the “reportable quantities” (RQ) database (U.S.
Comprehensive Environmental Response, Compensation and Liability Act –
CERCLA 1980). Therefore, we used the thresholds imposed by the European
Environmental Agency (EEA) to evaluate the toxicity level of each pollutant
(ANNEX B). In so doing, it was possible to capture the differences in levels of
68 pollution between sectors and to divide them into categories, identifying the most pollutant sectors of the sample, with highest record in pollution emission.
Table 12: Classification of pollution levels by sectors and pollution amounts statistics
Group Industry Sector N Avg. Std. Dev. Total 3,794 1,947.25 17,369.02 B - Mining and quarrying 187 722.76 1,883.91 High C – Manufacturing 2,486 1,971.58 21,037.22 D - Electricity, gas, steam and air conditioning supply 712 1,080.82 858.30 M - Professional, scientific and technical activities 409 3,867.5 10024.72 Total 1,041 86.36 602.046 E - Water supply; sewerage, waste management and remediation 817 62.86 646.71 activities G - Wholesale and retail trade; repair of motor vehicles and 87 99.73 298.92 motorcycles Medium K - Financial and insurance activities 54 327.06 529.547 N - Administrative and support service activities 35 265.42 495.868 S - Other service activities 48 60.72 21.470 Total 102 9.27 7.729 A - Agriculture, forestry and fishing 18 7.60 5.737 F – Construction 36 10.33 7.505 H - Transportation and storage 46 9.42 8.566 Low O - Public administration and defence; compulsory social security 1 1.04 -- Q - Human health and social work activities 1 2.84 -- Unspecified sector 0 -- -- Total 8 2.57 1.164 I - Accommodation and food service activities 8 2.57 1.164 J - Information and communication 0 -- -- L - Real estate activities ------Non- P – Education 0 -- -- pollutant R - Arts, entertainment and recreation 0 -- -- T - Activities of households as employers; undifferentiated goods- 0 -- -- and services-producing activities of households for own use U - Activities of extraterritorial organisations and bodies 0 -- --
Table 12 indicates the amount of air pollution (measured in kg/year) in emissions that exceed the EU threshold in absolute values. In other words, each pollutant is weighted according to its EU threshold to communicate the significance and the amount of harmful gases that are emitted into the
69 environment. The pollutant types include greenhouse gases, chlorinated organic substances, heavy metals, inorganic substances, other gases, pesticides and other organic substances.
70 Table 13: Main descriptive statistics by year
Variable Obs Mean Std. Dev. Min Max GDP Growth 105,247 2.343355 1.83 -4.94 6.556845 Fixed Assets 95,585 $159,126.20 2138436 0 2.56E+08 Y Est_Dep 98,651 $15,418.07 210512.4 0 $25,600,000.00 e a Revenue 97,816 $192,999.50 2150903 0 $256,000,000.00 r Net Income 81,920 $16,829.06 213648.9 0 $19,200,000.00
2 Employees 97,196 526.3695 7709.983 0 1554332 0 Sum of Pollution 105,247 2.891532 74.3518 0 2442.043 0 4 Subsidiaries 105,247 225.9872 408.2606 2 2512 GDP Growth 105,247 -3.117459 3.210015 -18 3.51 Fixed Assets 95,987 $164,404.70 2574619 0 $457,000,000.00 Y Est_Dep 99,402 $15,875.65 253018.4 0 $45,700,000.00 e a Revenue 98,395 $198,768.10 2288006 0 $286,000,000.00 r Net Income 81,320 $19,516.13 282666.9 0 $33,900,000.00
2 Employees 97,663 550.1274 8226.146 0 1705558 0 Sum of Pollution 105,247 0 0 0 0 0 5 Subsidiaries 105,247 225.9872 408.2606 2 2512 GDP Growth 105,247 0.9031395 2.074984 -4.24 7.926724 Fixed Assets 96,398 $188,874.30 2546015 0 $245,000,000.00 Y Est_Dep 100,248 $18,162.06 249691.1 0 $24,500,000.00 e a Revenue 99,012 $227,576.80 2386804 0 $316,000,000.00 r Net Income 81,598 $21,188.04 258861.3 0 $22,800,000.00
2 Employees 98,252 573.2004 8577.07 0 1799113 0 Sum of Pollution 105,247 0 0 0 0 0 6 Subsidiaries 105,247 225.9872 408.2606 2 2512 GDP Growth 105,247 3.542436 1.423857 0.115 10.49394 Fixed Assets 96,640 $219,088.50 2737177 0 $251,000,000.00 Y Est_Dep 100,863 $20,991.55 267962.2 0 $25,100,000.00 e a Revenue 99,393 $269,990.50 3103226 0 $357,000,000.00 r Net Income 80,799 $26,492.60 337114.6 0 $33,200,000.00
