Monitoring, Enforcement, & Environmental Compliance: 

Understanding Specific & General Deterrence 

State-of-Science White Paper 

October 2007 

Prepared for the Environmental Protection Agency’s Office of Research and 

Development (ORD) and the Environmental Protection Agency’s Office of Enforcement 

and Compliance Assurance (OECA) by Jay P. Shimshack. 

1 
AUTHOR CONTACT INFORMATION: 

Jay Shimshack 

Department of Economics 

8 Upper Campus Road 

Braker Hall 

Tufts University 

Medford, MA 02155 

617.627.5947 

jay.shimshack@tufts.edu 

DISCLAIMER: 

This document is prepared under contract in response to EPA RFQ TC0078. The views in 

this document are solely those of the author and do not necessarily represent the opinions 

of the Environmental Protection Agency or any of its employees. 

This report reviews research on the current state of science in environmental deterrence 

measurement and develops findings and recommendations based upon technical 

feasibility, without assessing implementation costs. No work has been done to determine 

the costs or practicality of implementing the deterrence measures discussed in this report. 

In other words, this report does not determine the costs to the EPA of conducting any 

specific information gathering, deterrence measurement, or statistical analysis nor does 

this report assess the feasibility of doing so given the OECA’s workload, workforce, and 

budget. 

ACKNOWLEDGEMENTS: 

The author is grateful to Ken Gigliello, Dinah Koehler, Jon Silberman, and Scott Throwe 

for sharing their institutional expertise. 

2 
Executive Summary 

Regulatory punishment for pollution violations is a mainstay of nearly every 

industrialized nation’s environmental policy, and a rich theoretical literature examines 

enforcement in general and environmental enforcement in particular. A smaller empirical 

literature studies the determinants of environmental compliance and behavior. 

Understanding real-world factors, however, is essential to the design and implementation 

of effective and efficient environmental regulation. This report reviews the policyrelevant environmental compliance literature, with emphasis on the growing empirical 

literature measuring the specific and general deterrence effects of environmental 

monitoring and enforcement. 

Major findings strongly supported by the related literature include: (1) 

Regulation, monitoring, and enforcement have historically been, and remain, critical 

determinants of environmental behavior. (2) Environmental monitoring and enforcement 

activities generate substantial specific deterrence. (3) Environmental monitoring and 

enforcement activities generate substantial general deterrence. (4) Environmental 

monitoring and enforcement activities may generate significant emissions reductions, 

even for sector/contaminant combinations where compliance is typically high. (5) 

Enforcement and monitoring responses are heterogeneous across sources. (6) 

Quantitative databases and statistical methods exist for deterrence measurement. (7) 

Qualitative survey methods exist for deterrence measurement. 

Major findings suggested, but not conclusively demonstrated, by the literature 

include: (1) The regulator reputation effect underlying general deterrence tends to decay. 

(2) There are limits to the “reach” of the reputation effect underlying general deterrence. 

(3) Deterrence varies across enforcement and monitoring instruments; inspections, 

intermediate enforcement actions, administrative fines, and criminal actions have 

different compliance impacts. (4) Sanctions should generally increase for repeat 

offenders, but important caveats to this rule exist. 

Key recommendations, based on the author’s subjective professional assessment 

of the related literature and the state of knowledge, include: (1) OECA should consider 

closely replicating statistical database analyses for measuring the specific and general 

deterrence effects of monitoring and enforcement. (2) OECA should consider closely 

replicating qualitative survey analyses for measuring the specific and general deterrence 

effects of monitoring and enforcement. (3) OECA should consider expanding existing 

qualitative survey analyses for measuring the specific and general deterrence effects of 

monitoring and enforcement. (4) For both quantitative and qualitative methods, studies 

based upon a universal metric are not advised. (5) Specific compliance rates and 

deterrence metrics should be statistically valid. 

3 
1. Purpose & Scope 

Regulatory punishment for pollution violations is a mainstay of nearly every 

industrialized nation’s environmental policy, and a rich theoretical literature examines 

enforcement in general and environmental enforcement in particular. A smaller empirical 

literature studies the determinants of environmental compliance and behavior. 

Understanding real-world factors, however, is essential to the design and implementation 

of effective and efficient environmental regulation. This report reviews the policyrelevant environmental compliance literature, with emphasis on the growing empirical 

literature measuring the specific and general deterrence effects of environmental 

monitoring and enforcement. 

The review highlights peer-reviewed research in domestic environmental settings. 

It focuses on peer-reviewed, published studies to ensure both quality and replicability. 

While many of the papers discussed were supported by the EPA’s Office of Research and 

Development (ORD) Science to Achieve Results (STAR) program, this white paper 

typically considers the ensuing journal articles rather than the underlying STAR Grant 

final reports. Particularly important conference or policy papers that have received 

substantial professional or institutional vetting are also investigated. The report 

emphasizes the domestic environmental context to reference results most directly 

applicable to the Agency’s mission. However, significant papers from other policy 

settings, like occupational safety, nuclear regulation, and tax compliance, are investigated 

when their methods or conclusions yield substantial insights for environmental 

deterrence. Similarly, the report discusses especially important results from other 

countries’ environmental contexts. 

This review stresses recent empirical research relevant to the measurement of 

general and specific deterrent impacts of compliance monitoring and enforcement. 

Seminal articles that strongly influenced the current state of science are referenced and 

explored, but less fundamental articles with publication dates prior to 1999 are generally 

not considered. This restriction avoids redundancies, generates a manageable final 

product, and emphasizes studies most relevant to today’s policy context. The particularly 

interested reader should consult Cohen’s [1999] excellent literature review or the EPA 

Compliance Information Project’s [1999] Literature Summaries. Theoretical studies are 

typically referenced only to establish an appropriate conceptual framework, since 

effective and efficient policy implementation requires additional research that validates 

and assigns empirical magnitudes to important theoretical predictions. However, where 

vital insights can be gained from theory and experiential data are unavailable, more 

abstract details are provided. 

This report reviews research on the current state of science in environmental 

deterrence measurement and develops findings and recommendations based upon 

technical feasibility, without assessing implementation costs. No work has been done to 

determine the costs or practicality of implementing the deterrence measures discussed in 

4 
 

this report.1 In other words, this report does not determine the costs to the EPA of 

conducting any specific information gathering, deterrence measurement, or statistical 

analysis nor does this report assess the feasibility of doing so given the EPA and OECA’s 

workload, workforce, and budget. 

This white paper proceeds as follows. Section 2 briefly reviews the literature on 

factors that influence environmental compliance. The discussion corresponds to 

Statement of Work sub-question 1C, “What confounding variables (variables other than 

monitoring and enforcement) impact environmental performance and responsiveness to 

regulatory actions?” The intent of this section is to place the literature examining the 

deterrence effects of monitoring and enforcement in context. 

Sections 3 and 4 explore the deterrence impacts of compliance monitoring and 

enforcement in detail. Section 3 highlights the deterrence effects of regulator actions on 

the sanctioned or monitored firm, otherwise known as specific deterrence. Section 4 

investigates the mechanisms and magnitudes of general deterrence, where regulatory 

actions directed towards one facility affect the behavior of other facilities. Sections 3 and 

4 correspond to the Statement of Work’s core question “How can OECA measure the 

general and specific deterrent impacts of compliance monitoring and enforcement?”, subquestion 2 “What approaches to deterrence measurement are suggested by the relevant 

literature?”, and sub-question 1 “What does the literature identify as the key factors 

motivating response to monitoring and enforcement actions?” Sections 3 and 4 dissect 

key studies to examine how the authors measured deterrence and consequently provide 

specific examples for OECA to follow. 

Section 5 synthesizes research methodologies and presents frameworks for 

deterrence assessment. Additionally, Section 5 references a technical appendix that 

provides specific quantitative tools for database analyses of deterrence. This discussion 

corresponds to Statement of Work sub-question 2, and addresses “What approaches to 

deterrence measurement are suggested by the relevant literature?” and “What information 

currently collected by the EPA could OECA use to measure deterrence?” 

Section 6 discusses major findings and recommendations. The section first 

presents conclusions that can be definitively supported by the literature, then reviews 

important findings suggested (but not definitely confirmed) by the literature, and finally 

offers ideas for further exploration. Subsequent recommendations follow Statement of 

Work sub-question 3 and suggest considerations for OECA actions and priorities. 

2. Factors Influencing Environmental Performance 

Numerous factors influence environmental compliance, and they can be loosely 

grouped into two categories. The first set of determinants is postulated by a standard 

economic model of the facility as a rational profit-maximizing decision-maker. Here, 

1

 Discussions with personnel at the Office of Enforcement and Compliance Assurance (OECA) suggest that 

estimating the costs and benefits of practical implementation from academic studies presents significant 

difficulties. 

5 
compliance factors include traditional regulatory monitoring and enforcement, pollution 

abatement costs, the ability to influence future regulatory action, community 

characteristics and components of consumer demand, and investor pressure. The second 

set of determinants goes beyond traditional law and economic models, and includes 

management knowledge, managerial attitudes, and organizational structure. 

The goal of this section is to briefly review the literature on non-regulatory 

compliance factors to put later discussions of the deterrence effects of monitoring and 

enforcement in context. There are two key messages. First, many factors influence 

environmental performance. Second, despite the diverse determinants of environmental 

behavior, the literature indicates that regulation, monitoring, and enforcement have 

historically been, and remain, critical determinants of environmental outcomes. 

2.1 Factors Predicted by the Standard Economic Model 

In the standard economic model, the facility chooses its environmental 

performance by balancing the expected costs of polluting with the expected benefits of 

doing so. This is consistent with the traditional law and economic framework that 

originated with Becker [1968] and Stigler [1970], and progressed as summarized in 

Polinsky and Shavell [2000]. Russell, Harrington, and Vaughn [1986] comprehensively 

applied this analytical framework to environmental behavior. 

Perhaps the most natural explanation for environmental performance is marginal 

variable costs of abatement, as determined by technology, industrial characteristics, and 

other factors. If marginal abatement costs are small, facilities may maintain low 

emissions even in the absence of significant regulations. Unfortunately, published peerreviewed studies examining abatement costs are limited. Relatively recent studies include 

McClelland and Horowitz [1999] for pulp and paper water pollution and Swinton [1998] 

and Rezek and Campbell [2006] for electricity air emissions. Results generally suggest 

significant marginal abatement costs, indicating that firms are unlikely to pollute at low 

levels in the absence of regulations or other pressures. Further, a key finding is that 

variable costs of contaminant reductions are extremely heterogeneous across both sources 

and industrial processes. Uniform regulatory policies and actions are therefore unlikely to 

achieve socially efficient outcomes. 

