Environmental Compliance Decisions in Urban, Suburban, and Rural Communities[*]
Matthew P. West
[*] This paper was part of a graduate class offered by professor Shelden in the fall, 2016, Department of Criminal Justice, UNLV.
Environmental Compliance Decisions in Urban, Suburban, and Rural Communities
Recent events, such as the water crisis in Flint, Michigan, illustrate why environmental issues are a primary concern for many communities (e.g., the disposal of hazardous waste; Edwards, 1996). Community characteristics influence the likelihood of, the types of, and the responses to environmental crimes (Clifford & Edwards, 2012; Wolf, 1983). For example, hazardous waste facilities are more likely to be located in low-income communities predominately composed of racial minorities (Bullard, 1990, 1993; Bullard, Mohai, Saha, & Wright; 2007), and regulatory responses to environmental crimes in those communities tend to be less severe (Bullard et al., 2007). Thus, the decision by a corporation’s top management to comply, not comply, or over-comply with environmental regulations might vary with community characteristics. The purpose of this research is to examine whether a facility’s location (i.e., urban, suburban, or rural) is related to compliance decisions.
A facility’s location might influence a manager’s decision to comply with environmental regulations; it might also be associated with the perceived likelihood of formal sanctions (e.g., criminal prosecution) and feelings of guilt and shame. Urban, suburban, and rural communities might vary in their quantity of organization (i.e., the capacity for collective action; Black, 2010), and therefore vary in the likelihood of, and type of, legal response to an environmental crime (see Black, 2010). Naturally, urban, suburban, and rural communities also vary in population density. In turn, a manager might make their compliance decision based on the perceived risk to human life if a violation occurs (e.g., toxins illegally released into a river in a rural area might harm less people than if it were to occur in an urban area). By understanding how a facility’s location relates to compliance decision-making, more effective regulatory strategies can be adopted (e.g., some locations might require more punitive sanctions than others), ensuring that all communities are equally protected from environmental crimes.
The Environmental Protection Agency (EPA), the primary agency charged with creating and enforcing environmental regulations in the U.S., generally defines environmental crimes as negligent and deliberate violations of environmental law (EPA, 2016). Environmental laws include laws such as the Clean Water Act (1972) and the Clean Air Act (1970). Importantly, this definition indicates that non-compliance with environmental regulation is only a crime insofar as the non-compliance is intentional. It is not surprising, then, that from 2011 through 2015 the average number of defendants facing criminal charges for environmental crimes was less than 250 (EPA, 2015). It is similarly not surprising that the number of environmental crime cases opened in 2015 was slightly over 200, whereas the number of initiated civil judicial and administrative cases (i.e., non-criminal cases) in 2015 was almost 2,500 (EPA, 2015).
In order to circumvent the EPA’s strictly legal definition, some environmental crime scholars invoke a “harm-based” definition (see Hillyard & Tombs, 2007) that includes any conduct (whether it is technically legal or illegal) of a corporation that negatively impacts ecosystems (e.g., Lynch & Barrett, 2015). Many of the same scholars point out that, although “street crime” is the typical domain of study for criminologists, environmental crimes have an equal—if not greater—effect on public health (Lynch & Stretesky, 2014; Lynch & Barrett, 2015; Stretesky, Long, & Lynch, 2013). For instance, a study by Lynch and Barrett (2015) compared estimates of death due to homicide with estimates of death due to small particle pollution from coal fired power plants (CFPPs) in 2010. Death due to homicide had a lower estimate of 14,612 and an upper estimate of 16,259, with a mean of 15,436; death due to CFPP small particle pollution had a lower estimate of 13,200 and an upper estimate of 34,000, with a mean of 23,600. Thus, a person was approximately 1.5 times more likely to die as a result of CFPP small particle pollution than as a result of homicide[P1] .