2 Employees 98,804 601.3201 9021.006 0 1993714 0 Sum of Pollution 105,247 39.8959 3117.698 0 304402.5 0 7 Subsidiaries 105,247 225.9872 408.2606 2 2512 GDP Growth 105,247 3.252094 1.604951 1.39 12.23323 Fixed Assets 96,968 208138.9 2474830 0 2.43E+08 Y Est_Dep 101,369 19910.24 242088.1 0 2.43E+07 e a Revenue 99,673 268903.1 2810742 0 3.76E+08 r Net Income 75,568 24439.31 301713.3 0 2.45E+07
2 Employees 99,174 622.2213 9167.851 0 1990827 0 Sum of Pollution 105,247 20.20736 1075.133 0 99805.88 0 8 Subsidiaries 105,247 225.9872 408.2606 2 2512 GDP Growth 105247 1.969199 2.130695 -2.16 10.60091 Fixed Assets 97120 223234.1 2633504 0 2.43E+08 Y Est_Dep 101694 21319.35 257401.3 0 2.43E+07 e a Revenue 99784 241659.4 2511609 0 3.74E+08 r Net Income 71937 26585.26 513658.5 0 1.05E+08
2 Employees 99541 611.3961 8961.704 0 1927850 0 Sum of Pollution 13576 0 0 0 0 0 9 Subsidiaries 105247 225.9872 408.2606 2 2512 GDP Growth 105247 3.20937 1.352686 -0.5 9.3 Fixed Assets 97259 233198.5 2776173 0 2.48E+08 Y Est_Dep 101822 22274.81 271368.3 0 2.48E+07 e a Revenue 99704 267179.3 2901214 0 4.19E+08 r Net Income 75929 27044.62 347480.6 0 3.49E+07
2 Employees 99688 624.8067 9112.549 0 1924048 0 Sum of Pollution 105247 8.064039 359.523 0 61011.03 1 0 Subsidiaries 105247 225.9872 408.2606 2 2512
71
Table 13 presents the main descriptive statistics that were used to perform the difference-in-differences procedure in the sample. It shows that over the period of analysis (2004-2010) average fixed assets grew slightly in
2005, grew more than 15% in the next 2 years, 2006 and 2007, and decreased in 2008, returning to increase in a slowly pace on 2009 and 2010. Notably, the decrease in fixed assets in 2008 coincided with the global economic crisis.
Much like asset levels, firm-operating revenues generally followed this seven- year trajectory: increasing from 2004 to 2007, stagnating in the year of 2008, decreasing in 2009 and by 2010, the turnaround from the 2008 crisis had started to gain a slow pace, but companies still closed 2010 with operating revenues 1 percent lower than the 2007 baseline. Therefore, to a certain extent, the financial data align with the recessionary years of the last decade.
Additionally, noticeable outliers are present. These findings suggest rightward skewness in the distribution of the sample data, demonstrated by high standard deviation levels and median values that are much lower than the corresponding mean values. These observations may be the first signs of heteroskedasticity in this study’s estimation models.
72 Table 14 Descriptive statistics by treatment and control group
Non-EU Group Variable Obs Mean Std. Dev. Min Max GDP Growth 95,032 2.688571 2.170064 -2.16 4.07 Fixed Assets 62,279 $995,947.10 6522082 0 $251,000,000.00 Est_Dep 62,352 $99,478.11 651835.2 0 $25,100,000.00 Revenue 52,604 $1,544,330.00 7943290 0 $419,000,000.00 Net Income 41,752 $134,059.10 921487 0 $105,000,000.00 Employees 48,621 5690.141 31750.15 0 1993714 Sum of Pollution 95,032 0 0 0 0 Subsidiaries 95,032 31.89268 91.40937 2 2512 EU Group Variable Obs Mean Std. Dev. Min Max GDP Growth 641,697 1.58675 3.034481 -17.95 12.23323 Fixed Assets 613,678 $118,772.40 1687299 0 $457,000,000.00 Est_Dep 641,697 $11,358.63 165022.9 0 $45,700,000.00 Revenue 641,173 $131,199.00 1439946 0 $357,000,000.00 Net Income 507,319 $13,897.96 217103.1 0 $33,900,000.00 Employees 641,697 200.7358 1715.652 0 261313 Sum of Pollution 550,026 13.59705 1451.582 0 304402.5 Subsidiaries 641,697 254.7316 428.6214 2 2512
As predicted in this study’s hypotheses, Table 14 shows that firms in the non-EU group (i.e., firms operating outside the European Union) maintain higher levels of assets, revenues, and employees than EU firms do. It should be recalled that the global group in the dataset, or the non-EU group, represents international subsidiaries spread worldwide or global figures minus the EU accounts. Thus, this study’s results are in line with expectations because the
EU group is only a fraction of the total group under analysis, which is international in scope. The difference in the mean number of employees between the EU group easily illustrates the differences between the international group and the local group.
