Social Time and Environmental Criminal Law

SOCIAL TIME AND ENVIRONMENTAL CRIMINAL LAW

MATTHEW JOHN WILLIAM GREIFE

(Under the Direction of Mark Cooney)

ABSTRACT

When corporations are prosecuted for violating federal environmental criminal laws the punishments vary widely. Some companies are fined millions of dollars, others only a couple thousands for violating the same laws. This dissertation uses Donald Black’s (2011) theory of moral time in an attempt to explain the variance in punishment previously mentioned. Black’s

(2011) theory argues that changes in social relationships or movements of social time cause all conflict – including legal conflicts. Movements of social time occur, for example, when people become more or less intimate with one another or see an increase or decrease in wealth. The greater and faster a movement of social time is, the more conflict it will cause. The greatest conflicts will thus attract the largest amounts of social control. Black’s (2011) theory predicts that when a company violates an environmental criminal law and causes the greatest movements of social time, those companies will be punished the most severely. This dissertation finds moderate support for Black’s (2011) theory.

INDEX WORDS: Social Control, Environmental Law, Corporate Crime, Environmental

Crime, Moral Time SOCIAL TIME AND ENVIRONMENTAL CRIMINAL LAW

MATTHEW JOHN WILLIAM GREIFE

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GEORGIA

Matthew John William Greife

MATTHEW JOHN WILLIAM GREIFE

Major Professor: Mark Cooney Committee: Tom McNulty Jim Coverdill Mark Pogrebin

Electronic Version Approved:

Suzanne Barbour Dean of the Graduate School The University of Georgia December 2016 DEDICATION

I dedicate my entire PhD to the Venor family, Mark Pogrebin, Paul Stretesky, Jennifer

Cross, Tara O’Conner-Shelly, my family and friends that supported me on this journey and most importantly to the men who fought and died in Afghanistan and Iraq.

iv ACKNOWLEDGEMENTS

Thanks to Jim Coverdill for agreeing to be on this committee at the last minute. Thanks to Mark Pogrebin who has been my emotional crutch during this project. Thanks to Tom

McNulty for helping guide me through some of the more intricate and nuanced statistical analysis required for this project. Finally, thanks to Mark Cooney for chairing the committee.

v TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ...... v

LIST OF TABLES ...... x

CHAPTER

1 INTRODUCTION ...... 1

A Sociology of Environmental Criminal Law ...... 6

Directional Roadmap ...... 9

2 CHAPTER 2: LEGAL FRAMEWORK ...... 12

Environmental Law ...... 12

Clean Water Act ...... 13

Clean Air Act ...... 17

Resource Conservation and Recovery Act ...... 19

Non-Environmental Law Criminal Acts ...... 22

Sentencing the Corporate Offender ...... 24

Conclusion ...... 27

3 CHAPTER 3: CONCEPTUAL DEFINITION AND LITERATURE REVIEW ...... 28

Conceptual Definition of Corporate Environmental Crime ...... 29

Defining White Collar, Corporate and Environmental Crime ...... 29

Offender Based Definitions ...... 29

vi Offense Based Definitions ...... 31

Typological Approaches ...... 32

Environmental Crime ...... 32

Corporate Crime Definition ...... 34

Corporate Environmental Crime Definition ...... 34

Literature Review ...... 35

Why Do Corporations Commit Crime ...... 35

Law and Society Scholarship ...... 40

Deterrence Based Models ...... 40

Cooperative Based Models ...... 42

How are Corporations Punished ...... 45

Law and Economics and Corporate Environmental Crime ...... 47

Environmental Justice Studies ...... 49

Conclusion ...... 53

4 CHAPTER 4: THEORETICAL FRAMEWORK ...... 56

Pure Sociology ...... 56

Social Time ...... 60

Crime, Punishment and Law as Movements of Social Time ...... 62

Environmental Crime and Punishment as Social Time ...... 66

The Pollution, Environmental Degradation and Resource Overuse Presumption ..70

Conceptual Shortcomings of Moral Time ...... 72

Conclusion ...... 75

vii 5 CHAPTER 5: Dataset Creation, Variable Operationalization, Conceptualization and

Hypotheses ...... 77

The Data Set ...... 86

Baseline Hypothesis: Pollution as a Movement of Social Time ...... 82

Pollution Causing Movements of Relational Time ...... 88

Pollution Causing Movements of Vertical Time ...... 93

Law Preventing Future Movements of Social Time ...... 96

Conclusion ...... 99

6 CHAPTER 6: ANALYSIS ...... 101

Crime as a Movement of Social Time ...... 103

Crime Causing Movements of Relational Time ...... 103

Crime Causing Movements of Vertical Time ...... 108

Law Preventing Movements of Social Time ...... 112

Supplemental Analysis: Why Law is Created to Prevent Future Movements of

Social Time ...... 118

Conclusion ...... 121

7 CHAPTER 7: CONCLUSION ...... 123

Summary of Findings ...... 123

Limitations of the Dissertation...... 129

Future Directions for Research ...... 135

REFERENCES ...... 138

APPENDICES

A APPENDIX A ...... 155

viii B APPENDIX B ...... 157

C APPENDIX C ...... 158

LIST OF TABLES

Page

Table 1: CWA Punishment Framework ...... 16

Table 2: Clean Water Act Prosecutions ...... 17

Table 3: CAA Punishment Framework...... 19

Table 4: RCRA Punishment Framework ...... 22

Table 5: Title 18 Crimes ...... 24

Table 6: Aggravating Factors Applicable to Environmental Crimes ...... 25

Table 7: Number of Prosecutions During the 2004 Through 2013 Time Period...... 80

Table 8: EPA Regional Breakdown of Prosecutions and Median Fine in Each Region Between

2004 and 2013 ...... 80

Table 9: Distribution of Monetary Sanctions across Prosecutions ...... 84

Table 10: Descriptive Statistics ...... 101

Table 11: Corporate Revenue Regressed Against the Total Fine ...... 103

Table 12: Environmental Crime Causing Movements of Relational Time ...... 106

Table 13: Crimes Causing Movements of Vertical Time ...... 111

Table 14: Repayment Plans Preventing Future Movements of Social Time ...... 114

Table 15: Curvilinear Relationship Between Total Fines, Yearly Revenues as Regressed against

Relative Fines...... 119

Table 16: General Summary of Findings ...... 124

ix CHAPTER 1: INTRODUCTION

Humans are likely the main force shaping the world’s environment (Purdy, 2015: 3). For hundreds of years, people have been living in what is known as the Holocene epoch where the environment has changed without much influence by human beings (Purdy, 2015: 1). As time has passed, human actions and behaviors have shaped the world’s environment so much that we have moved into what is being called the Anthropocene: the age of humans (Purdy, 2015: 1). In the Anthropocene, human actions change atmospheric pressures, sea levels, and even the genetic makeup of life (Purdy, 2015: 3). Nature in its purest form no longer exists; it is whatever human beings shape it into.

The relationship people have with the environment has taken many different forms that have consequently shaped the world we live in. In the past, people have held a romantic view of nature while in other periods the environment was viewed as serving utilitarian purposes (Purdy,

2015: 12-14). Regardless of what view people take of nature (e.g. romanticized or utilitarian), law is the primary mechanism through which modern societies mediate their relationship with the natural environment (Lazarus, 2004; Purdy, 2015: 228). Using law to dictate human relations with the natural environment is not a new concept. In Justinian times, the Romans had a series of legal codes and rules related to the taking of fish, ownership of eroded soil, and even the cultivation of bees (Sanders, 1984).

1 However, not all societies relied upon a set of legal codes and rules to govern their relationship with the natural environment. For instance, Native American tribes along the

Columbia River regulated their environment (e.g. the harvesting of fish, use of land for farming, etc.) through non-legal means for generations (Wood, 2001: 370-71). Nevertheless, law––and more specifically environmental law––is the legal membrane through which individuals act in relation to nature in the modern world (Wood, 2014: 5).

Environmental law dictates how society is to use nature by determining which lands can be used for farming, timber logging operations, and residential development (Nolan, 2002).

Further, environmental law dictates which bodies of water are used primarily for human consumption (e.g. drinking water) or those that can be diverted and used in various industrial operations (e.g. mining, manufacturing, large scale farming, etc.) (Tarlock, 2011).

The use of land and water for human purposes, whether through industrial projects or day-to-day living, creates pollution. Thus, environmental law serves another important purpose, which is to determine when society can pollute and just how much pollution may be released back into nature (Stretesky, Long, & Lynch, 2014). The regulation of pollutants into the environment is the central focus of this dissertation. Specifically, I seek to explain how law operates when environmental criminal statutes are violated by a corporation.

Too much pollution in the environment may result in severe consequences; therefore, most modern societies administer punishment for excessive polluting. Here in the United States

95% of all Americans have an increased likelihood of being diagnosed with lung cancer simply for breathing toxins found in outdoor air (USGAO, 2006). Further, American citizens have been advised not to swim in or eat fish from 24% of all rivers, 35% of all lakes (includes all five Great

Lakes), and 71% of all costal estuaries due to toxic contamination (EPA, 2016; NEJAC, 2002). 2 Finally, it is estimated that around 8% of all women childbearing age have unsafe blood mercury levels that can be passed on to their children and more than 300,000 children born each year have an increased likelihood of being born with a learning disability due to fetal exposure to methylmercury (EPA, 2015).

Polluting by a society does not mean the adverse effects stay within its borders. For instance, nitrogen and phosphate runoff directly linked to farming operations all over the world have made their way to the oceans and are a major contributing factor to the existence of over

200 oceanic dead zones found all across the globe (Schmid, 2008). Further, there is a literal floating continent of garbage known as the Pacific Trash Vortex (located just South of Midway

Island) covering an area twice the size of Texas (Weiss, 2006b). The Pacific Trash Vortex is responsible for the death of over 200,000 albatross chicks per year because during migration the birds end up consuming pieces of Lego toys, bottle caps, and styrofoam balls that are accidently plucked from the water during feeding (Weiss, 2006b). The dumping of toxic waste and oils from ships has led to the creation and spread of an unnatural toxic fireweed in Moreton Bay,

Australia that can spread and cover an area the size of a football field every half hour–– fishermen who have touched the fireweed have had their skin blister and eyes swell shut (Weiss,

2006a). Coastal areas around the globe have come to fear red tides that bring toxic wafts, which cause pneumonia and bronchitis and can lead to the development of asthma in children (Weiss,

2006). Additionally, climate change seems to exacerbate every environmental problem the world is facing (Weiss, 2008).

Pollution does not only cause environmental degradation or adverse human health; excess pollution can directly impact economies. For instance, generations of families have made their living as fishermen in the Chesapeake Bay. Due to increased pollutants in the water and the 3 spread of dead zones, many families have been forced to abandon the fishing vocation and search, usually unsuccessfully, for new forms of work (Malmquist, 2013). Excess pollution has been attributed to the rising costs of food and water because the resources themselves are becoming scarce (Barlow & Clarke, 2002; Pimentel, 2003; UCS, 2015). Finally, cleaning up overly polluted areas (e.g. superfund sites) costs governments, on average, between $25 and $30 million (MPCA, 2007; NHDES, 2015). Therefore, laws regulating how much pollution can go into the natural environment are necessary.

Often, laws that protect the natural environment are violated, sometimes intentionally and other times negligently. When environmental laws are violated by corporations, intentionally or not, they may be punished. For instance, in U.S. v. Crown Chemical ([2009] 06CR0545) the defendant corporation was fined $100,000 because management told employees to discharge large but unknown quantities of acidic and caustic wastewater into the local sewer systems during a three-month period. In the case of U.S. v. Wagner Construction JV ([2009] 07CR3443-

IEG) the defendant corporation was fined $27,503 because one of their employees operated a forklift in a negligent manner, which caused a 55-gallon drum filled with Plasti-Kote (20%

Acrylic Polymer and 80% Xylene, with a flashpoint of 80 degrees) to spill into a local creek. The spill was immediately reported and cleaned up by the company and local authorities. Both of these cases demonstrate that whether violation of environmental law is intentional or negligent, the offender may be punished. The goal of this dissertation is to explain why companies like

Crown Chemical and Wagner Construction received their particular fines.

A traditional argument used to explain why companies like Crown Chemical and Wagner

Construction received their fines is the particular seriousness of each offense. Referring back to the cases of Crown Chemical and Wagner Construction, a traditional legal argument would 4 likely claim that Crown Chemical received the higher fine because their offense was more severe and had a greater impact on the environment. After all, Crown Chemical intentionally violated environmental law, knowingly did so over a period of time, and, although the quantity of the discharged wastewater was unknown, it was likely to be more than 55 gallons worth. Therefore,

Crown Chemical should receive the highest penalty, and they did.

However, in U.S. v. Techmetals Inc. ([2009] 3:08-CR-142) the corporate defendant was fined $35,000 for allowing an unknown amount untreated wastewater to enter the local sewer systems between the dates of September 13, 2003 and January 31, 2004. Employees and management were aware that the untreated wastewater was being illegally discharged, but they took no corrective actions. The acts by Techmetals, Inc. were virtually the same as Crown

Chemical’s, yet they received a smaller fine. Comparing the transgressions of Crown Chemical with those of Techmetals, Inc. calls into question the adequacy of the argument that severity of the environmental crime is the best predictor of the fine a defendant will receive for violating an environmental law. Of course, one could point out that prosecutors have broad discretion when deciding what fines to assess against corporate defendants, hence the variability in outcomes for seemingly similar acts. Unfortunately, trying to understand discretion is incredibly difficult because it forces researchers to try and accurately decipher a prosecutor’s particular motivation.

Black’s (2011) theory of moral time is a relatively new theoretical framework that allows researchers to focus on measuring and analyzing quantifiable, sociological variables rather than attempting to discover the thought process used by each individual prosecutor. Therefore, I will rely upon Black’s (2011) theory of moral time to predict and explain why there is variation in punishment even when companies commit similar acts.

5 A Sociology of Environmental Criminal Law

This dissertation examines a study conducted primarily in the sociology of law discipline and focuses on understanding the role environmental criminal laws play in resolving conflicts between corporations that pollute the environment and the citizenry that is polluted.

Criminologists tend to focus on answering the question, Why do individuals engage in criminal and deviant conduct? Some criminologists argue that people will commit criminal or deviant acts because there is a lack of deterrence (Apel & Nagin 2010; Nagin, 2013). Others argue that negative strains or a lack of self-control will drive people to engage in criminal and deviant acts

(Agnew, 2006; Gottfriedson & Hirshi, 1990). Another group of criminologists argue that individuals become attracted to crime and deviance because those behaviors are learned and positively reinforced (Akers, 2009). Finally, there is a group of criminologists that argues socially disorganized communities with high levels of concentrated property will exhibit weak social controls, thus making it easier for individuals to become deviant or criminal (Sampson,

2012).

Legal sociologists, on the other hand, tend to be more focused on understanding the social forces that create law and how laws are used to shape societies and resolve conflicts within society (Milovanovic, 2003: 4). This dissertation, broadly speaking, focuses on the role law plays in resolving conflicts involving corporations that pollute and the communities that are polluted.

More narrowly, my dissertation attempts to provide a better understanding as to why some corporations are punished more severely for breaking environmental laws than others for the same or similar infraction.

One approach to understanding why punishments vary is to understand the type of society that the law and punishment exists in. For instance, Durkheim (1964) argues that 6 primitive societies will be more punitive and repressive against those that violate norms and laws while more complex societies attempt to reestablish social equilibriums (e.g. less punitive) and are more facilitative, rather than repressive, when a law or norm is violated. It is further argued, from a Durkheimian perspective, that within primitive or complex societies individual segments of a society (e.g. small community, state or even a nation) can be more or less punitive than others (Erikson, 1966). Thus, crime and deviance have a functional purpose in society because they serve as moral boundaries allowing for societies to know what acts are permissible and punishable (Erikson, 1966). Crime and deviance will be punished most severely when the actions cross the moral boundaries of a society.

Unlike the functional approach to understanding punishment in society, some scholars argue that punitiveness is determined by understanding the social, political, and economic contexts that crime control policies and decision making occur within (Garland, 2001). This approach is similar to the multi-causal explanations put forward by Max Weber arguing that law should be understood within political, economic, and ideological contexts (Milovanovic, 2003:

74). In particular, Garland (2001) asserted that the move away from a rehabilitative model to a punitive approach to criminal justice policies occurred in the United States because of (a) increased economic uncertainty, (b) a political shift to the right, (c) changes in family structures, and (d) increased visibility of crime through media outlets, among other changes, led to an increase in punitive actions not seen before. Scholars like Mona Lynch (2009) have demonstrated that some states (e.g. Arizona) were always highly punitive in their approach to criminal justice policies and thus did not undergo the same type of changes as argued by Garland

(2001). Nevertheless, the approach remains the same––punishment must be understood in the context of social, political, and economic contexts (Garland, 2001; Lynch, 2009). 7 Another approach to understanding punishment is to look at the different classes within a society. For instance, scholars from a Marxist tradition generally argue that the criminal justice system is used to keep the lower classes under control while allowing upper class individuals to maintain their positions of privilege and continue to acquire more wealth (Reiman & Leighton,

2012; Spitzer, 1975). Specifically, the criminal justice system will punish the lower classes more harshly when they begin to threaten the social standing of the upper class (Spitzer, 1975).

Therefore, punitive action taken in a society is a product of class relationships, particularly based upon the threat lower classes pose to the upper classes.

A final perspective within the sociology of law discipline worth discussing is the collateral consequences of punishment research. Generally speaking, the collateral consequences perspective looks at how punishments against individuals for criminal and deviant acts has lingering negative consequences even after formal and informal punishment has ended.

Research suggests that punishment has negative consequences for the individual such as not being able to find work (Pager, 2003; Western, 2006) along with being denied voter rights and losing out on public housing and welfare benefits (Mauer, 2002; Pager, 2003; Rubinstein &

Mukamal, 2002; Western, 2006). Punishment also extends beyond the individual and may produce negative consequences for family members, friends and the community at large

(Chesney-Lind, 2002; Clear, 2007; Geller et al., 2009; Turanovic, Rodriguez & Pratt, 2012).

There are certainly other orientations that scholars use to understand law, conflict, and punishment in society that can be discussed, but this dissertation is best characterized as a study in the sociology of law rather than a part of criminology. My focus is to provide a better understanding of how corporations that violate environmental criminal statutes are punished under the law. To do this I will be using Black’s (2011) theory of moral time. Chapter 4 will 8 discuss this theory in greater detail and provide an explanation as to why I do not use a different theoretical approach. Nonetheless, Black’s perspective of moral time incorporates in one way or another all of the perspectives previously discussed, which in turn provides sociologists a holistic understanding of conflict and punishment under one theoretical narrative.

Dissertation Roadmap

This dissertation has a total of seven chapters. This first chapter is meant to introduce the social problem of illegal polluting and its consequences. In Chapter 2, I will briefly introduce environmental law and the legal frameworks that dictate how individual and corporate polluters are to be punished when violating laws designed to protect the environment. Environmental law is complicated and not easily understood. Chapter 2 should do more than merely provide background information, but provide a more clear and organized view of environmental law.

In Chapter 3, I will provide an extensive literature review detailing what work has been done on white collar crime generally and corporate crime more specifically. In this chapter I treat corporate crime as a sub-type of white collar crime. Furthermore, I treat corporate environmental crime as a type of corporate crime. There are criminologists that would argue environmental crime is not to be studied within the field of corporate and white collar crime. The perspective that environmental crime is not a type has its merit, but as will be discussed in Chapter 3, I keep with the more traditional view of crimes against the environment. Finally, in Chapter 3, I will demonstrate that there is a lack of empirical evidence aimed at explaining why punishments against corporations vary even if the acts are similar, which shows the need for the research being presented in this dissertation.

Before moving on it should be noted that the literature review is focused on the definition of environmental crime, why people and corporations commit environmental crime, and how 9 those individuals and companies are punished after being found or pleading guilty to an environmental crime. Furthermore, as demonstrated by my dataset and empirical findings, this dissertation presents a study of sentencing and punishment under the law.

Scholars have spent a great deal of time trying to understand why there is variance in punishments between individuals. One strand of scholarship argues that variances in punishment exist because of racial biases within the criminal justice system. For instance, the state of

Georgia has a two strikes and you’re out sentencing law that allows district attorneys to seek, and courts to impose, a life sentence for an individual’s second felony drug conviction (Alexander,

2012: 114). Within this sentencing framework, Georgia district attorneys sought a life sentence against 1% of white defendants eligible for a life sentence and 16% of black offenders, which inevitably resulted in 98.4% of all people serving a life sentence under the two strikes and you’re out laws are black (Alexander, 2012, 114).

The argument that racial bias is the mechanism explaining why blacks and Hispanics are punished more severely than whites has been widely debated (Franklin, 2010; Shermer &

Johnson, 2010). Nevertheless, there is a significant body of evidence suggesting that people who are poor, black, or Hispanic are punished more harshly, for the same or similar crimes, than whites even when considering other factors such as (a) age, (b) employment status, (c) the social context of courts and courtroom actors, (d) differing stages and decision making points in the system, (e) type of conviction, and (f) victim characteristics (Byungbae, Spohn, & Hedberg,

2015; Johnson & Betsinger, 2009; Kutateladze, Andiloro, Johnson & Spohn, 2014; Mears,

Cochran, Stults, Greenman, Bhati, & Greenwald, 2014; Mitchell, 2005; Phillips, 2009; Spohn,

2000; Spohn & Holleran, 2000; Steffensmeier, Kramer, & Ulmer, 1995; Ulmer & Johnson, 2004;

Wang & Mears, 2010 ). 10 Although Chapter 3 focuses on white collar and corporate offenders, it should be recognized that there is a much larger body of knowledge regarding sentencing and punishment in which my dissertation fits. Generally speaking, the sentencing and punishment literature focuses on poor and minority individuals and how they are punished by the criminal justice system whereas my dissertation focuses on a more understudied offender––the corporation.

In Chapter 4 I provide the theoretical framework used to analyze my data. In particular I used Black’s (2011) theory of moral time to explain how law is being used within the conflict of environmental protection and economic expansion. Prior to detailing Black’s theory of moral time, I provide a brief background of the paradigm known as pure sociology to identify where this new theory of conflict originated. Finally, in Chapter 4 I show that law is something that forbids what Black (2011) calls movements of social time. Because law forbids changes in social time, I expect environmental laws will be utilized in a manner that maintains a type of status quo in spite of a change in social time.

In Chapter 5, I introduce the data set used to test the hypotheses derived from Black’s

(2011) theory. Once the data set is introduced, I conceptualize the variables used in the statistical models and present the hypotheses that were tested. In Chapter 6 I present the findings of my analysis of each hypothesis tested. Finally, in Chapter 7 I conclude by recapping the dissertation, summarizing my main findings and interpretations, discussing the limitations of this study, and presenting ideas for future research.

11 CHAPTER 2: LEGAL FRAMEWORK

In this chapter I describe the most frequently used environmental laws created to protect humans and the environment from excessive polluting (or in some cases the potential for excessive polluting) by individuals and corporations. Once the environmental law statutes are described I introduce laws that are not designed to protect the environment specifically, but are nonetheless used by prosecutors against corporations that violate environmental laws. Finally, I describe a set of laws prosecutors rely upon to determine how a corporation that violates an environmental law should be punished.

It must be understood that the descriptions I provide are meant to orient readers with environmental laws and sentencing schemes common to the practice of environmental protection through law. There are numerous exemptions and activities that do not apply to the statutes I describe below. Moreover, the laws described are by no means an exhaustive list of the statutes available to government officials to be used in punishing those that engage in excessive pollution.

Environmental Law

Currently a series of legal frameworks exists with the goal of alleviating or eradicating the negative effects pollution has on the natural environment, human populations and economic conditions. These frameworks are often referred to as legal typologies and are called natural resources law, water law, conservation law, land development law, and the like. Nevertheless, all of these legal typologies are sub-components of a larger framework called environmental law.

Environmental laws are meant to dictate how society uses natural resources, what types of

12 pollution can be generated, and what activities that have direct and indirect effects on the environment are prohibited. Further, environmental laws provide government officials with a wide array of administrative, civil, and criminal penalties that can be brought against individuals or corporations for excessively polluting and harming humans and nature. A brief description of the most popular and often used environmental laws follows.

Prior to reading the brief descriptions that follow, it is important to remember these laws are intertwined. There are manufacturing processes (e.g. semi-conductors) that create solid wastes, pollute water, and create air pollution and thus those companies will be under the purview of the Clean Water Act (CWA), Clean Air Act, and the Resource Conservation and

Recovery Act. If these statutes are violated, courts have multiple sentencing schematics and guidelines, which will be discussed in the next sections, that they may rely upon to determine what the fine against a corporation violating a federal environmental criminal law should be.

Clean Water Act

The CWA was passed in 1972 with the purpose to “restore and maintain the chemical, physical and biological integrity of the Nation’s waters” (CWA § 101(a); 33 U.S.C. §1251(a)). In reality the most ambitious goal of the CWA––the elimination of all discharges––was in abeyance the EPA and other environmental regulating agencies use the law to stop any avoidable loss of water quality (Gross & Stelcen, 2012: 57).

Generally speaking the Clean Water Act states the “discharge of any pollutant by any person without a permit shall be unlawful” (CWA § 301(a); 33 U.S.C. § 1311(a)). There are exceptions to what a pollutant is and they are found in sections 33 U.S.C. § 1312, 1316, 1317,

1328, 1342 and 1344. When addressing non-exempt substances, the CWA defines the discharge of pollutants as “any addition of any pollutant to navigable waters from any point source” (CWA 13

§ 502(12), 33 U.S.C. § 1362(12)). A pollutant is broadly defined to include any waste discharged into water, irrespective of the source, which includes dredged spoil, solid waste, incinerator residue, sewage, garbage, chemical wastes, biological materials, rock, sand, cellar dirt, agricultural waste, heat etc. (CWA § 502(6); 33 U.S.C. § 1362(6)). If a prosecutor is to prove a violation of the CWA, they must essentially show the following four elements are met: (a) there was an addition, (b) of a non-exempt pollutant, (c) into a navigable water of the United States, and (d) without a permit or over specified permitted limits.

With each element there are definitional hurdles prosecutors must get over. For instance, what is meant by the word addition? The word addition is treated by courts as a type of harm, thus forcing an inquiry into particular acts to determine if pollutants were in fact added or not.

For example, courts have determined the process of side-casting, which is the placement of dredged materials (e.g. usually sand, rocks, and small trace amounts of litter like plastics and paper which will dissolve) into excavated ditches for use as filler (e.g. filling of dug out trenches or holes part of a construction process) (United States v. Cundiff (2009, 6th Circuit. 555 F.3d 200:

213-14). However, other processes created incidental fallback such as excavated material falling out of a bucket or entrenching tool back into waters or wetlands during the excavation process does not constitute an addition (National Mining Association v. U.S. Army Corps of Engineers,

1998, D.C. Circuit. 145 F.3d 1399: 1403).