Whether one prefers the “harm-based” definition of environmental crime or the EPA’s “legalistic” definition of environmental crime, a key concern is the unequal distribution across communities of the negative consequences stemming from environmental issues. For example, pollution levels tend to be greater in low-income communities (Agyeman, 2005; Adamson, Evans, & Stein, 2002). Often, economic and business interests trump other interests when environmental issues emerge. Following Hurricane Katrina, for example, remediation efforts progressed more quickly in the Central Business District (a hub of tourism) than in low-income, non-Caucasian communities (Mata, 2012). A case study by Gaffin (1997) is also illustrative of this dynamic, but in the context of rural communities: A waste management company acquired land—originally purchased for recreation—in a rural town with a law banning the development of landfills; the company sued the town in order to change the law; the town’s leadership settled the case and altered the law, and the company secured a contract and approval to develop a landfill despite protests on the part of many citizens in the community. Many communities , particularly those which are economically depressed, might be less inclined to pursue sanctions against a corporation due to the economic benefits the corporation provides (e.g., employment, tax revenue). Thus, environmental non-compliance may be tacitly approved of or tolerated by some communities more than others (e.g., rural compared to urban communities)
Law and Organization
Gaffin’s (1997) study, in particular, might be viewed as an example of Black’s (2010) propositions concerning law and organization. According to Black, law and organization are quantitative variables. Law is governmental social control (e.g., the number of arrests is an indicator of law) and varies in style (e.g., penal law prohibits and punishes behavior); organization is the capacity for collective action (e.g., the number of administrative officers is an indicator of organization) and exists in any group. Black outlines a series of propositions concerning law and organization: 1) “Law varies directly with organization” (p. 86; e.g., corporations have more law than an individual); 2) “Law is greater in a direction toward less organization than toward more organization” (p. 92; e.g., a corporation is more likely to win a lawsuit against an individual than the reverse); 3) “In a direction toward less organization, law varies directly with organizational distance” (p. 93; e.g., a lawsuit against a corporation is more likely to be successful if it is initiated by a group compared to an individual); and 4) “In a direction toward more organization, law varies inversely with organizational distance” (p. 93; e.g., a lawsuit initiated by a corporation is more likely to win against an individual than a group).
These propositions are quite abstract, which is one of many reasons some scholars are critical of Black’s “theory” (see Greenberg, 1983), but his propositions provide a basic conceptual and predictive framework for understanding corporate compliance behavior in urban, suburban, and rural communities. In this framework, corporations, especially large corporations, tend to be more “immune” from penal law than individuals or small organizations: “Law is less likely to respond to organizational conduct as deviant, less likely to define it as criminal, and, even if so defining it, less likely to handle it as serious” (Black, 2010, p. 100). Particularly in circumstances where there is a “mismatch” in the quantity of organization, corporations have a distinct advantage. The waste management company in Gaffin’s (1997) case study acquired land in a rural town in 1990 and successfully sued the town in order to overturn a law banning landfills in 1991. It was not until 1994 that the ban was reinstituted (and it did not ban the original landfill proposed by the waste management company). According to Black’s propositions, this scenario would be less likely to occur if the rural town had instead been an urban city.
Black’s (2010) propositions, and elucidations of those propositions, suggest that corporations are generally treated leniently by the legal system (e.g., the EPA initiates more non-criminal cases than criminal cases; EPA, 2015), and when corporations are non-compliant, they are less likely to face legal sanctions as a result of action on the part of individuals or groups with less organization (e.g., a rural community might be less likely to successfully file and win a lawsuit against a large corporation than an urban or suburban community). Thus, Black’s theory predicts that environmental non-compliance should be most likely to occur in rural communities and least likely to occur in urban communities, with the likelihood of non-compliance in suburban communities falling in between these extremes. Furthermore, legal sanctions for non-compliance should be most likely to occur in urban communities and least likely to occur in rural communities, with the likelihood of legal sanctions for non-compliance in suburban communities falling in between.
An Integrated Approach to Environmental Compliance
There are two key limitations of Black’s (2010) theory with particular respect to the current study. First, the theory is entirely constrained to the social level—“it has nothing to do with how an individual experiences reality” (p. 7). Therefore, individual decisions can only be interpreted as an outcome of social variables. Second, and related, the theory is difficult to apply to scenarios of over-compliance with environmental regulation. Examination of over-compliance is important because of its implications for policy creation and law enforcement. When firms over-comply with environmental regulations there is no need for intensive regulatory oversight and new regulations can be more successfully adopted and implemented—prohibiting some behavior is only useful if it affects how often that behavior occurs (Tyler, 2006).
In Black’s framework, over-compliance can best be interpreted as a result of social control that is not law. For example, a firm’s managers might choose to over-comply with regulations to attain or retain a competitive advantage (Haines, 1997; see also Rorie, 2015), and therefore institute policies, and/or promulgate a particular corporate culture, which emphasize over-compliance (i.e., establish mechanisms of non-legal social control which promote over-compliance). In other words, this approach suggests the decision to over-comply is unrelated to the location of a facility. However, there may be individual-level variables that relate to the decision to over-comply in different locations. By integrating Black’s approach with an individual-level approach, compliance decision-making in urban, suburban, and rural communities can be more thoroughly examined (see Rorie, 2015, for a similar integrated approach to compliance decisions).