Additionally, the continued right skewness of the sampled data can be seen as average levels exceeding median values. Interestingly, the EU group maintains higher outlier quantities than the corresponding variables of the non-
73 EU group. In response to this skewness, the dependent variable, fixed assets, is log-transformed in the regression analyses.
Table 15: Frequency of industry sector type and treatment/control groups Industr y Se cto r Type Freq. Percent Null 2,611 0.35 A - Agriculture, forestry and fishing 5,299 0.71 B - Mining and quarrying 10,374 1.41 C - Manufacturing 189,119 25.67 D - Electricity, gas, steam and air 17,698 2.4 E - Water supply; sewerage, waste 9,877 1.34 F - Construction 40,054 5.44 G - Wholesale and retail trade; repair 163,506 22.19 H - Transportation and storage 32,137 4.36 I - Accommodation and food service 11,116 1.51 J - Information and communication 57,218 7.77 K - Finance and insurance 44,478 6.04 M - Professional, scientific, technical 92,183 12.51 N - Administrative and support service 39,683 5.39 O - Public administration and defense 364 0.05 P - Education 2,002 0.27 Q - Human health and social work 8,624 1.17 R - Arts, entertainment and recreation 4,277 0.58 S - Other service activities 6,013 0.82 T - Household activities 28 0 U - Extraterritorial organization 147 0.02 Total 736,729 100
DD Treatment/Control Freq. Percent Non-EU_Group 95,032 12.9 EU_Group 641,697 87.1 Total 736,729 100
DDD Estimator - Most Affected EU Sectors Freq. Percent Not Most Affected or Non-EU 483,762 65.66 Most Affected EU Firm 147,720 20.05 Firms in Policy Year (2006) 105,247 14.29 Total 736,729 100
Finally, Table 15 breaks down the sample distribution into the various categories. The first largest sector in the sample is manufacturing, corresponding to 25% of the total sample. The second largest sector is
74 wholesale, which makes up over 22% of the total sample. The third largest sector is professional/scientific firms, which comprises over 12% of the total sample. EU firms represent over 85 percent of the sample compared to non-EU firms. Finally, the high pollutants sector considered the most affected by the EU policy represents 20% of the sample.
75 CHAPTER IV – RESULTS
Difference-in-difference
Across the difference-in-difference models, the corrections for heteroskedasticity and serial correlation show lower standard errors among certain variables compared to the OLS model. However, all of the variables maintain their statistical significance across the models. The robust standard errors were subjected to the Huber-White sandwich standard error estimates, which assume that error variance differs across individuals, and they fell between the OLS and cluster bootstrapped standard errors in size. Cluster bootstrapped errors provided more refined estimations of the standard errors and were recommended for difference-in-difference models to anticipate biased standard errors. Ultimately, they emerged as the most conservative, carrying the largest standard errors, made it more difficult to reject the null hypothesis of the coefficient t-tests and for this reason were the standards errors reported.
Also, different measures for the depended variable (DV) were performed, and all of them result in the same directional effect, changing, obviously the magnitude of the impact once it refers to distinct measures. Tests excluding the outliers were also subject of analyzes.
76 Table 16: Difference-in-Difference model Fixe d Asse ts Fixe d Asse ts Inve st me nts Difference in Difference (DD) Model (Bootstrap) Full Sample No outliers Full Sample No outliers EU Group Dummy -0.9582*** -0.9864*** -0.1178 -0.0772 (0.0329) (0.0257) (0.0674) (0.0629) Post-Period Dummy 0.1456*** 0.1749*** 0.1119*** 0.1086** (0.0180) (0.0209) (0.0320) (0.0356) Treatment Interaction Term -0.0569* -0.0769*** -0.1030 -0.1078 (EU Group * Post-Period) (0.0224) (0.0213) (0.0656) (0.0619) Ln(Operating Revenues) 0.5101*** 0.4413*** -0.0026 -0.0053 (0.0101) (0.0093) (0.0050) (0.0059) Ln(GDP Growth Rate) -0.1180*** -0.1203*** 0.1464*** 0.1432*** (0.0108) (0.0095) (0.0082) (0.0072) Ln(Employees) 0.5087*** 0.5048*** -0.0918*** -0.0985*** (0.0096) (0.0093) (0.0054) (0.0061) Ln(Subsidiaries) 0.0629*** 0.0579*** 0.0029 0.0003 (0.0043) (0.0047) (0.0026) (0.0031) Manufacturing 1.0929*** 