It is also important to understand that the term navigable waters of the United States is continually being argued over. Case law has applied the term to bodies of water that have virtually nothing to do with actual navigability, and in United States v. Riverside Bayview Homes

Inc. (1985, 474 U.S. 121:133), the Supreme Court stated that “actual navigability of the water is of limited import and thus at least some waters not deemed ‘navigable’ will still be covered by 14

the Act.” At this point in time courts now rely upon the U.S. Constitution’s commerce clause to determine if a body of water is navigable or not. The commerce clause definition of navigable waters is incredibly broad and includes waters such as small tributaries (e.g. creeks – even if they only flow part of the year), wetlands, mudflats, and the like (40 C.F.R. § 122.2(1) – (7)). It is worth stating that discharges into groundwater (underground aquifers usually relied upon for drinking water) are not covered by the CWA (Exxon Corporation v. Train (1977, 5th Circuit,

544 F.2d 1310: 1329).

When the CWA is violated government officials have a number of ways to punish violators. The Environmental Protection Agency (EPA) or Department of Justice (DOJ) civil attorneys may bring administrative fines (CWA § 309(g); 33 U.S.C. § 1319(g)), civil (CWA §

309(b); 33 U.S.C. § 1319(b)), and criminal (CWA § 309(c); 33 U.S.C. § 1319(c)) penalties against individual and corporate violators. Because this dissertation is focused purely on criminal sanctions I will limit this discussion to presenting the criminal penalty framework only.1 Table 1 gives a full listing of acts and fines that can be punished under the CWA.2

1 Chapter 5 will discuss this in further detail. I only have data to analyze criminal prosecutions so my exclusion of civil and administrative fines is unfortunate but necessary. 2 The main difference between administrative, civil and criminal sanctions is the dollar amount a corporation can be fined. Administrative fines are generally small while criminal fines are large. The statutory language found in the administrative, civil and criminal laws are virtually identical and it is up to government lawyers to determine which statute they will use. 15 Table 1 CWA Punishment Framework Negligent Offense Statute Prohibited Activity Knowing Offense (Felony) (Misdemeanor) Discharge of pollutants or non- $5,000 to $50,000 per day $2,500 to $25,000 fine per treated waste water from a point 33 USC for 1st offense; Subsequent day for 1st offense; source into (a) navigable waters, 1319(c)(1) & (2) violations up to $100,000 Subsequent violations up to (b) a PTOW without or in per day $50,000 per day violation of a permit Discharges of oil or hazardous $5,000 to $50,000 per day $2,500 to $25,000 fine per 33 USC substances into waters, for 1st offense; Subsequent day for 1st offense; 1321(b)(3) shorelines or contiguous zone in violations up to $100,000 Subsequent violations up to harmful quantities per day $50,000 per day Failure to report the discharge of Sentencing scheme is set up Sentencing scheme is set up 33 USC oil or hazardous substance to in accordance with 18 § in accordance with 18 § USC 1321(b)(5) authorities immediately upon USC 35713 3571 learning of discharge $5,000 to $50,000 per day $2,500 to $25,000 fine per 33 USC Discharge of pollutants or non- for 1st offense; Subsequent day for 1st offense; 1319(c)(1)(B) & treated waste water that causes violations up to $100,000 Subsequent violations up to (2)(B) harm to a PTOW system per day $50,000 per day $250,000 fine for Any violation of CWA law that individuals and $1,000,000 33 USC puts a person in imminent for corporations. Fines N/A 1319(c)(3) danger of death or serious bodily double for subsequent injury violations

If individuals or corporations violate the CWA “knowingly” they may be prosecuted under a felony statute (e.g. 33 U.S.C. § 1319 (c)(2)(A)) and if the violation is “negligent” the prosecution will be as a misdemeanor (e.g. 33 U.S.C. § 1319(c)(1)(A)). To knowingly violate the CWA a person or corporation must have “consciously undertake the activity and the actor need not know that the activity is a violation of law.” United States v. Weitzenhuff (1993, 9th

Circuit, 35 F.3d 1275). The term “negligent” is not defined in the CWA but has generally been interpreted as “harm due to the carelessness of an individual or corporation that did not exercise a level of care a reasonably prudent person or corporation would have in the particular situation.”

United States v. Hanousek (1999), 9th Circuit, 1176 F.3d 1116.

3 This punishment scheme will be discussed further below. 16 An understanding of the CWA is important for this dissertation because over 50% of all prosecutions in my dataset (n=234) occur under the statute. As demonstrated by Table 2, 50% of all prosecutions in my dataset occur under 33 USC 1319(c)(1)(A) and 33 USC 1319(c)(2)(A).

Table 1. Clean Water Act Prosecutions4 33 USC 1319(c)(2)(A) 33 USC 1319(c)(1)(A) 57 Total Felony 61 Total Misdemeanor Prosecutions Prosecutions Percentage of all Prosecutions 24% 26% (all statutes)

Clean Air Act

The Clean Air Act of 1970 was designed to set primary and secondary National Ambient

Air Quality Standards (NAAQS), and to protect public health and the natural environment (CAA

§ 109; 42 U.S.C. §7409). The Clean Air Act gives the EPA authority to regulate any and all air pollutants that “may reasonably be anticipated to endanger public health or welfare” (CAA §

108(a)(1)(A), (B); 42 USC § 7408(a)(1)(A), (B)). The phrase public welfare is defined broadly and includes pollutants that can damage soil, water, vegetation, wildlife, and the like may also be regulated––the harm is not limited only to humans.

Though not an exhaustive list, the EPA has set primary and secondary quality standards for pollutants such as lead, carbon monoxide, sulfur dioxide, carbon dioxide, and nitrogen dioxide. Before any of these pollutants are subjected to regulation, the EPA must issue what is called an air quality criteria document, which provides the latest scientific knowledge about a pollutant and the harm it may/does cause when present in ambient air (CAA § 108(a)(2); 42 USC

§ 7408(a)(2)). It is unclear how much evidence is needed to determine a pollutant may cause an

4 It is worth noting that the cases prosecuted under the Clean Water Act, and other statutes designed to protect the environment, are concluded through the process of plea bargaining. Of the 234 cases in my data set (I will discuss the creation of my data set in Chapter 5) all but two were concluded through plea bargaining. 17

adverse health effect, thus courts will generally defer to the EPA’s judgment that the underlying science is sound and the conclusions of harm are rational and not contrary to the statute

(Allentown Mack Sales & Service Inc v. NLRB (1998) 522 U.S. 359: 374).

However, courts have mandated the EPA take into consideration, when determining to regulate a pollutant, the economic costs to industry and technological limitations that may undermine regulations. Thus even if the EPA regulates a pollutant, the industry that will be required to change in order to comply with regulations must currently be in or be allowed the opportunity to meet the new standards technologically, otherwise the regulation will be determined unreasonable and stuck down (American Lung Association (1998) D.C. Circuit, 134

F.3d 388; Lead Industries Association v. EPA (1980) D.C. Circuit, 647 F.2d 1130). Most recently, the Supreme Court held that the EPA cannot regulate pollutants (specifically green house gases) under the Clean Air Act if the financial costs to industry outweigh the benefits to human health and environmental conservation (Michigan et al. v. Environmental Protection

Agency (2015) 576 US ____).

Once the EPA has determined a pollutant is to be regulated and economic and technological concerns are met, the DOJ can use administrative (CAA § 113(a); 42 USC §

7413(a)), civil (CAA § 113(b); 42 USC § 7413(b)), and criminal (CAA § 113(c); 42 USC §

7413(c)) statutes to enforce regulations and punish violators. Like the CWA discussion, I limited this section to a discussion of the criminal statutes because it is the focus of this dissertation. The

CAA uses 18 § USC 3571, which is discussed in the following section, as its sentencing mechanism. Nevertheless, it is important to have a rough understanding of the activities prohibited by the CAA.

Table 2. CAA Punishment Framework Knowing Offense Negligent Offense Statute Prohibited Activity (Felony) (Misdemeanor) Sentencing scheme Sentencing scheme Construction or creation of a new polluting 42 USC § is set up in is set up in source or modification of old one without a 7413(c)(1)(A) accordance with 18 accordance with 18 permit § USC 3571 § USC 3571 Person who is working on any renovation Sentencing scheme 42 USC § project involving asbestos and causes is set up in N/A 7413(c)(1)(B) another person or employee to not comply accordance with 18 with workplace safety or disposal standards. § USC 3571 Importation of goods (e.g. engines, Sentencing scheme Sentencing scheme refrigerators, motor vehicles) that create o- 42 USC § is set up in is set up in zone harming material and do not fall 7413(c)(1)(C) accordance with 18 accordance with 18 within U.S. guidelines for permitted § USC 3571 § USC 3571 emission levels. Making false statement in connection to obtaining a permit, altering information, Sentencing scheme 42 USC § concealing documents or failing to maintain is set up in 7413(c)(2)(A) N/A records in connection to an existing permit; accordance with 18 through (C) failure to report a permit violation or § USC 3571 tampering with a monitoring device. Sentencing scheme Sentencing scheme 42 USC Violation of permit, performance standards, is set up in is set up in 7413(c)(3) emergency order or state ordinances. accordance with 18 accordance with 18 § USC 3571 § USC 3571 Sentencing scheme Sentencing scheme Knowing or negligent release of air 42 USC is set up in is set up in pollutant putting a person(s) in threat of 7413(c)(4), (5) accordance with 18 accordance with 18 death or serious bodily injury. § USC 3571 § USC 3571

Resource Conservation and Recovery Act

The Resource Conservation and Recovery Act (RCRA) establishes a cradle-to-grave framework of regulations designed to manage the handling of hazardous wastes (42 USC § 6901 et seq.). The RCRA program is extremely broad because it regulates the handling of waste generated by virtually any process (C.F.R. § 262 et seq), the transportation of waste (42 USC §

6922(a); C.F.R. § 263 et seq), and how waste is to be treated, stored, and disposed of (42 USC §

6925; C.F.R. § 265 et seq). Further, the RCRA provides specific standards for cleaning up of environmental hazards under the Superfund statute (42 USC §9601 et seq.) and the disposal of

19 non-hazardous waste (42 USC § 6945(a))5. Because the RCRA is so broad in its scope, it takes up over 1,000 pages of regulations and has even been referred to by courts as “mind-numbing” due to the fact perfect compliance with the law is virtually unattainable (American Mining

Congregation v. EPA (1987, D.C. Cir., 824 F.2d 1177, 1189).

Generally speaking, the RCRA applies only to solid waste that is hazardous (42 USC §

6903). For RCRA to apply, the EPA must first determine if the material in question is solid waste. If the waste is solid, then there needs to be an inquiry determining if the material is hazardous or not. The following three criteria must be met in order for a material to be considered solid waste: (a) Must be a physical form of a solid waste,6 (b) must be a discarded material,7 and (c) must not be excluded from regulation under one of the solid waste exclusions.8

Once a material is determined to be solid waste, there is an inquiry to determine if a waste is hazardous. 42 USC § 1004(5) defines the term hazardous waste as

a solid waste, or combination of solid wastes, which because of its quantity, concentration, or physical, chemical, or infectious characteristics may (a) cause, or significantly contribute to an increase in mortality or an increase in serious irreversible, or incapacitating reversible, illness; or (b) pose a substantial present or potential hazard to human health or the environment when improperly treated, stored, transported, or disposed of, or otherwise managed.

5 The Comprehensive Environmental Response, Compensation and Liability Act (“CERCLA”)––or more often referred to as the Superfund Act––was created in 1980 with the goal of providing local, state and federal agencies with large sums of money to clean up uncontrolled or abandoned hazardous waste sites. 6 Material can be considered solid waste under the RCRA if its physical form is either a solid, semisolid (e.g. gel or sludge), liquid or contained gas (42 USC § 6903(27)). 7 The EPA defines discarded material as anything that is “abandoned,” “recycled,” or “inherently waste-like” (40 CFR § 261.2(a)(2)). For simplicities sake effectively the EPA is saying that the material created out of a manufacturing, farming or consumption processes (e.g. secondary material) that has no real legitimate use at the moment of creation is discardable and thus presumptively discarded. 8 There are over 20 statutory and regulatory exclusions that can apply. These exemptions are things like “home scrap” and material generated out of a recycling process. 20 For the most part EPA has specifically listed what solid wastes are considered hazardous and thus subject to regulations (see generally 40 CFR § 261(D); CFR § 261.31, .32 and .33).

Legal practitioners commonly refer to the listed materials as the “F,” “K,” “P,” and “U” lists.

The EPA has created a list of solid waste that is exempt from RCRA regulation because it would be too “impractical, unfair, or otherwise undesirable to regulate the waste as hazardous”

(Bergson, 2004: 46). Some examples of exempt wastes are household waste, certain agricultural and animal waste, crude oil and natural gas, injected groundwater re-injected as part of hydrocarbon recovery operations, and dredged material. Even though some materials are exempt from the RCRA, it does not necessarily mean introduction of these wastes goes unpunished. For instance, in United States v. Brusco Tug & Barge (2009, N.D. CA, CR09: 0728 SI), the company released dredged material back into a navigable water without a permit. Though the dredged material was not subject to regulation and punishment under the RCRA, Brusco Tug & Barge

(2009) was prosecuted under the CWA because the discharge occurred without a permit to do so.

Like other federal environmental law statutes, the RCRA has a framework allowing government officials to use administrative, civil and criminal sanctions against individuals and corporations for violating its statutes. Similar to the previous subsections on the CWA and CAA

I limit this discussion to the criminal statutes found in the RCRA. Table 4 gives a brief description of the relevant law, prohibited activities and fines applicable to each outlawed act.

There is some confusion as to what a knowing offense is under the RCRA and if other offenses that would normally be treated as negligent under the CAA or CWA can be prosecuted.

Nevertheless, the majority of jurisdictions take the position that even if a defendant did not know the legal status of a waste or their actions were unlawful, they may still be prosecuted so long as he or she knew the waste was potentially harmful (Steinberg & Woodrow, 2004: 434). The 3rd 21 Circuit takes a different approach and has stated a defendant must know that: (a) the material in question was classified as a hazardous waste, (b) disposal requires a permit, and (c) the facility where disposal takes place lacks a permit (Steinberg & Woodrow, 2004: 434).

Table 4: RCRA Punishment Scheme

Knowing Offense Negligent Offense Statute Prohibited Activity (Felony) (Misdemeanor) Treatment, storage or 42 USC § 6928(d)(2)(A) disposal of hazardous waste Up to $50,000 fine per Up to $25,000 fine per through (C) without or in violation of a offense offense permit Transportation of Up to $50,000 fine per Up to $25,000 fine per 42 USC § 6928(d)(5) hazardous waste without a offense offense shipping manifest Transportation of hazardous waste to a Up to $50,000 fine per Up to $25,000 fine per 42 USC § 6928(d)(1) facility that does not have a offense offense permit to treat, store or dispose of hazardous waste Omission of a material fact or making of a false Up to $50,000 fine per Up to $25,000 fine per 42 USC § 6928(d)(3) statement in connection to offense offense receiving a permit Destruction, concealing, failure to maintain or Up to $50,000 fine per Up to $25,000 fine per 42 USC § 6928(d)(4) altering of records in offense offense connection with a permit Anybody who transports, Fine up to $250,000 for stores, or treats hazardous individuals and 42 USC § 6928(e) waste placing a person in N/A $1,000,000 for imminent danger of death corporations or serious bodily harm Knowing Offense Negligent Offense Statute Prohibited Activity (Felony) (Misdemeanor)

Non-Environmental Law Criminal Acts

Legislation like the CWA, CAA, and RCRA are created to protect the environment and human populations that may be adversely affected by environmental degradation. However, prosecutors are not limited to only using the CWA, CAA, and RCRA when prosecuting individuals and corporations that have caused environmental degradation. Take for instance the case of United States v. General Environmental Management (2009, N.D. Ohio, 1:08-CR-144). 22 General Environmental Management (GEM) was in the business of recycling industrial wastewater in Cleveland, Ohio. GEM had a permit to recycle wastewater and pump it back into local sewer systems. The permit required GEM to get pre-approval from the 5th District’s EPA office prior to accepting and recycling wastewater from their clients. EPA investigators believed

GEM was accepting and treating wastewater without prior approval because instruments at the processing plant showed more wastewater was being put into local sewers than claimed by

GEM. GEM stated in writings to the 5th District EPA office they never accepted or treated wastewater without pre-approval, but upon executing a search warrant investigators found hundreds of barrels filled with wastewater that had been accepted without prior approval.

During interviews, Scott Foster, GEM’s vice president and part owner, admitted to accepting and treating wastewater without prior approval. Though prosecutors could have used the CWA or

RCRA to prosecute GEM, they relied upon 18 USC § 2 (aiding and abetting a criminal act) and

18 USC § 1001 (fraudulent statements).

Title 18 is the criminal and penal code of the United States. Title 18 crimes include acts like arson (18 USC §81), assault (18 USC § 111) and the like. Crimes like conspiracy (18 USC §

371) and obstruction of justice (18 USC § 1505) are also included in Title 18. It is crimes like conspiracy, aiding and abetting, fraudulent statements, and the like that are used against individuals and corporations that commit harm against the environment. Sometimes prosecutors rely solely upon Title 18 crimes to prosecute those that commit harm against the environment – other times Title 18 crimes are added to CWA, CAA, RCRA and other environmental law statutes. Table 5 provides a list of the Title 18 crimes corporations in my dataset were prosecuted under.

23 Table 3. Title 18 Crimes Statute Prohibited Activity Punishment Whoever (1) aids, abets, counsels, commands, induces or procures Sentencing scheme is set up in 18 USC § 2 a crime or (2) causes a criminal act to occur though performed by accordance with 18 § USC another. 3571 Sentencing scheme is set up in 18 USC § Two or more actors conspire to commit an offense or defraud the accordance with 18 § USC 371 United States. 3571 Whoever (1) falsifies or conceals a material fact, (2) makes a Sentencing scheme is set up in 18 USC § materially false, fictitious or fraudulent statement, (3) makes use accordance with 18 § USC 1001 of a false writing containing materially false statements or facts. 3571 Whoever (1) knowingly and willfully, with intent to defraud the United States, smuggles, or clandestinely introduces any Sentencing scheme is set up in 18 USC § merchandise which should have been invoiced; (2) Whoever accordance with 18 § USC 545 fraudulently or knowingly imports or brings into the United States, 3571 any merchandise contrary to law Whoever, having devised or intending to devise any scheme or Sentencing scheme is set up in 18 USC § artifice to defraud, or for obtaining money or property by means of accordance with 18 § USC 1341 false or fraudulent pretenses, representations, or promises by and 3571 through the use of the U.S. Postal Service. Whoever corruptly, or by threats or force, or by any threatening letter or communication influences, obstructs, or impedes or Sentencing scheme is set up in 18 USC § endeavors to influence, obstruct, or impede the due and proper accordance with 18 § USC 1505 administration of the law under which any pending proceeding is 3571 being had before any department or agency of the United States

Sentencing the Corporate Offender

As previously noted, the CWA, CAA, and RCRA all make reference to 18 USC § 3571 when it comes to punishing certain acts. When prosecuting corporations for a violation of an environmental law, prosecutors can rely upon the sentencing frameworks listed in legislation like the CWA, CAA, and RCRA. However, prosecutors are free to rely upon 18 USC § 3571 when it comes to prosecuting corporations––and more often than not they do. The framework is pretty basic: 18 USC 3571(c) specifies the minimum and maximum fines corporations are subject to when found guilty of felonies and misdemeanors. For a felony conviction, a corporation can be fined up to $500,000 per day/offense (18 USC § 3571(c)(3)). For class A misdemeanors not resulting in death the maximum fine is $200,000 per day/offense (18 USC § 3571(c)(5)). For class B and C misdemeanors that do not result in a death the maximum fine is $10,000 per

24 day/offense (18 USC § 3571(c)(6)). Finally, misdemeanors that result in a death may be fined up to $500,000 per day/offense (18 USC § 3571(c)(4)).

The sentencing framework in 18 USC § 3571 is just a starting point for prosecutors and courts when deciding what the fine against a corporate defendant should be. Within the U.S.

Sentencing Guidelines (2015) there is a list of aggravating circumstances that gives the prosecutors the ability to increase the amount of a fine a corporation would be subject to under the CWA, CAA, RCRA, and 18 USC § 3571. Prior to U.S. v. Booker (2005, 543 U.S. 220), courts were mandated to use aggravating factors to craft fines against corporations violating environmental law. However, U.S. v. Booker (2005) made it so that using aggravating factors to determine a fine was discretionary. Nevertheless, prosecutors may still rely upon the aggravating factors listed in the U.S. Sentencing Guidelines (2015) for help in creating appropriate penalties for corporations violating environmental criminal laws. USSG §2Q1.1 through § 2Q2.1 contain the full list of aggravating factors that are specific to environmental crimes. The specific aggravating factors and their citations are listed in Table 6.

Table 4. Aggravating Factors Applicable to Environmental Crime Statute Prohibited Activity Offenses with hazardous materials that involve: (a) continuous/repetitive release, (b) substantial USSG § likelihood of serious bodily injury or death, (c) disruption of public utilities or evacuation of 2Q1.1(b) citizens, or (d) the violation was with or without a permit. Offenses with non-hazardous materials that involve: (a) continuous/repetitive release, (b) USSG § 2Q1.3 substantial likelihood of serious bodily injury or death, (c) disruption of public utilities or evacuation of citizens, or (d) the violation was with or without a permit. The offense involves the actual, or threat, of tampering, mishandling, misuse of monitoring instruments or public treatment water-work systems. Offense severity is to increase if the USSG § 2Q1.4 underlying act violating this provision results in substantial clean up costs or evacuation of communities/citizens. USSG § 2Q2.1 An offense that involves the destruction of wildlife or the death of wild animals.

It is not just aggravating circumstances that gives courts the ability to increase, or sometimes decrease, a fine against a corporation. Specifically, 18 USC § 3553 and 18 USC §

3572 have a set of factors that may be considered when crafting fines against corporation for violations of environmental law. In 18 USC § 3553(a) there is a list of factors courts and prosecutors are to consider when creating fines: (a) nature and circumstances of the criminal act and the defendant’s past history, (b) promotion and respect for law, (c) afford adequate deterrence of criminal conduct, (d) protect the public from the defendant, and (e) provide defending with educational, vocational training or medical care in an effective manner. The factors found in 18 USC § 3553(a) can be used to go above the maximum fines stated in 18 USC

§ 3571(c) and/or force corporate defendants to engage in community service and create environmental compliance/training programs to ensure no future violations of environmental law.

Prosecutors may also rely upon the framework specified in 18 USC § 3572 to assist in crafting and implementing punishments against corporations for violating environmental law.

Specifically, 18 USC § 3572 has prosecutors look at the following factors (this list is not wholly inclusive but is most relevant to corporations): (a) Defendant’s income, earning capacity, and financial resources, (b) the burden the fine will impose on the defendant or dependents or employees, (c) pecuniary loss inflicted upon others, (d) whether restitution is ordered and the fine will impede the paying of it, (e) whether defendants can pass onto consumers or customers the burden of the fine, and (f) the size of the corporation, number of employees, and if the offense was committed or known by higher ups in a company or not.

With regards to corporations, prosecutors may use the aforementioned factors to increase or reduce a fine against a corporation. For instance, if a corporation is fined but does not have enough income to pay a fine they can be put on a payment plan or the amount owed can be reduced prior to sentencing. If the sentencing framework laid out in laws like the CWA would 26

cause a corporation to lay off employees the fine may be lowered to preserve jobs. However, fines may also be increased because the defendant’s conduct caused a pecuniary loss upon others and thus restitution along with a fine may be necessary

Conclusion

The purpose of this chapter was to introduce the basic legal framework in which environmental criminal laws are applied. The CWA, CAA, and RCRA, along with many other statutes designed to specifically protect the environment, list out what acts are acceptable or prohibited and provide a set of punishments applicable to each outlawed act. However, prosecutors are not limited to protecting the environment and punishing offenders by relying only on laws like the CWA, CAA, and RCRA. Prosecutors may use Title 18 crimes (e.g. conspiracy, aiding and abetting) to punish individuals and corporations that cause environmental harm. Moreover, courts may rely upon punishment and sentencing frameworks found in Title 18 to determine what the fine against a defendant should be. Specifically, the fines can be significantly increased or decreased, which represents a departure from the CWA, CAA, and

RCRA sentencing frameworks.

In the end, prosecutors and courts have a great deal of discretion when determining what the fine against a corporate defendant should be. There is a wide array of statutes to use when coming up with a punishment. Thus, to say determining what the punishment of a corporation should be when violating an environmental law is complicated may be an understatement. The purpose of this chapter was to introduce the basic tenants of environmental law and the punishments associated with violation of those laws. However, this chapter raises the question:

How are corporations being fined under these laws?

27 CHAPTER 3: CONCEPTUAL DEFINITION AND LITERATURE REVIEW

Lynch (1990) was the first to forcefully argue that criminologists should place the study of crimes against the environment at the center of their inquiries. Today, a limited number of studies attempt to understand why individuals and corporations violate or obey environmental laws. Further, few studies have attempted to explain why individuals and corporations are punished differentially for violating the same environmental laws. Thus, little is known about why corporations obey/violate the law and how they are punished for violating administrative, civil and criminal laws protecting the environment (Wolf, 2011). This dissertation attempts to explain the variance (e.g. why does one corporation receive a different punishment for the same crime) seen in criminal punishments given to corporations for violation of environmental laws.

Legal scholars and commentators devote most of their attention to discussing the theories that corporate criminal liabilities are founded upon (Beale, 2009; Brickey, 1996; Lazarus, 1995;

Weissmann & Newman, 2007). The debates surrounding theories of applicability and fairness of environmental criminal laws are important (Beale, 2009; Brickey, 1996; Lazarus, 1995, 1997;

Weissmann & Newman, 2007). However, they have distracted researchers from investigating other issues such as variance found in sentencing decisions for defendants violating environmental criminal statutes (O’Hear, 2004).

In this chapter I will define the concept of corporate environmental crime. Second, I give an overview of the prior empirical work relevant to corporate crime generally and corporate environmental crime specifically. To do this I provide the two main research questions guiding the study of corporate crime and provide a description of the relevant literature. In conclusion I

28 describe how this dissertation expands on the current empirical knowledge base related to violations of environmental laws.

Conceptual Definition of Corporate Environmental Crime

The conceptual focus of this dissertation is corporate environmental crime. The empirical goal of the study is to explain the variance found in criminal penalties levied against corporations that violate federal environmental criminal laws. This dissertation’s conceptual focus and empirical goal are situated within the broader concept of white collar crime. The purpose of this section is to present the definition of corporate environmental crime used in this study and to demonstrate how it is based in and consistent with the broader literature of white collar crime.

Defining White Collar, Corporate, and Environmental Crime

There is considerable disagreement regarding the concept and definition of white collar crime (Simpson, 2013: 310). In fact, there is no agreed upon conceptual definition of white collar crime. The purpose of this sub-section is to highlight the various approaches to studying white collar crime and give the general definition of white collar crime specific to each approach. The first approach to studying white collar crime is offender based. The second approach is offense based. The third approach is typographical.

Offender based definitions.

In his study, Sutherland (1939) defined white collar crime as an act committed by individuals of high social status and respectability within an occupation. This is an offender- based definition of white collar crime because the focus of study was on the individual and their personal characteristics (Benson & Simpson, 2009: 9; Simpson, 2013: 311). Sutherland (1949) amended the 1939 definition to include corporations as potential white collar offenders, but the focus remained on high status and respectable actors. Sutherland (1941: 12) wrote that the point 29

of his definition was not precision, but to call attention to an ignored type of criminal behavior and show that white collar crime is “identical in its general characteristics with other crime rather than different from it.” Sutherland was initially praised for his definition and focus on white collar crime, but criticisms and weaknesses were inevitably pointed out by other scholars (Payne,

2013; Simpson, 2013). Nevertheless, Benson and Simpson (2009: 5) argued that Sutherland’s definition intentionally excluded certain crimes that can be committed by the upper class (e.g. murder) and gave attention to acts usually only committed by business managers and executives.