Rational Choice Theory
Paternoster and Simpson’s (1993, 1996) application of rational choice theory to corporate crime identified the certainty and severity of formal sanctions as one of the factors that predict the likelihood of corporate offenses. The decision to not comply or over-comply with environmental regulations might be associated with a manager’s perceived risk of formal sanctions if they do not comply. Based on Black’s (2010) propositions, a corporation would be most likely to receive formal sanctions for non-compliance in urban communities, followed by suburban communities, and then rural communities. However, this does not necessarily mean that individuals have different perceptions of risk of formal sanctions in urban, suburban, and rural communities. Logically, if an individual’s compliance decision is based on the risk of formal sanctions, then an individual has to have some perception of risk of formal sanctions. In other words, an individual cannot base their compliance decision on the risk of formal sanctions unless they first determine the risk of formal sanctions. Thus,
Research Question 1: Do individuals perceive the risk of formal sanctions as a result of environmental non-compliance differently in urban, suburban, and rural communities? More specifically, do individuals perceive the risk of formal sanctions as greatest in urban communities, followed by suburban communities, and then rural communities?
Hypothesis 1: If the answer to Research Question 1 is “yes,” then community type will indirectly relate to compliance decisions through perceptions of risk.
Guilt and Shame
Two additional individual-level variables are expected to relate to compliance decisions: Anticipated feelings of guilt and anticipated feelings of shame. Guilt and shame are both negative emotions that are likely to occur when a person commits a transgression against others (Cryder, Springer, & Morewedge, 2012). Guilt occurs when the person feels responsible for the transgression itself (Tangney, 1996; Tagney & Dearing, 2002), whereas shame occurs when the person feels bad about themselves for committing the transgression (Cryder et al., 2012). Guilt and shame differentially predict behavior. In general, guilt is associated with prosocial behavior (e.g., when people experience guilt, they attempt to remedy the harm they caused; Cryder et al., 2012; Tagney & Dearing, 2002) and shame is associated with antisocial behavior (e.g., when people experience shame, they tend withdraw from society; de Hooge, Zeelenberg, & Breugelmans, 2007; Hosser, Windzio, & Grieve, 2008). Guilt is negatively associated with recidivism but shame is positively associated with recidivism (Hosser et al., 2008; Tangney, Stuewig, & Martinez, 2014; Tangney et al., 2011; see also, Murphy & Harris, 2007). In the current study, then, an individual who anticipates feelings of guilt as a result of non-compliance will be less likely to not comply and more likely to over-comply. Conversely, an individual who anticipates feelings of shame as a result of non-compliance will be more likely to not comply and less likely to over-comply. Thus,
Research Question 2: Are anticipated feelings of guilt and shame related to a facility’s location (i.e., urban, suburban, or rural)?
Research Question 3: If the answer to Research Question 2 is “yes,” does the facility’s location indirectly relate to compliance decisions through anticipated feelings of guilt or shame?
Hypothesis 2: Anticipated feelings of guilt as a result of non-compliance with environmental regulations will be positively related to over-compliance and negatively related to non-compliance.
Hypothesis 3: Anticipated feelings of shame as a result of non-compliance with environmental regulations will be negatively related to over-compliance and positively related to non-compliance.
A group of researchers from the University of Maryland and Vanderbilt University recruited businesspeople to participate in a web-based survey. The sample of businesspeople was obtained from a company that provides targeted databases. Participants were mailed letters that described the study and included a link to the survey. A total of 717 participants completed the survey.
A factorial survey design was implemented. Participants responded to three hypothetical vignettes that depicted a manager engaged in environmental non-compliance (e.g., polluting the local waterway) and two hypothetical vignettes that depicted a manager engaged in environmental over-compliance (e.g., maintaining pollution levels lower than what is required). The facility’s location was manipulated across scenarios, such that each vignette described the facility as located in an urban, suburban, or rural community.