Another important determinant of environmental behavior is firms’ ability to 

influence future regulatory action. In a seminal paper, Maxwell, Lyon, and Hackett 

[2000] proposed a game theoretic framework where firms preempt future regulations by 

voluntarily reducing pollution emissions. The intuition is straightforward. Firms that 

expect consumers to lobby for stringent environmental regulation might voluntarily incur 

the necessary costs to reduce current emissions if this reduction prevents consumers from 

lobbying in the first place. In other words, facilities accept increased current abatement 

costs to prevent even larger regulatory costs in the future. 

The published empirical evidence supporting the influence of preemptive 

behavior on environmental outcomes is suggestive, but not yet definitive. Maxwell, 

6 
Lyon, and Hackett [2000] tested their theory by regressing state’s releases of 17 TRIreported toxic chemicals (per dollar value of shipments) on state-level demographics, 

political variables, and other controls. They concluded that the negative relationship 

between increases in a state’s conservation group membership and that state’s reported 

toxic releases was evidence for their regulatory threat theory. However, their sample size 

was small, it was difficult to disentangle the effects of regulatory threats from TRI 

reporting itself, and other mechanisms like consumer demand might well explain their 

empirical regularity. 

Community characteristics may influence environmental behavior in several 

ways. For example, community pressure may influence the threat of future regulation as 

discussed above. Locational characteristics may also correlate with political action or 

bargaining power more broadly, and facilities in wealthier, better educated, and more 

socially-active neighborhoods may face greater incentives for pollution abatement. Sitespecific factors may also relate to consumer purchasing power and preferences, and 

therefore community composition may influence environmental performance (Arora and 

Gangopadhyay [1995], Kirchoff [2000], and Cavaliere [2000]). Finally, it is possible that 

pure discrimination influences emissions, although the empirical evidence for this 

“environmental injustice” hypothesis is debatable (Hamilton [1995], Wolverton [2002], 

Becker [2004]). Of course, community characteristics may also influence the level of 

monitoring and enforcement directed at a specific facility. This indirect effect is 

subsumed in the specific and general regulatory deterrence discussed in Sections 3 and 4. 

An empirical literature does indeed detect a significant influence of community 

characteristics on environmental behavior. Arora and Cason [1999] found that zip-code 

level TRI-reported toxic releases were correlated with numerous economic and 

demographic characteristics. Results varied significantly across regions, however. 

Earnhart [2004b] determined that a community’s income, population density, 

unemployment rate, and average political affiliation influenced facility-level 

environmental performance at Kansas wastewater treatment plants. Becker [2004] found 

that some ideological and socio-economic factors influenced air pollution abatement 

expenditures at manufacturing plants. In contrast, however, several notable characteristics 

like voter turnout, education, and race/ethnicity did not yield consistent environmental 

implications. Most recently, Stafford [2007] analyzed the impacts of consumer 

preferences (as proxied by market size and the number of competitors) on hazardous 

waste violations reported by federally regulated facilities in the EPA’s Biennual 

Reporting System. The author hypothesized that more competitive local markets would 

be associated with fewer violations because consumers favoring environmental 

performance have more consumption options. Indeed, results indicated that increased 

local market competition induced increased compliance with waste management statutes. 

Locational composition results are not restricted to domestic institutions, and may 

matter more in settings with limited formal regulation. For example, Pargal and Wheeler 

[1996] found statistically and economically strong relationships between the BOD 

pollution intensity of Indonesian industrial sources and income, education, and 

population density. During the sample period, formal regulations in Indonesia were 

7 
largely absent. In contrast, Dasgupta et al. [2000] established that the degree of direct 

influence by neighbors and community activism on Mexican firm’s environmental 

practices was insignificant. Results suggested that local pressures operated primarily 

through regulation in the more formal Mexican legal setting. None-the-less, despite the 

mature regulatory regimes in the US, Canada, Australia, and New Zealand, Kagan et al. 

[2003] discovered that managers from pulp and paper mills located in these countries all 

reported significant effects of social pressures on water pollution and spill control. 

Ownership structure and investor pressure may also affect environmental 

performance in the traditional economic model. For example, subsidiaries may be less 

inclined towards emissions reductions since liability rules protect their parent companies. 

Indeed, Grant and Jones [2001] studied TRI toxic releases by 2000 chemical facilities and 

found that subsidiaries had higher release rates than their non-subsidiary counterparts. In 

contrast, Grant and Jones [2004] found that foreign ownership had no detectable impact 

on chemical plant releases, presumably because other factors trumped any potential 

disregard for local communities by overseas parent companies. 

In addition to ownership type, ownership concentration and influence may affect 

pollution outcomes. Suppose, for example, that fewer owners are more risk averse 

because risks are spread out over fewer individuals. Or, suppose fewer owners are better 

able to control costs due to more direct contact with facilities. In a study of Czech 

manufacturing firms, Earnhart and Lizal [2006] discovered that more concentrated 

ownership, including that of a single public owner, resulted in reduced air pollution 

emissions. Of course, regardless of their concentration, investor owners may express 

environmental preferences or concerns over future environmental regulation by 

decreasing demand for shares. Konar and Cohen [1997] and Konar and Cohen [2001] 

studied the relationship between stock prices and TRI-reported toxic emissions and 

environmental lawsuits. The authors determined that poor environmental performance 

resulted in lower asset values and negative stock price changes produced lower future 

toxic releases. 

2.2. Factors Not Predicted by the Standard Economic Model 

Management knowledge, managerial attitudes, and organizational structure also 

determine compliance and performance. These corporate culture considerations do not fit 

neatly into the standard law and economic model of environmental behavior and 

compliance. Managers’ understanding of complex legal requirements partially determines 

compliance. Dasgupta el al. [2000] conducted a survey of Mexican industrial facilities 

and found that environmental management strategies and training significantly improved 

compliance with pollution control laws, largely because managers were learning about 

the evolving regulatory structure. Winter and May’s [2001] survey of Danish livestock 

operators determined that farmers with high levels of awareness of rules exhibited 

compliance levels 28 percent higher that farmers with low levels of awareness. While it 

may be that farmers most concerned with compliance expend the most effort to learn 

about regulations, Stafford’s [2006] later study supports Winter and May’s original 

8 
interpretation. Facilities with more complex regulations (exogenously determined) are 

more likely to violate RCRA hazardous waste requirements, all else equal. 

Manager attitudes also affect environmental behavior. Kagan et al. [2003] 

uncovered both qualitative and quantitative evidence that managerial outlooks 

significantly affected water pollution emissions of 13 pulp and paper mills in the US, 

Canada, Australia, and New Zealand. In particular, the authors found that firms with 

proactive environmental management styles invested in more advanced abatement 

equipment and more day-to-day environmental oversight. May [2005] surveyed 421 

marinas and boatyards in the western US and determined that facility operators’ attitudes 

towards government strongly influenced their deterrence perceptions. ‘Conservative’ 

boatyard operators were 13 percent more likely to have significant concerns about 

regulatory action and 16 percent less likely to feel obligated by a sense of duty to comply. 

Recently, several authors have peered inside the “black box” of organizational 

structure to understand its role in environmental performance. Delmas and Toffel [2005] 

used survey and archival data to study institutional pressure and internal power 

distribution at US industrial sources. They found that facilities with strong market 

pressures and marketing departments adopted different environmental practices than 

facilities with influential non-market pressures and legal departments. For example, 

facilities that were more receptive to market constituents like customers, suppliers, the 

media, and community groups were more likely to adopt international environmental 

management systems. In contrast, facilities that were more receptive to non-market 

constituents like legislators and regulators were more likely to adopt governmentsponsored voluntary pollution control programs. Howard-Greenville [2006] conducted a 

detailed ethnographic study of a large computer chip manufacturer, and results indicated 

that different sub-cultures framed environmental priorities, definitions, and solutions very 

differently. Therefore, firms with heterogeneous internal power dynamics are expected to 

have heterogeneous environmental outcomes, even in the presence of similar external 

pressures. 

2.3 The Importance of Regulation 

Despite the many factors influencing environmental performance reviewed in 

sub-sections 2.1 and 2.2, the literature strongly suggests one result central to this report. 

Regulation, monitoring, and enforcement have historically been, and remain, critical 

determinants of environmental behavior. 

Table 1 summarizes key empirical studies that compare regulatory factors to other 

compliance determinants. Every reviewed study indicated that regulation/legislation is a 

more important determinant of environmental behavior than any other single factor. 

While Khanna and Anton’s [2002] survey of S&P 500 firms indicated that second-order 

environmental practices like total quality management were largely attributable to market 

factors, first-order practices like environmental staffing, audits, and internal policies were 

attributable to legal and regulatory factors. Kagan et al. [2003] argued that regulation is 

principally responsible for large environmental gains for pulp and paper facilities in the 

9 
 

US, Canada, Australia, and New Zealand. In a survey of Canadian pulp and paper 

facilities, Doonan et al. [2005] discovered that a full 70 percent of managers rated the 

government as the single most important source of environmental pressure. Similarly, 

Delmas and Toffel’s [2005] survey of 493 US industrial sources indicated that the 

influence of regulators and legislators on environmental performance was considerably 

stronger than community organizations, activist groups, and the media. Finally, May 

[2005] showed that traditional regulation had a considerably stronger influence on marine 

industry managers’ deterrence viewpoints than non-mandatory regulation. 

3. Monitoring and Enforcement: Specific Deterrence 

Given the critical importance of regulation, monitoring, and enforcement for 

environmental performance and compliance, it is important to assess empirical deterrence 

magnitudes. This section reviews the recent literature on the specific deterrence effects of 

monitoring and enforcement. In this context, specific deterrence refers to the effects of 

regulatory actions on the evaluated or sanctioned firm itself. General deterrence effects, 

in which regulatory actions aimed at one facility impact the environmental performance 

at other facilities, are considered in the next section.2 

The key goal of this section is to dissect important studies. The examination of 

each paper’s approach to measuring specific deterrence should provide concrete 

examples for possible replication. Collectively, the results of the specific deterrence 

literature indicate that both qualitative and quantitative methods exist to measure specific 

deterrence. Further, joint results show that significant reductions in non-compliance and 

emissions are obtainable with traditional monitoring and enforcement. Inspections and 

enforcement actions consistently produce improved future environmental performance at 

the evaluated or sanctioned facility. Results hold both historically and currently. 

3.1 The Specific Deterrence Literature 

Table 2 summarizes key empirical studies that measure the specific deterrence 

effects of domestic environmental monitoring and enforcement. The seminal paper 

empirically assessing specific deterrence is Magat and Viscusi [1990]. Magat and Viscusi 

[1990] explored the relationship between EPA inspections and self-reported biochemical 

oxygen demand (BOD) discharges at 77 major US pulp and paper mills for the period 

1982-1985. The authors found that lagged inspections had statistically significant 

negative impacts on both BOD compliance status and continuous BOD discharges. The 

results were also economically significant. Lagged inspections induced as much as a 20 

percent reduction in mean BOD discharges in the quarter following an inspection. 