1.1078*** -0.1979*** -0.1986*** (0.0162) (0.0188) (0.0107) (0.0131) Construction 0.1775*** 0.1727*** 0.0908*** 0.0953*** (0.0272) (0.0344) (0.0251) (0.0223) Transportation 0.8216*** 0.7669*** -0.0251 -0.0263 (0.0354) (0.0396) (0.0215) (0.0281) Information 0.3250*** 0.2472*** 0.3023*** 0.3259*** (0.0282) (0.0315) (0.0194) (0.0181) Financial 2.8951*** 2.8904*** -0.1251*** -0.1448*** (0.0853) (0.0744) (0.0317) (0.0375) Professional 0.7667*** 0.7015*** 0.1202*** 0.1319*** (0.0325) (0.0317) (0.0171) (0.0233) Administrative 0.2587*** 0.2482*** 0.2381*** 0.2486*** (0.0394) (0.0478) (0.0280) (0.0257) All Other Sectors 1.7252*** 1.5997*** -0.0816*** -0.0778*** (0.0278) (0.0359) (0.0176) (0.0187) China -0.9214*** -0.7568*** 0.7483*** 0.6726*** (0.1639) (0.1797) (0.1150) (0.1470) Japan 0.1088** 0.1806* -0.2464*** -0.3440*** (0.0406) (0.0775) (0.0384) (0.0619) Germany -0.2934*** -0.2242*** -0.2197*** -0.2221*** (0.0453) (0.0517) (0.0302) (0.0473) France -0.3916*** -0.3498*** -0.2805*** -0.2931*** (0.0452) (0.0535) (0.0251) (0.0487) South Africa -0.5456 -0.4062 -0.0087 -0.2757* (0.2826) (0.3329) (0.1451) (0.1369) Brazil 0.0111 -0.1959 0.5271 -0.2032 (0.2690) (0.5234) (0.3591) (0.1517) Russia -0.1123 -0.1885 0.2785 0.1791 (0.4099) (1.1699) (0.3753) (1.3171) UK -0.4555*** -0.4373*** -0.1409*** -0.1506** (0.0469) (0.0515) (0.0291) (0.0483) India -0.2588 -0.3269 0.5736*** 0.4682 (0.1337) (0.1815) (0.1647) (0.2874) Mexico -0.1625* -- -0.1088 -- (0.0746) (.) (0.2735) (.) All Other Countries -0.0927* -0.0668 -0.1371*** -0.1469** (0.0421) (0.0450) (0.0235) (0.0474) Constant 0.8082*** 1.4919*** -0.9317*** -0.8906*** (0.0914) (0.0816) (0.0535) (0.0726) N 274,729 262,814 141,139 133,214 R2 0.5568 0.4530 0.0264 0.0260 Adj. R2 0.5568 0.4529 0.0263 0.0259 F Wald 365,567.88 155,724.26 8,673.73 6,898.49 p-value 0.0000 0.0000 0.0000 0.0000 * p<0.05, ** p<0.01, *** p<0.001 standard errors in parenthesis omitted categories: non-eu_group, pre-period, Wholesale sector, United States NO OUTLIERS (between 5th and 95th percentile) Mexico dropped due to multicollinearity
77 Table 16 shows the results of the diff-in-diff test. As expected, the EU firms have a 62 percent lower fixed asset level than the non-EU firms have. An overview of the total sample (i.e., the EU and non-EU groups) shows that the post-policy period (2007-2010) yielded almost 20 percent higher fixed asset levels than the pre-policy years of 2004-2005. This result demonstrates the positive growth tendency in firms’ fixed assets during these years. The elasticity of operating revenue to fixed assets is approximately 0.44, which means that with every 1 percent change in operating revenues, fixed assets increase by
0.44 percent. As well, with every 1 percentage change in GDP growth rate, employees and subsidiaries fixed assets increase/decrease by -12/+50/+0.05 percent respectively.
Additionally, all the sectors in the model maintain higher fixed asset levels than the omitted category, the Wholesale sector, which is the largest sector in the dataset. Specifically, manufacturing, a capital-intensive sector, emerged with 1.1078 percent higher log fixed assets, that is, 200 percent higher than the fixed assets of the Wholesale sector. Following the same exponential transformation to interpret the other results, it is clear that construction is 18 percent higher than the wholesale sector’s fixed assets; transportation is 115 percent higher; information is 28 percent higher; professional industry is 101 percent higher and administrative is 28 percent higher than the wholesale sector’s fixed assets.
Certain countries’ fixed asset levels showed a negative significance in comparison to those of the United States, the largest economy in the world.
China reported 53 percent lower fixed assets than the U.S.; Germany, 20
78 percent less; France, 29 percent less; the U.K., 35 percent less; and all other countries, 6 percent less. It is possible that, as a net creditor nation, the U.S. continues to play an influential role in global fixed asset investments.
Difference-in-difference-in-difference
In Table 17 a more refined model were performed to add another control group comprised by the sectors that have high levels of pollution report and therefore, are subject of report and the most affected by regulation controlling with the other sectors.