One gap in Sutherland’s (1939) definition is the exclusion of crimes like tax evasion because they do not necessarily occur in the course of an occupation (Payne, 2013). A second limitation is that the definition excluded acts that occur within an occupation, but are committed by lower-level and entry-level employees who are not of high social status and respect (Simpson,

2013). This is problematic because Weisburd, Chayet, Waring, and Bode (1990: 353) found that a large number of white collar offenses are actually committed by people in the middle class of society. Moreover, Sutherland’s (1949) study of white collar crime examined offenses like workplace theft, fraud by mechanics, and deception by shoe salespersons; these offenses are not per se committed by business managers, executives, and people of high respect.

For the most part, offender-based definitions of white collar crime have been abandoned but not completely so. For instance, Reiss and Biderman (1980: 4) used an offender-based approach when they defined white collar crime as

violations of law to which penalties are attached and involve the use of a violator’s position of significant power, influence, or trust in the legitimate economic or political institutional order for the purpose of illegal gain or to commit an illegal act for personal or organizational gain.

Nevertheless, offender-based approaches have lost influence and are infrequently used.

Offense-based definitions.

An offense-based approach looks primarily at the means through which an offense is carried out. Edelhertz (1970) was the first to use an offense-based definition of crime and defined white collar crime as an illegal act or series of illegal acts committed by non-physical means and by concealment or guile or property, or to obtain business or personal advantage. Edelhertz continues to serve as the baseline definition for scholars using an offense based approach to white collar crime.

For instance, Gibbs, Cassidy, and Rivers (2013: 340) used an offense-based approach and defined white collar crime as: “Nonviolent crime for financial gain committed by means of deception.” Gibbs et al. (2013: 343) explicitly stated they were trying to stay consistent with

Edelhertz’s definition. Coleman (1987) defined white collar crimes as: “a violation of the law committed by a person or group of persons in the course of an otherwise respected and legitimate occupation or financial activity.” Albanese (1995) defines white collar crime as planned or organized illegal acts of deception or fraud, usually accomplished during the course of legitimate occupational activity, committed by an individual or corporate entity. Collectively, these definitions remove the social status restrictions found in offender-based approaches thus allowing for the reality that people of low social status also commit white collar offenses.

Further, the approach allows for the investigation of crimes not committed in the course of employment. Finally, offense-based definitions exclude certain types of crimes (e.g. murder, assault, etc...) thus keeping a focus away from street crimes and the like. Though popular with researchers, Geis (2002) argued that offense-based approaches risk capturing too many low-level minor offenders and do not focus on the crimes committed by the rich and powerful.

31 Typological Approaches

Some scholars argue that white collar crime should be studied using a typological approach (Clinard & Quinney, 1973: 132). Typological approaches are based on the nature of employment and can be separated into subcategories of corporate and occupational crime

(Simpson, 2013). Corporate crimes are acts that benefit the offending company and occupational crimes are acts benefiting the individual. Whether studying corporate or occupational crime, the typological approach focuses research on specific acts. Thus rather than studying corporate fraud, generally a typological approach is used by researchers to study corporate real estate fraud or corporate pharmaceutical fraud.

The typological approach has been embraced by some scholars. Because there were so many definitions in use, Friedrichs (1992) argued the definition of white collar crime should be driven by an individual’s research goal. This is because the field of white collar crime “is so large and diverse offender and offense based definitions often times become too over or under inclusive regarding the types of offenders and acts they are trying to study” (Tomasic, 2005:

267). Thus, focusing on a specific type of white collar crime allows researchers to avoid the seemingly endless debate about the proper definition of white collar crime (Friedrichs &

Schwartz, 2008; Piquero & Moffit, 2014).

Environmental Crime

Like white collar crime there is disagreement over what constitutes a proper definition of environmental crime. An environmental crime can encompass a vast array of behaviors ranging from individual acts of littering to large scale oil and toxic spills by corporations (Clifford &

Edwards, 2012. Environmental crime is generally treated as a subtype of white collar crime that can be committed by individuals or corporations (Brickey, 1995; Burns & Lynch, 2004; Geis, 32

2011; Jerrel, 2009; Payne, 2013; Pontell & Geis, 2007). For example, Payne (2013: 233) defined white-collar environmental crimes as: “Situations where individuals or businesses illegally pollute or destroy the environment as part of an occupational activity.” Situ and Emmons (2000:

3) define environmental crime as “An unauthorized act or omission that violates the law [e.g. environmental law] and is therefore subject to criminal prosecution and criminal sanctions.”

Both of these definitions do not require offenders to be of high status, respectable (e.g. offender- based approach), or the acts to be committed through deceit or guile (e.g. offense-based approach).

Some scholars argued that definitions of white collar crime should include acts that are immoral or unethical (Ermann & Lundman, 1978; Simon & Eitzen, 1982). This type of definition presumes crime is a social construction and subjective. Thus a social constructionist view of white collar crime necessarily requires scholars to include acts that are not prohibited by law but still cause social/economic harm when committed. The same arguments have been made regarding environmental crimes (Goode, 2011). One such definition argued that environmental crimes should include actions that “may or may not violate existing rules and environmental regulations; has identifiable environmental damage outcomes; and originated in human action”

(Lynch & Stretesky, 2003: 227). More recently social constructionists have defined environmental crime as: “Acts that cause or have the potential to cause significant harm to ecological systems for the purposes of increasing or supporting production” (Stretesky, Long &

Lynch, 2014: 2).

Social constructionist definitions have their merit––for instance they allow scholars to investigate actions that normally fall outside the purview of traditional criminology. However, social constructionist definitions can be too subjective, which may lead to difficulties in 33

measurement and non-objective results which will vary from study to study. Take the Stretesky et al (2014) definition for example: How does one actually define significant harm? The definition of significant harm will change from researcher to researcher, participant to participant, study to study. It is because of this definitional inconsistency that I chose not to use a social constructionist definition of environmental crime.

Corporate Crime

Corporate crime is a subcategory of white collar crime (Simpson, 2002). Braithwaite

(1984: 6) defined corporate crime as: “Conduct of a corporation, or of employees acting on behalf of a corporation, which is proscribed and punishable by law.” Simpson (2002) argued that this is the simplest and most popular definition of corporate crime. Beyond simplest,

Braithwaite’s (1984) definition of corporate crime allows for the reality that crimes are not always intentional acts by corporations or employees. Therefore, in this dissertation, I used

Braithwaite’s definition of corporate crime as the base concept for defining corporate environmental crime.

Corporate Environmental Crime Definition.

I used the following definition of corporate environmental crime: “Acts by a corporation, or of employees acting on behalf of a corporation in the course of business that violate laws meant to protect the environment and thus subject to criminal sanctions.” My definition is consistent with Friederichs’ (1992) recommended typographical approach. I used the typographical approach for two reasons. The first is my focus on all corporate actors that violate environmental laws. High and low status corporations are analyzed, thus an offender based approach is inappropriate. Second, environmental crimes are not necessarily committed though acts of guile or deceit. Therefore, an offense based approach is not required because I was 34

interested in all acts that are violations of environmental law. Finally, because I examine corporations only and focus specifically on environmental crimes, a typological definition is appropriate because it limited the scope of inquiry to a specific unit of analysis (corporations) and the particular actions that can be prosecuted under federal environmental laws.

Literature Review

White collar crime research is guided by two main questions. The first is Why do corporations and individuals commit white collar crime (Simpson, 2013)? Law and society scholars state the question differently and ask Why do corporations and individuals obey the law

(Simpson, 2013; Tyler, 1990)? The second question is How are corporations and individuals punished when they violate the law (Simpson, 2013)? I discuss each one of these questions individually and present the relevant empirical literature.

Why Do Corporations Commit White Collar Crime?

When scholars have attempted to answer this question, they have relied primarily upon traditional theories of criminology. Sutherland (1983) used differential association theory to argue that white collar crime is learned behavior. When people are associated with those who define white collar criminal behavior favorably and in isolation from those who definite it unfavorably, the odds of committing a white-collar criminal offense increases. Often, differential association theory is used when explaining non-white collar crime. Based on the research conducted for this study, Benson (1985) was the only other researcher to use differential association theory to explain why people commit white-collar crime.

Other scholars have used anomie theory to explain why corporations and individuals commit white-collar crime. Passas (1990) provided evidence suggesting that corporations commit crime because capitalist societies are based upon economic principles, such as monetary 35

success or competition, that have cultural and structural contradictions, which inevitably promote widespread corporate deviance. Like differential association, anomie theory was popular with scholars explaining street crime, but it was not widely used to explain white-collar crime.

Control theories have also been used to explain why individuals and corporations commit white-collar crime. In this context, Gottfredson and Hirschi (1987) argued that white-collar criminals, like other criminals, exhibit low levels of self-control even though they excel in school and are usually in managerial positions within a business. People with low self-control are more likely to commit crime (Gottfredson & Hirschi, 1987). Therefore, white-collar crime rates should be low, but when they occur it is because the offender has low self-control (Gottfredson &

Hirschi, 1987: 960-962). However, scholars have been extremely critical of this view. For instance, there is no reason to believe white-collar crime rates are low. In fact, the evidence suggests white-collar crime is rather common; it is just hard to detect (Benson & Moore, 1992:

264-266; Benson & Simpson, 2009: 63). Second, Geis (2002) and Benson and Moore (1992:

266) argued that many documented white-collar crimes take in-depth planning and discipline, thus the low-self control argument is false. Moreover, there is no reason to believe white-collar criminals have low self-control because the act of committing the crime itself requires high levels of self-control to do well in school and advance in the business world (Benson & Simpson,

2009: 64).

The most common perspective used in white-collar crime studies was rational choice.

Simply put, rational choice theory assumes all actors are self-interested and behave according to a cost/benefit analysis (Benson & Simpson, 2009: 66). Paternoster and Simpson (1993) extended rational choice theory to corporate crime by arguing that corporations act at the behest of individuals. Thus, if individuals controlling corporations are rational actors, then corporate actors 36 are also rational actors. Paternoster and Simpson (1993) extended the scope of rational choice theory and argued that part of the cost/benefit analysis was in corporate officers taking outside factors into account like moral beliefs and the legitimacy of law in conjunction with traditional rational choice variables (e.g. greater profits and risk of detection).

Other writers used a rational choice rationale as the basis for their theory. For instance,

Benson and Simpson (2009) argued that rational actors operate in structures that give the potential offender legitimate access to their target, thus providing the opportunity to engage in white-collar crime, which spurs potential offenders to make their decisions within a rational choice framework. Similarly, Gibbs, Cassidy, and Rivers (2013) used a routine activities theory to explain the opportunity for white-collar crime in carbon markets––rational choice theory serves as the base perspective for decision making in routine activities theory (Stretesky, Long,

& Lynch, 2014: 8).

Finally, critical criminologists attempt to explain white-collar crime using theories based in a political economy perspective. For instance, Calavita, Pontell and Tillman (1993) argued there was a deep connection between the state and savings and loan banks that inevitably led to a major economic collapse. In particular Calavita et al. (1993) argued that the state deregulated the entire industry at the command of corporate leaders, allowing for acts that were once illegal to become legal. Further, Calavita et al. asserted that the state de-funded regulatory agencies so that the corrupt acts of corporate actors went virtually undetected.

Regarding environmental crime Stretesky, Long, and Lynch (2014) argued the state is complacent in crimes against the environment. Namely, Stretesky et al. believed the state continually de-regulates industry, allowing for once illegal activities to become legal, and inadequately enforces environmental laws out of fear that monetary sanctions against 37 corporations will have a negative impact on a growing economy. Thus corporations and individuals break the law because the social structure effectively encourages it through a lack of enforcement of laws and status quo regulatory efforts.

Within the environmental law world there is sufficient evidence to suggest the perspectives argued by Calavita et al. (1993) and Stretesky et al. (2014) may be correct. For instance, there was empirical data suggesting that major corporate polluters contribute about

98% more to political campaigns of democrats and republics than environmental protection groups. There is great fear that politicians will not regulate or punish major polluters because of their political donations. This may be the case in regards to the Waste Technologies Industries

(“WTI”) toxic waste incinerator, which released over 3.5 million pounds of toxic pollutants into the air each year, which could possibly be linked to noticeable health effects likely tied to WTI’s operations in East Liverpool, Ohio (Collins, 2010: 13).

Prior to the Clinton/Gore presidency, Al Gore promised residents in East Liverpool that once in office, he and Clinton would close down the WTI incinerator because it had over 130 permit violations despite being a mere 400 yards from a school and 320 feet from the first home.

After eight years in office the Clinton/Gore presidency did not close down WTI. As it turned out,

Jackson Stephens, who was the chairman of Stephens, Inc., the largest U.S> investment bank not on Wall Street, was the biggest financial backer of the Clinton/Gore campaigns (Collins, 2010:

13; Truell & Gurwin, 1992).

Another example comes from the Overland Park neighborhood in Denver, Colorado. The

Shattuck Chemical Corporation has a location in Denver that is listed as a superfund site

(Collins, 2010: 21). The EPA ordered the corporation to remove tons of radioactive waste located at the site (Collins, 2010: 21). The removal of radioactive waste is incredibly costly and 38

after complaints from Shattuck Chemical, the EPA allowed for a mound-and-cap method of disposing of the toxic waste (Collins, 2010: 21). The mound and cap method consists of placing all toxic waste within a concrete block, sealing it with a clay cap, and burying it underground

(Collins, 2010: 21). This may be a more affordable method, but the EPA Ombudsman’s office also asserts that it is potentially faulty and dangerous (Collins, 2010: 21). Interestingly, Shattuck

Chemical Company was owned by Citigroup a company that was listed on EPA director

Christine Todd Whitman’s public finance disclosure form that showed she and her husband owned over $250,000 of Citigroup stock (Collins, 2010: 21). Additionally, Whitman’s husband worked for Citigroup for 15 years and then left to work for Sycamore Ventures, in which

Citigroup was a primary investor (Collins, 2010: 21).

In both the WTI and Shattuck Chemical examples there are clear conflicts of interest between politicians, regulators, and private companies. It might be true that the connections between Clinton and Gore and WTI led to no action being taken to close down a toxic incinerator that continually violated its permit. It may also be true that Christine Whitman’s connections to Shattuck Chemical through Citigroup influenced her decision to allow for a less expensive and possibly unreliable method for disposing of toxic waste. However, there was no actual proof offered to show impropriety. Calavita et al. (1993) and Stretesky et al. (2014) asserted alternative perspectives for consideration; however, Calavita et al. and Stretesky et al. presumed that improper connections between the public and private sectors influence whether or not companies are prosecuted for violating laws. Thus, there is a need in social science to find a more concrete explanation supported by empirical evidence for the variance in punishment levied against corporations for violating laws.

Law and Society Scholarship

Most law and society scholars ask the question Why do corporations obey the law? It is essentially the same question of Why do corporations break the law that is most often asked by criminologists. There is merit in discussing the law and society scholarship separately from the previous criminological work because the two branches have pursued the answer to their questions slightly differently. Particular to law and society scholarship, these studies attempted to identify what legal and extra-legal factors are most important in motivating corporations to comply with written law. There were two primary models used in the white-collar crime and corporate crime literature to determine why corporations obey the law (Simpson, Gibbs, Rorie,

Slocum, & Cohen, 2013). One model was deterrence-based and the second was cooperative- based. Both models assumed corporations are run at the behest of individuals who are rational actors, thus assuming that corporate decision making is rational (Benson & Simpson, 2009;

Simpson, 2002). Rational actors act in self-interest and make decisions about whether to engage in deviant or conventional behavior according to a cost/benefit analysis (Paternoser & Simpson,

1993; Shover and Hochstetler, 2006).

Deterrence Based Models

This model is popular with policy makers and serves as the primary rationale behind the use of administrative, civil, and criminal punishments for law violation (Tyler, 1990: 3).

Deterrence-based models argue that the fear of legal sanctions renders people compliant with the law (Gibbs, 1975). Thus people may change their behavior to comply with law when the penalties outweigh the benefits of violation (Simpson, 2002: 9; Tyler, 1990: 3).

The formal legal system is the central element in the deterrence process (Simpson, 2002:

9). Fear of actions being detected followed by an arrest and punishment resulting from a 40

conviction serves as the core of these models (Gibbs, 1975). Punishment can be delved out through administrative, civil, or criminal systems. Administrative punishment is non-judicial. In other words, regulatory agencies like the EPA and the Securities and the Exchange Commission can fine offenders without resorting to the filing of a civil lawsuit or criminal prosecution (Frank

& Lombness, 1988: 24-25). If regulatory agencies do not resort to administrative punishment, they can refer their grievances against offenders to the Department of Justice, where a civil action or criminal prosecution may be pursued (Lazarus, 1996). Thus compliance with law is achieved through aggressive enforcement (e.g. regular, thorough, and complete inspections) and credible punishment for law violation (Gray & Deily, 1996; Shimshack & Ward, 2005).

For the most part deterrence-based models have virtually no empirical support (Simpson,

2002: 153). For example, York (2004: 358) argued that broader goals of economic growth trump the goals of deterrence because fines are simply too small and inconsistently meted out to change behavior. If fines are going to change behavior, they need to be consistently levied and large enough to actually change behavior, rather than become a mere cost of doing business. Further,

Stretesky, Long, and Lynch (2013) found that even when polluters are fined for excessive pollution, the fines and follow up inspections are rare and do not make corporations more compliant with environmental laws. Thornton, Gunningham, and Kagan (2005) argue that most companies are already in compliance with environmental law; thus, if there is a deterrent effect, it is that the threat of legal punishment serves to reassure companies compliance with the law is sound policy.

Cooperative Based Models

The fundamental goal of cooperative models of punishment is to work with corporations to bring them into compliance with the law rather than simply administer a punishment for 41

violating laws (Garrett, 2013).9 Proponents of cooperative-based models argued that strict deterrence has not worked. Regarding corporations, the argument was that the number of punishments (e.g. fines and prison time) for violations has increased, yet there is no evidence of increased compliance with the law because there is little effort to detect white-collar and corporate crimes. When they are discovered, the fines are lenient and inconsistently imposed to have any deterrent effect (Snider, 1990). Cooperative models do not completely discard administrative, civil, and criminal punishments. Rather, these punishments are one tool among many to increase compliance with the law.

One such additional tool available in a cooperative-based model is self-regulation, which means companies police themselves and voluntarily turn themselves in when the law is violated in exchange for more lenient punishments (Braithwaite, 1982). Another tool available is informal social control, which essentially argues the shame, embarrassment, or impact (e.g. loss of revenue or future job prospects) one will face if caught and punished for a criminal act deters criminal acts more than formal punishments (Fisse & Braithwaite, 1983).

There is some empirical support for the assertion that cooperative-based models ensure compliance with law by corporations. Simpson (2002: 116) used factorial surveys (e.g. instruments that combine experimentally manipulated summaries with survey techniques) that were administered to current corporate executives and MBA students to test the efficacy of deterrence and cooperation based models. Overall Simpson (2002: 151) found that people

9 For instance, companies that violate environmental laws are given a small fine, or no fine at all, and in exchange have to create a compliance plan that will eventually bring the corporation into compliance with law. Usually, this means hiring compliance managers or independent monitors to create policies that will bring the company into compliance with law. 42

running corporations were not likely to adjust their behaviors based upon formal legal threats even though there was a fear of detection and the consequences of law violation.

The biggest deterrent to corporate crime was potential informal sanctions, specifically fear of family condemnation and inability to secure future employment after the illegal act has been detected (Simpson, 2002: 152). Further, corporate managers who morally believe in the law are more likely to conform to law without the threat of formal sanctions. These findings do not invalidate the deterrence based models; rather, the results indicate formal sanctions are only one part of a whole system to ensure corporate compliance with law. Since Simpson’s (2002) study, others have found similar results regarding informal sanctions. For instance, Wu and Wirkkala

(2009) found that personal values and beliefs of upper management toward environmental stewardship increases the likelihood of compliance with law despite the threat of formal sanctions. Further, Gunningham, Thorton, and Kagan (2005) found that most companies within the chemical and electroplating industry (companies surveyed are in Ohio and Washington State only) were in compliance with environmental laws because they sought to protect their social standing within a community and believed that they have a moral duty to take care of the environment.

Recently, Garrett (2013) demonstrated that rather than relying only upon fines to gain corporate compliance with law, legal officials are implementing new penalties and agreements, the goal of which is to rehabilitate a firm’s culture so it will voluntarily comply with law

(Garrett, 2013: 47). Rather than be prosecuted and subsequently receive a fine, a corporation will be asked to hire internal, independent moderators to ensure compliance with law; in exchange, criminal charges against the corporate defendant are not sought in court. This process is called a

deferred prosecution.10 Further, companies may be asked to create compliance programs requiring managers and employees to undergo training to ensure they understand what laws govern their business and that they are followed (Garrett, 2013: 175). However, Garrett (2013:

64) noted that these deferred prosecution agreements are being reserved for large corporations, while convictions of smaller and non-public corporations remain relatively stable. However, corporations prosecuted for environmental crimes rarely receive a deferred prosecution agreement (Garret, 2013: 65).

Within cooperation-based models, the enforced self-regulation component has been tested in the environmental law context. There was some evidence to suggest corporations do self-report violations of law. Toffel and Short (2007) argued that under the Clean Air Act, corporations that engage in self-policing substantially improve compliance records and are less likely to be subject to multiple future inspections. However, Stretesky and Gabriel (2005) found that corporations working in industries and regions with high inspection rates are more likely to self-report violations, which in turn suggested that cooperative models have some elements of deterrence in them. Further, Press (2007) found that self-policing policies only sometimes deliver on the promise that firms will comply with environmental laws; effectiveness varies with industry (Press, 2007).

Not surprisingly then, there were some studies that called into question the effectiveness of self-policing and audit policies. Stretesky (2006) found that the threat of inspections and enforcement do not increase corporate self-policing. Further, Stretesky (2006) found that large companies are most likely to self-report when law violations involve lesser environmental

10 Refer back to footnote 9. Sometimes independent monitors and compliance officers will be hired even when a company is fined, or receives no fine at all. Not every company will get a deferred prosecution. 44

hazards. Finally, Stretesky and Lynch (2009) found that companies that reported violations under the EPA’s self-policing and audit policy continue to have the same or increased chemical emission rates as those companies that do not self-report violations.

How are Corporations Punished?

When individual white collar offenders are punished, a combination of probation, fines, and prison time can be used. As with corporations, no two white-collar defendants receive the same punishment. A common belief was that people of high social status would receive less severe punishments (e.g. less prison time) for crimes of fraud and embezzlement, but interestingly some scholars found the opposite to be true. For instance, Wheeler, Weisburd, and

Bode (1982) found that when controlling for crime seriousness (e.g. number of victims, amount of money stolen, and complexity of the scheme), white-collar defendants with high social status are more likely to be sent to prison and received longer sentences than offenders that committed similar crimes and were of lower social status. In a follow up study Weisburd, Waring, and

Wheeler (1990) found that even when controlling for crime seriousness (e.g. number of victims, amount of money stolen, complexity of scheme, and spread of illegality) and class status, offenders of high status were still more likely to go to prison and received longer sentences for white-collar crime. Similarly, Nagel and Hagen (1982) found highly educated offenders convicted of white-collar offences received longer prison sentences when convicted.

Not all scholars agreed with this finding. In particular Benson and Walker (1988) analyzed one federal courthouse during 1970 and found white-collar offenders of high socioeconomic status were no more likely than low socioeconomic offenders to go to prison or receive longer prison sentences. However, Benson and Walker’s (1988) study was severely flawed because it only looked at one courthouse and one year where the aforementioned studies 45

cover multiple courthouses and years. The Wheeler et al. (1982), Nagel and Hagen (1982), and

Weisburd et al. (1990) studies raised an important issue: When corporations violate the law, would corporate actors of higher socioeconomic status be punished more severely, too?

When corporations violate the law punishments primarily take the form of monetary fines

(Stretesky, 2006). There was some evidence to suggest that corporations with large amounts of economic and political power are less likely to be punished for law violation and more likely to receive lenient punishments (Hagan & Parker, 1985). Unfortunately, this work was never expanded upon. The majority of studies looking at how corporations are punished used deterrence models and argued punishments are crafted to be a general and specific deterrent.

Therefore, there remains a need to use the social characteristics of corporations to explain why they receive differing punishments.

The deterrence model of compliance mandates that punishments be severe enough to deter corporations and individuals from committing future violations of law and prevent others from committing similar acts. To do this, the fines must be optimal (Becker, 1968). An optimal fine is one that increases with the severity of harm caused, but is not so high it creates an undue burden and financial harm to the offender yet not too low so the fine is treated merely as a cost of doing business (Cohen, 1996). It was argued by law and economics scholars that fines against corporations for violation of law are usually optimal (Cohen, 1996). For example, Piquero and

Davis (2004) found that fines assessed against corporations are strongly correlated with economic solvency. Thus fines will be high so long as the corporation can pay it (Piquero &

Davis, 2004). Simpson and Koper (1992) found that there is some evidence to suggest that past guilty verdicts and increasing severity in punishments may increase the likelihood corporations will not engage in future criminal activity. However, Simpson and Koper (1992) pointed out that 46

informal norms within an industry are much stronger in promoting future compliance with law.

Moreover, other scholars were wary of deterrence models and the use of optimal fines because corporations can receive fine modifications or have their fines eliminated with a showing of economic instability (Green & Bodapati, 1999). While this could be viewed as a court’s attempt to craft an optimal penalty, an equally plausible explanation is that modification or elimination of fines is really about keeping a corporation in business and people employed, thus making deterrence at best a secondary goal that violates the principles of an optimal fine theory. A deterrent effect and the goal of not creating undue burdens for a company are of equal importance––one goal should not outweigh the other (Green & Bodapati, 1999).

Law and Economics and Corporate Environmental Crime

Cohen (1992) was the first study to analyze corporate environmental crime. Using an optimal fines model, Cohen (1992) analyzed the total number of corporate environmental crime prosecutions between the years 1983 and 1990 (n=709). The main findings were that with each passing year the total number of corporations prosecuted for environmental crimes and the aggregate of fines collected increases (Cohen, 1992: 1087). This finding was similar to prior findings regarding fines against corporations for all white collar violations (Cohen, 1990; Cohen,

1991). Cohen (1992: 1090) also demonstrated that violations of certain environmental laws–– specifically, the CWA and Hazardous Waste Act––results in larger fines than violations of other environmental laws. These findings were interesting but did not explain any variance that existed between assessed fines because Cohen never once explained when a fine was or was not an optimum fine; he seemed to presume the fines are optimal.

Brickey (2001) analyzed the total number of prosecutions brought under the Resource

Conservation and Recovery Act (RCRA) for the 10-year time period between 1983 and 1992 47

(n=140) and the total amount of fines collected for each fiscal year (Brickey, 2001: 1102-1133).