2

 Although rare, some authors (see, for example, Earnhart [2004a], Earnhart [2004b], and Glicksman and 

Earnhart [2007]) use the phrase ‘general deterrence’ to describe facility-specific deterrence associated with 

the predicted threat of an inspection or enforcement action. For the purposes of this report, all facilityspecific responses to monitoring and enforcement are considered specific deterrence. Only impacts on 

facilities other than the sanctioned facility are considered general deterrence here. 

10 
 

 

 

Other notable early papers included Deily and Gray [1991], Gray and Deily 

[1996], and Nadeau [1997]. Deily and Gray [1991] studied 49 steel plants between 1976 

and 1986, a period of rapid decline in the steel industry. They studied the response of 

plant-closing probabilities to all EPA/state enforcement actions in the industry. The 

authors found that a 12 percent increase in the predicted probability of EPA and state 

enforcement activity over the entire sample period increased the likelihood that a plant 

would close by 1 percentage point. In a related study, Gray and Deily [1996] analyzed the 

impact of non-monetary enforcement actions on the self-reported annual particulate, 

sulfur dioxide, and nitrogen oxide compliance of 41 US steel manufacturers for the 

period 1980-1989. Here, the authors detected a substantial impact of actual lagged 

inspections and non-monetary enforcement actions on air pollution compliance, but they 

found limited evidence that the predicted threat of these actions directly influenced 

environmental behavior.3

 Actual enforcement actions seem to have spurred changes in 

environmental behavior. 

In a study of self-reported air pollution compliance rates for 175 US pulp and 

paper plants from 1979-1989, Nadeau [1997] showed that EPA regulatory activity can 

significantly affect plants’ duration of non-compliance. In particular, Nadeau [1997] 

studied the response of time spent in violation of air pollution statutes to EPA/state 

inspections and enforcement actions. Results indicated that a 10 percent increase in a 

plant’s predicted threat of enforcement actions, including monetary penalties, resulted in 

a 4 to 5 percent reduction in the violation length. Results for predicted inspection activity, 

however, were largely indeterminate. A 10 percent increase in a plant’s predicted threat 

of inspection resulted in a 0 to 4 percent reduction in the length of non-compliance. 

More recently, Earnhart [2004a] and Earnhart [2004c] investigated the 

relationship between regulator activity and self-reported BOD discharges from 40 Kansas 

wastewater treatment facilities for the period 1990-1998. The key metric in both studies 

was the response of BOD discharges to EPA/state enforcement and monitoring actions. 

The most consistent result across the analyses was that lagged actual inspections and 

lagged actual enforcement actions caused reductions in BOD discharges (as a percentage 

of permitted levels). Interestingly, the author detected no deterrence difference between 

actual EPA inspections and actual local authority inspections. Other findings, including 

deterrence effects of the threat of inspection and the threat of enforcement were 

inconsistent across papers and statistical specifications. As Earnhart [2004c] highlights, 

these papers also suggest that empirical tests of specific deterrence may be sensitive to 

statistical specification. In other words, the same data can sometimes yield different 

conclusions when assessed via alternative statistical methods. 

May [2005] analyzed survey results from approximately 200 marine facilities 

(marinas and boatyards) in Washington and California. In a study of how managers’ 

reported deterrence fears responded to regulator inspections and sanctions, the author 

3

 Many studies reviewed in this document contributed significantly to the literature in ways not discussed. 

For example, Gray and Deily [1996] also analyzed the impact of firm compliance on regulator behavior. 

However, this review only focuses on determinants of compliance, specific deterrence, and general 

deterrence. 

11 
found that inspections significantly contributed to managers’ deterrence viewpoints. 

Facilities inspected in the last 5 years had higher deterrent fears than those not inspected. 

In contrast, past sanctions for water quality violations were not associated with higher 

deterrence fears. Keohane, Mansur, and Voynov [2006] studied the impact of new source 

review (NSR) enforcement lawsuits on the sulfur dioxide and utilization rates of 249 

electric power plants for the period 1996-2000. The authors determined that the threat of 

a NSR lawsuit, as primarily predicted by lagged emissions rate increases, lagged large 

capital investments, and parent company characteristics, significantly decreased 

emissions by these plants. Actually implemented lawsuits, however, had little impact on 

facilities’ air contaminant emissions. The authors interpreted their results to indicate that 

firms strongly sought to avert enforcement ahead of time, but imposed enforcement 

actions did not contribute to any further improvements above and beyond changes 

already made. 

Most recently, Glicksman and Earnhart [2007] analyzed both qualitative and 

quantitative evidence of specific deterrence for water discharges in the chemical industry. 

The statistical database analysis examined how plants’ composite BOD and TSS 

discharges responded to EPA/state enforcement actions. The survey component covered 

267 of the 1003 originally solicited chemical facilities (a 26.6 percent response rate). 

Glicksman and Earnhart [2007] established that inspections and fines both produced 

significant deterrence effects. Nearly 75 percent of survey respondents reported that fines 

were an effective deterrent and approximately 87 percent reported that inspections 

definitely or probably effectively induced water pollution compliance. Similarly, the 

statistical results indicated that both inspections and enforcement actions resulted in 

decreased aggregate conventional water pollution emissions. The authors further explored 

the relative contribution of state actions vs. federal actions as well as administrative 

actions vs. judicial actions, but results interpreted broadly failed to reveal consistent 

patterns. 

A particularly notable recent paper is Gray and Shadbegian [2005]. In addition to 

measuring specific deterrence in a framework consistent with earlier studies, the authors 

also considered factors that influenced the responsiveness to monitoring and enforcement 

actions. They examined 116 pulp and paper mills for the period 1979-1990, and their key 

metric was the response of air pollution compliance to EPA/state inspections and 

enforcement actions. Results indicated that plants increased their CDS-reported air 

pollution compliance rates by approximately 10 percent in response to a marginal 

regulator action. This result held roughly equally for inspections and enforcement 

instruments. Additionally, the authors found heterogeneous enforcement responses across 

plants. For example, pulp mills were less sensitive to inspections than paper mills and 

plants owned by larger parent companies were less sensitive to inspections, but more 

sensitive to enforcement actions, than plants owned by smaller parent firms. 

Specific deterrence effects are not restricted to domestic environmental 

institutions. For example, Laplante and Rilstone [1996] explored the impact of 

inspections and the predicted threat of inspection on the biochemical oxygen demand 

(BOD) and total suspended solid (TSS) discharges of 47 Canadian pulp and paper mills. 

12 
 

The authors found inspections and their associated threat reduced mills’ absolute BOD 

discharges by approximately 28 percent. Similarly, Dasgupta et al. [2001] demonstrated 

that inspections on manufacturing facilities in Zenjiang, China reduced common water 

pollutants by between 0.4 and 1.2 percent and reduced air particulates by approximately 

0.3 percent. Outside of the environmental arena, Gray and Mendeloff [2005] found that 

OSHA inspections reduced workplace injuries by approximately 19% in 1979-1985, 11% 

in 1987-1991, and 1% in 1992-1998. Further, they showed that inspections with penalties 

had a significantly stronger effect than inspections without penalties. 

3.2 Specific Deterrence: Repeat Offenders 

The empirical literature on repeat offenders is very limited. Miller [2005] 

explored the effect of administrative, civil, and criminal enforcement activities on US 

companies’ recidivism for the period 1970 to 1997. The key metric was the response of 

recidivism to various regulatory actions. The analysis revealed that both civil and 

administrative penalties reduced repeat offenses. However, both did so about equally, 

despite the fact that administrative penalties are constrained to be lower on average. 

Criminal enforcement appeared to dominate other enforcement actions, and results 

suggested that criminal sanctions significantly reduced repeat offenses. Also, there was 

some suggestive evidence that criminal fines that increased in the number of offenses 

were particularly effective for reducing recidivism. All of these results, however, were 

extremely preliminary. Clearly, recidivism represents a promising area of future 

empirical research. 

Given the lack of observational studies, it is perhaps informative to consider the 

theoretical literature. Law and economic models indicated that it is frequently optimal to 

sanction repeat violators more severely than first time violators. See, for example, 

Polinsky and Shavell [1998]. This view is consistent with numerous environmental 

penalty structures, including Clean Water Act fines. The basic intuition is simple. When a 

potential violator considers violating for the first time, the specific deterrence effect will 

potentially be enhanced by knowing that a violation now may not only trigger a penalty 

but may also enhance penalties in the future. Guttel and Harel [2005] extended the 

traditional model to incorporate behavioral economic considerations. They also 

concluded that penalties should be increasing in the number of violations.4

 However, economic support for increased penalties for repeat environmental 

offenders rests on one key assumption. The assumption is that it must be socially optimal 

to deter violations from the recidivating facility. It is possible that large numbers of 

repeat violations may signal extremely high costs of compliance (due, for example, to 

plant vintage or industrial subcategory) at the relapsing facility. In some cases, this 

facility may actually be expending greater costs to reduce marginal units of pollution than 

4

 There is also a theoretical literature that suggests that penalties should be decreasing in the number of 

violations. See, for example, Emons [2003]. However, the assumptions in this strand of the literature 

generally don’t seem appropriate for the majority of environmental regulations. In particular, this area of 

the literature assumes that imposed fines are extremely large relative to the sanctioned firm’s wealth and 

that increasing penalties in some period inherently implies lower penalties in another period. 

13 
society is receiving in benefits from these marginal reductions. Further, given limited 

agency enforcement budgets, significantly greater abatement and environmental benefits 

may be achieved by targeting resources towards violators with smaller marginal 

abatement costs and greater ability to comply. In short, while it is likely that increasing 

sanctions for repeat offenders is an optimal policy for many recidivists, this strategy may 

not achieve the greatest social and environmental benefits when applied to facilities with 

numerous repeat offenses driven by extreme abatement costs. 

4. Monitoring and Enforcement: General Deterrence 

Law enforcement agencies and legal scholars have long postulated that 

monitoring and enforcement spills over to deter violations at facilities beyond the 

sanctioned entity. If this is true, this regulator reputation-building ‘general deterrence’ 

effect has critical implications. Notably, focusing on deterrence effects at the sanctioned 

facility alone may seriously underestimate the efficacy of fines and other sanctions. 

This section reviews the literature on the general deterrence effects of monitoring 

and enforcement. A key goal is to dissect important studies. The examination of each 

paper’s approach to measuring general deterrence should provide specific examples for 

possible replication. Collectively, the results of the general deterrence literature indicate 

that both qualitative and quantitative methods exist to measure general deterrence. 

Further, joint results show that environmental facilities learn from the experiences of 

their neighbors, and this learning impacts compliance behavior. Inspections and 

enforcement actions consistently produce significant spillover effects on non-sanctioned 

facilities. Consequently, a substantial improvement in environmental quality might be 

achieved from a relatively small additional investment in traditional monitoring and 

enforcement. 