79 Table 17: Difference in Difference in Difference (DDD) Model
Difference in Difference in Difference (DDD) Model (Bootstrap) Fixed Assets Fixed Assets Investments Full Sample No outliers Full Sample No outliers EU_Group -1.2019*** -1.1845*** -0.1401 -0.0966 (0.0376) (0.0420) (0.0905) (0.1089) AffectedSector 0.9749*** 0.8661*** 0.1376* 0.1554 (0.0668) (0.0709) (0.0609) (0.0875) Interaction: EU_Group*AffectedSector 0.4784*** 0.3986*** 0.0455 0.0453 (0.0366) (0.0467) (0.1192) (0.1573) Post-Period 0.1641*** 0.1930*** 0.0239 0.0421 (0.0308) (0.0356) (0.0462) (0.0586) Interaction: EU_Group*PostPeriod -0.0796** -0.1032** 0.0435 0.0199 (0.0295) (0.0357) (0.0959) (0.1111) Interaction: Affected Sector*PostPeriod -0.0249 -0.0285 0.1370* 0.1071 (0.0405) (0.0372) (0.0624) (0.0879) Diff-In-Diff-In-Diff Estimator 0.0474 0.0577 -0.2553* -0.2297 (EU Group * AffectedSector * PostPeriod) (0.0430) (0.0409) (0.1290) (0.1651) Ln(Operating Revenues) 0.4833*** 0.4245*** -0.0039 -0.0063 (0.0087) (0.0094) (0.0041) (0.0060) Ln(GDP Growth Rate) -0.1184*** -0.1227*** 0.1456*** 0.1423*** (0.0116) (0.0095) (0.0080) (0.0072) Ln(Employees) 0.5325*** 0.5259*** -0.0900*** -0.0968*** (0.0085) (0.0097) (0.0040) (0.0062) Ln(Subsidiaries) 0.0560*** 0.0522*** 0.0021 -0.0004 (0.0046) (0.0048) (0.0025) (0.0031) Manufacturing -0.3517*** -0.1684** -0.2813*** -0.2906*** (0.0651) (0.0646) (0.0353) (0.0359) Construction 0.1600*** 0.1583*** 0.0908*** 0.0952*** (0.0375) (0.0339) (0.0223) (0.0225) Transportation 0.7980*** 0.7499*** -0.0228 -0.0259 (0.0419) (0.0394) (0.0251) (0.0282) Information 0.2832*** 0.2156*** 0.3104*** 0.3315*** (0.0255) (0.0314) (0.0231) (0.0186) Financial 2.8608*** 2.8615*** -0.1183*** -0.1378*** (0.0892) (0.0740) (0.0339) (0.0373) Professional -0.6838*** -0.5704*** 0.0381 0.0397 (0.0711) (0.0705) (0.0396) (0.0409) Administrative 0.2325*** 0.2260*** 0.2386*** 0.2487*** (0.0443) (0.0475) (0.0334) (0.0259) All Other Sectors 1.2765*** 1.2596*** -0.1079*** -0.1028*** (0.0330) (0.0357) (0.0160) (0.0208) China -0.8068*** -0.6631*** 0.7030*** 0.6278*** (0.1601) (0.1809) (0.1064) (0.1445) Japan 0.1875*** 0.2330** -0.2700*** -0.3634*** (0.0392) (0.0738) (0.0322) (0.0615) Germany -0.3690*** -0.2851*** -0.2055*** -0.2071*** (0.0421) (0.0495) (0.0325) (0.0470) France -0.4333*** -0.3872*** -0.2615*** -0.2739*** (0.0467) (0.0509) (0.0336) (0.0487) South Africa -0.6489* -0.5047 0.0213 -0.2471 (0.2585) (0.2921) (0.1821) (0.1432) Brazil 0.1345 -0.0736 0.4858 -0.2576 (0.2715) (0.5158) (0.3622) (0.1531) Russia -0.3034 -0.3647 0.2690 0.2017 (0.4341) (1.0969) (0.3419) (1.3012) UK -0.5248*** -0.4912*** -0.1240*** -0.1331** (0.0449) (0.0486) (0.0348) (0.0479) India -0.2850 -0.3819 0.5722** 0.4712 (0.1729) (0.2014) (0.1847) (0.2859) Mexico -0.0618 -- -0.1603 -- (0.1062) (.) (0.2989) (.) All Other Countries -0.1709*** -0.1290** -0.1199*** -0.1293** (0.0421) (0.0416) (0.0344) (0.0475) Constant 1.3255*** 1.8641*** -0.9677*** -0.9374*** (0.0788) (0.0854) (0.0692) (0.0885) N 274,729 262,814 141,139 133,214 R2 0.5619 0.4572 0.0268 0.0263 Adj. R2 0.5619 0.4571 0.0266 0.0261 F Wald 351,024.69 170,560.23 11,171.57 6,992.24 p-value 0.0000 0.0000 0.0000 0.0000 * p<0.05, ** p<0.01, *** p<0.001 standard errors in parenthesis omitted categories: non-affected sectors, non-eu_group, pre-period, Wholesale sector, United States NO OUTLIERS (between 5th and 95th percentile) Mexico dropped due to multicollinearity
80
Table 17 presents a closer view of the pollution policy effect across high sectors compared with other sectors. However the effect treatment is only significant in the full sample using fixed assets as the depended variable.