Though not her main finding, Brickey (2001) asserted that with each passing year the number of individuals and corporations prosecuted plus the aggregate fines collected increases. Like Cohen

(1992), Brickly offered no real explanation in this study; instead Brickey (2001: 1118) argued that most prosecutions occur against defendants that are operating without a permit which suggests prosecutors are interested in going after criminals operating as “rogue corporations” outside the permitting system. Unfortunately, Brickey did not provide any indication that defendants operating without a permit are fined more than those with one. Thus, a full explanation as to why offenders receive different fines is still lacking.

O’Hare (2004: 137) stated that the empirical data he presented was descriptive, not explanatory. Like Cohen (1992) and Brickey (2001), O’Hare (2004) showed that as each year passes the number of individuals and corporations prosecuted for violations of environmental criminal laws increase. Moreover, fines and prison sentences increase for both corporate and individual offenders (O’Hare, 2004). Using completed prosecutions of individuals and corporations between the years of 1996 and 2001 (n = 663), O’Hare (2004) argued that implicit with the increase of prosecutions, fines, and prison sentences, it is highly probable that the use of sentencing guidelines when determining the sentence of a corporation leads to low-culpability offenders receiving overly harsh penalties in violation of the optimum penalty principle.

However, he never actually demonstrated that low culpability offenders actually receive overly harsh penalties violating the optimum penalty principle, he only assumed it.

The overarching problem with the aforementioned studies in this subsection was that there is no explanation provided in regards to whether or not a fine is optimal. Cohen (1992) merely assumed the fines meted out were optimal. O’Hare (2004) made a similar assumption 48

because he argued that it is likely fines are too burdensome for low culpability offenders––but he never actually demonstrated it. Brickey (2001) does not try to explain why corporations receive the fines they do; she simply stated that as the years pass the number of prosecutions and aggregate fines increase. It may be that even if the fines assessed are not optimal, they may carry a deterrent effect. Nevertheless, based upon the studies by Brickey (2001), Cohen (1992), and

O’Hare (2004), there was not enough information to know whether a fine is optimal or not.

Environmental Justice Studies

Deterrence models have been used extensively when explaining white-collar crime generally and environmental crimes specifically. While deterrence models remain popular, theories of environmental justice have also been used to analyze punishments of corporations for violation of environmental law. The EPA defined environmental justice in the following manner:

Environmental Justice is the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies. EPA has this goal for all communities and persons across this Nation. It will be achieved when everyone enjoys the same degree of protection from environmental and health hazards and equal access to the decision-making process to have a healthy environment in which to live, learn, and work.

Here, the idea is that the enforcement of laws at both the inspection and penalty phases ought to take environmental justice factors into consideration. Regarding penalties for violations of environmental law, fines should be higher in communities where the residents are primarily of low socioeconomic status and racial minorities because the majority of pollution causing facilities are located in poor and minority communities (Taylor, 2014). Thus the higher fines are an attempt to bring some semblance of equality in environmental quality. The logic here is that if poor and minority communities are to have the highest concentration of polluting facilities and

therefore more pollution, then violation of environmental laws will be met with harsher punishment.

There were only a small number of studies that drew upon an environmental justice framework to analyze legal outcomes for corporations that violate environmental laws. These studies investigated cases handled in state court or federal court and focused on civil and administrative law outcomes. Only Greife, Stretesky, O’Conner-Shelly, and Pogrebin (2015) analyzed criminal law outcomes at the federal level.

At the state level, Mennis (2005) found that air-polluting facilities in New Jersey located in areas with high percentages of minorities received fines that were lower than those received by facilities located in predominantly white areas. This is the only study that analyzed environmental justice concerns at the state level. Unfortunately, this study did not control for factors such as offense severity and company size. Moreover, Mennis (2005) only examined facilities that were regulated by the Clean Air Act and had active permits; therefore, it is unknown whether the findings would remain consistent if other types of environmental hazards

(e.g. CWA or RCRA violations) were analyzed or non-permitted facilities that were prosecuted were included in the analysis.

The National Law Journal (hereinafter NLJ) published a study examining the fines assessed against corporations for civil environmental violations and how those fines varied in size based upon how many minorities (e.g. Blacks, Hispanics, and Asians) lived near a hazard site (Lavelle & Coyle, 1992). The NLJ findings claimed that the EPA and the courts engaged in a pattern of discriminatory practices; more specifically, as the number of minorities increased around a hazard site, the size of the fine assessed against a corporation would decrease. In particular, the NLJ articles claimed that penalties in communities predominantly occupied by 50

whites were 46% higher than penalties in zip codes with minority residents ($153,067 versus

$105,028), and penalties in zip codes with high-income residents were 53% higher than penalties in zip codes with low-income residents ($146,993 versus $95,664).

Ringquist’s (1998) follow up to the NLJ study adhered to the methods as much as possible in an effort to replicate NLJ’s findings; however, he improved on the NLJ study by controlling for judicial characteristics (e.g. Republican or Democratic judge) and corporate status

(e.g. if corporation is a fortune 500 company or not), among other extra-legal factors. Ringquist concluded that there was no negative statistically significant relationship between the percentage of minority residents, income, and the civil fines assessed against corporations for environmental hazards (Ringquist, 1998: 1162).

Following Ringquist (1998), Atlas (2001) set out to create the definitive study analyzing civil law fines in federal court from an environmental justice perspective. Atlas (2001) improved methodologically on Ringquist’s (1998) study in a number of ways. First, Atlas (2001: 644-648) eliminated the judicial characteristics variable because he correctly noted that most cases end in settlement agreements and judges have no real influence in how the parties settle their cases.

Second, Atlas (2001: 653) dropped the zip code boundary when measuring the number of poor and minority individuals that live near a hazard site and instead used a one-mile concentric ring around the location where the environmental harm occurred. The reason for this change was that zip code boundaries change for a variety of reasons like redistricting and re-zoning but nobody actually moves from their homes. Thus a zip code measure one year can yield a demographic picture that is different the next year simply because the boundaries were re-drawn, not because the population actually changed. A third improvement was that Atlas (2001: 657-658) attempted to control for offense severity by breaking cases up and determining whether or not the law 51

violation was for a permit violation or not. The presumption was corporations that do not have a permit would be punished more harshly. Ultimately, Atlas (2001: 676) found that there was no negative statistically significant relationship between race, income, and monetary penalties assessed against corporations for environmental hazards. Atlas (2001: 677) goes on to argue, but does not actually empirically demonstrate, that the facts of a case best predict the outcome of legal actions against corporations for violating environmental law; in particular, that the more severe the environmental hazard, the greater the fine would be.11

Following Atlas (2001), Lynch, Stretesky, and Burns (2003) used an environmental justice framework to analyze penalties assessed against petroleum refineries in Florida. Lynch et al. (2003) found that the racial, ethnic, and income characteristics (excluding Hispanic populations) of census tracts surrounding petroleum refineries were not related to assessed civil penalties. Specifically, Lynch et al. (2003) did find petroleum refineries in Florida located in zip codes primarily made up of low-income Hispanics received smaller civil penalties than high- income Hispanic areas. Following their 2003 study, Lynch, Stretesky, and Burns (2004) found the mean penalties against petroleum refineries all across the country––not just in Florida––are lower in Black and low-income census tracts when controlling for EPA regions.

Finally, Greife, Stretesky, O’Conner-Shelly, and Pogrebin (2015) were the first to use an environmental justice framework to analyze federal criminal prosecutions against corporations between the years 2005 and 2010 (n=121). Greife et al. used the one-mile concentric ring

11 In support of his argument Atlas (2001: 677) found that corporations that violate environmental laws without a permit receive the highest fines. However, it is certainly possible that a company polluting with a permit may actually cause more harm to the environment than one without a permit. Thus, what Atlas (2001) actually found is certain types of violations are punished more harshly than others, but he is unable to actually explain why.

measure to determine the number of poor and minority individuals living near an environmental hazard. However, Greife et al. improved upon the hazard severity measures found in Atlas

(2001) by also looking at whether or not the crime is a felony or misdemeanor, if a death (human or animal) occurred, and the number of aggravating circumstances found in each individual prosecution. Like Atlas (2001), Greife et al.(2015) found that fines against corporations are not related to the number of poor and minority individuals living within one-mile of a hazard site, but that the most severe crimes attract the largest monetary penalties.

Conclusion

This dissertation’s conceptual focus is on corporate environmental crime. Past studies with a similar research question used an optimal fine theory or environmental justice orientation–

–the findings tended to provide weak or mixed support for both theoretical perspectives.

Regarding optimal penalties, prior studies did not actually show fines were optimal, this fact was just inferred.

Due to limitations in my own dataset, if I were to use an optimal fines theory to analyze the outcomes of prosecutions against corporations committing environmental crime, the same inferences would have to be made. In particular, optimum fine theory states the fine should deter future violations of environmental law. This information is not impossible to collect information of corporate compliance with law after a fine. However, due to current conditions regarding inspections of corporations, it is just not possible for inspectors to go to every corporation fined and determine if they have begun to comply and remain in compliance with environmental law

(Esworthy, 2014; GAO, 2009a; GAO, 2009b). An optimal fine analysis would be limited to a very small number of cases; the purpose of this dissertation was to examine a large number of

cases and make broad generalizations. Therefore, an optimal fine analysis is not appropriate for this dissertation.

Another problem with optimal fine theories is they seem to ignore the fact some criminal acts are committed negligently. Deterrence models presume behavior can change because people are rational actors; thus they act with intentionality. However, some environmental criminal laws, like 33 USC § 1319(c)(1)(A), allow for prosecution even when the crime is an accident. In other words, prosecutions can occur even if the corporation acts negligently rather than intentionally. It was argued by many scholars that optimal fine theories cannot explain outcomes in negligence cases for a variety of reasons. One reason was that judges (including Richard

Posner and Learned Hand – the two judges who created optimal fine theory) rarely, if ever, use an optimal fine approach to decide negligence cases; therefore, there is no empirical basis for the theory (Green, 1997; Wright, 2003a; Wright, 2003b; Zipursky, 2007). Further, other scholars argued that in reality there are just some acts that cannot be deterred because individuals and corporations do not respond to fines in negligence cases the same way as intentional tort/criminal cases because negligent acts are accidents (Bayern, 2010; Schwartz, 1994). I agree with the critiques regarding optimal fine theory’s shortcomings in explaining the outcome of negligence cases. This dissertation attempted to explain both intentional and negligent acts so an optimal fine theory approach was not appropriate.

Regarding environmental justice perspectives, the evidence was mixed. Some studies found support for the claim that fines will be lower in communities with a high percentage of low socioeconomic status and minority individuals – others have not. The studies that found support for environmental justice claims were narrowly focused on a particular industry or specific state (Lynch et al., 2003, 2004). The studies that did not focus on a specific industry or 54

state found no support for environmental justice claims (Atlas, 2001; Greife et al., 2015;

Rindquist, 1998), which means a new theoretical orientation is needed to explain the variance found in punishments of corporations for violation of environmental laws.

This dissertation seeks to go beyond the law and economics and environmental justice literature to provide a deeper explanation regarding the variance seen in punishment of corporations that violate federal environmental criminal and civil laws. To do this, I use Black’s

(2011) theory of moral time. Unlike optimal fine theories of punishment, Black’s theory allows for the analysis of negligent acts, not just intentional actions. Further, Black’s theory allows me to incorporate environmental justice concerns, but not be too narrowly focused on them. Finally,

Black’s theory allows for the use of a typographical conceptualization of corporate environmental crime because the analysis was primarily concerned with the social status of a defendant as determining what the fines would be, not the manner in which the criminal act was committed (e.g. guile or deceit). Black’s theory is appropriate because it has never been applied to explaining corporate environmental crime prosecutions and was a general theory allowing for a full and complete examination of case outcomes. Black’s theory is discussed in the next chapter.

CHAPTER 4: THEORETICAL FRAMEWORK

In this chapter I introduce and discuss Black’s (2011) theory of moral time. This dissertation uses the theory of moral time to explain the handling of prosecutions against corporations that violated environmental criminal statutes. To date Black’s theory of social time has been applied to forms of violence such as genocide, family honor killing, suicide, terrorism, and online hostility towards death row inmates (Campbell, 2013; Cooney, 2014; Cooney &

Bigman, 2015; Manning, 2015; Phillips & Cooney, 2015). Applying Black’s theory to corporate environmental crime serves as an important theoretical expansion because it moves the paradigm beyond inter- and intra-personal conflicts to conflict that encompasses large numbers of victims along with economic and political considerations.

Before moving on and fully laying out Black’s (2011) theory of moral time, I discuss

Black’s prior theoretical perspectives. An understanding of Black’s past work may help solidify the logic used in the theory of moral time. After discussing Black’s prior works, I then lay out the theory of moral time and demonstrate that polluting in violation of federal environmental criminal statutes are actions relevant to the theory of moral time.

Pure Sociology

Within the field of sociology there is a multitude of paradigms scholars use as a framework for inquiry. When it comes to the study of environmental crime and law, scholars have put forth the notion that a green criminology paradigm be the primary framework for scientific inquiry (Lynch & Stretesky, 2003; South, 1998). The basis of the green criminology framework is the idea that crimes are social constructions generally, and specifically an

environmental crime is merely a reflection of a political process that is influenced by a multitude of other factors related to the political-economy (Lynch & Stretesky, 2003: 219). Therefore, scholars studying environmental crime and law should use a green criminology paradigm as the framework for inquiry if their focus is on how the political-economy shapes the creation of environmental crimes and punishment under these laws (Lynch & Stretesky, 2003; Stretesky et al., 2013). Using a green criminology paradigm necessarily forces researchers to focus upon the psychological state of human actors, their motivations, and to focus upon people or organizations as the unit of analysis.

Black (1976, 1995, 1998, and 2000) introduced, and expanded upon, a theoretical paradigm known as pure sociology. In particular, Black (2000: 344) argued that the majority of sociological theories and paradigms are psychological, teleological, and centered on ‘persons’ as the unit of analysis. Black (2000: 345) argued that past sociological theories, because they are too psychological, teleological, and human-centered, are not really sociological at all12. For instance, Black (1995, 2000) generally argues that it is extremely difficult, if not impossible, to measure emotional states (e.g. psychology) and personal motivations (e.g. teleology), which are necessary components of many sociological theories. If emotional states are not measurable then the theories relying upon such constructs are not testable and therefore unfalsifiable (Black,

1995: 831-832). The same goes for theories that try to understand a person’s motivation for acting. Motivations, for Black (1995: 862-863) are unobservable and often force researchers to adopt value-laden definitions about behavior therefore making theories relying upon understanding motivation untestable/unfalsifiable and more metaphysical rather than factual.

12 In fact, Black (1995: 862) argues that theories relying upon teleological inquiry and the like constitute bad science because psychological states and motivations are not directly observable or quantifiable. Theories relying upon non- observable phenomena are then, in Black’s judgment, not testable, general, empirically valid, original or simple and should be left out (Black, 1995). 57

Finally, Black’s pure sociology removed people as the unit of analysis and replaces them instead with ‘social life’ (Black, 1995: 859). By removing people as the unit of analysis, Black (1995:

860) argues he is giving sociologists new conceptions of human behavior, a different logic, and new possibilities for exploration. Black’s paradigm of pure sociology then is a way to move past prior paradigms like green criminology which tend to rely upon understanding psychological states, motivations, while using a people-centered focus, and direct inquiry only upon sociological variables.

Black’s (1976) book The Behavior of Law is the first major publication introducing the pure sociological paradigm where the unit of analysis is legal conflict. Black argued that his perspective can predict and explain the handling of legal conflicts at the case level simply by understanding the sociological makeup of the conflict–– also known as the social geometry of a conflict – thus there is no need for any inquiry into the subjective state of an actor or their desires/motivations. In particular, Black argued that the handling of a legal conflict is generally understood by determining its location, direction, and distance in social space. Black’s theory argued that law, which is defined as governmental social control, varies based upon the location of disputants within five dimensions of social space: vertical, horizontal, cultural, relational, and normative.13 Furthermore, the quantity and severity of law increases relatively to the social distance between disputants and the direction it travels (Black, 1976).

To help illuminate the idea of Black’s (1976: 21) theory, consider the following prediction: downward law is greater than upward law. In particular, this means that a legal

13 Though not discussed in detail with this dissertation each dimension is roughly measured in the following ways: vertical (e.g. wealth), horizontal (e.g. involvement in other people’s lives), cultural (e.g. education levels, race, sexual orientation, religious beliefs, etc...), organizational (e.g. groups and organizations such as the same family, membership in the PTA, NRA, DNC/RNC, etc...) and normative (e.g. respectability – people who are respectable do not have criminal records or engage in other deviant activities). 58

complaint directed downward in social space (e.g. rich person against a poor person) attracts greater quantities of law (more punishment) than the opposite. Thus, a rich man who kills a poor man will receive less law than when a poor man kills a rich man. By less law, Black (1976) argued that the rich man killing a poor man will be less likely to be prosecuted; if prosecuted less likely to go to prison; if sent to prison less likely to be imprisoned for a great length of time. On the other hand, a poor man killing a rich one will be more likely to be prosecuted, go to prison, and be imprisoned for a long period of time. If Black’s prediction is correct, and there is ample evidence to suggest this is so, these patterns are explained by the vertical direction the acts travel in social space (Cooney, 2009).

While there are other aspects to Black’s (1976) theory, it is unnecessary to discuss them at length. It is important to understand that Black (1976, 1995, 2000) claims his theory predicts and explains the handling of legal conflict. However, the reality is very few conflicts actually turn into lawsuits. In later years, Black (1998) developed theories predicting and explaining the handling of non-legal conflicts. Staying consistent with his previous perspective, Black’s theories regarding non-legal conflict management were determined by a conflict’s location, direction, and distance in social space. For example, Black’s perspectives can predict when a person will rely upon self-help rather than law to resolve a conflict and when third parties will intervene on behalf of a principle to end conflicts (Black, 1993, 1998: Chapter 7, 2004; Phillips

& Cooney, 2005).

For all its promise, the pure sociological paradigm was missing something. To be precise, pure sociology could not adequately explain the cause of legal conflicts and why some illegal conduct attracted more law or social control than others. For instance, Black (1976) did not provide a way of explaining or predicting why homicide would attract more law (e.g. longer 59

prison sentences) than an assault or theft. For Black (1979), to call homicide more severe than assault or theft necessarily invokes a subjective statement about the severity of one act over another. Traditionally, homicide is considered more severe, thus attracting more social control than assault or theft because of the seriousness of the act (e.g. killing is more severe than punching somebody or stealing from them).

The theory of moral time (Black, 2011) potentially remedies this shortcoming found in the pure sociology paradigm. The severity of an act is measured by how rapidly and great a movement of social time, which is discussed in the next section of this chapter, is rather than traditionally subjective conceptions. In Black’s (2011) framework, a homicide may actually be considered less severe than an assault or robbery because the movement of social time caused by a killing could in fact be less than assaulting or robbing another person. Conduct, according to

Black (2011), is capable of being objectively measured.

Taking everything in this section together, pure sociology predicts and explains the handling of legal and non-legal conflicts by determining a conflict’s location within social space.

Determining a conflict’s location within social space is merely taking a snapshot of a particular moment––a static state (Black, 2011; Phillips & Cooney, 2015). The theory of moral time predicts and explains the origins of conflict and why some conflicts are more serious than others by looking at the dynamic aspects of social geometry. The dynamic aspect of social geometry is called social time (Black, 2011).

Social Time

Conflict is the clash of right and wrong (Black, 1993, 1998, 2011). The cause of conflict is a movement of social time (Black, 2011: 3). Just as physical time changes in physical space

(e.g. moon orbiting around the earth), social time is a change in one or more dimensions of social 60

space. The action of the moon orbiting the earth is dynamic––a picture showing the moon and earth together is static. Social time, then, is the dynamic aspect of social space. Black’s (1976,

1995, 1998, 2000) prior perspectives of social geometry provide only a snapshot of social space at any one time––social time is the movement of social space (Black, 2011; Phillips & Cooney,

2015).

Under Black’s (2011) conception, social time is divided into three categories: vertical time, cultural time, and relational time. Vertical time is an increase or decrease in inequality

(Black, 2011: 2). An example of a movement in vertical time is when a worker is fired from a job, thus experiencing a sudden loss of income (Phillips & Cooney, 2015: 732). The firing causes the individual to experience a decrease in vertical status because of the lost income whereas a promotion or raise constitutes an increase in vertical status due to an increase in salary. Each movement, whether upward or downward, is a movement of vertical time. The larger the movement of vertical time (e.g. the distance an upward or downward movement travels), the more likely conflict will occur and the more severe it will be. Cultural time is an increase or decrease in diversity (Black, 2011: 2). An example of an increase in cultural diversity is when two or more ethnically distinct groups (e.g. different languages, skin color, religious beliefs, etc.) interact (Phillips & Cooney, 2015: 733). Finally, relational time is an increase or decrease in intimacy (Black, 2011: 2). An example of an increase in physical intimacy is rape. The act of raping someone is an increase in intimacy because it is unwanted sexual intercourse (Black,

2011: 4). Moreover, a stranger rape has a greater and more rapid increase of relational time (e.g. intimacy) than a rape by an intimate because it traverses a larger amount of relational distance than an intimate rape (Black, 2011: 18).

It is important to understand how Black’s (2011) theory defines conflict severity. For instance, take the crime of rape as causing a change in relational time. What makes one rape more severe than another is the greater and faster a change in relational time that occurs and whether it covers a greater amount of relational distance. At its most basic level, the qualitative nature of the rape is generally irrelevant. Rape is rape; it does not matter if the unwanted penetration is anal, vaginal, or oral. What matters is the change in relational time.

It is worth mentioning that Black (2011: 23) argued the more violent an act is, the greater the movement of social (e.g. relational) time it entails. It could be argued that an anal rape causes the greatest movement of relational time because it is more violent and painful than a vaginal or oral rape. Of course, there needs to be some empirical basis to make such a claim, which to my knowledge none, other than an individual’s logical expectation, exists. Nevertheless, if the empirical basis to say anal rape is more violent and painful than other forms of rape, then it does matter what opening is penetrated.

Considering all three categories together, the theory predicts that the severity of conflict depends upon how rapid and great a shift in social time is (Black, 2011: 3). The more rapid a shift and the greater the increase or decrease within social time, the more severe the conflict will be (Black, 2011: 3). Moreover, some conflicts may affect other parties that were not part of the underlying conflict (Black, 2011: 9). This idea is discussed in further detail in the upcoming section.

Crime, Punishment, and Law as Movements of Social Time

Within the theory of moral time comes the argument that crime, punishment, and law are themselves movements of social time. Crime is a movement of social time (Black, 2011: 4). As already asserted, rape is a movement of social time because it is increase levels of intimacy. 62

Theft is a crime that causes both increases and decreases in vertical time (Black, 2011: 74). For instance, a person who creates and runs a successful Ponzie scheme will see increased incomes–

–albeit through ill-gotten means––and subsequently experience an increase in vertical status.

The movement of vertical time that occurs is called oversuperiority because the perpetrator of the Ponzie scheme acquires more wealth than others. The more money brought in by the perpetrator of a Ponzie scheme, the more their vertical status increases. Regarding victims, a wealthy man who loses 50% of his fortune in a Ponzie scheme will experience a rapid movement in vertical time, which is called overinferiority because of the large loss of wealth. A second wealthy man who loses only 5% of his fortune in the same scheme will experience overinferiority, but it is not a great or rapid movement of vertical time because this second wealthy man did not lose as much as the one who lost 50% of his wealth. Black’s (2011) theory predicts that the conflict between the thief and wealthy man who lost 50% of their fortune will be more severe than between the thief and second wealthy man who only lost 5% because losing half of one’s wealth is a greater and faster change in vertical time than a loss of only 5%.

Black (2011: 7) stated “Just as crime is a movement of social time, so the punishment of a crime is a movement of social time that corresponds to the movement of social time to which it reacts.” Thus punishment, like crime, is a movement of social time. Fines and payments of compensation are movements of social time (Black, 2011: 6). Staying in line with the above stated principle that punishment corresponds to the movement of social time to which it reacts, the theory predicts that larger fines will be assessed for greater and faster changes in social time.

However, large fines will cause future changes of social time. Therefore, severe punishments may cause future changes of social time leading to the possibility of continued and increasingly severe conflicts. 63

However, punishment does not end with those being punished and their disciplinarian.

Punishment can cause movements of social time not just for the individual person being punished, but for their dependents as well. For example, if a father of three is the primary money maker for the family and is sent to prison, not only does the individual father experience punishment, but his three children do, too, because they lose everything that is contributed to their lives (e.g. wages, love, support, mentorship, etc.). A true to life example can be seen with

Bernie Madoff’s imprisonment for running a massive Ponzi scheme. The punishment for Madoff did not end with him alone. Madoff’s wife, Ruth, has since been deprived of significant income and has been forced to completely change her lifestyle; one son Andrew sought isolation in trying to escape the stigma of his father’s acts, and another son Mark committed suicide after his father’s crimes were made public (Safer, 2011).14

Like losing a father, the loss of an employer can cause future conflicts because of the change in social time caused by losing a job. Therefore, punishment does not simply constitute a singular movement of social time; secondary movements may occur as well and potentially lead to future conflict (Black, 2011: 8). For instance, the fraud committed by the Enron Corporation led to the company and many of its top executives facing criminal and civil charges that totaled over $40 billion. The company eventually went bankrupt; all the employees lost their jobs and retirement investments, and continued to feel the negative consequences of the company’s failing

(McLean & Elkind, 2013). In connection with the Enron scandal, the accounting firm Arthur

Andersen, which helped perpetrate Enron’s fraud, was able to stay in business, but went from having over 84,000 employees to 200 due to the significant fines they had to pay, and lost credibility as a reputable accounting firm (Garrett, 2014; McLean & Elkind, 2013). After the

14 Suicide is a conflict that is caused by, and may cause, future movements of social time (Manning, 2015). 64

Enron and Arthur Andersen schemes were exposed, John Baxter, a former Enron employee that made around $35.2 million off of the fraudulent acts, committed suicide rather than face prison time and losing his fortune (McLean & Elkind, 2013). Black (2011) did not argue that all punishments cause movements of social time that lead to future conflict. However, the Madoff and Enron examples suggest large fines and multiple years in prison may lead to future conflicts.

Finally, law can be either a movement of social time or a force that limits it (Black, 2011:

11). Conceptually speaking, law and punishment can be one and the same because legal prohibitions against murder also call for the punishing of it. However, when law is not being used as an instrument to punish, and consequently lead to movements of social time, it may be working to limit movements of social time. Take the prohibition of insider trading, which is codified in 17 CFR § 240.10b5-1. The purpose of this law is to prohibit people from gaining financially based upon material information that cannot be known to others. Take, for instance, pharmaceutical company executives purchasing their own company’s stock prior to the issuance of a new wonder drug that is needed by millions of people. If the executives purchase the stock knowing that the demand for their wonder drug will increase their company’s stock price, but never tell anybody about the release of the wonder drug, then they are trading in stocks based upon information only they have. In other words, the executives are trading on material information (e.g. the release of the wonder drug) that is only known to them because of their position. Legally speaking, 17 CFR § 240.10b5-1 is a way to ensure a free, fair, and open market. Theoretically speaking, 17 CFR § 240.10b5-1 is an attempt to prevent a person from experiencing a rapid and significant increase in vertical status thus limiting the movement of vertical time.