4.1 The General Deterrence Literature 

Despite regulators’ mature beliefs in general deterrence, an empirical literature 

investigating spillover effects of monitoring and enforcement has only recently emerged. 

Early studies included Stafford [2002] and Stafford [2003]. Stafford [2002] studied the 

response of 8411 hazardous waste-generating facilities to a rule change that significantly 

increased financial penalties for violation. The 10 to 20 fold increase in potential fine 

magnitudes generated an approximately 15 percent reduction in regulated plants’ 

violation probabilities. In contrast, Stafford’s complementary 2003 study found no 

consistent effect of state-level strict liability rules on hazardous waste violations. This 

more recent study, however, did find that increased state-level environmental spending 

decreased Class I pollution violations. While these findings may indicate improved 

environmental performance, results may also suggest the presence of firm-level strategic 

reporting behavior since Class II record-keeping violations increased at the same time. In 

other words, while it is possible that increased spending reduced violations, it is also 

possible that increased spending increased instances where firms cheat by no longer selfreporting violations. Collectively, Stafford [2002] and Stafford [2003] considered the 

impact of rule and budget changes that may have enhanced the regulator’s reputation for 

14 
 

toughness. Therefore, these papers importantly informed the deterrence spillovers 

literature. They did not, however, directly differentiate between specific and general 

deterrence. 

Table 3 summarizes important recent empirical studies that directly measure the 

general deterrence effects of domestic environmental monitoring and enforcement. In 

companion studies, Thorton, Gunningham, and Kagan [2005] and Gunningham, Thorton, 

and Kagan [2005] used survey and interview evidence from chemical and electroplating 

facilities to directly take up the issue. Using a survey of 233 firms across 8 industries, 

Thorton et al. [2005] examined managers’ knowledge of important ‘signal enforcement 

case’ and firms’ self-reported environmental responses to knowledge of enforcement 

actions levied against other facilities. Gunningham et al. [2005] used interview evidence 

from 35 chemical and electro-plating facilities to explore managers’ reasons for 

environmental performance and their stated responses to enforcement actions levied 

against other facilities in their industry. The authors found that 89 percent of survey 

respondents could recall some enforcement actions imposed on other firms, 63 percent of 

survey respondents took an environmental action in response to learning about a sanction 

at another facility, and a large majority of interview respondents reported that other 

firm’s regulatory activity impacted their own attention to environmental issues. Results 

indicated that general deterrence, interpreted broadly, is a significant factor for 

environmental behavior. 

Thorton, Gunningham, and Kagan [2005] and Gunningham, Thorton, and Kagan 

[2005] also explored the specific mechanisms that might drive general deterrence. The 

authors detected that respondents were frequently aware of infractions at other facilities 

that triggered sanctions but not could recall other details like the penalty amount. The 

authors further found that only a small number of interview respondents reacted to 

knowledge of penalties at other facilities by performing a specific environmental action 

to immediately avoid a similar infraction. Yet, many officials indicated that learning 

about someone else’s fine focused their attention on environmental issues or altered their 

long-term compliance motivations. Collectively, the companion studies concluded that 

general deterrence may operate more as focusing, reminder, or reassurance mechanism 

than a short-run fear-generating mechanism. The authors also note that fines may serve as 

a trigger mechanism for social stigma effects, which may be particularly strong for larger 

firms. 

Shimshack and Ward [2005] used data on BOD and TSS discharges from 217 

pulp and paper mills to explore the regulator reputation-building effect of general 

deterrence. In particular, the authors statistically analyzed the response of plants’ BOD 

and TSS discharges to lagged enforcement and monitoring activity at both the sanctioned 

plant and other plants in the same regulatory jurisdiction. Shimshack and Ward [2005] 

found that an additional fine induced about a two-thirds reduction in the statewide water 

pollution violation rate in the year following that fine. Nearly all of this large result was 

attributable to spillover effects, as the average deterrence impact on each of the other 

plants in a state was almost as strong as the impact on the sanctioned facility. The authors 

also showed that general deterrence was increasing in the amount of the penalty. In other 

15 
words, larger fines induced fewer subsequent violations by other plants in the state. Other 

results included the finding that non-monetary enforcement responses like notices of 

violation alone had little influence on plant-level compliance. 

For both the existence of fines and fine magnitude specifications, Shimshack and 

Ward [2005] detected that the regulator reputation deterrence effect decayed significantly 

after one year. This portion of the analysis was based on the response of BOD and TSS 

violations to separate one year and two year lagged fine variables. While the marginal 

penalty induced an approximately 67 percent reduction in statewide violation rates in the 

subsequent year, this regulator-reputation effect declined to about 27 percent in the 

second year following the fine. Results suggested that pulp and paper plants regularly 

updated their beliefs regarding regulatory stringency. 

State of Oregon Department of Environmental Quality [2004] conducted an 

extensive survey of 450 regulated companies in Oregon for one month in 2002. The 

sample was selected primarily from high priority industries, and thus results are not 

representative for less visible industrial sectors. Key metrics included the response of 

managers’ self-reported environmental behavior to hearing about inspections and 

sanctions at other facilities. Respondents frequently reported significant general 

deterrence effects. For example, 38 percent of surveyed companies made changes in 

response to hearing about inspections at another facility and 14 percent reportedly made 

changes in response to hearing about sanctions at other facilities. Such changes included 

improved water treatment, investment in new equipment, environmental personnel 

additions, and employee training. Oregon DEQ estimated that inspections and penalties 

directly generated an average of 1.6 environmental changes per company. 2.6 additional 

environmental actions per company occurred when other facilities later heard about the 

enforcement response. 

In Oregon DEQ [2004], inspections seemed to have stronger specific and general 

deterrence effects than penalty actions, largely because inspections appeared to be better 

publicized. Additionally, concerns about penalty actions depended critically on the size 

of the penalty and the number of facilities penalized in a given period. Notably, the study 

also found heterogeneous responses to enforcement. In particular, small companies were 

significantly less likely to respond to general deterrence, in part because these 

organizations were considerably less likely to be aware of regulatory activities devoted 

towards other facilities. Further, a substantial portion of company officials reported that 

enforcement actions sent strong signals to non-regulatory constituents and therefore 

regulatory actions significantly, yet indirectly, contributed to compliance. 

In a study of particulate, sulfur dioxide, and toxics emissions from 521 US 

manufacturing plants located within 50 miles of three major cities, Gray and Shadbegian 

[2007] analyzed the response of toxic and conventional air pollution violations and 

emissions to EPA/state inspections and enforcement actions. The authors found evidence 

for both specific and general deterrence. Inspections increased compliance with 

regulations for both the inspected facility and for nearby plants. The specific and general 

deterrence effects of inspections were found to be approximately the same magnitude. 

16 
Interestingly, the authors also determined that general deterrence was restricted to states, 

suggesting that regulatory jurisdictions may be important determinants of the “reach” of 

the regulator reputation effect underlying general deterrence. 

Spillover effects of environmental enforcement not only affect compliance, but 

beyond compliance behavior as well. For example, Shimshack and Ward [2007] 

examined the impact of enforcement-induced regulator reputation on the beyondcompliance water pollution discharges of 251 major pulp and paper mills for the period 

1990-2004. In particular, the authors examined the response of BOD and TSS discharges 

to lagged enforcement activities at other facilities in the same state. Shimshack and Ward 

[2007] found that, even in an industry where compliance was generally high, an increase 

in enforcement through fines can cause a significant reduction in discharges. General 

deterrence induced plants with discharges typically below legally permitted levels to go 

further beyond compliance when regulators issued fines. Also, likely non-compliant 

plants often responded to sanctions on other facilities by reducing discharges well below 

levels simply required for compliance. In aggregate, BOD and TSS discharges within a 

state fell by approximately 7 percent in the year following a sanction in that state. Most 

of the reduction was due to plants going beyond compliance, rather than simply a 

reduction in violations. 

Shimshack and Ward [2007] also demonstrated how these empirical regularities 

can be rationalized by economic theories of discharge randomness and discharge 

jointness. Plants with partially random discharges may have some possibility of a fine 

from accidental releases over the permitted amount (Brannlund and Lofgren [1996], 

Bandyopadhyay and Horowitz [2006]) and therefore may reduce discharges even further 

beyond compliance when they perceive a more stringent enforcement environment. 

Similarly, a plant compliant in one pollutant may face some possibility of a fine for 

violations on a different, but jointly-produced, pollutant. If such a plant perceives an 

increased regulatory threat by observing fines on other facilities, it may reduce the 

pollutant with the binding limit and correspondingly push the jointly-determined 

pollutant even further beyond compliance. 

Finally, evidence for general deterrence effects is not restricted to domestic 

environmental policies. Dubin et al. [1990] and Dubin [2007] found significant impacts 

of tax audits on compliance. For example, Dubin [2007] showed that a doubling of 

criminal investigation cases would yield nearly $17 billion in increased tax revenues. 

Nearly all of these increased collections stemmed from general deterrence, as 94 percent 

of new inflows came from people not actually investigated. These large spillover effects 

strongly suggest that is cost effective to increase IRS sanctions. In fact, the author 

estimated that a $25 million dollar investment in additional investigations, prosecutions, 

and sanctions would yield nearly $1.7 billion in additional revenues. Despite the strength 

of the regulator reputation effect, the impact decayed over time. Taxpayers adjusted their 

behavior to correspond to updated audit perceptions every two to three years. Finally, the 

author found suggestive evidence that media information dissemination enhanced 

deterrence spillovers. 

17 
General deterrence effects beyond tax and the environmental settings are more 

uncertain. Feinstein (1989) found no evidence for either specific or general deterrence 

effects from safety sanctions on 17 nuclear power generators. However, this institutional 

context was quite unique, as each facility had a resident inspector who has primary 

responsibility for plant safety. Additional inspectors also conducted an average of more 

than one evaluation per month. 

5. Replication considerations 

A key goal of Sections 2 and 3 was the examination of diverse approaches to 

measuring specific and general deterrence. Reviewed papers provide an excellent 

foundation for possible replication. While several definitive findings arise from the 

existing literature, care should be exercised before extrapolating more nuanced results 

beyond the relevant institutional settings. Consequently, replication for new sectors, 

contaminants, and time periods could importantly contribute to the state of knowledge on 

deterrence. This section discusses replication considerations, examines studies 

particularly promising for replication, and reviews the strengths and weaknesses of the 

diverse approaches to compliance and deterrence measurement. All discussions are based 

upon the author’s subjective assessment of the related literature and the state of 

knowledge. 