However, the interaction between regulated group and post period remains significant in the fixed assets variable.
Major Results
Therefore, the sample is tested to address the following question: Has the change in environmental policy in Europe caused a reallocation of industrial production to other locations? The results show that, after the change in policy in 2006, there was a statistically significant negative effect on the EU group’s fixed asset levels in comparison to those of the non-EU group using fixed asset variable. However, changing the depended variable the results are no longer significant. This result corroborates our null hypothesis and provides some evidence of a pollution haven movement away from the EU after the implementation of strict environmental policies, however evidence is weak once it doesn’t remains after changing the dependent variable.
81
Fixed Assets Trends - Treatment & Control Pre & Post Policy Period
EU_Group NonEU_Group EU_Non-Treatment
2
1
0
-1
-2 Ln(FixedAssets)
-3
-4 Pre Period (2004 -2005) Post Period (2007-2010)
Figure 5: Diff-in-Diff graph
The treatment effect, which is also illustrated in the corresponding trend-line graph, using fixed assets depended variable results in a 0.0769 percent reduction in fixed asset log dollars o r, in proper unit terms, a 7.40 percent reduction in fixed asset do llars. I t is important to emphasize that all coefficient estimates reported have been exponentiated ( 1 100 to reflect the percentage changes in fixed asset dollar values rather than fixed asset log dollar values. Visually, the corresponding line g raph shows the slight negative slope of the EU group between the pre - and post-policy periods and the positive slope of the non-EU Group, which indicates the treatment effect difference. The increase in assets at a lower baseline is an important indicator that investments in production in regulated areas do not parallel investments in production
82 worldwide for the same companies. This evidence that global parent companies either focused their investments in fixed assets outside the EU or chose not to invest additional fixed assets there makes a convincing case for the existence of a pollution haven in the EU-regulated area. However, changing the depended variable the results no longer remain significant what affect the robustness of the results. Indeed, adding most affect sectors to control against the other sector groups also didn’t turn up significant, being an evidence that high pollutant sectors are probably the ones with high fixed assets structure, and for this reason, the most difficult industries to reallocate.
83
CHAPTER V – CONCLUSIONS
Discussion
The initial objective of this work was to investigate European environmental regulation and to contribute to an ongoing discussion over the use of domestic and foreign emissions as strategic substitutes or complements.
The results pointed to a strategic substitutes scenario, in which a decrease in emissions in one country could lead to an increase in another. When this study compared the evolution of fixed assets as a proxy for international companies’ production capacities and divided its sample according to EU and non-EU groups, it shows that local changes in fixed assets did not correspond to the trajectory of a company’s international parent group. Fixed assets decreased after the regulatory change, however the results don’t remain significant changing the dependent variable. Therefore, the results support that after the regulation there might be an effect on firms competitiveness, however this evidence is not robust enough to prove the pollution haven hypothesis, even though there is an evidence of it.
If EU is suffering from collateral effects, such as carbon leakage, due to the implementation of the 2006 regulations, this finding corroborates Copeland and Taylor’s (2005) statement that carbon leakage will offset 25 percent of the emissions cuts in the Annex I countries of the Kyoto Protocol.
84 First, by satisfying the secondary objectives of this work, it was possible to identify the companies that have been affected by stringent environmental regulations in the EU. This study examined the massive emitters of industrial pollution in Europe, primarily in the sectors of Manufacturing, Mining, Electricity and Professional and Scientific activities. This new information allowed us to test PHH and to sidestep common problems related to proxies and data sources.
Second, this study performed tests to determine whether environmental regulation adversely affects international competition. Results show a general support to pollution haven hypothesis, failing however in provide robust evidence of the pollution haven hypothesis. At the same time, results aligns with some studies, like Ederington, Levinson and Minier (2005), once high-pollutant industries, which are typically the firms that suffer the most with strict environmental regulation, are less geographically mobile than others as a result of three potential factors: transportation costs, plant fixed costs and agglomeration economies. Therefore, less mobile industries will be insensitive to differences in regulatory stringency between countries because these firms cannot easily relocate.
Third, the central question of this study was whether stricter environmental policy in the EU has caused the reallocation of production to other locations. This study found evidence, however failed to find robust suport.