Environmental Crime and Punishment as Social Time

Overuse of natural resources, environmental degradation, and the pollution of the natural environment all may constitute movements of social time. Elements like nickel and tungsten are harvested from mountainsides, and through multiple processes are turned into microchips

(Williams, Ayers, & Heller, 2002). The individuals and companies with the ability to turn natural elements into microchips and sell them at a profit will experience an increase in vertical time.

However, the process of making microchips creates pollution, in particular waste-water, which is water used in a manufacturing or cleaning process that becomes contaminated with elements not natural to it (e.g. lead, copper, cyanide, etc...) (Williams et al., 2002). Waste water is released back into the environment and in some cases could be used for human consumption (e.g. drinking, showering, etc...). Pollution is an increase of intimacy because it involves a corporation or individual’s unwanted intrusion of toxins into our most intimate space––the body. Pollution is an increase of intimacy and a movement in relational time. The more pollution a person is exposed to, the greater the movement of relational time and therefore, more conflict.

Pollution is also a decrease in vertical status for an individual because the body itself is a form of wealth and the most fundamental means of production (Black, 2011:62). People who are subjected to large amounts of pollution are more likely to suffer from adverse health effects

(ranging from respiratory problems to death) and will not be able to work as hard as healthy individuals who do not suffer from the effects of being over-polluted. The more pollution a person is exposed to, the greater the movement of vertical time and increase in the likelihood of more conflict. Therefore, populations subjected to pollution, whether it be drinking contaminated water or breathing in noxious chemicals, experience changes in relational time and vertical time simultaneously. 66

If law is a method to prevent rapid and sudden changes of social time, then there will be laws regulating the use and polluting of the natural environment. The foregoing discussion focuses on water, but can be applied to laws related to air and land use as well. Water is the essential element of all life and the backbone of economic development (Wennersten, 2012).

Thus, water use must be regulated. Water can be found both on the surface and underground and the laws governing each source vary (Glennon, 2002: 12-18). In regards to ground water, most states have adopted the reasonable use doctrine, which essentially means anybody (individual, corporation, or municipality) can pump water from underground aquifers so long as the use of the water is for a beneficial purpose (e.g. economic development, drinking, etc...) and not in conflict with “the wants of the community” (Glennon, 2002: 15, 210, 242). Though in practice it has not been successful, the spirit of the reasonable use doctrine is to ensure no one person receives all the benefit of groundwater pumping at the expense of all others. In theoretical terms then the reasonable use doctrine ensures no rapid and great movements of vertical and relational time.

Laws not only dictate the use of water, they can punish those who use it inappropriately or pollute it. Take 33 USC §§ 1251 et seq, more commonly known as the CWA, as an example.

The stated goal of the CWA is to restore and maintain the chemical, physical, and biological integrity of the Nation’s waters” (CWA § 101(a), 33 USC § 1251(a)). The CWA does not regulate who can or cannot use water and for what purpose; instead, the CWA is meant to determine what pollutants and at what rate they can be introduced or re-introduced back into

America’s waters. Moreover, the CWA has administrative, civil, and criminal penalty schemes that can be used to punish those that pollute or over-pollute in a manner inconsistent with the law

(see generally 33 USC § 1319(a) through (c) and CWA § 309(b) through (g)). As stated, people 67

subjected to pollution experience an increase in relational time. If the CWA, in legal terms, is meant to punish excessive and un-permitted pollution, then in theoretical terms the CWA’s punishment scheme is a method of social control that arises in response to movements of relational time. More simply put, punishment under the CWA is a response to overintimacy and overinferiority with the people being polluted.

As discussed above, fines and payments of compensation are movements of social time – in particular vertical time (Black, 2011: 6). Punishment under the CWA can take the form of fines and possible jail/prison time for individuals. For corporations, punishment under the CWA is a fine, the same is true for corporations under other environmental laws like the CWA).

Further, larger fines may be assessed with greater movements of relational time.

Simply due to their size, the largest corporations, which likely have bountiful annual revenues and large numbers of employees, violating environmental laws will likely pollute more than smaller corporations. For instance, a billion-dollar company with thousands of employees that dumps waste-water illegally into a river during a manufacturing process is likely to be dumping a greater volume than a company barely making a hundred thousand dollars a year and employing only three people. In other words, larger companies likely pollute the most because their operations are bigger than smaller companies (Prechel & Zheng, 2012).

As previously stated, punishment affects more than just the principals to a conflict; other parties may feel the effect as well. Thus under Black’s (2011) theory of moral time, laws may prevent movements of social time as related to the principal and third parties to a conflict.

Interestingly enough, there are laws that force prosecutors and judges to consider the consequences of their actions when fining corporations that violate environmental laws. For instance, 18 USC § 3572(a)(2) mandates prosecutors and judges take into consideration the 68

burden a fine will have on the defendant and any dependents reliant upon the defendant. In context of corporations, this means prosecutors and judges must consider the effect the fine will have not on just the corporation, but also its employees. In practice it appears fines are crafted in a way so as to exert some level of punishment upon the corporation, but not have the fine be so burdensome that the corporate defendant will have to lay off employees or go out of business

(Garrett, 2014).

It may be useful to provide a couple of real life examples to emphasize the point being made. The first example comes from the savings and loan scandals that came to light during the late 1980s and early 1990s. Banks that were part of the savings and loan business engaged in pervasive and widespread fraudulent activities that eventually led to massive failings, which cost citizens and the government upwards of $160 billion just to save the industry (Calavita, Pontell

& Tillman, 1997). Though there was massive fraud, laws were passed so that bailout money could be given to banks to prevent them from going out of business. Furthermore, prosecutors focused on prosecuting individuals rather than corporations so as not to cause more layoffs than necessary (Calavita et al., 1997: Chapter 5). Prosecutors focused on high-status prosecutions, which allowed many smaller fraudulent acts to either go completely unpunished or be tried in civil and administrative courts (Calavita et al., 1997: Chapter 5). In the end, the savings and loan crisis caused massive changes in social time (financial loss, ill-gotten gains, etc.), but nevertheless law was used to bring about equilibrium. Punishments were doled out and the industry was saved, which spared some banks from going out of business and the laying off employees.

Similar to the saving and loan crisis was the great recession that occurred in 2008. There were many accusations that the great recession was caused by rampant fraud in the financial 69

sectors of the American economy (McLean & Nocera, 2011; Sorkin, 2010). Like the savings and loan crisis and notwithstanding the issues of fraud, the financial industry received over $700 billion in bailout funds so no other banks beyond Leman Brothers and Bear Stearns would falter and fail (McLean & Nocera, 2011; Sorkin, 2010). Though Wall Street banks were fined for their activities, they did not have to admit any wrongdoing and to this day they continue to make millions of profits and employ thousands of individuals (Garrett, 2014; McLean & Nocera, 2011;

Sorkin, 2010). What is important is that law was used to grant the financial industry bailout funds so they would not go out of business and create large numbers of people who were unemployed.

The Pollution, Environmental Degradation, and Resource Over-use Presumption

In this study I make the following presumption: That pollution, environmental degradation, and resource overuse is, in one way, shape, or form, always a movement of social time in the relational, vertical, and cultural dimension. In Chapter 2, I discussed the legal framework that criminalizes certain actions that harm, or could potentially harm, the environment. An understanding of the legal framework is important because Black’s (2011) theory argues that some movements of social time will be treated as criminal. Thus, the prosecutions in my study are movements of social time that are being treated as criminal acts.

Here, I presume all pollution, environmental degradation, and resource overuse is a movement of social time because it causes harm, even if those harms do not directly affect people. For instance, in U.S. v. Brusco Tug and Barge, the corporate defendant was fined a total of $1.5 million for intentionally releasing dredged material into the San Francisco Bay.

Prosecutors who criminally charged Brusco Tug and Barge supported Black’s (2011) argument that crime is a movement of social time because the fine represents a loss of income, which is 70

conceptualized as overinferiority. While there is no evidence presented that suggests Brusco Tug and Barge’s crime directly affected people in a negative way, it is not unreasonable to think they might have. For instance, the dredged materials released may find their way to shore and into the drinking water of a population, thus resulting in overintimacy because most (but not necessarily all) people would likely unknowlingly ingest harmful substances, which is a wrongful act.

Companies that release toxins into the air may not directly cause harm to a population.

For instance, in U.S. v. Belvan Corporation, the corporate defendant was fined $500,000 for negligently released toxic substances such as hydrogen sulfide, sulphur dioxide, and other noxious pollutants into the air. Belvan’s plant is not located anywhere near a population center and local citizens were not in any real risk of breathing these substances in. However, these same substances are considered to be contributors to global climate change, which is argued to cause harm to human populations through droughts, heat waves, and the like (Klein, 2013).15

Like all presumptions, the one I make here could be false. My presumption was based upon possibilities people may find logically correct. Unfortunately, there was very little empirical evidence to support my stance. One study that moderately supported my presumption argued that local communities may support the punishing of corporations for environmental harms, but that support is contingent upon current economic contexts in which each community finds itself (O’Conner-Shelly & Hogan, 2013). Therefore, the ‘wrongness’ of pollution, environmental degradation, or resource overuse may be contingent upon other factors.

Nevertheless, this presumption is logical and may be used. However, if there was no empirical support for my presumption, then the results stemming from its use would be false positives.

15 Droughts make it harder to grow food which increases the cost of food at the grocery stores. Heat waves have been known to kill the elderly. 71

Conceptual Shortcomings of Moral Time

Prior to the creation of moral time, scholars have written pieces that are very critical of

Black’s pure sociology (Greenberg, 1983; Marshall, 2010a, 2010b; Sarat, 1989). The most critical critique was that the studies and examples Black used to promote his theoretical propositions were cherry picked at worse and anecdotal at best (Marshall, 2010a). Thus, there exists some doubt as to Black’s (1976, 1998) propositions because they may not be as strongly supported empirically as he argued. The obvious response to this critique is that the propositions are falsifiable (Black, 1995; 2000) and thus still valid. However, critics argued that even if falsifiable, studies supporting Black’s (1976, 1998) propositions may be logically correct, but empirically false (Greenberg, 1983; Marshall, 2010a, 2010b). Moreover, these critics expressed concern that when studies do not support Black’s (1976, 1998) propositions, the response from

Blackian scholars is that the authors do not understand the theory or, the findings are anomalies

(Marshall, 2010b).

Black’s (2011) theory of moral time presents propositions formulated through the same process from which his prior works were developed (Black, 1976, 1998). Thus, the same critiques levied at the pure sociology paradigm may be true for moral time. An example or two may demonstrate this assertion. Black’s (2011) argument essentially claims conflict is caused by movements of social time and with greater and faster movements of social time comes greater conflict. However, when multiple people experience the same movement of social time they may, and often do, react very differently. One example is seen in the documentary Kumare

(Ghandi, 2011). In Kumare, Vikram Ghandi pretended to be an Indian guru that can help others obtain spiritual enlightenment and personal fulfillment; yet, Mr. Ghandi admitted at the beginning of the documentary he can do no such thing (Ghandi, 2011). Nevertheless, Mr. 72

Ghandi, with his new persona as Kumare, the Indian guru, had multiple followers that truly believed in his teachings (Ghandi, 2011). At the end of the documentary, Mr. Ghandi revealed that Kumare is a fake persona and that he is no Guru (Ghandi, 2011). In Black’s (2011) theory of moral time, the followers experienced a fast and rapid change in relational time because of the underintimacy they now have with Kumare, their guru who is not a guru. Kumare’s followers are all of similar social status, yet do not react the same to the rapid and fast change in relational time. Some followers felt closer to Kumare even though he was a fictitious persona, while others were skeptical of the method, but appreciated and stayed in touch with Mr. Ghandi, while others were upset and completely cut off communication with Mr. Ghandi (Ghandi, 2011). The Kumare

(2011) documentary demonstrated that the same movement of relational time can produce drastically different reactions, some of which do not lead to conflict at all.

Other examples exist as well. For instance, in the book Missoula by Jon Krakauer (2015) a series of rapes, and how they were handled by the University of Montana and local district attorneys, was chronicled. There were three examples in Missoula (Krakauer, 2015) that demonstrate the same movement of relational time through the act of acquaintance rape produces completely different outcomes. Beau Donaldson, a starting and popular tight end on the

University of Montana’s football team, pled guilty to raping his childhood friend Allison Huguet while she was drunk and passed out.16 He was sentenced to 10 years in prison (Krakauer, 2015).

Jordan Johnson, the quarterback for the University of Montana’s football team was accused of raping a college friend, Cecilia Washburn, after a night of heavy drinking and was expelled from school, found not guilty at trial, and then allowed to return to campus (Krakauer, 2015). Finally,

16 The University of Montana football team is treated with the same revenance as other major schools like the University of Alabama, Florida State University, and Ohio State (Krakauer, 2015:7). 73 Calvin Smith was expelled from the University of Montana for raping Kaitlynn Kelly during a night of drinking at a local bar; Kelly only knew Smith for a few hours, but they were both students at the University of Montana (Krakauer, 2015). Despite evidence strongly suggesting

Smith may have actually raped Kelly, local prosecutors declined to bring charges (Krakauer,

2015).

Viewing these examples collectively, it is hard to understand how they completely fit with Black’s (2011) theory. All three rapes were acquaintance rapes, but Donaldson’s rape was of his childhood friend. Thus, his act represented the smallest movement of relational time because it was against an intimate, yet he received the greatest amount of social control.17 Both

Johnson and Smith received the same initial punishment of expulsion despite their alleged rapes of intimates that were at different levels of intimacy. It was surprising that Johnson would be prosecuted instead of Calvin Smith under Black’s (1976, 1998, 2011) conceptions.

Finally, there were the collateral consequences surrounding the arrest of Bernie Madoff.

As stated earlier, after his arrest, Madoff’s family was forced to forfeit vast amounts of their ill- gotten fortune, which was a large and rapid change in vertical time, and their personal reputations were deeply tarnished, which is a large and rapid change in vertical time (Safer,

2011). However, the family members do not react the same. Madoff’s wife, Ruth, and his son

Andrew went into isolation while another son Mark committed suicide (Safer, 2011). Using

Black’s (2011) conception of moral time, it is not clear why there are drastically different reactions to similar movements of social time.

17 It is worth noting that Black (2011) uses sources to discuss his conception of rape and relational time that are at least 13 years old and older. If more recent studies conform or diverge from Black’s (2011) theory exist we simply do not know because he does not tell us, thus serving for the basis of critiques by scholars such as Marshall (2008a, b) and Greenberg, 1983). 74 The point of this section is not to suggest Black’s (2011) theory is inappropriate to use as a lens for understanding the beginnings of legal conflicts, or other styles of conflict. Black’s theory is generally testable and his propositions can be falsified. However, if studies using

Black’s theory prove the theory correct, then there is still more work to be done to truly demonstrate the accuracy of Black’s theory of moral time. It may be that the examples presented are exceptions to the way conflicts begin and are inevitably handled, which provide results conforming to Black’s propositions are generally correct. However, without more evidence, the propositions offered by Black may not accurately reflect the way the majority of conflicts begin and are handled. Black attempted to present a general theory of conflict in less than 300 pages.

Nevertheless, with such a small space to present a complex and large idea, it is not surprising

Black cannot and does not attempt to explain why other examples presented in the same manner as the sources he uses to support his propositions, do not conform to those propositions.

Therefore, the findings in this dissertation, and in any other study using Black, regardless of their level of support, should be interpreted with a healthy level of skepticism.18

Conclusion

In this chapter I introduced Black’s (2011) theory of moral time and demonstrated how illegal pollution is a concept appropriate for inquiry within the theoretical framework. First, I have argued that illegal pollution, which is a crime, causes movements of relational and vertical time. Pollution is a form of overintimacy in relational time because it is an unwanted invasion of

18 Taking this idea one step further, all studies should be viewed with doubting eyes. Classic sociologists such as Durkheim, Weber, and Marx have certainly come under such scrutiny and it has been for the betterment of social science because doubting the empirical reality of their respective propositions has led to the creation of new and better theoretical orientations that inevitably after scrutiny must admit their own conceptual shortcomings. Given the possible shortcomings in the empirical reality to Black’s (2011) propositions it would have been better for Black (2011) to state he has conceptualized a new idea regarding the beginnings and handling of conflicts rather than claim to have discovered the cause of conflict (Black, 2011:3). 75

the body by toxic substances. Furthermore, pollution can be a form of violence when it causes the death or people or animals, which are forms of overinferiority. Pollution is also a form of overinferiority when it destroys or damages property. The greater and faster the change in relational and vertical time, the more severe the corresponding punishment will be. Finally, I laid out Black’s (2011) arguments regarding how the punishing of corporations may lead to further conflict. Law, which forbids movements of social time, must be applied in a fashion to limit or ensure there are no future conflicts that result from punishment.

Shortcomings of Black’s (2011) theory aside, the argument that movements of social time cause conflict and it is the largest and fastest movements of social time that cause the most severe conflicts that result in the severest punishments gives researchers a new way of understanding conflict in our society. New theories of social reality need to be tested in order to move the field of sociology forward. Therefore, Black’s (2011) theory is an appropriate theoretical perspective to view and understand the variance in legal conflicts involving corporations that violate federal environmental laws.

CHAPTER 5: DATASET CREATION, VARIABLE OPERATIONALIZATION,

CONCEPTUALIZATION AND HYPOTHESES

This chapter will accomplish two goals. First, this chapter introduces the data set I created to test Black’s (2013) theory. Second, I introduce the hypotheses to be tested, present conceptual models for testing, and operationalize the variables that were used to each conceptualization of Black’s (2013) theory. My description of the data set is broad; not all of the data I collected was used in this study. Nevertheless, I provide a broad look into the data set to give all readers a general idea of the efforts undertaken to complete this study, and invite criticism as to collection methods or the folly in not using certain variables in the analysis.

The Data Set

The unit of analysis in this dissertation was corporations. More specifically, the unit of analysis was corporations that were prosecuted for violating a federal environmental criminal law such as the CWA or the Clean Air Act. The current dataset analyzed a total of 234 criminal prosecutions against corporations that were prosecuted under federal laws meant to protect the environment. Currently, there is no national dataset that researchers can use to analyze prosecutions against corporations for violating federal environmental criminal law statutes. In order to determine the names of corporations that have been prosecuted for violating federal environmental criminal statutes, I filed two freedom of information act (FOIA) requests with the

DOJ (FOIA No: 2014-03784) and the EPA (HQ-FOI-02150-11). From these two FOIA requests

I was able to get the names and case numbers of all prosecutions against corporations for

77 violation of federal environmental criminal statutes between the years 1980 and 2013. For this study, I analyzed prosecutions during the 10-year period between 2004 and 2013.

With the names and case numbers I was able to log onto the federal government’s Public

Access to Court Electronic Records (PACER) to download criminal complaints, plea agreements, factual statements, and judgment. From these documents I was able to determine: (a) what the total fine assessed against a corporation was; (b) if any humans or animals were killed/injured by the environmental hazard; (c) if the prosecution was a felony; (d) if there was an actual discharge of pollutants; (e) if there was a continuous discharge of pollutants; (f) if the corporate defendant violated a permit or not; (g) if the corporation was given a repayment plan;

(h) if the corporation was mandated to create and implement an environmental law compliance program; and (g) if the corporation was ordered to hire an independent monitor to ensure future compliance with environmental law.

Once the proper information was gleaned from court records, I went to the

ReferenceUSA and Dun & Bradstreet databases to get information regarding the individual corporate defendants. From these two databases I was able to determine a corporation’s yearly revenues, the number of employees companies have; the corporation’s advertising budget; and whether or not the prosecuted company was a subsidiary or not.

After company information was gathered, I used the EPA’s ECHO website to collect demographic information on the individuals living within one mile of an environmental hazard.

The ECHO website provides the following demographic information for the populations living within one mile of an environmental hazard; (a) total number of people living within the one- mile radius of a hazard; (b) the percentage of white, black, Hispanic, Asian, and Native

American people living within a one-mile radius of a hazard; (c) the percentage of people with a 78

high school degree (or less), some college or a bachelor’s degree (or higher) living within a one- mile radius of a hazard; and (d) the percentage of households with incomes below the poverty line or above $75,000. Additionally, I was also able to get the latitudinal and longitudinal coordinates for each environmental crime. Using these three separate sources, I constructed an original dataset quantifying a multitude of factors that influenced the fines assessed against corporations for violating federal environmental criminal statutes, which was not in the possession of any other researcher in the country.

During the time period of 2004 through 2013, there were a total of 388 criminal prosecutions of corporations. Unfortunately, I was forced to exclude 154 cases from the dataset because complete records could not be collected from one of the three data bases used to construct my dataset. Of the 154 cases excluded, I was unable to collect financial records for 83 cases because they were either foreign companies that do not report earnings to U.S. agencies or the company simply was not in the ReferenceUSA or Dun & Bradstreet database. Neither database stated exactly why a company may not be listed by their service. Of the 154 cases excluded, 61 cases did not have complete records in the PACER database. PACER is an ongoing project by the U.S. Federal Courts to put all federal cases online, which requires paralegals and other individuals to scan and upload past filings to the system, so it is understandable that some cases simply will not be found on the website. Furthermore, 12 of the 61 cases were on PACER but for unknown reasons documents were not uploaded or could not be viewed. Finally, the last

10 cases were excluded because, even though they were initially prosecuted by the federal government, jurisdiction was handed over to state authorities and the corporations were then prosecuted under state laws instead of federal. Table 7 provides a frequency table showing the

total number of prosecutions against corporations for violating federal environmental criminal statutes through the 2004 to 2013 period.

Table 5. Number of Prosecutions During the 2004 through 2013 Time Period 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Number of 23 19 25 25 30 35 28 3 29 17 Prosecutions

The only major issue with the frequency distribution in Table 7 is the three prosecutions in the year 2011. When looking at the FOIA requests from the DOJ and EPA I found 12 corporations were excluded. Eight of the corporate prosecutions I excluded from 2011 were against foreign companies, one case was excluded because the defendant was a city instead of a corporation, and three other cases did not have documents on PACER.

The prosecutions in this dataset varied not just by year, but EPA region and state. Table 8 presents the total number of prosecutions in each region and state during the 10-year time period this study examined. Generally speaking the fewest number of prosecutions occurred in the

Northeastern part of the country. However, region 2, which includes only New York and New

Jersey in its jurisdiction, had 17 total prosecutions and represented about one-third of all criminal cases brought in the Northeastern U.S.

Table 6. EPA Regional Breakdown of Prosecutions and Median Fine in Each Region Between 2004 and 2014 (n=234) Region 1 Region 2 Region 3 Region 4 Region 5 Region 6 Region 7 Region 8 Region 9 Region NH, VT, NY, NJ PA, WV, KY, TN, MN, WI, NM, TX, IA, MO, MT ND, CA, NV, 10 ME, VA, DE, AL, MS, IL, OH, OK, AR, NE, KS SD, WY, AZ, HI AK, MA, RI, MD, DC GA, FL, MI, IN LA UT, CO WA, CT NC, SC OR, ID 15 17 16 39 34 27 27 18 21 17 $150,000 $150,000 $200,000 $170,000 $226,003 $500,000 $130,000 $57,500 $100,000 $75,000

At this time, I do not know why there are more prosecutions in one region versus another, as reflected in Table 8. What is more interesting is the variance of the median fines between

each region. It could be that region 6 experienced environmental hazards that released more pollutants into the environment than any of the other regions. I found no indication within my dataset to demonstrate conclusively that this is true. In fact, the crimes prosecuted in regions 5 and 6 have no unique qualities that I can see, suggesting more pollution was released into the environment by corporations in these two regions than any others. I have no evidence to suggest this is correct, but it may be true that fines vary between each region because of some underlying political or economic concerns that mediates or moderates the severity attributed to a movement of social time. Future research could investigate this issue.

The data set I used is wholly original. Nevertheless, I am not the first person to try and create an original data set to analyze corporate crimes. Garrett (2014) collected data on 1,012 corporations that committed crimes ranging from fraud and embezzlement to illegally polluting and violating international treaties. In his data set, Garrett quantified the following variables: (a) year of prosecution; (b) prosecuting office; (c) offer and acceptance of a plea agreement; (d) total fine; (e) creating of a compliance program or hiring of an independent monitor; (f) if corporate defendant is a subsidiary or not; (g) the number of employees a corporation has; and (h) if the company is public, private, or foreign. Garrett’s work is monumental, but unfortunately has its flaws.

One of the major flaws I found in Garrett’s (2014) data set is that there are corporations that committed an environmental crime that were not listed. For instance, Yazdian Construction,

Madison Contractors Inc, Seven-Up Bottling, and Crystal Extrusion Systems are not in Garrett’s

(2014) data set, even though they all committed a federal environmental crime. Another flaw I found in the dataset as that Garrett did not accurately calculate the fines assessed against a corporate defendant. For example, with Energy Partners LTD he did not calculate that the 81

corporate defendant was forced to pay a $25,000 community service payment on top of a

$75,000 fine. Similarly, in Frazer Jones Company’s prosecution, Garrett accurately showed the company was fined $250,000, but did not report there was also a $135,000 restitution payment assessed. There were over 40 cases I found where Garrett either did not list the corporate defendant in his data set or he mistakenly cited the fines assessed against the companies.

Garrett’s (2014) efforts advanced the field and serve as an important first step when trying to construct a data set in order to study corporate crime. Nevertheless, I constructed the data set used in this study with the errors of Garrett in mind and hope to have constructed a more complete, thorough, and accurate database for analysis.

Baseline Hypothesis: Pollution as a Movement of Social Time

In Chapter 3 I indicated that this study presumed all acts of pollution are movements of social time. Because of the way this baseline hypothesis was constructed, it could not determine that dimension in which the movements of social time occurred.

[BH]: As corporations release more pollution into the environment, they will receive

larger fines.19

Support for this hypothesis provided credence for Black’s (2011) theory. In particular, support demonstrated that there were underlying movements of social time occurring––and the greater and faster a movement was, the more social control it attracted. In which dimension of social time the movements occurred was unknown within the base hypothesis. Therefore, I constructed other hypotheses to identify which dimensions within the theory of moral time these underlying movements of social time occurred.

19 BH = Baseline Hypothesis 82

Black (1976:3) states that law is governmental social control. Fines assessed against an individual or a corporation by the government is an objective, direct measure of law (Black,

1976: 3). Larger fines therefore equal greater amounts of social control (Black, 1976: 3). Black

(2011) argued that larger and faster movements of social time will attract the greatest amounts of social control. Thus, the largest fines should be interpreted as the greatest amounts of social control. Therefore, the proper dependent variable for BH was the total fine assessed against a corporation for violating federal environmental criminal law statutes.20

When companies are prosecuted they can be fined, but may also be forced to make community service payments, restitution, or pay other assessed fees at the court’s discretion.

These sanctions are used to accomplish different goals. Restitution payments are meant to pay back government agencies for cleanup costs or private entities that had their property damaged.

Community service payments are usually given to a charitable organization like the Raptor

Society. Criminal fines simply go into government coffers and can be used for any purpose by federal courts and agencies. Finally, concurrent and other payments are just a mixing of fees or some other financial sanction a company pays as a result of prosecution. Like criminal fines, the money here goes into a general fund for use by courts and agencies.