Participants reported the likelihood they would take the same action as the manager depicted in the scenario on a scale from 0 (no chance of environmental non-compliance or over-compliance) to 10 (100% chance of environmental non-compliance or over-compliance). Participants reported their perceived likelihood of formal sanctions by responding to three questions: “What is the chance the firm would be criminally prosecuted if you did what the manager did under these circumstances?”, “What is the chance that the firm would be sued if you did what the manager did under these circumstances?”, and “What is the chance that the firm would be investigated by a regulatory agency if you did what the manager did under these circumstances?” on scales from 0 (no chance) to 11 (100% chance). Participants reported their anticipated feelings of guilt by responding “yes” or “no” to the prompt: “Assume that you did what the manager did and it did not become known within or outside of the company. Would you feel guilty for acting as the manager did?” Participants reported their anticipated feelings of shame by answering “yes” or “no” to the question: “Would you feel a sense of guilt or shame if others knew that you had done this?”
To examine these research questions and hypotheses, a random coefficient regression analysis and a mediation analysis will be conducted. In this study’s factorial design, individual participants responded to multiple scenarios. Thus, when using the scenario as the unit of analysis, there may be random differences in the influence of the independent variables on the dependent variables across scenarios. For instance, the relationship between facility location and the likelihood of non-compliance might vary randomly across the offending scenarios. As a result, the residual variance is heteroscedastic. Random coefficient (RC) regression models are typically applied to data “clustered” in groups or levels (e.g., hierarchical linear models; see Raudenbush & Bryk, 2002; Cohen, Cohen, West, & Aiken 2003). Similar to multilevel models, the RC model is built by combining multiple regression equations. In the following illustration, the first equation is the basic regression equation and the second equation models the random effect.
These equations are then combined in the full equation:
Yi is a given outcome value for case i, β0 is the intercept (i.e., the average Y value when variables are mean centered), γ10 is the grand slope (i.e., the effect of the independent variable when accounting for random effect), γ11 is the effect of the interaction between the independent variable and the random variable, u1i is the random slope error (i.e., random deviation from grand slope), and ri is the case-level error. Notably, the final equation is essentially equivalent to a regression equation that includes an interaction with an additional error component (i.e., random slope error).
After the RC model is examined, a mediation analysis will be performed in order to estimate indirect effects (e.g., perceptions of the likelihood of formal sanctions might mediate the association between facility location and compliance decisions). Mediation analysis will be conducted following Hayes’ (2013) suggested procedures. Data will be screened and checked for statistical assumptions prior to analysis (e.g., missing value analysis, variable distribution inspection).
The primary purpose of this research
is to examine if a facility’s location (urban, suburban, or rural) is related to
compliance decisions. Community characteristics are generally related to the
likelihood formal sanctions and the types of responses to environmental harm
(Agyeman, 2005; Adamson et al., 2002; Bullard et al., 2007; Clifford & Edwards,
2012; Wolf, 1983). This study will apply an integrated approach drawing on
Black’s “pure” sociological approach to the behavior of law (2010), Paternoster
and Simpson’s (1993, 1996) application of rational choice theory to corporate
crime, and literature concerning the emotions of guilt and shame (e.g., Cryder
et al., 2012). Results will contribute to a theoretical understanding of
environmental compliance decisions, and thereby will have implications for
regulatory strategies aimed at ensuring environmental compliance in urban,
suburban, and rural communities.
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 Sampson (2009) makes a roughly analogous point in emphasizing perceptions of neighborhood disorder rather than so-called “objective” measures of neighborhood disorder.
 These definitions of guilt and shame are typically found in the psychological literature and are analogous to Braithwaite’s (1989) “reintegrative shame” and “disintegrative shame,” respectfully (Tangney, Stuewig, Mashek, & Hastings, 2011).
 It is worth emphasizing that there are nuances to the relationship between shame and offending. The literature generally suggests that this relationship is mediated by other variables, such as the externalization of blame (e.g., see Tangney et al., 2014). Thus, it is not necessarily shame in-and-of-itself that results in recidivism—hence Braithwaite’s (1989) distinction between reintegrative shame and disintegrative shame.
 Although the terms “guilt” and “shame” are used virtually interchangeably in this measure, it is a measure of shame because it stresses the negative emotional experience as a result of social awareness of the transgression. Whether or not others are aware of the transgression, a person can feel guilty because they feel responsible for the transgression. Conversely, whether or not a person is responsible for the transgression, a person can feel shame because others are aware of the transgression—a person can experience anxiety that stems from the fear that others perceive them negatively because of the transgression.