Quantitative Database Analyses 

Theoretically, the quantitative database analysis approach could be replicated for 

other sectors by OECA. This approach is most directly applicable to regulations and subsamples with continuous self-reported emissions data over many facilities and several 

time periods. Such comprehensive data allow for month-by-month, quarter-by-quarter, or 

year-by-year compliance determination and emissions assessment for all analyzed 

facilities. For example, major facilities’ water pollution data from the Permit Compliance, 

air pollution data from the Continuous Emissions Monitoring System, and toxics data 

from the Toxic Releases Inventory fit this framework. The approach is also applicable to 

regulations and sub-samples with comprehensive compliance indicators over several 

periods. Major facilities’ water pollution violation data from the Permit Compliance 

System, air pollution violation data from the Continuous Emissions Monitoring System, 

hazardous waste violation data from the RCRA Biennial Reporting System, and air 

pollution violation data from the Compliance Data System fit this framework. 

Quantitative database analyses are particularly powerful for individual sectors 

with census or near census monitoring coverage. Sector-specific analyses of common 

discharges or violations from CWA major sources are a notable example. The technical 

appendix summarizes rigorous statistical procedures for implementation in such contexts. 

Strengths of the quantitative approach include the identification of actual empirical 

deterrence magnitudes. It is difficult to assess the extent to which enforcement and 

monitoring affect compliance in qualitative frameworks. Well-designed quantitative 

analyses also provide the opportunity to control for confounding factors. Further, 

database analysis reveals how regulated facilities actually responded to monitoring and 

18 
enforcement, rather than how these facilities say they responded to monitoring and 

enforcement. In other words, quantitative analyses avoid survey response bias, recall 

bias, and strategic reporting bias. This latter concern, where respondents may have 

incentives not to reveal certain information truthfully in a survey, may be particularly 

crucial for surveys conducted by the regulatory agency itself. Quantitative database 

analyses tie monitoring and enforcement actions to actual measures of compliance and 

emissions, which are better connected to environmental quality than the intermediate 

factors like process and management modifications that are typically measured by 

qualitative methods. Finally, quantitative analyses do not require approval from OMB, 

via an Information Collection Request (ICR). 

The basic analyses in Shimshack and Ward [2007] and Gray and Shadbegian 

[2005] provide particularly promising foundations for replication to other sectors, other 

contaminants, and other time periods. In Shimshack and Ward [2007], the key question is 

‘What is the empirical relationship between pollution discharges and lagged enforcement 

actions on other facilities?’ Answers directly measure general deterrence. The study’s key 

metric is the response of plants’ BOD and TSS discharges to inspections and EPA/state 

fines. Monthly BOD and TSS discharge data, expressed as a percent of permitted levels, 

are the dependent variables. The key explanatory variable is a 0-1 indicator that reveals 

the existence of a fine on another plant in a given plant’s state in any of the 12 months 

preceding the evaluated period. The log of total fine magnitudes on other plants in a 

given plant’s state in any of the 12 months preceding the evaluated period is another 

possible independent variable. Other explanatory variables include year indicators to 

control for general time trends, seasonality indicators to control for seasonal variation, 

and plant-specific indicators to control for time invariant confounding factors like 

community characteristics, industrial sub-sector, and basic control technology. Techical 

appendix A.1 discusses statistical methods for Shimshack and Ward [2007] type 

analyses. 

In Gray and Shadbegian [2005], the key question is ‘What is the empirical 

relationship between a plant’s pollution discharges and lagged enforcement and 

monitoring actions directed at that facility?’ An important supplemental question is 

‘What factors determine a plant’s sensitivity to monitoring and enforcement activities?’ 

Answers directly measure specific deterrence and its sensitivity to important confounding 

factors. The study’s key metric is the response of plants’ air pollution compliance status 

to EPA/state inspections and enforcement actions. The dependent variable is a 0-1 

indicator reflecting whether the specific plant was in compliance with air pollution 

regulations throughout the year. The key explanatory variables are predictions of total 

enforcement actions and total inspections directed towards a plant in the given year. To 

measure the sensitivity of deterrence to other factors, the key explanatory variables are 

the predictions interacted with plant-specific characteristics like industrial sub-sector 

(pulp/paper in their analysis), plant age, and plant size. Since Gray and Shadbegian 

[2005] do not use plant-specific indicator variables to control for time invariant 

confounding factors, the study also includes detailed plant and firm characteristics. 

Technical appendix A.3 discusses statistical methods for Gray and Shadbegian [2005] 

type analyses. Confounding variable data is typically not collected by the EPA itself and 

19 
 

must be obtained externally. This may be a disadvantage of this specific quantitative 

analysis approach for OECA. 

Note that the metrics in both Shimshack and Ward [2007] and Gray and 

Shadbegian [2005] are sector-specific (both pulp and paper) and regulation-specific (the 

former analyzes conventional water pollutants regulated under the Clean Water Act and 

the latter analyzes criteria air pollutants regulated under the Clear Air Act). These metrics 

are similar to those used in other quantitative database analysis studies, including 

Earnhart [2004a], Earnhart [2004c], Shimshack and Ward [2005], and Keohane et al. 

[2006].5 While these metrics are narrowly defined, they are of significant supplemental 

value to OECA’s mission since they directly measure the general and specific deterrence 

for pollution. This stands in contrast to the agency’s commonly utilized output-oriented 

metrics (number of inspections, total penalty amounts, etc.) or intermediate processoriented metrics (value of injunctive relief). More specific compliance and deterrence 

metrics are also more statistically rigorous, as it is difficult to credibly create statistically 

valid indices across pollutants or sectors. Finally, more universal or output-oriented 

metrics mask important heterogeneity across sectors and are therefore more likely to 

obscure, rather than illuminate, our understanding of specific and general deterrence. 

Another notable feature of most quantitative database analyses, including 

Earnhart [2004a], Earnhart [2004c], Gray and Shadbegian [2005], and Shimshack and 

Ward [2007], is lagged monitoring and enforcement variables. Lags serve two important 

purposes. First, lags reduce statistical simultaneity (endogeneity) and help isolate the 

direction of causality. If contemporaneous monitoring and enforcement variables are 

included in the analysis, any statistically detected correlations between these factors and 

compliance or emissions may reflect the causal effect of compliance and emissions on 

monitoring and enforcement actions due to regulator targeting. This reverse causation 

influence is likely to understate true deterrence effects. Second, lags allow time for firms 

to alter their environmental behavior in response to monitoring and enforcement actions. 

Qualitative Survey Analysis 

Qualitative deterrence assessments have significantly contributed to the state of 

science. Key advantages of the survey approach include the ability to examine 

organizational culture issues, knowledge of enforcement and monitoring activity across 

facilities, and the interactions between policies in greater depth. An additional advantage 

is that deterrence and compliance metrics from survey methods can be defined more 

broadly than metrics for quantitative analysis. For example, May [2005], Oregon DEQ 

[2004], Thorton et al. [2005], and Gunningham et al. [2005] all simultaneously explore 

multiple regulations across multiple sectors. Finally, while effective surveys require 

considerable care, less statistical expertise is needed for implementing qualitative 

approaches. 

The basic methods and questions in Oregon DEQ’s [2004] company survey 

provide particularly promising foundations for replication to other regions, other sectors, 

5

 See Tables 2 and 3 for more detail on the metrics used in these studies. 

20 
 

and other time periods. Oregon DEQ [2004] surveyed 450 regulated facilities by 

telephone over a one month period in 2002. Most facilities were randomly selected from 

high priority sectors. The response rate was 68 percent.6

 Key questions assessed company 

attitudes about environmental compliance, frequency and kinds of process modifications 

made because of environmental issues, most important factors stimulating compliance, 

and stated perceptions of regulatory risk. Questions specifically targeting general 

deterrence examined awareness of inspections and enforcement actions at other facilities 

and company response to awareness of inspections and enforcement actions at other 

facilities. The actual survey is attached as an appendix to Oregon DEQ [2004]. 

Theoretically, many features of Thorton et. al. [2005] could also be replicated to 

other sectors, other time periods, and other geographic areas. Results would provide 

meaningful insights into specific and general deterrence. Thorton et. al. [2005] surveyed 

233 facilities in 8 industries by telephone sometime after December of 2000. A random 

sample of approximately 290 facilities in 8 pre-selected industries was determined, and 

calls yielded a response rate of 80 percent. Key questions assessed awareness of 

important regulatory signal cases in the industry, awareness of inspections and 

enforcement actions at other facilities, stated response of regulatory risk perceptions to 

such awareness, and stated compliance action response to such awareness. In particular, 

respondents were asked if hearing about a fine or prison sentence at another company in 

their industry induced them to (1) review their environmental programs, (2) change their 

management plans, (3) change their environmental record-keeping, (4) change employee 

training, (5) change equipment, and (6) change their physical plant. The actual survey is 

not publicly available, but should be available directly from Neil Gunnigham at the 

Australian National University. 

How often should replication occur? 

Given resource constraints, it is not practical to frequently repeat either qualitative 

or quantitative analyses for the same regulated facilities and same regulations. 

Implementation of any technique is costly. Unfortunately, the literature does not examine 

how frequently replication should occur. The key consideration is likely the internal 

benefits and costs of assessment, although this white paper does not attempt to determine 

the costs or practicality of implementing any specific information gathering, deterrence 

measurement, or statistical analysis. My subjective assessment is that broad conclusions 

are likely applicable for 10-20 years for sectors/regulations in which technical change is 

modest, regulations are fairly static, and managerial attitudes are not evolving rapidly. 

More detailed results, including the specific magnitudes of deterrence effects, probably 

have a lifespan of less than 10 years for most industry/regulation combinations. 

It is not the case, however, that rapidly decaying deterrence effects over time 

render any given study obsolete. The key consideration is whether the underlying 

decision-making process has importantly changed, on average, for the regulated facilities. 

For most regulated industries, average deviations from organizational structure and 

managerial attitude trends remain relatively small over multiple year periods. Suppose, 

6

 May’s [2005] 49 percent response rate is more typical of the broader literature. 

21 
 

for example, a quantitative database study finds that an additional fine reduces aggregate 

discharges in a state by 8 percent but that this effect is no longer significant after one 

year. These findings suggest that, X years from now, firms in the studied industry are 

expected to pollute baseline levels (after controlling for trends) if they observe no fines in 

the preceding year (X-1 years from now). If they do observe a fine in the preceding year 

(X-1 years from now), however, they are still expected to reduce pollution by 8 percent 

below baseline levels (X years from now) unless dramatic organizational, managerial, or 

technical changes have occurred in the industry. 

How should samples be collected? 

Sample selection is an important component of both quantitative and qualitative 

methods. No comprehensive assessment of an entire regulated community is 

recommended, as implementation costs would be prohibitive. Further, since the regulated 

universe changes over time, a universal study yields no additional information for future 

policy making than a statistically valid sample. Every study reviewed in Sections 3 and 4 

examines specific regulations for a limited number of sectors, and most study few 

regulations for few industries (see, for example, Earnhart [2004a], Gray and Shadbegian 

[2005], Shimshack and Ward [2005], Thorton et al. [2005], Gunningham et al. [2005], 

and Gray and Shadbegian [2007]). Further, results from such a broad analysis would 

likely obscure, rather than illuminate, important heterogeneity in deterrence assessment. 