To calculate the total fines a corporation paid, I added all of these payments together. For example, in the case of Rockwood Lithium, the company was initially fined $15,000 for illegally dumping wastewater that was highly acidic and salinized, which killed over 300 migratory birds during a three-year period. However, Rockwood Lithium was also mandated to pay $75,000 in

20 Fines can be given to both private and public entities when they violate an environmental law. I ran models using only the criminal fine, and not total fine as the dependent variable and found the analysis presented in Chapter 6 does not change. 83

restitution to the state of Nevada for cleanup costs and make a $650,000 community service payment to the Raptor Society of Nevada. Thus, Rockwood Lithium’s total fine was $740,000.

The highest fines are an expression of greater and faster changes in social time.

Therefore, the dependent variable for BH is the total fine assessed against a corporation for violating federal environmental criminal laws. Table 9 gives a breakdown of how fines, restitution, community service payments, and other monetary sanctions were distributed among the 234 prosecutions in my data set. Appendix B provides a correlation matrix for each of the possible fines a corporation may be assessed.

Table 7. Distribution of Monetary Sanctions across Prosecutions (n=234) Totals Mean Median Min Max Criminal Fines21 213 $5,889,872 $100,000 $0 $1,900,000,000 Community Service 64 $32,448,731 $150,000 $0 $2,700,000 Restitution Payments 77 $27,047,759 $87,000 $0 $2,000,000,000 Concurrent and Other Payments 33 $305,809 $4,000 $0 $8,000,000

The primary independent variable introduced to measure the BH is the yearly revenue of a corporation. This information was taken from the ReferenceUSA and Dun & Bradstreet databases. Simply due to their size, companies with large yearly revenues and the greatest number of employees violating environmental laws could pollute more than smaller corporations. For instance, a billion-dollar company with thousands of employees that dumps waste-water illegally into a river during a manufacturing process is likely to be dumping a greater volume (e.g. more gallons) than a company barely making a hundred thousand dollars a year and employing only three people. In other words, larger companies pollute the most because their operations are bigger than smaller companies. Unfortunately, official records often do not state exactly the total amount of pollutants that are released into the environment and so it is

21 In 60 prosecutions only a criminal fine was assessed. In the remaining 174 prosecutions, there was a combination of a criminal fine, restitution, community service, or concurrent/other payments. 84

generally impossible to determine if one corporation actually pollutes more than another.

Nevertheless, it is perfectly logical to believe larger corporations may pollute more than smaller ones simply due to the size of their operations, and this is the same presumption Prechel and

Zheng (2012) rely upon in their discussion about pollution and corporate structures.

A limitation of this study is the use of the yearly revenue of a corporation as a measurement for the amount of pollution a company releases because more often than not official records do not often state the exact amount of total pollutants released into the environment. Thus, I presumed that companies with the largest revenues polluted the most.

However, there are examples within my dataset where companies received substantial fines yet did not release any pollutants into the environment. For instance, Pyramid Chemical Sales

Company was fined $1,908,712 (the fine took up about 42% of the corporation’s annual revenue) for shipping chemical wastes from a superfund site in the U.S. to the Netherlands for disposal in a manner that did not comply with reporting requirements; no chemicals were actually spilled.

There were examples of companies with incredibly large annual revenues that released pollutants into the environment, yet received smaller fines than corporations with a lower annual revenue. In 2009, Corn Plus LLC negligently released wastewater into Rice Lake, which is in

Minnesota. Corn Plus LLC had an annual revenue of $16 million and was fined $150,000. In

2007, Chief Ethanol Fuels negligently discharged wastewater into Blue Lake, which is in

Nebraska. Chief Ethanol Fuels has an annual revenue of $11.6 million and was fined $200,000.

Finally, Cedyco Corporation negligently discharged oil into the Louisiana Bayou. Cedyco had an annual revenue of $620,000 and was fined $557,000. These case comparisons directly contradicted my presumption that corporations with the largest yearly revenues were fined the most because they produced the greatest amount of pollution. 85 My point here is that the presumption that corporations with the largest revenues pollute the most may not be completely accurate; thus, unwittingly my results could produce a false positive. Looking at Appendix A, it is clear that there was a positive correlation between corporate revenue and the fine assessed. However, I cannot say with any empirical certainty that corporations with the largest revenues pollute the most (i.e., cause the greatest movements of social time) and therefore receive the larger fines. Prosecutors may in fact just craft fines based upon a corporation’s ability to pay, as prescribed by 18 USC § 3572(a). This issue will be discussed in more depth in the conclusion.

Within the models constructed to test the BH, I introduced two control variables. The first control variable was whether or not the violation was prosecuted as a felony or misdemeanor.

As shown in Chapter 2, felony prosecutions have higher minimum and maximum fines than misdemeanors. However, prosecution as a felony does not necessarily mean a greater and faster movement of social time occurred. If a corporation accidentally releases 100,000 gallons of oil into a river, it is a ‘negligent’ act and thus prosecuted as a misdemeanor. If the 100,000 gallons of oil are released because a supervisor ordered an employee to do so the act is ‘knowing’ and thus prosecuted as a felony. It is not the amount or type of pollutants that are released into the environment that determine whether a prosecution is a misdemeanor or felony; it is the state of mind of an actor as determined by a prosecutor. Therefore, the felony status variable was a control rather than one of interest (e.g. independent) within this, or any model measuring movements of social time. To capture the effect of felony prosecutions, I created a nominal variable where felonies were coded as 1 and misdemeanors as 0.

The second control variable I introduce controls for instances where pollutants were not actually released into the environment. The overwhelming majority of cases in this dataset 86

involved the release of toxic substances into the environment. However, not every prosecution occurred because corporations released toxic substances. Companies can be fined simply for improperly filling out shipping manifests, not having a permit to store chemicals, or using tools in an un-prescribed manner. For example, in U.S. v. Herbst Construction the corporate defendant was fined $7,500 for using concrete slabs to hold steel rebar in place for bridge repair work without having a permit to place these materials in the river. The concrete slabs were not natural to the environment and did not result in the release of cyanide, nickel, or any other controlled substance into the environment. In the case of U.S. v. Goedecke Inc., the corporate defendant was forced to pay $45,272 in restitution because employees transported, on a haphazardly secured flatbed truck, containers filled with hydrofluoric acid, along with other toxic, flammable, and hazardous waste without a shipping manifest or any other markings to identify what exactly was in each container. Again, there was no allegation that any of these chemicals were spilled or leaked into the environment. Finally, in U.S. v. Michigan Industrial Finishes the company knowingly stored 2,500 drums of hazardous wastes without a permit - namely spent solvents like xylene, toluene, and methylethylketone. Michigan Industrial did not have the resources to remove all of the illegal drums so the EPA performed the removal at a cost of $7,096,579.

Michigan Industrial Finishes was forced to pay $1,000,000 in restitution. Like the previous cases, Michigan Industrial’s crime did not result in any of the chemicals being leaked or released into the environment. Therefore, in all three cases, it can be asserted there was not any actual environmental hazard (e.g. movement of social time), only the potential of one. Thus, this control variable was treated as binary with a code of 1 for no release of toxins into the environment and 0 for actual releases.

Pollution Causing Movements of Relational Time

Illegally polluting the environment is a change in relational time. One way a change in relational time occurs is through overintimacy (Black, 2011: 21). Black (2011: 21) stated that

“intimacy is a relational distance: a degree of participation in the life of someone else.” Intimacy can be measured by how much contact people have with each other, how many activities they share, and how long they have done so (Black, 2011: 21). Overintimacy occurs when there is increase in closeness from one entity to another (Black, 2011: 21).

There are many different types of overintimacy described by Black (2011: 22-42). The type of overintimacy that is specific to this study was overinvolvement, which is some form of trespass that can include anything from “a personal question to a taboo sexual relationship, a burglary, a rape, or an invasion” (Black, 2011: 22). Any one of these trespasses causes a change in social time. The greater and faster the change in social time, the more severe the conflict will be.

Pollution is generally an unwanted invasion of toxins into a person’s body and therefore a change in relational time potentially causing conflict. Companies releasing pollutants into a community are invading the bodies of the people living there. When pollution is released into the environment it generally does not invade the body of only a single person, multiple people are affected. The more people that are affected by pollution, the greater the change in relational time will be and correspondingly the more severe the ensuing conflict. Furthermore, if the illegal polluting continues over a period of time, rather than as a single event, the ensuing legal conflict will be more severe because, as Black (2011: 21) implies, intimacy is something that can change over time. Thus, overintimacy occurring over a period of time brings a greater change in relational time than a onetime event. 88

The conflict examined in this study is legal conflict resulting from companies polluting in violation of environmental laws. Corporations causing the most severe legal conflicts, which are caused by large and fast changes in relational time, will be punished most severely. Thus, my first hypothesis was the following:

[H1]: Companies that continuously, and at greater levels, pollute in communities with the largest number of people living within one mile of the pollution site will receive the largest fines.

Black (2011: 22) went on to say that downward intimacy (e.g. intimacy with a social inferior) is greater than upward intimacy (e.g. with a social superior). Corporations polluting against individuals is an example of downward overintimacy because companies tend to have greater financial resources, more employees, and greater name recognition than individual people. However, the greater the distance downward overintimacy travels, the less severe the punishment should be because harm committed against an equal is always worse (Black, 2011:

23). In prior work, Black (1976; 1998) argued that people who are wealthy, have high levels of education, and are not of a minority racial/ethnic group are of high social status while people that are poor, with little education, and are a racial/ethnic minority have a low social status. A company that commits an environmental crime against a community that is made up primarily of white, wealthy, and educated individuals and households should receive a larger fine than if the crime occurs in an area composed primarily of minority, poor and lowly educated individuals and households. Black’s (2011) argument was when a high status actor causes a change in relational time against a low status actor, the amount of social control that results will be less than if the change in relational time occurs against a social equal or of someone with only slightly less social status. Thus, my second hypothesis was the following:

[H2]: Companies that continuously, and at greater levels, pollute in communities with a greater percentage of low status individuals and households should receive smaller fines than areas with a larger percentage of high status individuals and households.

To test H1 and H2, I used the total fine as the dependent variable. I also used the corporate revenue variable from BH as the primary independent variable because it is my proxy measure for harm. The first new independent variable I introduced to test H1 and H2 was whether or not the illegal pollution released by a corporation is more than a one-time event. This variable is called continuous release. With this variable I differentiated between prosecutions where the illegal pollution occurred over a period of time rather than just as a singular event.

This variable was a dummy-variable where illegal pollution that occurs only once was coded as 0 and pollution that is released over a period of time was coded as 1.

For H1 only the third independent variable I used was the total population living within one mile of the location an illegal release of pollution by a corporation occurs. This information, as previously stated, was gathered from the EPA’s ECHO database. To test H2, the third and fourth independent variables were composite variables. As stated, ECHO provided demographic data for individuals living within one mile of an environmental hazard. To create the composite variables, I relied upon data regarding a household’s family income, education level, and the racial makeup of a community. The first composite variable I created was an indicator of low status individuals/families living within one mile of an environmental hazard. First, I computed z-scores for the following variables: (a) % of households living below the poverty line, (b) % of individuals that are minorities (e.g. Black, Hispanic, Asian), and (c) % of individuals with a high school diploma or less.22

22 The procedure looks like this. In the case of U.S. v. Corn Plus LLC, I added the following z-scores together: .5289645 (z-score for % below poverty line) + -.700937 (z-score for % minority) + .3055347 (z-score for % with 90

To create the composite variable that indicated high status individuals/families living within one mile of an environmental hazard, I computed z-scores for the following variables: (a)

% of households with an income of $75,000 or greater, (b) % of individuals that are white, and

(c) % of individuals with a college education or greater. I took the z-scores of each variable and add the together for each case. The sum of each z-score was the value indicating the concentration of high status or low status individuals in a community. The composite variable measuring the concentration of low status individuals/families in a community was called Low

Status. The composite variable measuring the concentration of high status individuals/families in a community was called High Status. Finally, I used the Felony Status and No Release control variables in the models testing H1 and H2.

Before moving on, it is necessary to justify why I use a 1 mile concentric ring around the hazard cites analyzed in this dissertation. The ECHO database allows people to look up the demographic information for populations living within 1, 3 or 10 miles of an environmental hazard. I use the 1 mile concentric ring measure for two reasons. First, prior studies analyzing environmental harms and their relationship to local populations use (Atlas, 2001; Greife et al.,

2015) the 1 mile concentric ring measure because people living closest to an environmental hazard will be the most likely victims. When pollution is released into the environment its effects are worse for those immediately subjected to it (Atlas, 2001). The further away people live from an environmental hazard the more likely they will not be affected (Williams, 1996).

Second, I use the 1 mile concentric ring because the boundaries of the rings do not change due to political decision making. With ZIP codes, one year a particular ZIP code could be concentrated

high school or less education). Thus the value for Corn Plus’ low status composite variable is .1335615. I do this same procedure for the remaining 233 cases. 91

with wealthy, white, individuals and then two years later due to redistricting, rezoning, or gerrymandering, be concentrated with poor, minority individuals (Williams, 1999). Therefore, to get a truly accurate picture of the population demographics surrounding environmental hazards researchers are recommended to use the concentric ring measure (Liu, 2001).

To help set context, it is necessary to present a case comparison between Frazer Jones

Company and Acuity Specialty Inc. to demonstrate what I expected to find under H1 and H2.

Frazer Jones was prosecuted for intentionally discharging wastewater with a temperature over 70 degrees and highly acidic pH levels, in violation of their permit, into a local river on multiple occasions between 1990 and 2007. Acuity Specialty, Inc. was prosecuted for allowing wastewater containing phosphorous, which also contains highly acidic pH levels, in violation of their permit, into a neighboring river on multiple occasions between 2002 and 2003. Acuity

Specialty, Inc. was fined $3,800,000 while Frazer Jones received a fine of $405,000. Both crimes were virtually the same and on balance Frazer Jones’ offense lasted much longer yet they received the smaller fine. As it turned out, the total population living within one mile of Acuity

Specialty, Inc.’s hazard was 9,472 people. On the other hand, there were 5,362 people living within one mile of Frazer Jones’ environmental hazard. Despite the similarity of facts, Acuity

Specialty’s illegal pollution affected a greater number of people than Frazer Jones. This meant

Acuity Specialty caused a larger change in relational time by becoming more overly intimate with a larger number of people than Frazer Jones. The comparison between Frazer Jones and

Acuity Specialty provided support for H1 and I expected the same general findings at the aggregate level.

Pollution Causing Movements of Vertical Time

Pollution that results in the injury or death of a person is a change in vertical time. A change in vertical time occurs when people “gain or lose money, authority or anything else that raises or lowers one person or group above or below another” (Black, 2011: 59). Wealth is something that people groups can gain or lose (Black, 2011: 76). In Black’s (2011: 71) theory of moral time when a person or group falls below others due to a loss of wealth, the ensuing conflict is caused because the type of change in vertical time that occurs is overinferiority. It is important to know that wealth is not just monetary, it includes “any material condition of human existence such as food or shelter, currency and even the human body itself” (Black, 2011: 76).

The human body is by arguably one of the most fundamental means to produce wealth (Black,

2011: 76). A person’s health, then, is important because healthy people are much more likely to be able to work and generate wealth than those who are unhealthy (Black, 2011: 76). Thus, actions harming or destroying the means of production and cause people or groups to lose wealth creates situations of overinferiority within the dimension of vertical time. Making people sick is one form of overinferiority (Black, 2011: 79) as is killing a person (Black, 2011: 76).

Similar to relational time, the greater and faster the change in vertical time the more severe the ensuing conflict will be. Illegal pollution discharged into the environment by corporations that cause people to become sick is damaging, but killing an individual is worse because a dead person cannot generate wealth. On the other hand, a person may, but not always, recover from being sick. Pollution does not necessarily kill people only; it may also result in the death of wildlife. For centuries, human populations have survived, and created businesses, built upon the harvesting and dissemination of animals for consumption––even if some individuals decide not to partake in the eating of an animal. When companies illegally pollute, the forbidden 93

discharges may result in the death of fish, waterfowl, and the like. Though not always the case, people may make their living by harvesting fish or other animals from the environment and selling the meat in the marketplace. Other people may prefer to fish and hunt on their own so they do not have to go to the grocery store and pay for their meat. In either case, the killing of wildlife may cause overinferiority within vertical time because people either lose the means to sustain their livelihood or are forced to expend funds for food in the marketplace when they did not have to before. Based upon the foregoing I tested the following hypothesis:

[H3]: Companies that continuously, and at greater levels, pollute, which then causes the death of wildlife or a human being, will receive higher fines than corporations that release pollution that does not kill people or animals.

Like the relational time discussion, the dependent variable used to test H4 was the total fine assessed against a corporation. I also keep the continuous pollution variable in the models as well. However, in the models measuring change in relational time, I dropped the variables related to population and replaced them with the death variable. The death variable was a dummy-variable where any prosecution that occurred because people or animals were killed was coded as 1 and prosecutions with no deaths of any kind were coded as 0. Finally, like with the relational time models, I included the control variables of felony status and corporate revenue.

The logic behind the two control variables remained the same for the vertical time models as described in the relational time models.

As with the section on relational time, a case comparison gives an idea of what is being predicted with H3. Novozymes Biological negligently allowed 4,015 gallons of wastewater mixed with toxic chemicals, in violation of its permit, to be discharged into a local river, which resulted in the death of 660 fish. Novozymes was fined $525,000 for its violation. On the other hand, Phoenix Products, Inc. allowed for 1,500 gallons of toxic wastewater, in violation of its 94

permit, to enter a local river. Phoenix Products was fined $225,000 for its violation. The comparison between Novozymes and Phoenix Products demonstrates that, despite engaging in similar acts resulting in pollution, Novozymes received the greater fine because 660 fish were killed. The killing of 660 fish may have resulted in the overinferiority of people living around the hazard site because they relied upon healthy and unpolluted fish to eat from the river so they do not have to spend money at a grocery store. The comparison between Novozymes and Phoenix

Products provided support for H3 and I expected these trends to continue in the aggregate.

It should be remembered that the deaths I am measuring are immediately caused by the environmental hazard. My data did not allow me to capture deaths caused by environmental hazards that occur many years down the road, which is a very likely scenario. For instance,

Donald Carlson worked at the Koch Refining Company’s Pine Bend Refinery in Rosemount,

Minnesota for over 20 years (Meyer, 2014: 120). Carlson’s job was to clean, by hand, large leaded gasoline tanks that sometimes released toxic vapors and chemicals causing burns to his legs and hands (Meyer, 2014: 120). In 1995 it was found that Carlson had developed leukemia and died in 1997 (Meyer, 2014: 122). Subsequent investigations culminating in a wrongful death lawsuit by Carlson’s wife determined the leukemia was developed because of the chemicals

Carlson had been breathing in and came in contact with; Koch Refining Company was not properly storing and cleaning the chemicals prior to human contact as required by their operating permits (Meyer, 2014: 122-125). Carlson’s death was caused by violations of environmental laws, but it took almost 23 years for him to pass on. Cases like Carlson’s were not captured in my dataset but certainly should be considered in future research.

Law Preventing Future Movements of Social Time

The final set of models that were used in this study tested hypotheses derived from

Black’s (2011) general argument that like crime, punishments for committing crimes can cause movements of social time.23 Black (2011: 9) stated that “every reaction to deviant behavior alters social space: Social control is a movement of social time.” Black (2011: 9) noted that punishment “does not occur in a social vacuum...it may radiate into social space and can entail different movements of social time from one case to the next.” Thus, because punishment is a movement of social time it may cause future conflicts between the punished, punisher, or other third parties (Black, 2011: 9).

Laws are created to forbid movements of social time (Black, 2011: 12). As discussed, environmental criminal statutes were created to prevent movements of social time caused by polluting the environment. Laws do more than just prevent movements of social time occurring because of a crime; they also prevent movements of social time that occur because of punishment. In particular, when punishing corporations, 18 USC § 3572(a) (2) states24.

the burden that the fine will impose upon the defendant, any person who is financially dependent on the defendant, or any other person (including a government) that would be responsible for the welfare of any person financially dependent on the defendant, relative to the burden that alternative punishments would impose

Clearly, the written law tells prosecutors and judges that the fines assessed against corporations for violation of environmental criminal statutes must be created with the impact the fine will have on others in mind. Punishments that are too harsh may force companies to go out of

23 Black (2011) uses the terms punishment and social control interchangeably. In this dissertation I use the term punishment in the same fashion. 24 This type of law is not unique to environmental crimes. Prosecutors and judges can give repayment plans to any criminal defendant that is fined as a result of a trial or plea agreement at both the state and federal levels. 96

business or lay employees off, both of which are losses of wealth within vertical time and may cause future conflicts.

When corporations are punished for violating environmental criminal laws, the punishment is a movement of social time. As stated, there are multiple changes of vertical time that can occur (e.g., overinferiority, underinferiority, etc.). When corporations are fined they lose wealth and the change in vertical time is overinferiority (Black, 2011: 71). The type of overinferiority that may occur when corporations are fined is a form of hard times (Black, 2011:

74). Hard times occur because people and corporations become poor. However, it should be understood that fines will differentially affect companies. If one company has yearly revenue of

$1 billion and is fined $100,000, there will be little chance of facing hard times in the future. Yet, if a company only has an annual revenue of $1 million and is fined $100,000 the likelihood of facing hard times is significantly increased because the corporation may now have to lay employees off just to stay in business. Laying employees off likely hurts the company’s ability to generate large amounts of wealth due to the loss of manpower and may even increase the chance a corporation violates future environmental laws because there are fewer people employed to ensure compliance. Worst case scenarios may occur and fines can cause corporations to simply go out of business, which is the ultimate way of becoming poor because the owners and employees completely lose their means of producing wealth. Furthermore, governments lose the tax revenue paid by the corporation and may even have to pay out unemployment benefits. If

Black (2011: 9) was correct, laws dictating how punishments against corporations are to be crafted should try to at least limit, but possibly forbid, future movements of social time.

Therefore, I hypothesized the following:

97 [H4]: Companies causing movements of relational and vertical time will be fined in a manner that will not likely lead to corporations going out of business or laying off employees.

To test H4 I used a new dependent variable called Repayment Plan. This was a dummy variable where companies under a repayment plan were coded as 1 and corporations that do not were coded as 0. Repayment plans are given to corporations at the sentencing phase of a prosecution. They tend to be given to companies when they do not have the immediate resources to pay a fine after they violate environmental criminal statutes. An example of a repayment plan was seen in the case of U.S. v. Ecosolve LLC ([2007] 3:07cr98-2). Ecosolve LLC pled guilty to knowingly violating the CWA under 33 USC § 1319(c) (2) (A) and was fined $161,000.

Ecosolve had an annual revenue of $1.9 million and employed 16 people. Without taking into account Ecosolve’s expenses, the $161,000 fine encumbers 8.3% of the company’s yearly revenue. It was likely the $161,000 fine would be difficult for Ecosolve to pay, so the court granted a repayment plan where the company paid $445 to the court per month until the fine was paid in full.

The independent variable used to test H4 was called Relative Fines. To calculate this variable, I divided the fine by corporate revenue and then multiplied by 100. For example, if a corporation was fined $5,000 and had corporate revenue of $100,000, I divided 5,000 by 100,000 and get 0.05. I then multiplied the 0.05 by 100 to get 5. Thus, the $5,000 fine would take up 5% of corporate revenue. Fines that encumber large amounts of a corporation’s revenue are most likely to force a company to lay employees off or completely go out of business. I expected that as the relative fines increased, the likelihood of getting a repayment plan would be greater.

Furthermore, I used the following control variables from previous models: (a) continuous polluting, (b) Total Population living with one mile of an environmental hazard, and (c) death of 98

animals or people. I used these three variables as controls because variables measuring the effect of law being used to limit or forbid future movements of social time should remain statistically significant, the movement of social time notwithstanding.

Finally, to test H4 I also used the felony status variables as controls. I did not control for corporate revenue to test H4 because of its high correlation with the relative fine independent variable. When two independent variables in a regression model are highly correlated, there is an effect that occurs which is called multi-colliniarity. Though multi-colliniarity is not always a problem it is better to avoid it as it can increase the variance of the coefficient estimates in a model and make regression models very sensitive to even the slightest changes (Frost, 2013).

Put more simply, when the issue of multi-colliniarity arises, it makes it very difficult to know if the causal model being analyzed is actually correct because it is very easy for coefficients to go from negative to positive (and vise versa) or gain and lose statistical significance (Frost, 2013).

Therefore, I did not control for corporate revenue because it was highly correlated with the relative fines dependent variable.

Conclusion

In this chapter I introduced the data set created to analyze five hypotheses derived from

Black’s (2011) theory of moral time. In particular, I will test one set of hypotheses which predicts that corporations engaging in the criminal act of illegally polluting will receive the highest fines when the illegal pollution causes greater and faster movements of relational and vertical time. Furthermore, I will test a hypothesis based on the argument that greater and faster movements of relational and vertical time notwithstanding, punishments assessed against corporations for illegally polluting the environment would be crafted in a way to prevent future movements of social time, specifically vertical time. 99

In the next chapter I present the descriptive statistics for each variable used in this analysis. I also present the test models described to see if Black’s (2013) arguments were borne out empirically.

100

CHAPTER 6: ANALYSIS

In this chapter I construct models analyzing the particular aspects of Black’s (2011) theory discussed in Chapter 4. First, I analyze the argument that greater movements of relational time will lead to severe sanctions. Second, I analyze the position that greater movements of vertical time will lead to increased sanctions. Finally, I analyze the argument that law forbids, or at least limits, future movements of social time. Before presenting the findings from my analysis,

I wish to give basic descriptive statistics for all the variables used in this chapter, which are shown in Table 10.

Table 8 Descriptive Statistics

Standard Mean Median Skewness Minimum Maximum Deviation Total Fine $1,795,014 $170,000 10,500,000 13.168 $400 $5,000,000,000 Total Fine 12.025 12.062 2.258 0.406 5.991 22.333 (logged) Continuous Release 0.571 1 0.496 -0.286 0 1 Total Population 6,249 3,391 11,034 6.194 0 125,103 Total Population 6.955 8.102 2.982 -1.272 0.023 11.737 (logged) Composite Variable: -0.00000029 -0.1223 2.534 0.233 -4.563 6.898 Low Status Composite Variable: 0.0053 0.0169 2.462 -0.186 -4.712 6.087 High Status Death or Injury 0.159 0 0.366 1.867 0 1 Human Death 0.683 0 0.252 3.42 0 1 or Injury Felony 0.704 1 0.458 -0.893 0 1 Revenue $4,630,000,000 $7,735,000 390,000,000 12.054 $80,265 $47,600,000,000 Revenue (logged) 16.54 15.86 3.16 0.927 11.29 26.889 Fine as a percentage 13.86% 1.19% 38.132 4.988 0.000006 352.349 of revenue Fine as a percentage 0.001 0.176 2.895 -0.717 -12.024 5.864 of revenue (logged) Repayment Plan 0.312 0 0.465 0.812 0 1 No Hazard 0.192 0 0.394 1.561 0 1

101 The only noteworthy variables are: total fines, total population living within one mile of an environmental hazard, corporate yearly revenue, and fines as a percentage of corporate yearly revenue. Each one of these variables is continuous and has a high positive skew. To eliminate the skew, I logged each one of these variables (Kim & Pridemore, 2005: 1383). As expected, Table

10 shows that the high positive skews associated with each of the aforementioned variables were eliminated. Eliminating the high, positive skews gave each of these variables a normalized distribution making a traditional OLS regression appropriate.