A statistically significant and negative effect of fixed asset levels shows that companies decreased their investments in fixed assets in EU firms more than in their international counterparts. Although it is possible that companies
85 discouraged fixed asset investments without any shift to overseas operations, this result nevertheless suggests the existence of a pollution haven in non-EU areas. This study analyzed international groups and found that just a fraction of their accounts had to face strict environmental regulation, and it s this fraction that reduce fixed asset investment levels mismatching with international companies counterparts. However, changing the dependend variable results are no longer significant.
The support for the PHH found in this study converges with the traditionalist view of neoclassical environmental economics (Jaffe, Peterson,
Portney, & Stavins, 1995). Traditionalists argue that environmental regulation has a negative effect on firms and diminishes GDP through disinvestments in a country. Moreover, this study is related to Henderson (1996), which finds that local regulation can lead to improved air quality simply because polluters move.
The findings of this study support previous work by Kellenberg (2009) and the general support for the pollution haven hypothesis found by Xing and Kolstad
(2002) and Kahn and Yoshino (2004).
Finally, it is important to emphasize that due to limitations in data availability before 2003, this study could not test the immediate impact of stringent environmental regulations after the first European-wide register of industrial emissions, which was established in 2000. It is possible that firms adjusted their operations in the early 2000s after the major overhaul of environmental regulations in the wake of the Kyoto Protocol. Even though the results of this study present some evidence of a pollution haven, this effect may
86 have been attenuating since the year 2000 and the first change in environmental policy in the EU.
This study provides evidence that firms respond negatively to free trade and heterogeneous environmental regulations across facilities in order to maintain firm competitiveness. This study therefore provides new insights into the relationship between environmental regulation and competition in a carbon- constrained world.
Limitations and Future Research
The following section addresses some of the limitations of this study.
We assume that the difficulties associated with measuring environmental standards and pollution intensity were dealt with in the best way to present reliable results. As mentioned above, the available data permit the analysis of the period from 2003 onwards. Therefore, the use of 2006 as the policy-change year is the best possible accommodation to the data, even though this method fails to capture the effects of previous years.
One limitation in this work is its analysis of companies that report air emission pollution to the EEA only after they have exceeded the pollutant threshold. This procedure, which also requires public financial information, automatically excludes small companies that do not disclose financial information.
The absence of some information in statistical analysis may form a limitation in this study. For example, according to Smarzynska and Wei (2001), bureaucratic corruption may deter foreign direct investment (FDI) and be
87 positively correlated with less stringent environmental standards. Empirical tests show that corruption reduces inward FDI (Smarzynska; Wei, 2000). The environmental policy effects of FDI have been found to be conditional on a government’s degree of corruptibility (Cole et al., 2006), and the omission of this information may produce misleading results. Future studies may consider variables such as poverty, as addressed in Dam and Scholtens (2008); corruptibility, addressed in Cole, Elliott and Fredriksson (2006); and national income, studied in Cave and Blomquist (2008).
This study attempts to measure, as accurately as possible, the impact of changes in environmental policies. Another important limitation is the difficulty in measuring the strength of environmental protection. This limitation arises because the de jure laws may not always be the ones that are enforced
(Smarzynska; Wei, 2001).
Future research might adjust the stringency of environmental policy and pollution proxies to observe if this study’s results are replicable. Researchers might also use a wider database that includes firms other than European MNEs and employ a counter-hypothesis to test this study’s findings.
Final considerations
The present study found evidence that a pollution haven (or at least affect firm’s competitiveness) has come into existence since the EU’s adoption of stricter environmental regulations, brought into effect in 2006 to attain the
Kyoto Protocol targets. Therefore, this study demonstrates a need for universal participation in this treaty and for global consistency in environmental
88 regulations in our carbon-constrained world. Such policies would foster action on climate change to correct the carbon leakage problem, in which non- participating countries increase their emissions in response to the cutbacks of others.