Sample selection should first identify sectors, contaminants, and even 

characteristics (‘majors’) based upon agency priorities. Such priorities may include 

environmental impacts, traditional compliance rates, or assessment costs. For quantitative 

analyses of specific deterrence, a random sample may then be obtained. For quantitative 

analyses of general deterrence, a near-census for the sector(s)/contaminant/characteristic 

is most desirable. This is possible for major facilities in much of the PCS, CEMS, CDS, 

TRI, and the RCRA Biennial Reporting Systems. For qualitative survey approaches, 

random samples should be obtained. Note that the originally selected survey sample 

should be considerably larger than necessary for statistical validity, as response rates will 

be significantly less than 100 percent. In all cases, replications should explain their 

sampling procedure in detail and should acknowledge that individual generated results 

are most useful for assessing compliance and deterrence effects in the context of this 

sector/contaminant/characteristic combination. More general statements can be made in 

conjunction with the larger literature, much of which is reviewed in this document. 

6. Major Findings & Recommendations 

Regulatory punishment for pollution violations is a mainstay of nearly every 

industrialized nation’s environmental policy. This report reviews the relevant 

environmental compliance literature, with emphasis on measuring the specific and 

general deterrence effects of environmental regulatory activity. This section first presents 

conclusions that can be conclusively supported by the literature. The major criterion for 

this categorization is broad-based support across several studies in several sectors. The 

section then reviews important findings suggested (but not definitely confirmed) by the 

22 
literature. The major criterion for this categorization is support by three or fewer notable 

studies and the absence of strong contradictory evidence. This section concludes with 

recommendations for OECA consideration. All findings and recommendations are based 

upon the author’s subjective assessment of the related literature and the state of 

knowledge. 

Major Findings Strongly Supported by the Related Literature 

•	 Regulation, monitoring, and enforcement have historically been, and remain, 

critical determinants of environmental behavior. 

As demonstrated in Section 2 of this paper, many factors influence environmental 

compliance. However, every reviewed study that compared traditional regulation 

to other compliance determinants indicated that regulation/legislation is a more 

important determinant of environmental behavior than any other single factor. 

This includes Khanna and Anton [2002], Kagan et al. [2003], Doonan et al. 

[2005], Delmas and Toeffel [2005], and May [2005]. In many cases, regulators 

and legislators were extremely influential. For example, Doonan et al. [2005] 

found that a full 70 percent of pulp and paper managers rated the government as 

the single most important source of deterrence pressure. 

•	 Environmental monitoring and enforcement activities generate substantial specific 

deterrence. 

Every reviewed study that examined the impacts of domestic environmental 

monitoring and enforcement actions on the inspected or sanctioned facility found 

at least some evidence for specific deterrence. This includes Magat and Viscusi 

[1990], Deily and Gray [1991], Gray and Deily [1996], Nadeau [1997], Earnhart 

[2004a], Earnhart [2004c], May [2005], Gray and Shadbegian [2005], Keohane et 

al. [2006], and Glicksman and Earnhart [2007]. In many cases, the specific 

deterrence effects were large. For example, Gray and Shadbegian (2005) found 

that the threat of an additional enforcement action or inspection increased future 

compliance with air pollution regulations in the pulp and paper sector by 10 

percent. 

Inspections and enforcement activities directly affect future compliance and 

environmental performance. Further, many other important compliance factors 

relied on the enforcement and monitoring process for information. For example, 

Kagan et al. [2003] found that facilities frequently reported that enforcement 

activities significantly impacted consumer and community perceptions of their 

environmental performance. 

23 
•	 Environmental monitoring and enforcement activities generate substantial general 

deterrence. 

Every reviewed study that examined the spillover effects of environmental 

monitoring and enforcement on other facilities uncovered support for the 

existence of general deterrence. This includes Thorton et al. [2005], Gunningham 

et al. [2005], Shimshack and Ward [2005], Oregon DEQ [2004], Gray and 

Shadbegian [2007], and Shimshack and Ward [2007]. In many cases, general 

deterrence effects are practically important. For example, Shimshack and Ward 

[2005] found a nearly 2/3 reduction in the statewide probability of a conventional 

water pollution violation in the year following a fine. Failure to account for such 

general deterrence may significantly underestimate the efficacy of fines, 

inspections, and other regulatory actions. 

•	 Environmental monitoring and enforcement activities may generate significant 

emissions reductions, even for sector/contaminant combinations where 

compliance is typically high. 

Reviewed studies consistently find specific and general deterrence effects of 

environmental monitoring and enforcement, even in industries with relatively 

high compliance rates. Earnhart [2004a] and Earnhart [2004c] find substantial 

specific deterrence from monitoring and enforcement actions in the Kansas 

wastewater industry where average conventional water pollution discharges are 

45 percent of permitted levels. Shimshack and Ward [2005] find significant 

general deterrence for conventional water pollutant fines in a sector with 98 

percent average compliance rates. Gray and Shadbegian [2005] find significant 

specific and general deterrence for 521 US manufacturing plants that exhibit an 

89 percent average compliance rate with air regulations. 

Further, Shimshack and Ward [2007] find that plants with discharges typically 

below legally permitted levels reduce discharges further when regulators issue 

fines, even on other plants. Likely non-compliant plants often respond to 

sanctions by reducing discharges well beyond reductions required to meet 

statutory requirements. Failure to account for this often overlooked aspect of 

deterrence may understate the efficacy of enforcement activities. Because this 

enforcement-induced beyond compliance effect magnifies the impact of 

regulatory activity, results again suggest that a substantial improvement in 

environmental quality may be achieved with a relatively modest additional 

investment in traditional monitoring and enforcement. 

•	 Enforcement and monitoring responses are heterogeneous. 

The literature on both specific and general deterrence effects indicates 

heterogeneous responses to enforcement and monitoring. For example, the 

quantitative database analysis literature (see, for example, Earnhart [2004a], Gray 

and Shadbegian [2005], Shimshack and Ward [2005], and Gray and Shadbegian 

24 
 

 

 

[2007]) finds significantly different deterrence magnitudes for different sectors, 

different time periods, different enforcement actions, and different monitoring 

instruments. Factors like facility-specific abatement costs, industrial sub-sector, 

and facility size determine responses to regulatory activity. A few studies examine 

heterogeneity in more detail. For example, Gray and Shadbegian [2005] found 

that pulp mills were less sensitive to inspections than paper mills and plants 

owned by larger parent companies were less sensitive to inspections but more 

sensitive to enforcement actions than plants owned by smaller parent firms. 

Gunningham et al. [2005] also found that deterrence was a smaller overall 

concern to larger facilities. 

• Quantitative databases and statistical methods exist for deterrence measurement. 

Sections 3 and 4 of this white paper discuss more than 15 recent papers that 

quantitatively measure the specific and/or general deterrence effects of 

monitoring and enforcement actions. Quantitative databases and statistical 

methods exist for deterrence measurement. Tables 2 and 3 list the critical metric 

for the studies most directly relevant to OECA’s own assessment and priorities. A 

notable feature of the quantitative database analyses (see, for example, Earnhart 

[2004a], Gray and Shadbegian [2005], Shimshack and Ward [2005], and Gray and 

Shadbegian [2007]) is that the key metrics are restricted to specific regulations 

and specific populations.7 

• Qualitative survey methods exist for deterrence measurement. 

Sections 3 and 4 of this white paper discuss more than 5 important recent studies 

that qualitatively measure the specific and/or general deterrence effects of 

monitoring and enforcement actions. Qualitative survey methods exist for 

deterrence measurement. Tables 2 and 3 list the critical metric for the studies most 

directly relevant to OECA’s own assessment and priorities. A notable feature of 

the qualitative survey analyses (see, for example, May [2005], Thorton et al. 

[2005], and Gunnigham et al. [2005]) is that key metrics are typically restricted to 

specific populations (or a small number of specific populations), but not specific 

regulations. State of Oregon DEQ [2004] represents a notable exception, where 

metrics are defined for several regulations in eight industries. 

Major Findings Suggested, but Not Conclusively Demonstrated, by the Literature 

• The regulator reputation effect underlying general deterrence tends to decay. 

The results of the general deterrence literature indicate that environmental 

facilities learn from the experiences of their neighbors, and this learning impacts 

compliance behavior. However, the main results of Shimshack and Ward [2005] 

7

 This restriction is very likely a result of implementation costs and benefits, although this white paper does 

not attempt to determine the costs or practicality of implementing any specific information gathering, 

deterrence measurement, or statistical analysis. 

25 
and the sensitivity analyses of Shimshack and Ward [2007] also show that 

facilities regularly update their beliefs about regulatory stringency. At least for the 

studied pulp and paper sector, regulator reputation begins to decay within a year 

after a fine for water pollution violation. Within 2 years of a fine, general 

deterrence has decayed by more than 50%. Therefore, regulators must maintain a 

monitoring and enforcement presence to induce consistent environmental 

performance over time. This result is not inconsistent with Gunningham et al.’s 

[2005] findings that spillover effects may operate through “implicit general 

deterrence,” where the perceived threat of legal sanctions is a function of 

enforcement history and the mere existence of regulations. Enforcement history is 

itself significantly determined by relatively recent regulator behavior. 

•	 There are limits to the “reach” of the reputation effect underlying general 

deterrence. 

Theory suggests that plants learn primarily from the experiences of their most 

similar neighbors (Sah [1991]). Indeed, empirical evidence suggests that the 

“reach” of the regulator reputation effect underlying general deterrence is 

restricted to a single regulatory entity. For example, Gray and Shadbegian [2005] 

found that plants seem inclined to respond to general deterrence created by the 

experiences of facilities in the same state, but not in neighboring states. 

•	 Deterrence varies across enforcement and monitoring instruments; inspections, 

intermediate enforcement actions, administrative fines, and criminal actions have 

different compliance impacts. 

Inspections, intermediate enforcement actions, administrative fines, and criminal 

actions have different compliance impacts. Unfortunately, relative deterrence 

effects seem somewhat sensitive to context. For example, there is no consensus on 

whether the marginal inspection induces greater compliance than the marginal 

penalty. Two largely consistent results, however, do emerge. First, criminal 

actions seem to affect environmental behavior more significantly than 

administrative or civil actions. See, for example, Miller [2005] and Cohen [1999]. 

Second, most evidence suggests no detectable consistent difference between 

deterrence associated with state actions and federal actions (Earnhart [2004c], 

Glicksman and Earnhart [2007]). 

•	 Sanctions should generally increase for repeat offenders, but important caveats 

exist. 