For my analysis I used a traditional ordinary least square regression analysis. I did this because both of my dependent variables were transformed to their natural log. Initially, I was going to use a negative binomial regression analysis because the dependent variable had a very heavy positive skew. However, negative binomial regressions are most appropriate for count level dependent variables (Berk & MacDonald, 2008; Osgood, 2000). A second option for the analysis was to create ordinal or nominal dependent variables. However, creating each category in either an ordinal or nominal variable was difficult because there were no natural categorical cut-offs in my dataset. No natural cut-offs in the data leaves me open to a charge of creating arbitrary categories. This is a fair charge and I decided it better to avoid the issue all together. So the variables of transforming the total fine, fine as a percentage of revenue, total population, and corporate revenue took away the heavy skews. Subsequent descriptive statistics showed very little skew in these variables, which allowed for an OLS regression against the logarithmic dependent variables and more readable coefficients with the logged independent and control variables.

102

Crime as a Movement of Social Time

In this study I make the general presumption that all acts of pollution, environmental degradation, and resource over-use were a movement of social time, whether the acts were legal or illegal. In this study the cases I analyzed were movements of social time because prosecutions occurred and the actions taken by the corporate defendants in my dataset were criminal.25 In

Tables 11 and 12 I attempted to discover whether the movement of social time being punished was relational (e.g. unwanted invasion of the body) or vertical (e.g. causing death thus destroying wealth). Before moving on and testing these hypotheses, a baseline hypothesis needed to be established. In particular, I needed to determine that as companies illegally pollute at greater levels (as measured by the proxy variable Corporate Revenue), they would be punished in a corresponding manner (e.g. releasing of most pollutants = greater Total Fine). Therefore, I tested the following:

[BH]: As corporations release more pollution illegally into the environment, they will receive larger fines.26

If this hypothesis was correct, then would establish an empirical justification for using Black’s

(2011) theory of moral time to explain why some companies were assessed larger fines than others. Furthermore, support for this baseline hypothesis would provide general support for

Black’s (2011) theory that larger and faster movements of social time would be met with increasingly greater amounts of social control.

Table 11 demonstrates that as corporate revenue increases, the total fine increases as well. This relationship was statistically significant at the .01 level and held in model 2 after the introduction of the control variables I used in later models. In particular, model 2 demonstrates

26 BH = Baseline Hypothesis 103

that corporations violating an environmental law but do not actually create an environmental hazard will not be fined as much as companies that do cause environmental harm.27 Furthermore, companies that commit a felony offense will receive likely receive larger fines than corporations that commit a misdemeanor. Both of these relationships were to be expected.

Table 9 Corporate Revenue Regressed against the Total Fine (n=234) Model 1 Model 2 Corporate Revenue 0.37*** 0.38*** Felony Status 1.51*** No Hazard -0.98*** Constant 5.96*** 4.74*** F-Statistic 83.42 45.01 R^2 0.26 0.37 a. p > 0.01 = ***; p > 0.05 = **; p > 0.10 = * b. Dependent Variable: Total Fine levied against a corporate defendant.

At this point, a major question arose: Are the fines levied against corporations based upon an ability to pay or movements of relational or vertical time? Recall that in Chapter 2, I pointed out 18 USC 3572(a) required prosecutors to look at the ability of a corporate defendant to pay their fine. It is certainly possible that prosecutors give larger fines to corporations with the most revenue because the fine can be paid. However, Black (2011) argues that larger fines are given because there are greater and faster movements of social time preceding the fine. Table 11 provides strong support for Black’s (2011) argument that larger and faster movements of social time will be met with increasingly great amounts of social control. Therefore, Table 11 provides strong support for Black’s (2011) theory.

It must be remembered that support in Table 11 is based upon a presumption that all acts of pollution, environmental degradation, and resource over-use is a movement of social time

27 This finding actually supports Black’s (2011) theory and will be discussed in more detail with Table 13. 104

generally. Without more, I cannot say that it is a movement of relational/vertical time that is driving higher fines or some other phenomena. Tables 12 and 13 in the next section include independent variables that allowed me to directly measure whether or not it was larger and faster movements of relational time, vertical time, or both that provided the general support for Black’s

(2011) theory. Nevertheless, accepting the presumption that releasing pollution into the environment is always a movement of social time, the dimension the movements take place in notwithstanding, Table 11 provides strong support for Black’s (2011) theory.

Crime Causing Movements of Relational Time

Black’s (2011: 5) theory of moral time argues that crimes are movements of social time within the relational, vertical, or cultural dimensions. In this first section, I focus on crimes causing movements of relational time. The greater and faster a movement of social time is the more severe the conflict could be (Black, 2011: 5). The most severe conflicts then attract the greater amount of punishment (Black, 2011: 5). Black (2011) further argued that crimes committed by a superior against an inferior were less severe and would therefore attract less legal punishments.

As previously argued, illegally polluting the environment is a movement of relational time because it is an act of overinvolvement. Overinvolvement according to Black (2011) is a

“trespass, and might include anything from an overly personal question to a taboo sexual relationship, a burglary, a rape, or an invasion.” Pollution is overinvolvement because it is an invasion of toxins into the human body and natural environment.28 As stated in Chapter 5, companies that pollute over a period of time (rather than a single instance of illegal polluting)

28 It should be noted that 49 of the cases used in my dataset do not actually involve the release of pollutants into the environment. The violations are typically for things like shipping manifest violations or storage of chemicals without a permit. 105

should be considered the most severe. Furthermore, because corporations are presumed to have a higher social status than individuals, I expected to find that crimes against wealthy people would attract more punishment than those against poor people.

Therefore, in this first set of models I expected to find the following: (a) the most severe crimes (conceptualized as corporate revenue and the continuous release of pollutants) will lead to greater fines, (b) when the total population of people living within one mile of an environmental hazard increases, it will lead to greater fines, and (c) when the percentage of people with household incomes below the poverty line living within one mile of an environmental hazard increases, there should be a negative relationship with the total fine.

Table 10 Environmental Crime Causing Movements of Relational Time (n=234)29 Model 1 Model 2 Model 3 Model 4 Continuous Release 0.26 0.26 0.24 0.23 Corporate Revenue (logged) 0.38*** 0.38*** 0.38*** 0.38*** Total Population (logged) -0.07* -0.07 -0.05 -0.02 Low Status 0.01 -0.04 High Status -0.05 -0.07 No Hazard -0.81** -0.80** -0.80** -0.81** Felony Status 1.48*** 1.48*** 1.48*** 1.49*** Constant 5.18*** 5.21*** 5.07*** 4.83*** F-Statistic 28.01 23.23 23.45 20.07 Adjusted R^2 0.37 0.38 0.38 0.38 c. p > 0.01 = ***; p > 0.05 = **; p > 0.10 = * d. Dependent Variable: Total Fine levied against a corporate defendant

Table 12 shows partial support for Black’s (2011) argument that greater and faster changes of relational time lead to more social control. The continuous release independent variable was positively related with larger fines, but was not significant in any model. The

29 In a prior analysis I used an ordinal dependent variable to see if the results in Table 12 would be different from the OLS regression. Nothing changed and the models may be requested. I was also curious to see if the total population and concentration of low and high status people around a hazard site moderated the effect between corporate revenue and the total fine dependent variable. The idea here is that the total fine given against a corporation might be amplified if the crime occurs where there is a larger concentration of high status individuals. To test this, I split the total population, low and high status variables at their median and explored models with these factors along with interaction terms formed by multiplying each new dummy variable with corporate revenue. Results found no statistically significant relationships which shows there are no moderating interactions occurring. 106 corporate revenue variable is positively related to the total fine dependent variable and was statistically significant at the .01 level in all four models. Similarly, in all four models the felony status variable is positively associated with the total fine dependent variable and was statistically significant at the .01 level. This was expected because, as stated in Chapters 2 and 5, felony prosecutions automatically invoked a punishment scheme that has higher financial penalties than misdemeanors. Finally, the no hazard control variable had a negative relationship with the dependent variable and was statistically significant at the .05 level in all four models. This was also expected because actually releasing toxins into the environment is an aggravating factor, as stated in Chapter 2, and generally leads to a higher fine.

Surprisingly, models one through four showed that as fines against a corporation increase, the total population living within one mile of the environmental hazard decreases. This finding is only slightly significant at the .10 level in model one and lost its significance when I introduced the low and high status composite variables. Models one through four seems to suggest that as the total population living within one mile of an environmental hazard increases, the total fine will likely decrease. Thus, even though there may be a large amount of toxins released into the environment, possibly on a continuing basis, the total population living within one mile of the hazard is not a good predictor as to what a corporation will be fined. Therefore, I found only weak support for H1.

In models two through four I introduce the low and high status composite variables to test

H2. Model two shows that as the concentration of low status individuals increases, the fines assessed against corporations would likely get larger. This relationship is not significant at any level, and not predicted. Black’s (2011) theory predicts that the fines against low status individuals by a high status actor should decrease rather than increase. Similarly, the high status 107

composite is not in the predicted direction. When corporations commit an environmental crime in an area where a large concentration of high status individuals reside, their fines will become smaller; this relationship is not predicted by Black (2011). Like the low status composite variable, this relationship is not significant at any level. These findings are not surprising. In previous models I ran that were not reported here, my results indicated that the demographic makeup of the community living within one mile of an environmental hazard did not matter. For instance, both the percentages of households below the poverty line and with incomes above

$75,000 per year are statistically insignificant and both had a negative relationship with the total fine dependent variable. Therefore, looking at H1 and H2 together, it does not appear that larger, faster, and continuous movements of relational time result in greater amounts of social control.

A case comparison should help demonstrate what I found in Table 12. Based upon the findings in Table 12, a comparison between U.S. v. Radiators Inc and U.S. v. Northlake

Environmental best illustrates what is seen in Tables 2 and 3. Radiators, Inc., with an annual revenue of $648,000, pled guilty to discharging wastewater contaminated with lead and zinc into the local sewer system over a 7-month period without a permit and was fined $40,000.

Northlake Environmental, with an annual revenue of $2.6 million, plead guilty to discharging contaminated wastewater for a 2-month period in violation of their permit and was fined

$350,000. Both releases were continuous so they constitute a larger movement of relational time than if the illegal discharges were only one time occurrences. Northlake Environmental received the higher fine because, due to their size, they likely released more wastewater into the environment than Radiators, Inc. However, a total of 610 people live within one mile of

Northlake Environmental’s hazard while a total of 12,052 live within one mile of Radiators

Inc.’s hazard. Even though Northlake Environmental caused a greater change of relational time 108

because it released more wastewater into the environment, the spill affected a smaller number of people. Thus, in the end what matters is the greater movement of relational time caused by an environmental hazard(s)––the total number of victims (actual or potential) and their economic demographics do not seem to matter.

Before moving on it is worth discussing the no hazard variable and its relationship to the total fine. As Table 12 shows, in all the models the no hazard variable has a negative and statistically significant relationship with the total fine. In Table 12, I use the no hazard variable as a control. However, the no hazard variable’s relationship with the total fine is predicted by

Black’s (2011) theory. As I discussed in chapter 5, some violations of environmental law do not actually result in the release of pollutants. These violations of law are acts such as improperly filling out a shipping manifest, or storing chemicals without a permit or applying for a new permit. In these instances, it is expected the fines will be smaller because no movement of social time is actually occurring. Although I did not create a hypothesis for this finding, it nevertheless does provide some support for Black’s (2011) theory.

Crimes Causing Movements of Vertical Time

In the previous section I focused on the idea that an environmental crime causes movements of relational time and the more intense and severe the movement, the greater the punishment will be against the corporate defendant. Black’s (2011) theory also argued that a crime can cause a movement of vertical time. In this section I present models demonstrating that when environmental crime causes a movement of vertical time the most intense and severe movements attract greater punishments. The concepts in this section are the same because Black

(2011) argued that the most severe crimes cause a more intense and severe movement of vertical time, which should lead to greater punishment. 109

Black (2011) argues that the loss of wealth can lead to the social phenomena of overinferiority. As stated before, overinferiority occurs when a person or group falls below others that are not their social superior. People or groups may fall due to their own mistakes or the actions of a third party (Black, 2011). Nevertheless, individuals and groups falling below others that are not their social superior cause conflict, and the greater and faster the loss, the more trouble it will cause (Black, 2011). Therefore, Black (2011) proposes that conflict is a direct function of overinferiority.

Black (2011) argues that violence is a movement of vertical time. In particular, violence may cause a loss of wealth and is therefore a movement of vertical time (Black, 2011). Wealth includes the material conditions of existence such as food, shelter, currency, or means of production (Black, 2011). The body is also a form of wealth because if a person cannot work or move around, then they cannot produce wealth (Black, 2011). Thus, injury and sickness of a person may cause wealth fluctuations and the death of a person would have the greatest effect

(Black, 2011). Considering all factors, I expected to find that corporations violating environmental laws that result in the death or injury of humans or wildlife will receive higher fines than companies not causing a death or injury.

When companies illegally pollute they run the risk of causing people to get sick or even killing them. Illegal pollution may also kill off wildlife, which people use to feed their families, run businesses, and the like. Of the 234 cases analyzed in this dataset, a total of 38 cases resulted in the death or injury of a human being or wildlife. Of those 38 cases, 16 resulted in the death or sickness of people. Of those 16 cases, six resulted in the actual death of one or more human and the remaining 10 were known incidents of people developing sickness due to exposure to chemicals. In this study, I use known cases of death or injury. I focused on actual deaths or 110

injuries because the law does not allow individuals to claim compensation simply for the potential of being sick; in legal cases complainants must show “actual damages.” Thus, I focused only on actual, known cases of death or injury to humans or wildlife. My results are displayed in

Table 13.

Table 11 Crime Causing Movement of Vertical Time (n=234) Model 1 Model 2 Model 3 Model 4 Death or Injury 0.67 0.28 Human Death or Injury 1.56*** 0.86* Continuous Release 0.22 0.24 Corporate Revenue 0.38*** 0.38*** (logged) Felony Status 1.47*** 1.41*** No Hazard -0.86** -0.77** Constant 11.37*** 10.73*** 4.67*** 4.69*** F-Statistic 4.39 6.71 27.34 28.13 Adjusted R^2 0.02 0.05 0.36 0.37 a. p > 0.01 = ***; p > 0.05 = **; p > 0.10 = * b. Dependent Variable: Total Fine levied against a corporate defendant.

In model one, I ran a binary regression, which shows the death or injury independent variable is positively related to the total fine dependent variable but it is not significant. This provides no support for Black’s (2011) theory that overinferiority leads to greater punishment. I found this to be rather surprising and decided to break up the death and injury variable.

Specifically, I created a new dummy variable that was coded as 1 for cases that resulted in human deaths and injury only; all other cases were coded as 0. In model two I ran the same binary regression with the new human death only dummy variable and found that it was positively and statistically significant at the .05 level with the total fine dependent variable.

Model two provides strong support for H3 because the killing of a human being, which is a great and rapid change in vertical time, significantly increases the chances of receiving higher fines for their hazard than companies that do not kill a person as a result of violating environmental laws.

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In models three and four I introduced the relational time variables as controls along with the felony status and no hazard control variables. I did this to see if there was potentially a relationship that may explain why there was so little support for H1 and H2 in Table 12. In model three I saw the original death/injury variable remained statistically insignificant while the continuous release, corporate revenue, felony status, and no actual hazard variables30 remained virtually the same in their significance with the dependent variable as in Table 12. Curiously, model four shows that the human death/injury variable remained statistically significant but it is only at the .10 level instead of the .05 level. The subsequent models searching for the moderating effect were not shown above and can be requested; not surprisingly the corporate revenue variable had a partially moderating effect on the human death variable and its relationship with the total fine dependent variable.

Law Preventing Movements of Social Time

Black (2011) states that “law forbids various movements of social time…” On its face, it would appear that Black is arguing that law bans actions like rape, burglary, and homicide.

Rape, burglary, and homicide are crimes, which, when committed, will cause a movement of social time (Black, 2011). Law, then, is enacted to prevent these movements of social time, which in turn decreases the likelihood of conflict. Black further noted that when a person or corporation was punished for a crime, there may be other movements of social time because social control does not “occur in a social vacuum.” For instance, Black states that if a child’s father murders its mother, not only will that child lose his mother, but the father, too, if imprisoned, and all he contributes to the family. Though there is no guarantee of future conflict,

30 Again, the no hazard variable’s relationship with the dependent variable does provide support for Black’s (2011) theory. See discussion of Table 12. 112

it is possible that by losing the mother and father, the movements of social time corresponding to the loss of family members may result in criminal activity later in life (e.g. retaliation against the father, stealing to survive, etc.). Therefore, not only does law forbid immediate movements of social time like rape, burglary, and homicide, but should also attempt to halt or at least limit future movements of social time.

In the environmental law context, legislation like the CWA and the Clean Air Act have criminal statutes in them prohibiting certain types of pollution. As stated earlier, with conceptual qualifications, pollution is a movement of relational time because it is an invasion of the body with foreign chemicals or a movement of vertical time if people or wildlife are killed or injured.

When these movements of social time occur, they attract various levels of social control. If a corporation is fined for violation of environmental criminal statutes, it is possible the punishment could cause other movements of social time if companies are forced to go out of business or lay employees off. Therefore, there should be laws in place that work to limit or forbid future movements of social time. In fact, there are laws that exist that give prosecutors the ability to lower potential fine amounts or offer repayment plans so that corporations do not have to go out of business or lay employees off because of the punishment assessed for violating environmental criminal laws (see Chapter 2 discussion on 18 USC § 3572).

Table 14 presents the models I used to test the argument that law limits future movements of social time. In this table, I introduce a new dependent variable and independent variable. The dependant variable is whether or not a repayment plan is given to the corporate defendant to make it easier for the company to pay their fines. Cases where companies that were given a repayment plan are coded as 1 and all others were coded as 0. Because the repayment plan dependent variable is nominal, I had to use a logistic regression analysis rather than a traditional 113

ordinary least squares regression. Non-logistic regression models are necessary for nominal dependent variables for a number of reasons. First, having a nominal dependent variable means the model is inherently non-linear, which is required for an ordinary least square regression analysis. Second, using an ordinary least squares regression analysis with a nominal dependent variable means the error terms will not approximate properly to a normal distribution; further the error terms in the output are not going to be homoscedastic (e.g. error terms have an unequal variance across levels of the independent variables). Finally, ordinary least square regression analysis on a nominal dependent variable presents standard errors on the regression line slopes that are incorrect because they are inefficient, inconsistent or larger/smaller than they should be.

Thus, significance tests are likely misleading.

Table 12 Repayment Plans Preventing Future Movements of Social Time (n=23431) Model 1 Model 2 Model 3 Model 4 Fine as percentage of 1.25*** 1.27*** 1.26*** 1.28*** Corporate Revenue (logged) Death 0.62 0.64 Human Death 0.36 0.37 Continuous Release 1.92** 1.96** 1.97** 2.01** Total Population (logged) 1.01 1.02 1.02 1.02 Felony 0.72 0.75 No Hazard 1.67 1.54 Constant 0.31*** 0.37*** 0.29*** 0.36*** Chi Squared 21.61 22.46 22.88 23.56 Pseudo R^2 0.07 0.07 0.08 0.08 a. p > 0.01 = ***; p > 0.05 = **; p > 0.10 = * b. Dependent Variable: Repayment Plan.

The new independent variable introduced was the total fine as a percentage of corporate revenues. As Tables 11, 12 and 13 demonstrate there were certain variables that predicted when corporations would receive higher fines than other companies for committing similar crimes.

31 Table 14 does not present coefficients. The numbers presented are odds ratios. 114

The main predictor was corporate revenue. The larger the annual revenues a corporation has, the more likely that same company will receive a large total fine.

There is good reason to believe that if a company has high yearly revenue and receives a large monetary fine, there will be little trouble with paying the fine. For example, Tyler Pipe, which is one of the largest pipe manufacturers in the country, released carbon monoxide and lead into the local air and water systems without a permit for about 30 days. For this continuous violation of environmental law, Tyler Pipe was fined $4.5 million. Tyler Pipe’s annual revenue is

$1.5 billion. While the $4.5 million fine was certainly substantial, the fine only represented a total of 0.31% of Tyler Pipe’s billion-dollar revenue. Because the fine was such a small percentage of Tyler Pipe’s annual revenue, it was unlikely that they would need a repayment plan or be offered to assist in repaying the fine. On the other hand, ECO-Solve, which knowingly installed faulty grease traps in a local restaurant allowing around 3,000 gallons of wastewater to enter local sewers per day, was fined $161,000 and the company had an annual revenue of $1.9 million. For ECO-Solve, the fine made up 8.25% of their yearly revenue.

Unlike Tyler Pipe, ECO-Solve likely needed help paying their fine. In fact, prosecutors gave ECO-Solve a repayment plan of $445 per month until the entire $161,000 fine was paid in full. Without the repayment plan, there was good reason to think ECO-Solve would lay employees off or go out of business. Therefore, Black’s (2011) theory predicted that as fines took up larger amounts of a corporation’s annual earnings, the likelihood of a repayment plan increased so as to limit or prevent future movements of social time.

Generally speaking, Table 14 provides strong support for H4. The primary finding from

Table 14 is that as fines encumber larger percentages of corporate revenue, the likelihood of receiving repayment plans increases. This relationship was statistically significant at the .01 level 115

in all four models as presented in Table 14. In models one through four the odds of receiving a repayment plan was 1.25 times to 1.28 times greater than the odds for corporations that were assessed fines taking up a smaller percentage of their annual revenue. The findings held even when controlling for changes in relational and vertical time. Therefore, Table 14 demonstrates strong support for H4 because it shows prosecutors are looking forward and using law to prevent future movements of social time; the prior bad acts of a corporate defendant notwithstanding.

The support for H4 remained even after introducing variables measuring movements of relational and vertical time and controlling for felony status and no actual hazard. Interestingly, the death of people or animals did not increase the odds of receiving a repayment plan. In models one and two, corporations that killed people or animals were 38% and 36% less likely to receive a repayment plan. When the death was limited to a human, only the odds of receiving a repayment plan were reduced. In models three and four, corporations that killed people only are

64% and 65% less likely to receive a repayment plan than companies not causing the death of a person. Though the relationships between death and the likelihood of receiving a repayment plan is not significant, the findings do make conceptual sense in Black’s (2011) theory. Remember, law is supposed to forbid movements of social time. The death of a person is a large and great movement of vertical time because it causes overinferiority. When companies kill, they are supposed to pay money to the victim’s family to compensate them for the loss. Denying repayment plans in these situations makes sense because if law is supposed to forbid movements of social time, forcing companies that kill people to compensate a victim’s family then should be immediate and in full. This was essentially what Table 13 demonstrates–– companies that kill people in connection to their environmental hazard are forced to pay their fines and compensate

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the victim’s family as quickly as possible to limit the amount of future conflict resulting from the change in vertical time.

Finally, in Table 14 I found that with the continuous release variable there was a statistically significant relationship with the dependent variable at the .05 level in all four models. Effectively, this means that corporations polluting over a period of time are anywhere between 1.92 and 2.01 times more likely to receive a repayment plan than companies that pollute on a single occasion. This finding makes sense because, as Tables 11, 12, and 13 demonstrated, polluting continuously increases the chances of receiving a large fine. Large fines for most, but not all companies, will take up a larger percent of corporate earnings hence the greater odds of receiving a repayment plan. Regarding the total population variable, there was no statistical significance with the dependent variable, which was expected because Tables 11, 12, and 13 all showed that the population living within one mile of a hazard had no influence on how corporations were treated by courts when levied punishment for committing an environmental crime. Lastly, the felony status variable was statistically insignificant in models two and four.

This also made practical sense. Essentially, companies that commit felonies are 28% and 25% less likely to be given a repayment plan than corporations committing misdemeanors. Felonies, in the environmental law world, are intentional acts; in other words, companies intended to violate the law. Practically speaking, it is unlikely courts and prosecutors would want to allow for repayment plans in these situations. The individual effects of each variable notwithstanding, whether the crime is a felony, results in a death, or is a continuous release or singular event, when the fines assessed against corporations take up large percentages of a corporation’s yearly earnings, the greater the odds those same companies will be given a repayment plan. This was exactly what Black (2011) predicted and I concluded Table 14 provides strong support for H4. 117

Supplemental Analysis: Why Law Is Created to Prevent Movements of Social Time

As previously stated, law can itself be a movement of social time, but also prevent movements of social time too (Black, 2011). Black (2011: 71) stated that conflict is a direct function of overinferiority. Overinferiority occurs when superiors fall below inferiors (Black,

2011: 71). Overinferiority happens for a variety of reasons as described by Black (2011). The most important reason for the purposes of this supplemental analysis was that law can cause overinferiority directly, or by exacerbating hard times (Black, 2011: 74).

Black (2011: 74-75) argued that poverty does not, by itself, cause crime; it is becoming poor that causes crime. Fines naturally cause companies to lose revenue––that is the nature of a fine. Because fines can cause overinferiority, companies can be given repayment plans to help alleviate the loss of social status experienced with over-inferiority. However, fines may also exacerbate the loss of social status a company is already experiencing.

It is certainly possible that as corporations lose revenue, they cut corners to maintain profitability; one way to do this is to not comply with environmental laws. Non-compliance with environmental laws can take many forms. For instance, in the case of United States v. Synpep

Corporation, the corporate defendant was in the business of selling polypeptides to the government. The strands were to be pure of peptide, but they were not. Recreating polypeptides is an expensive and time consuming endeavor and it was alleged by prosecutors that Synpep falsified their test results to show their product was pure of peptide in an effort to save money and maintain profitability. Ultimately, Synpep Corporation was losing money at the time of their crime. Using the Reference USA database, I was able to determine that Synep had an annual revenue of $10 billion two years prior to their crime. Synpep’s yearly revenue dropped to $2 million one year prior to, and in the year of their crime. 118 When a company like Synep is fined, it is possible the fine exacerbates the financial loses companies may be experiencing due to outside forces such as shrinking market demand for a product or service. Synpep was fined $43,972, which made up about 22% of the company’s annual revenue. Synpep’s loss of revenue, independent of the criminal fine, was conceptualized by Black (2011: 59) as a decrease in social status. Therefore, law (e.g. criminal fine) is a movement of social time because it causes a decrease in social status. However, law is also supposed to prevent movements of social time as well (Black, 2011). As discussed, a repayment plan is a way law can prevent too drastic of a movement in social time. In this section, I provide the empirical basis for my previous analysis in Table 11.

In particular, I want to empirically demonstrate that as fines increase, they will take up a larger percentage of yearly revenues. However, I also wish to show that this relationship is curvilinear because eventually companies will have so much income that even a large fine will still only be a fraction of a company’s yearly revenue. Additionally, without a repayment plan, they will not see a major decrease in their social status. I used a supplemental analysis because many of the variables used below were conceptualized to accomplish a significant goal in the main analysis. A supplemental analysis allowed me to re-conceptualize these variables to make a particular point that does not violate construct validity above.