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96 ANNEX A
ENERGY CODES FOR EPER – 1.1, 1.2, 1.3, 1.4 CODES FOR PRTR – 1(A), 1(B), 1(C), 1(D), 1(E), 1(F)
METALS CODES FOR EPER - 2.1, 2.2, 2.3, 2.4, 2.5, 2.6 CODES FOR PRTR – 2(A), 2(B), 2(C), 2(D), 2(E), 2(F)
MINERAL CODES FOR EPER - 3.1, 3.2, 3.3, 3.4, 3.5 CODES FOR PRTR – 3(A), 3(B), 3(C), 3(D), 3(E), 3(F), 3(G)
CHEMICAL CODES FOR EPER – 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 CODES FOR PRTR – 4(A), 4(B), 4(C), 4(D), 4(E), 4(F)
WASTE CODES FOR EPER – 5.1, 5.2, 5.3, 5.4, 6.5 CODES FOR PRTR – 5(A), 5(B), 5(C), 5(D), 5(E), 5(F), 5(G)
WOOD CODES FOR EPER -6.1 CODES FOR PRTR – 6(A), 6(B), 6(C)
ANIMALS CODES FOR EPER – 6.6 CODES FOR PRTR – 7(A), 7(B)
FOOD AND BEVERAGE CODES FOR EPER – 6.4 CODES FOR PRTR – 8(A), 8(B), 8(C)
OTHERS CODES FOR EPER – 6.2, 6.3, 6.7, 6.8 CODES FOR PRTR – 9(A), 9(B), 9(C), 9(D), 9(E)
97 ANNEX B
Threshold for releases Pollutant to air to water to land kg/year kg/year kg/year 1,1,1-trichloroethane 100 - - 1,1,2,2-tetrachloroethane 50 - - 1,2-dichloroethane (EDC) 1000 10 10 1,2,3,4,5,6-hexachlorocyclohexane(HCH) 10 1 1 Alachlor - 1 1 Aldrin 1 1 1 Ammonia (NH3) 10000 - - Anthracene 50 1 1 Arsenic and compounds (as As) 20 5 5 Asbestos 1 1 1 Atrazine - 1 1 Benzene 1000 200 200 Benzo(g,h,i)perylene - 1 - Brominated diphenylethers (PBDE) - 1 1 Cadmium and compounds (as Cd) 10 5 5 Carbon dioxide (CO2) 100000000 - - Carbon monoxide (CO) 500000 - - Chlordane 1 1 1 Chlordecone 1 1 1 Chlorfenvinphos - 1 1 Chlorides (as total Cl) - 2000000 2000000 Chlorine and inorganic compounds (as HCl) 10000 - - Chloro-alkanes, C10-C13 - 1 1 Chlorofluorocarbons (CFCs) 1 - - Chlorpyrifos - 1 1 Chromium and compounds (as Cr) 100 50 50 Copper and compounds (as Cu) 100 50 50 Cyanides (as total CN) - 50 50 DDT 1 1 1 Di-(2-ethyl hexyl) phthalate (DEHP) 10 1 1 Dichloromethane (DCM) 1000 10 10 Dieldrin 1 1 1 Diuron - 1 1 Endosulphan - 1 1 Endrin 1 1 1 Ethyl benzene - 200 200 Ethylene oxide 1000 10 10 Fluoranthene - 1 - Fluorides (as total F) - 2000 2000 Fluorine and inorganic compounds (as HF) 5000 - -
98 Halogenated organic compounds (as AOX) - 1000 1000 Halons 1 - - Heptachlor 1 1 1 Hexabromobiphenyl 0,1 0,1 0,1 Hexachlorobenzene (HCB) 10 1 1 Hexachlorobutadiene (HCBD) - 1 1 Hydro-fluorocarbons (HFCs) 100 - - Hydrochlorofluorocarbons (HCFCs) 1 - - Hydrogen cyanide (HCN) 200 - - Isodrin - 1 - Isoproturon - 1 1 Lead and compounds (as Pb) 200 20 20 Lindane 1 1 1 Mercury and compounds (as Hg) 10 1 1 Methane (CH4) 100000 - - Mirex 1 1 1 Naphthalene 100 10 10 Nickel and compounds (as Ni) 50 20 20 Nitrogen oxides (NOx/NO2) 100000 - - Nitrous oxide (N2O) 10000 - - Non-methane volatile organic compounds (NMVOC) 100000 - - Nonylphenol and Nonylphenol ethoxylates - 1 1 (NP/NPEs) Octylphenols and Octylphenol ethoxylates - 1 - Organotin compounds(as total Sn) - 50 50 Particulate matter (PM10) 50000 - - PCDD + PCDF (dioxins + furans) (as Teq) 0,0001 0,0001 0,0001 Pentachlorobenzene 1 1 1 Pentachlorophenol (PCP) 10 1 1 Perfluorocarbons (PFCs) 100 - - Phenols (as total C) - 20 20 Polychlorinated biphenyls (PCBs) 0,1 0,1 0,1 Polycyclic aromatic hydrocarbons (PAHs) 50 5 5 Simazine - 1 1 Sulphur hexafluoride (SF6) 50 - - Sulphur oxides (SOx/SO2) 150000 - - Tetrachloroethylene (PER) 2000 10 - Tetrachloromethane (TCM) 100 1 - Toluene - 200 200 Total nitrogen - 50000 50000 Total organic carbon (TOC) (as total C or COD/3) - 50000 - Total phosphorus - 5000 5000 Toxaphene 1 1 1 Tributyltin and compounds - 1 1 Trichlorobenzenes (TCBs) (all isomers) 10 1 - Trichloroethylene 2000 10 -
99 Trichloromethane 500 10 - Trifluralin - 1 1 Triphenyltin and compounds - 1 1 Vinyl chloride 1000 10 10 Xylene - 200 200 Zinc and compounds (as Zn) 200 100 100 Source: European Commission (2006)
100