Theory suggests that sanctions should increase with the number of violations, 

supporting current agency practice. See, for example, Polinsky and Shavell [1998] 

and Polinsky and Shavell [2000]. There is some empirical evidence to support this 

theoretical recommendation (Miller [2005]). However, the agency should 

carefully consider the application of this rule to recidivists who can demonstrate 

extremely high costs of compliance. Targeting these facilities for stringent 

26 
sanctions may be socially wasteful and may achieve significantly less 

environmental benefits than an equivalent resource allocation towards facilities 

that are able to reduce emissions more cost effectively. 

Recommendations 

All recommendations are based upon the author’s subjective professional 

assessment of the related literature and the state of knowledge. 

How can OECA measure the general and specific deterrent impacts of compliance 

monitoring and enforcement? 

•	 OECA should consider closely replicating statistical database analyses for 

measuring the specific and general deterrence effects of monitoring and 

enforcement. 

Replication of existing quantitative studies for new sectors, contaminants, and 

time periods could importantly contribute to the state of knowledge on deterrence, 

and general deterrence in particular. The database analysis studies in Section 4 

and Table 3 are concrete examples for replication. Particular notable studies for 

consideration include Gray and Shadbegian [2005] and the first half of Shimshack 

and Ward [2007]. These studies themselves, coupled with the technical appendix 

to this document, should in theory allow replication by statistically trained agency 

personnel. Perhaps a small pilot study for a sector with a relatively small number 

of similar major facilities could be performed to gauge internal feasibility. 

•	 OECA should consider closely replicating qualitative survey analyses for 

measuring the specific and general deterrence effects of monitoring and 

enforcement. 

Replication of existing qualitative studies for new sectors, contaminants, and time 

periods could importantly contribute to the state of knowledge on deterrence, and 

general deterrence in particular. The survey analysis studies in Section 4 and 

Table 3 are concrete examples for replication. Particular notable studies for 

consideration include Oregon DEQ [2004] and Thorton et al. [2005]. These 

studies themselves, coupled with the actual surveys, should in theory allow 

replication by agency personnel. Perhaps a small pilot survey to gauge internal 

feasibility would be an appropriate start. 

•	 OECA should consider expanding existing qualitative survey analyses for 

measuring specific and general deterrence effects. 

Replication of Oregon DEQ [2004] and Thorton et al. [2005] to other sectors and 

time periods could importantly contribute to the state of knowledge on deterrence, 

and general deterrence in particular. However, it is also possible that modestly 

expanding such surveys may significantly increase the state of knowledge about 

27 
the “reach” and decay of reputation effects underlying general deterrence. OECA 

might consider asking respondents: (1) How long do deterrence fears from 

regulatory activity last? (2) If the regulated plant observed a significant 

enforcement action on another facility in the same state and industry last year, 

would that plant change its processes or environmental behavior? (3) If the 

regulated plant observed a significant enforcement action on another facility in 

the same industry but in a different state, would that plant change its processes or 

environmental behavior? (4) If the regulated plant observed a significant 

enforcement action on another facility in a different industry but in the same state, 

would that plant change its processes or environmental behavior? (5) If the 

regulated plant observed a significant enforcement action on another facility 2, 3, 

4, etc. years ago, would that plant change its processes or environmental 

behavior? (6) If the regulated plant did not observe a significant enforcement 

action on another facility last year (or 2, 3, etc. years ago), would that plant 

change its processes or environmental behavior? (7) If the regulated plant was 

aware of enforcement and monitoring actions at other facilities, was that plant 

approximately aware of the sanctioned or inspected plants’ pollution levels? (8) If 

so, did this knowledge impact its responses to sanction awareness? 

•	 For both quantitative and qualitative methods, studies based upon a universal 

metric are not advised. 

No comprehensive assessment of an entire regulated universe is recommended, as 

implementation costs for a credible analysis would be prohibitive. Every study 

reviewed in Sections 3 and 4 of this document examines specific regulations for a 

limited number of sectors, and most study few regulations/contaminants for few 

industries (see, for example, Earnhart [2004a], Gray and Shadbegian [2005], 

Shimshack and Ward [2005], Thorton et al. [2005], Gunningham et al. [2005], 

and Gray and Shadbegian [2007]). In addition to implementation concerns, 

studies based upon universal compliance assessment provide no additional 

information for future policy over those with statistically valid samples since the 

regulated universe changes over time. Finally, results from broad studies would 

likely obscure, rather than illuminate, important heterogeneity in deterrence and 

compliance assessment. 

•	 Specific compliance rates and deterrence metrics should be statistically valid. 

Generating near-census compliance metrics for major facilities is technically 

possible for those sector/contaminant combinations with relatively complete and 

accurate self-reported data. Quantitative studies such as Shimshack and Ward 

[2005], Shimshack and Ward [2007], and Glicksman and Earnhart [2007] used 

near-censuses. In other instances, randomly selected samples would yield 

statistically valid results. Qualitative survey results with credible sector-specific 

random sampling include Oregon DEQ [2004], Gunningham et al. [2005], and 

May [2005]. 

28 
What data currently collected by EPA could be used to measure deterrence? 

•	 OECA should consider utilizing the extensive data already available to the EPA 

for quantitative compliance and deterrence evaluations. 

Extensive Permit Compliance System water pollution discharges and violations 

data, Continuous Emissions Monitoring System air pollution discharges and 

violations data, Toxic Releases Inventory toxics data, RCRA Biennial Reporting 

System hazardous waste violations data, and Compliance Data System air 

pollution violations data are available for analysis. In many cases, near-censuses 

of major facilities can be obtained. See, for example, Shimshack and Ward 

[2005], Shimshack and Ward [2007], and Glicksman and Earnhart [2007]. 

Published studies have examined a relatively small number of sectors for few 

periods, and considerable insight would be gained by further analysis. The studies 

reviewed in this document, coupled with the technical appendix, should in theory 

allow replication to new sectors, contaminants, and time periods by statistically 

trained agency personnel. Note that some quantitative assessment methods use 

only EPA collected data (see, for example, the first half of Shimshack and Ward 

[2007]). 

• OECA should consider collecting new data from qualitative survey analyses. 

As discussed above, replication and modest extension of existing qualitative 

studies for new sectors, contaminants, and time periods could importantly 

contribute to the state of knowledge on deterrence, and general deterrence in 

particular. All of the collected data would be new, but no additional data beyond 

the information collected in the survey would be required. 

What confounding factors impact deterrence and deterrence assessment? 

•	 OECA should consider important confounding factors when assessing deterrence. 

The literature suggests that plant characteristics like size, industrial sub-category, 

age, and abatement technology may influence compliance decisions. Similarly, 

firm characteristics like managerial attitudes, organizational structure, and 

ownership may influence environmental behavior. Finally, community and 

customer characteristics may influence compliance choices. Therefore, OECA 

studies should consider these confounders when analyzing specific and general 

deterrence. In qualitative settings, this can be done by collecting plant-specific 

information from respondents and controlling for these covariates when analyzing 

survey results. In quantitative settings, two possible methods exist. First, panel 

data techniques can control for factors that don’t vary much over time (and most 

of those listed above are relatively static over multiple year periods). With these 

techniques, the researcher does not need data on these confounding variables. 

Earnhart [2004c], Shimshack and Ward [2005], and Shimshack and Ward [2007] 

demonstrated this approach. Sections A.1 and A.2 of the technical appendix 

29 
briefly discuss such methods for the interested reader. Alternatively, quantitative 

analyses can include observations on plant, firm, and community characteristics. 

Some of this data is collected by the EPA, but some is not. This may represent a 

disadvantage of this particular quantitative approach for OECA replication. 

Other recommendations 

•	 If universal compliance rates must be constructed, they should be weighted by 

priorities. 

As discussed above, universal or even broadly defined compliance rates are not 

recommended. Examining the relationships between such broad metrics and 

enforcement and monitoring activities is unlikely to yield statistically valid 

deterrence assessments. If such metrics are essential for agency reporting to 

constituents, however, they should be constructed to overweight 

sector/contaminant combinations that have particularly important environmental 

benefits. Priorities can be determined, for example, by a panel of internal and 

external experts that rank sector/contaminant combinations in terms of 

environmental implications. These rankings can than be used to construct weights 

for use in developing the broad-based compliance metric. 

•	 As general deterrence importantly motivates environmental compliance, the 

Agency should consider more vigorously publicizing its actions. 

General deterrence requires that facilities know about monitoring and 

enforcement actions at other regulated entities (Oregon DEQ [2004], Gunningham 

et al. [2005], Thorton et al. [2005], Shimshack and Ward [2005], Shimshack and 

Ward [2007]). Enforcement and compliance alerts are infrequent and highly 

aggregated, suggesting that plants may not be sufficiently informed of monitoring 

and enforcement at facilities like theirs. The qualitative general deterrence 

literature somewhat supports this hypothesis, as many company officials were 

unaware of sanction details even for high profile cases. See, for example, Thorton 

et. al [2005] and Gunningham et al. [2005]. Therefore, the agency should perhaps 

consider pilot programs that release sector-specific monitoring and enforcement 

details. Modern communications technology suggests that the costs of such 

information dissemination may be decreasing. Of course, it is possible that 

facilities actually overestimate their perceived risk of sanction. Therefore, the 

effects of such pilot programs should be carefully monitored. 

•	 The agency should examine the net social benefits or environmental gains from its 

current inspection targeting regime. 

Inspection targeting plays an important role in agency behavior, and this is borne 

out in the specific deterrence literature. See, for example, Helland [1998]. At the 

industry level, inspection targeting is primarily a function of national priorities. 

Resource devotion to these priorities, however, significantly cannibalizes 

30 
resources from core monitoring activities. At least for major programs like air, 

water, and hazardous waste, facility-specific inspection targeting is typically 

determined by statutory requirements for inspection frequency, suspicion of high 

environmental discharges due to ambient environmental quality observations, 

citizen complaints, and offense history. 

However, overly compromising random targeting and core programs may induce 

many facilities that can easily reduce emissions to fail to do so. These entities 

simply do not expect frequent evaluations since targeting is devoted to priority 

sectors and previously identified “bad” actors. While targeting priority sectors and 

bad actors may significantly reduce emissions for this small subset of sources, 

general deterrence is most effective when randomness across both space and time 

plays a key role. In other words, randomness may not necessarily achieve large 

reductions at any given facility, but it is quite likely to induce reductions at a very 

large number of facilities due to general deterrence. If randomness is part of the 

targeting process, even facilities that typically comply (or even typically overcomply) may remain especially vigilant since there is some possibility of 

monitoring in any given period. This conclusion is borne out in the tax literature. 

See, for example, Andreoni et al. [1998], for a survey. 

•	 OECA or related offices should consider modestly funding new academic 

research. 

As reviewed in the findings section of this document, many conclusions are 

definitively supported by the existing literature on domestic environmental 

deterrence. However, many insights could be gained by funding further external 

research. Studies examining commonly over-looked features of the enforcement 

process, like enforcement-induced beyond compliance behavior and general 

deterrence mechanisms, may fill particularly import knowledge gaps. 