Table 13. Curvilinear Relationship between Total Fines, Yearly Revenues as Regressed against Relative Fines (n=234) Model 1 Model 2 Model 3 Model 4 Total Fine (logged) 0.33 *** 1.06*** 1.07*** Yearly Revenues (logged) -0.62*** -1.01*** -1.01*** Felony -0.11 Population (logged) 0.0000018 Ongoing Discharge -0.03 Constant -4.02*** 10.27*** 3.93*** 3.97*** F-Statistic 16.31*** 189.58*** 1843.02*** 728.28*** Adjusted R^2 0.06 0.45 0.94 0.94 a. p > 0.01 = ***; p > 0.05 = **; p > 0.10 = * b. Dependent Variable: Relative Fine (logged) 119

In Table 15, models three and four are the most important to look at. In model three, I demonstrate that as total fines increase, they are more likely than not to take up a larger percentage of a company’s yearly revenue. However, as a company’s yearly revenue increases, fines will take up smaller percentages of their yearly income. Both of these relationships are significant at the .01 level. In model four, these relationships held even when I introduced the felony, continuous release, and total population variables as controls, none of which are significant at any level. What Table 15 demonstrates is that as fines increase they will likely lead to greater losses of social status. However, this only matters to a point. Eventually, companies will be making so much money (e.g. billion dollar revenues) that multi-million dollar fines will not lead to a massive decrease in social status. This is evidenced by United Stated by STX Pan

Shipping where the defendant corporation was fined $29 million, but with yearly revenue of $4.5 billion, the fine only made up 0.64% of the annual income. Despite receiving a multi-million- dollar fine, STX experienced a very small drop in their social status.

Table 15 demonstrates a curvilinear relationship between yearly revenues and total fines with relative fines. It was not my intention to determine where the peak of this curvilinear relationship occurred. My only goal was to demonstrate that there was an objective, empirical reason to believe a repayment plan would be given to corporations to prevent large drops of social status. Companies like STX simply do not need a repayment plan while others like Synpep might. Table 15 also helps us put Table 14 in context. Repayment plans are likely to be given as the relative fine increases; a goal of future research might be to determine where this tipping point occurs. In other words, future research needs to attempt to determine at what point a company’s revenue will decrease the likelihood of receiving a payment plan for their fines.

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Conclusion

In this chapter I presented the empirical findings for the models used to test Black’s

(2011) theory of moral time. Table 12 displayed models testing the concept that a change in relational time causes conflict. Specifically, I tested the idea that as corporations become overly intimate with people by continuously polluting and affecting larger numbers of populations the ensuing legal conflicts will be more severe. Table 12 provides weak support for H1 and no support for H2. Therefore, I concluded that corporations may pollute and cause large and rapid changes in relational time, but the ensuing legal conflicts will not necessarily be severe.

On the other hand, Tables 13 and 14 provide weak to moderate support for Black’s

(2011) theory of moral time. Specifically, I found in Table 13 that when corporations kill people they will likely receive a large fine. The killing of a person is a large and rapid change in vertical time because it creates overinferiority by taking away the ability to generate wealth not just for the individual but the victim’s family. Black predicted that such a large and rapid change in vertical time would cause severe conflict. As it stands, companies that kill people receive larger fines than those that do not cause a death but the variable loses meaning when the corporate revenue variable is intrudced. Larger fines are indicative of a severe conflict. Therefore, Table 13 provides strong support for Black’s theory.

Finally, in Table 14 I found that companies receiving fines that make up a large percentage of their annual revenue increases the odds of receiving a repayment plan to assist in paying a fine. As stated, corporations losing a large percentage of their yearly earnings will fall below other companies, which is a form of overinferiority. However, law is meant to forbid drastic movements of relational, vertical, and cultural time. Giving a corporation a repayment plan is a way to limit or even eliminate the movement of vertical time caused by fining a 121

company for committing an environmental crime. Therefore, Table 14 provides strong support for Black’s theory.

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CHAPTER 7: CONCLUSION

Black’ (2011) theory of moral time is a new and original approach to the study of conflict. This study is the first attempt to apply Black’s theory of moral time to legal conflict resulting from corporations violating federal criminal statutes designed to prevent corporations from releasing too much toxic material into the environment. Overall, the results of this study offer moderate support for Black’s theory of moral time. Additionally, these results suggest that with continued efforts to collect, refine, and analyze data related to corporate environmental crime, a more thorough understanding of why corporations receive differing punishments for violating environmental laws could be established. In this chapter I summarize the findings of my study, address limitations of the study, and suggest future directions for research.

Summary of Findings

All in all, conflict is, for Black (2011), ubiquitous. Conflict exists in one way, shape, or form as a natural part of the human experience (Black, 2011). The reality is most conflict will not result in the mobilization of law to settle a dispute. However, some conflicts need the law to end disputes. Conflicts involving the environment are among those instances where law is engaged to end conflicts between polluters and the polluted. Therefore, it is important to understand how law responds to individuals and corporations that pollute at levels that violate statutes designed to protect the environment and people from excess polluting.

As previously discussed, Black’s (2011) theory of moral time argues that large and rapid changes in social time result in an amount of social control (formal or informal) equal to the

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original change in social time. This study focused primarily on analyzing how the amount of social control levied upon corporations that violate federal criminal laws designed to protect the environment from excess polluting varied due to the changes in relational and vertical time caused by the violation of environmental law. Specifically, I argued that when corporations pollute, they cause a change in relational time because pollutants entering the body is an unwanted invasion that constitutes an act of overintimacy. Furthermore, some acts of pollution result in the death of humans and animals, which is a change of vertical time because the deaths constitute an act of overinferiority. When corporations pollute and cause larger and rapid changes in relational time and vertical time through the acts of overintimacy and overinferiority, the fines levied against them for law violation should be largest. Finally, I focused on the idea that punishment causes a change in vertical time because the fining of a corporation constitutes the act of overinferiority, thus potentially forcing corporations into hard times (e.g. laying off employees, going out of business, etc.). However, law is generally created to forbid rapid and large changes of social time and thus I argued when fines make up large percentages of a corporation’s annual revenue, those same companies will be most likely to get a repayment plan to help mitigate potential hard times (Black, 2011). The general summary of my findings is presented in Table 16.

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Table 14. General Summary of Findings Level of Support Hypothesis Strong Moderate Weak/No BH: As corporations release more pollution into the environment, they will receive larger fines. x

H1: Companies that continuously, and at greater levels, pollute in communities with the largest number of people living within 1 mile of the pollution site will receive the largest fines. x

H2: Companies that continuously, and at greater levels, pollute in communities with a greater percentage of low status individuals and households should receive smaller fines than areas with a larger x percentage of high status individuals and households.

H3: Companies that pollute which then causes the death of wildlife or a human being will receive higher fines than corporations that x release pollution that does not kill people or animals.

H4: Companies causing movements of relational and vertical time will be fined in a manner that will not likely lead to corporations x going out of business or laying off employees.

As stated, I found strong support for the baseline hypothesis stemming from Black’s

(2011) argument that larger and faster movements of social time, whether relational, vertical, or cultural, will attract the greatest amounts of social control. However, it should be remembered that these results are to be interpreted with caution. It is unclear from the models in Table 11 which movements of social time were occurring and being punished. It is certainly possible that that Table 11 is capturing multiple movements of social time occurring simultaneously. If this is true then it makes perfect sense why when looking at movements of social time, independently, in the relational and vertical dimensions, I find little to no support for Black’s (2011) theory.

However, it must be made clear I am only presuming that there are underlying movements of relational, vertical, or cultural time, occurring simultaneously, that drive the statistically significant relationship between the corporate revenue independent variable and total fine dependent variable. Tables 12 and 13 attempt to flesh out the results presented in Table 11 more

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fully. Nevertheless, and the incorrectness of my presumptions notwithstanding, Table 11 provides strong support for Black’s theory of moral time.

My results for hypotheses one and two were generally surprising. As Table 12 shows, companies that pollute the most, causing greater changes in relational time through the act of overintimacy, do receive the greatest fines. Surprisingly, the continuous release variable and the population demographics surrounding a hazard site do not have a statistically significant relationship with the dependent variable. In regards to the continuous releases of pollutants, the variable may be insignificant because the corporate revenue variable, which is my proxy measure for the total amount of pollution released into the environment, is already taking all types of hazards into effect. For instance, ongoing discharges may not actually be releasing more pollution into the environment than a onetime occurrence. It is certainly possible that Company

A releases 100,000 gallons of ethanol into a river over the course of 4 months while Company B releases 1,000,000 gallons of ethanol into the same river in one day. It is also possible that

Company A that is releasing 1,000,000 gallons of ethanol over a period of time and Company B only releases 100,000 gallons of ethanol at one time. Regardless of which scenario is occurring most often, the corporate revenue variable may already be taking them into account and thus controlling for continuous releases or one time releases will not have a significant relationship with the total fine dependent variable.

Concerning local populations living within 1 mile of an environmental hazard, the number of people subjected to pollution and their social status do not have any influence on the total fine assessed against corporations for violating environmental laws. My findings raised the question why do the victims of an environmental crime not have any influence on the outcome of a prosecution? The answer may be simpler than initially thought. The reality is that it is often 126

very difficult to determine if someone, or a group of people, have been a victim of an environmental crime (Williams, 1996). The examples rape, burglary, and homicide as used by

Black (2011) can easily identify an offender and a victim because the harms are immediate and visibly intrusive. Pollution is not something we ever actually see entering our bodies or immediately notice its harmful effects. Take for instance the example of Carlson (Meyer, 2014) discussed in Chapter 5. It took over 20 years for Carlson to realize he was victimized by over- exposure to toxic chemicals; his death was the result of almost 20 years of over exposure to toxic chemicals while the cases in my dataset do not even come close to detailing the same type of exposure as occurring to Carlson. Most companies that illegally pollute on a large scale or continuously disperse their pollutants over a large area of air, land, and water meaning individuals will only be overly exposed to a small amount of illegal pollution. I suspect that prosecutors are sanctioning the potential harms caused by illegal pollution, not any immediately and knowable harm. The issue of identifying victims of environmental harm is not new and is a source of great frustration to scholars studying victimization caused by pollution (Williams,

1996).

For instance, in U.S. v. Bugman Pest and Lawn (2012) two small children died while sleeping because insecticide was accidently sprayed into their bedroom. The Bugman case is not only tragic but rare. The most common scenario of people being harmed by pollution is demonstrated by the case of U.S. v. Luxury Wheels Plating (2004) where employees were getting sick on a continuous basis because they were being exposed to reactive acid waste without proper protection during discharge procedures under the company’s permit. Even the Luxury

Wheels prosecution is a rarity; the overwhelming majority of cases in my dataset never mentioned the harms of pollution within a community unless a victim can be readily identified. 127

Thus, what prosecutors seem to be punishing is the potential harm to populations living within a one-mile radius of an environmental hazard.

My findings regarding H1 and H2 suggest that prosecutors are punishing the potential movement of relational and vertical time. Black’s (2011: 59, footnote 21) does state that threats to the altering of social space constitute movements of social time and thus may be punished.

Black’s (2004: 16) article on terrorism, also discusses this issue, when he notes that a foiled and incomplete terrorist attack is a form of social control by would be attackers and may be met with legal punishments. A similar phenomenon seems to occur in my study. Even though there was no evidence that an environmental hazard caused immediate and direct harm to a local population, the potential for harm is being punished. This finding is even more evident in the fact that in every model run there is a negative relationship between the total fine dependent variable and the no hazard control variable. Companies that do not actually cause a movement of relational or vertical time when they violate an environmental law are punished, but just not as harshly as corporations that do cause movements of relational or vertical time. I did not construct a hypothesis to test this issue, but nevertheless the finding does provide support for Black’s (2011) argument that potential movements of social time will be punished, but just not as harshly, as actual movements of social time.

In regards to H3 and H4, the results strongly support Black’s (2011) theory of moral time. With H3, once I created the dummy variable for human deaths/injuries only, the results indicated killing a person led to a greater level of social control in response to the large and rapid change in vertical time. While it is true that corporate revenue has a slight moderating effect when introduced in Table 13 as a control variable (along with all the others), the end results remain generally the same. Finally, regarding H4 the models clearly show that fines making up a 128

large percentage of a corporation’s yearly revenue will lead to the increased likelihood of receiving a payment plan to help mitigate the change in vertical time resulting from legal punishments due to the violation of federal environmental criminal statutes. All in all, my findings provide general, but not total support, for Black’s (2011) theory of social time.

Limitations of the Dissertation

This study is the first of its kind. I have taken Black’s (2011) theory of moral time and applied it to corporate environmental crime; to date no researcher has done this. Despite applying a new theory to corporate environmental crime, thereby creating original research, there were limitations to my findings. I discuss a number of those limitations in this section.

The first major limitation is the generalizability of my results. Of the 388 criminal prosecutions brought between the 10-year time period between 2004 and 2013, I excluded 154 cases from my analysis. This means I was only able to analyze 60% of all prosecutions argued between 2004 and 2013. Being able to analyze 60% of the prosecutions in my 10-year time frame means I could argue my results are generalizable for the cases prosecuted from 2004 to

2013. I make this claim because there is no reason to indicate that the 40% of cases that were missing from my dataset were handled any differently than the included cases. It should be noted that criminal prosecutions against corporations for violating environmental laws date back to the

1970s. This means there is over 30 years’ worth of prosecutions I cannot generalize to.

Therefore, a limitation of this study was that it is not broadly applicable to all criminal prosecutions against corporations that violate environmental laws. A continued goal then would be to expand this dataset so that it can be more broadly generalized.

A second limitation is that I only analyzed prosecutions in federal courts. All 50 states have their own state specific environmental laws. This means that corporations can be prosecuted 129

in federal and state courts. Moreover, state agencies are able to prosecute corporations for violation of federal laws if the DOJ chooses not to pursue a prosecution. The point here is that there are prosecutions of corporations at the state level that I did not analyze. The major limitation here then is that my dataset is far from complete.

Similar to the first two limitations, a third limitation is that corporations can be punished for violating environmental laws through administrative and civil actions rather than criminal prosecutions. Administrative actions are brought by regulatory agencies like the EPA and civil actions can be brought by the DOJ or individual citizens.32 Furthermore, administrative and civil claims can be pursued in federal or state courts. Legal conflicts do not occur only in criminal court, and any study that does not account for administrative or civil actions cannot be considered comprehensive.33 Therefore, this study cannot be considered a holistic analysis of conflicts involving corporations and the violation of environmental laws because I did not account for administrative and civil claims occurring in lieu of criminal prosecutions.

A fourth limitation is that I could tell why one corporation may only receive an administrative penalty or a civil fine instead of a criminal prosecution. It is necessary to not only understand why two corporations committing similar environmental crimes receive different punishments, but also why similar acts result in punishments from an administrative or civil action instead of criminal prosecutions. Without incorporating administrative and civil claims

32 It is worth noting that without actual case information for administrative and civil actions, I do not know if companies, and their actions, that are prosecuted criminally rather than sanctioned administratively or civilly differ from one another. More specifically, I simply do not know if companies committing the worse environmental hazards are more likely to be prosecuted criminally or sanctioned administratively or civilly. 33 Even homicide claims and theft claims can be pursued in civil courts. For instance, plaintiff’s can bring a wrongful death claim against a person who kills but is not found guilty in criminal court or sue pursue a thief under civil theft or conversion claims. 130

into my dataset, I simply cannot say why some corporations are given administrative or civil penalties while others are prosecuted criminally.

The fifth limitation of this study has to do with measuring the social status of both the corporate defendant and victims. Black’s (2011) theory of moral time is geometrical, incorporating both movements of social time and the social status of disputants in a conflict. A full and complete analysis using the theory of moral time should include variables that measure changes in social time and the social status of disputants to the conflicts being studied. In the analysis chapter of this dissertation, I measured movements of relational time with the corporate revenue variable and was able to control for victims or potential victims’ vertical status (e.g. household incomes). However, corporate revenue is also a measure of a corporation’s vertical status and overall social status (Black, 1976; 1995; 2000). If I were to try and control for a corporation’s social status in this study, the corporate revenue variable would be used to measure two different concepts within the same model which would mean losing construct validity.34 I did presume corporations had a higher social status than the communities they pollute, but a presumption about social status was not the same as having an actual way to measure it.

If future research into corporate environmental crime were to take place using a Blackian perspective, an interesting question must be asked: Do corporations have a higher social status than governments and the natural environment? There is good reason to believe that corporations may have greater social status than a government (see generally Rothkopf, 2010; Stretesky et al.,

2013). Black (1976, 1998) generally presumed that the actor with the highest social status is

34 I should mention that there are variables in my dataset which could be used to measure the social status of a corporation besides yearly revenue. For instance, I have data regarding the number of employees a company has, how many years they have been in business, advertising budgets, etc. However, all of these variables are highly correlated with the corporate revenue variable. Though each variable may be conceptually distinct from corporate revenue their high correlations make using them problematic due to issues of multicoliniarity. 131

government, but in the corporate world this may not be true. Through lobbying actions and outright influence, corporations tell governments what laws to enact/repeal, and how they will conduct business and be taxed (Rothkopf, 2010; Stretesky et al., 2013). For my study, this issue simply did not come up because I did not control for each actor’s social status and the conflicts analyzed were between corporations and communities. However, if a researcher were to attempt a complete study using Black’s (2011) theory of moral time this issue would likely need to be addressed.

Further, in my analysis of movements of vertical time, I was able to measure human and animal deaths. However, I was unable to determine the social status of human victims. In the complaints, I analyzed that resulted in human death(s) there was never any mention of the decedent’s age, income levels, family memberships, and the like. My analysis could determine if larger changes in vertical time result in a greater penalty, but I am unable to say how penalties vary when controlling for the social status of the corporation and deceased human. Therefore, my study is limited because the changes in social time I was able to measure did not account for the social status of corporations or deceased victims.

A sixth limitation is that I used Donald Black’s (2011) theory of social time but did not measure certain variables that are often used when conducting a study within a Blackian perspective. For instance, I did not control for a corporate defendant’s normative status. Black

(1976) argues that people and organizations that have previously violated the law have low normative status and will therefore be punished more severely. Furthermore, another Blackian variable I did not measure is the levels of intimacy corporations have with the community where the environmental hazards occur (Black, 1976). According to Black (1976, 2011), the more intimacy that exists between individuals and organizations it can be expected that: (1) there will 132

be less punishment and (2) the movement of social time that causes conflict and future punishment will be considered smaller and slower. Black’s theories of social life (1976, 1995,

2000, 2011) are geometrical. To fully test Blackian concepts of social life, researchers need to incorporate as many variables that measure the social status of offenders and offended in a conflict in their studies. Unfortunately, this dissertation did not do this and is therefore an incomplete test of Black’s (2011) theory.

The seventh limitation to this dissertation is the lack of information regarding the cases excluded from my analysis. It is unknown if the missing cases in this dissertation bias my results. One way to help determine if these missing cases do cause any bias is to analyze the limited information I had for each one. For instance, there were some cases that I was able to locate company information (e.g. yearly revenue) for, but unable to get prosecution information off of PACER. Had I systematically recorded the yearly revenues for the missing cases, I could determine if companies that did not have information available on PACER had low, medium or high yearly incomes. Having this information would help determine if the missing cases are different in any meaningful way from the cases included in my data set and thus allow insight into the type of bias, if any; the missing cases have on the results. Unfortunately, I did not collect any information about missing cases as I did not realize at the time that the information might be useful for some readers.

The final limitation to discuss was that I could not determine if Black’s (2011) conception of moral time was actually correct. In Chapter 2 I gave descriptions of the major environmental laws like the CWA along with statutes laying out different factors prosecutors and judges must consider when crafting punishments against corporations or individuals. For instance, 18 USC § 3572(1) tells prosecutors to craft fines against corporations violating federal 133

environmental criminal laws with the corporate defendant’s income, earning capacity, and financial resources in mind. In other words, the law has prosecutors create fines based upon a corporation’s ability to pay as major determinant. A corporation’s ability to pay is measured by looking at annual revenue because companies with large revenues can pay the largest fines more easily than a smaller company. However, to measure movements of relational time under Black’s

(2011) theory of moral time, I used the corporate yearly revenue variable.

Earlier in this dissertation I compared cases which demonstrated some companies will release less pollution than others yet still receive higher fines because they have greater yearly revenues. I did this in order to call attention to the fact my using of the corporate revenue variable as a proxy measure for the amount of pollution being released into the environment by a corporation may be inaccurate. In order to determine exactly how the corporate revenue variable should be conceptualized, researchers need to sit down with prosecutors and ask them directly how they use, and to what extent, a company’s income helps determine what a criminal fine will be. I will discuss this issue further in the next section.

There is no doubting the positive relationship between higher yearly revenues and large fines. However, it was unclear if it was the movement (or potential movement) of relational and vertical time that really led to larger fines or the corporation’s ability to pay. Again, it must be stated that the presumption that corporations with the largest yearly revenues receive the greatest fines because they pollute the most, thus causing increasingly fast and great movements of social

(e.g. relational) time may be wrong. Therefore, without talking to prosecutors I just do not know and thus the limitation.

134

Future Directions for Research

In regards to future research, an effort to collect data on all prosecutions against corporations violating environmental laws is necessary. As stated, my dataset covers the years

2003 through 2014, yet prosecutions go back to the early 1970s. To fully understand the social dynamics that underpin prosecutions against corporations for violating environmental laws, researchers need to expand my dataset to include all past cases. Furthermore, researchers need to collect complete data on administrative and civil cases along with criminal prosecutions. This will provide scholars a true and complete understanding of how legal conflicts arising from the violation of environmental laws are handled. This will not be a simple task as many of these cases will not be uploaded to PACER thus requiring hours of searching through federal courthouse storage facilities. Nevertheless, the task needs to be undertaken.

Another direction for future research is to collect case information for administrative, civil, and criminal actions at the state level. This will address the second limitation addressed above. Just like my call to collect information on all past federal administrative, civil and criminal prosecutions collecting data on state cases will be extremely difficult. To my knowledge, there is no database like PACER that exists at the state level. Moreover, at the federal level, the DOJ is primarily responsible for pursuing civil and criminal actions for violations of environmental law. This is not necessarily true at the state level. For instance, in

California the attorney general may take administrative, civil, and criminal action against corporations that violate state and federal environmental laws. However, counties like Los

Angeles have dedicated environmental task forces in the local district attorney’s office that may pursue administrative, civil, and criminal actions also. Putting together a dataset that analyzes how state agencies pursue corporations that violate environmental laws requires navigating a 135

myriad of state level bureaucracies on top of locating the physical location of case files because there is no known state level electronic database like PACER to pull information from.

Finally, researchers must do more than quantitative analysis. As stated, my study cannot conclusively say Black’s (2011) perspective was actually correct; at best my results are suggestive. Qualitative researchers need to interview prosecutors, defense lawyers, and judges to get a realistic and fuller understanding about how these legal professionals approach the job of crafting punishments for corporations that violate environmental laws at both the state and federal levels.

Nevertheless, my dissertation does put forward the following predictions: (a) as corporate yearly revenue increases, the fine for violating an environmental law will increase; (b) corporations that kill a human being in the course of violating an environmental law will receive higher fines than those that do not kill; and (c) as fines encumber larger amounts of a corporation’s yearly revenue the likelihood of receiving a repayment plan will increase. These predictions hold true even when controlling for socially and legally relevant variables.

My findings also have relevance for other theoretical perspectives. For instance, the fact that fines do not seem to be at all influenced by the total population or the particular economic demographics living around a hazard site can inform future studies using an environmental justice framework. Furthermore, my finding that repayments plans are more likely to be given to a corporation when the fine take up increasingly large percentages of a corporation’s yearly revenue lends credence to Stretesky et al.’s (2014) argument that punishments against corporations for violating environmental laws will be titrated to ensure the companies are not put out of business and continue to produce. These implications are important because they can inform other perspectives without, as Black so often argues, researchers basing their findings on 136

propositions that are value-free and free of psychological and teleological leanings (Black, 2000,

2013).

That said, there is reason to be cautious in accepting Black’s (2011) argument that my predictions can be explained by movements of social time. Whether you accept Black’s (2011) argument or not does not matter at this point. What matters is that there is still so much more work to be done in order to understand why corporations violating environmental laws receive different punishments for similar acts.

137

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Appendix A

Correlation Matrix: Non-Logged Variables

Fine as Total Repayment Felony Continuous Death Revenue Total Below Over No % of Fine Plan Release Population Poverty $75K Hazard Revenue Line Fine as % 1.00 of Revenue Total Fine 0.25 1.00 Repayment 0.09 0.09 1.00 Plan Felony 0.17 0.04 0.03 1.00 Continuous 0.07 0.06 0.14 0.01 1.00 Release Death 0.01 0.15 -0.07 0.08 -0.04 1.00 Revenue -0.05 0.01 -0.08 -0.03 -0.03 0.003 1.00 Total 0.15 -0.04 0.16 0.001 -0.06 -0.08 0.19 1.00 Population Below -0.12 -0.09 0.03 0.01 0.13 -0.11 0.08 0.19 1.00 Poverty Line Over $75K -0.08 -0.09 -0.11 0.05 -0.05 -0.03 -0.01 0.06 -0.31 1.00 No Hazard -0.04 -0.03 -0.06 0.14 -0.56 0.14 0.09 0.12 -0.12 0.07 1.00

Correlation Matrix: Logged Variables

Fine as Fine Total Revenue No Over Below Continuous Felony Death Repayment % of (log) Population (log) Hazard $75K Poverty Release Plan Revenue (log) Line (log) Fine as % 1.00 of Revenue (log) Fine (log) 0.26 1.00 Total -0.01 -0.10 1.00 Population (log) Revenue -0.67 0.51 -0.05 1.00 (log) No Hazard -0.01 -0.19 -0.04 -0.12 1.00 Over $75K -0.01 -0.08 0.32 -0.04 0.07 1.00 Below -0.07 -0.01 0.52 0.06 -0.12 -0.31 1.00 Poverty Line Continuous -0.003 0.22 0.07 0.16 -0.56 -0.05 0.13 1.00 Release Felony 0.32 0.19 0.01 -0.16 0.15 0.05 0.01 0.01 1.00 Death 0.01 0.07 -0.08 0.03 0.14 -0.03 -0.11 -0.04 0.08 1.00 Repayment 0.25 0.10 0.02 -0.16 -0.06 -0.12 0.03 0.14 0.03 -0.07 1.00 Plan

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Appendix B

Fines Correlation Matrix

Total Fine Restitution Community Concurrent Repayment Fine Service Payments Plan Total Fine 1.00 Fine 0.99 1.00 Restitution 1.00 0.99 1.00 Community 0.99 0.99 0.99 1.00 Service Concurrent 0.29 0.35 0.29 0.27 1.00 Payments Repayment 0.09 0.09 0.09 0.09 -0.04 1.00 Plans

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Appendix C

Correlation Between Fines and Community Demographics Total Fine as Total White Black Hispanic Total High College Below Over Fine % of Population Minority School and Up Poverty $75K Revenue or Less Line Total Fine 1.00 Fine as % 0.25 1.00 of Revenue Total -0.04 0.15 1.00 Population White -0.13 -0.21 -0.09 1.00 Black -0.04 -0.05 0.16 -0.45 1.00 Hispanic -0.04 -0.004 0.35 -0.08 -0.03 1.00 Total -0.06 -0.05 0.38 -0.44 0.79 0.52 1.00 Minority High -0.13 -0.13 0.12 0.31 0.37 0.26 0.43 1.00 School or Less College -0.09 -0.04 0.19 0.41 -0.06 0.005 0.02 -0.17 1.00 and Up Below -0.09 -0.12 0.19 0.05 0.54 0.21 0.55 0.77 -0.20 1.00 Poverty Line Over -0.09 -0.08 0.06 0.46 -0.19 0.13 -0.03 -0.02 0.71 -0.31 1.00 $75K

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