Important unresolved questions include: (1) How, exactly, do firms learn about 

regulator reputation and how do they update their threat perceptions? (2) Which 

factors in the theoretically-postulated causal chain of deterrence most drive 

compliance behavior? (3) Does the absolute level of enforcement or the number of 

enforcement actions per violation matter more for general deterrence? (4) How do 

repeat offenders systematically differ from their peers? (5) What do differences 

between typical facilities and recidivists imply for optimal targeting and 

sanctioning? (6) How does general deterrence interact with other compliance 

factors? (7) What does an optimal inspection targeting regime look like for 

domestic environmental performance? 

31 
References 

*J. Andreoni, B. Erard, and J. Feinstein, Tax Compliance, J. Econ. Lit. 36, 818-860 

(1998). 

*S. Arora and T.N. Cason, Do Community Characteristics Influence Environmental 

Outcomes? Evidence from the Toxics Release Inventory, Southern Econ. J. 65(4), 

691-716 (1999). 

*S. Arora and S. Gangopadhyay, Toward a Theoretical Model of Voluntary 

Overcompliance, J. Econ. Behav. Org. 28, 289-309 (1995). 

*S. Bandyopadhyay and J. Horowitz, Do Plants Overcomply with Water Pollution 

Regulations? The Role of Discharge Variability, Topics in Econ. Analysis and 

Policy 6, Art. 4 (2006). 

*G.S. Becker, Crime and punishment: an economic analysis, J. Polit. Economy 76, 169-

217 (1968). 

*R. Becker, Pollution Abatement Expenditure by Manufacturing Plants: Do Community 

Characteristics Matter? Contributions to Econ. Analysis and Policy 3, Art. 6 

(2004). 

*R. Brannlund and K. Lofgren, Emission standards and stochastic waste load, Land 

Econ. 72, 218–230 (1996). 

*A. Cavaliere, Overcompliance and Voluntary Agreements, Env. Res. Econ. 17, 195-202 

(2000). 

*G. Chamberlain, Analysis of covariance with qualitative data, Rev. Econ. Stud. 47, 225– 

238 (1980). 

*M. Cohen, Monitoring and Enforcement of Environmental Policy in 

INTERNATIONAL YEARBOOK OF ENVIRONMENTAL AND RESOURCE 

ECONOMICS, III (Tom Tietenberg & Henk Folmer, eds.) (1999). 

*S. Dasgupta, H. Hettige, and D. Wheeler, What Improves Environmental Compliance? 

Evidence from Mexican Industry, J. Environ. Econ. Manage. 39: 39-66 (2000). 

*S. Dasgupta, B. Laplante, N. Mamingi, and H. Wang, Inspections, Pollution Prices, and 

Enviornmental Performance: Evidence from China, Ecological Econ. 36, 487-

498. 

*M. Deily and W. Gray, Enforcement of Pollution Regulations in a Declining Industry, J. 

Environ. Econ. Manage. 21, 260-274 (1991). 

32 
*M. Delmas and M. Toffel, Institutional Pressures and Environmental Strategies, Center 

for Responsible Business Working Paper, UC Berkeley. Paper 32 (2005). 

*J. Doonan, P. Lanoie, and B. LaPlante, Determinants of Environmental Performance in 

the Canadian Pulp and Paper Industry: As Assessment from Inside the Industry, 

Ecological Econ. 55, 73-84 (2005). 

*J. Dubin, M. Graetz, and L. Wilde, The Effect of Audit Rates on the Federal Individual 

Income Tax, 1977-1986, National Tax Journal, 43, 395-409 (1990). 

*J. Dubin, Criminal Investigation Enforcement Activities and Taxpayer Noncompliance, 

Public Finance Review, 35, 500-529 (2007). 

*D. Earnhart, Regulatory Factors Shaping Environmental Performance at PubliclyOwned Treatment Plants, J. Environ. Econ. Manage. 48, 655-681 (2004a). 

*D. Earnhart, Panel Data Analysis of Regulatory Factors Shaping Environmental 

Performance, Rev. Econ. Stat. 86, 391-401 (2004c). 

*D. Earnhart, The Effects of Community Characteristics on Polluter Compliance Levels, 

Land Econ. 80, 208-432 (2004c). 

*D. Earnhart and L. Lizal, Effects of Ownership and Financial Performance on Corporate 

Environmental Performance, J Comparative Econ. 34, 111-129 (2006). 

*W. Emons, A note on the Optimal Punishment for Repeat Offenders, Int. Rev. Law 

Econ. 23, 253-259 (2003). 

*J. Feinstein, The Safety Regulation of U.S. Nuclear Power Plants: Violations, 

Inspections, and Abnormal Occurrences, J. Political Economy, 97, 115-154 

(1989). 

*D. Grant and A.W. Jones, Are Subsidiaries More Prone to Pollute? New Evidence from 

the EPA’s Toxics Release Inventory, Social Sci. Quart. 84, 162-173 (2003). 

*D. Grant and A.W. Jones, Do Foreign-Owned Plants Pollute More? New Evidence from 

the U.S. EPA Toxics Release Inventory, Society & Nat. Res. 17, 171-179 (2004). 

*W. Gray and M. Deily, Compliance and Enforcement: Air Pollution Regulation in the 

U.S. Steel Industry, J. Environ. Econ. Manage. 31, 96-111 (1996). 

*W. Gray and J. Mendeloff, The Declining Effects of OSHA Inspections on 

Manufacturing Injuries, 1979 to 1998, Ind. Labor Relations Rev. 58, Article 3 

(2005). 

33 
*W. Gray and R. Shadbegian, When and Why do Plants Comply? Paper Mills in the 

1980s, Law and Policy, 27, 238-261 (2005). 

*W. Gray and R. Shadbegian, The Environmental Performance of Polluting Plants: A 

Spatial Analysis, J. Regional Science, 47, 63-84 (2007). 

*R. Glicksman and D. Earnhart, The Comparative Effectiveness of Government 

Interventions on Environmental Performance in the Chemical Industry, Stanford 

Env. Law J. Forthcoming (2007). 

*N. Gunningham, D. Thornton, and R. Kagan, Motivating Management: Corporate 

Compliance and Environmental Protection, Law and Policy, 27, 289-316 (2005). 

*E. Guttel and A. Harel, Matching Probabilities: The Behavioral Law and Economics of 

Repeated Behavior, Univ. Chicago Law Rev. 72, 1197-1236 (2005). 

*J.T. Hamilton, Testing for Environmental Racism: Prejudice, Profits, or Political? J. 

Policy Analysis & Manage. 14, 107-132 (1995). 

 *E. Helland, The Enforcement of Pollution Control Laws: Inspections, Violations, and 

Self-Reporting, Rev. Econ. Statist. 80, 141-153 (1998). 

*J. Howard-Greenville, Inside the “Black-Box”: How Organizational Culture and SubCultures Inform Interpretations and Actions on Environmental Issues, 

Organization & Enviro. 19, 46-73 (2006). 

*R. Kagan, N. Gunninham, and D. Thornton, Explaining Corporate Environmental 

Performance: How Does Regulation Matter? Law & Society Rev. 37, 51-89 

(2003). 

*N. Keohane, E. Mansur, and A. Voynov, Averting Enforcement: Strategic Response to 

the Threat of Environmental Regulation, Yale University mimeo (2006). 

*M. Khanna and W. Anton, What is Driving Corporate Environmentalism: Opportunity 

or Threat? Corporate Environmental Strategy 9, 409-417 (2002). 

*S. Kirchhoff, Green Business and Blue Angels, Environ. Resource Econ. 15, 403-420 

(2000). 

*S. Konar and M. Cohen, Information as Regulation: The Effect of Community Right to 

Know Laws on Toxic Emissions, J. Environ. Econ. Manage. 32, 109-124 (1997). 

*S. Konar and M. Cohen, Does the Market Value Environmental Performance? Review 

Econ. Stat. 83, 281-289 (2001). 

34 
*B. LaPlante and P. Rilstone, Environmental Inspections and Emissions of the Pulp & 

Paper Industry in Quebec, J. Environ. Econ. Manage. 31, 19-36 (1996). 

*W. Magat and W.K. Viscusi, Effectiveness of the EPA’s Regulatory Enforcement: The 

Case of Industrial Effluent Standards, J. Law Econ. 33, 331-360 (1990). 

*P. May, Regulation and Compliance Motivation: Examining Different Approaches, 

Public Admin. Rev. 65, 31-44 (2005). 

*J. Maxwell, T. Lyon and S. Hackett, Self-Regulation and Social Welfare: The Political 

Economy of Corporate Environmentalism, J. of Law and Economics 43: 583-618 

(2000). 

*J. McClelland and J. Horowitz, The Costs of Water Pollution Regulation in the Pulp and 

Paper Industry, Land Econ. 75, 220-32 (1999). 

*A. Miller, What Makes Companies Behave? An Analysis of Criminal and Civil 

Penalties Under Environmental Law, New York University mimeo (1995). 

*L. Nadeau, EPA Effectiveness at Reducing the Duration of Plant-Level NonCompliance, J. Environ. Econ. Manage. 34, 54-78 (1997). 

*A.M. Polinsky and S. Shavell, On offense history and the theory of deterrence. Int. Rev. 

Law Econ. 18, 305-324 (1998). 

*A.M. Polinsky and S. Shavell, The economic theory of public enforcement of law, J. 

Econ. Lit. 38, 45-76 (2000). 

*J.P. Rezek and R. Campbell, Cost Estimates for Multiple Pollutants: A Maximum 

Entropy Approach, Energy Econ. 29, 503-519 (2007). 

*C.S. Russell, W. Harrington and W.J. Vaughan, Economic models of monitoring and 

enforcement: Enforcing pollution control laws, Resources for the Future, 

Washington, DC (1986). 

*R. Sah, Social Osmosis and Patterns of Crime, J. of Political Econ. 99, 1272-1295 

(1991). 

*J. Shimshack and M.B. Ward, Regulator Reputation, Enforcement, and Environmental 

Compliance, J. Environ. Econ. Manage. 50, 519-540 (2005). 

*J. Shimshack and M.B. Ward, Enforcement and Over-Compliance, J. Environ. Econ. 

Manage. Forthcoming (2007). 

*S. Stafford, The Effect of Punishment on Firm Compliance with Hazardous Waste 

Regulations, J. Environ. Econ. Manage. 44, 290-308 (2002). 

35 
*S. Stafford, Assessing the Effectiveness of State Regulation and Enforcement of 

Hazardous Waste, J. Regulatory Econ. 23, 27-41 (2003). 

*S. Stafford, Rational of Confused Polluters? Evidence from Hazardous Waste 

Compliance, Contributions to Econ. Analy. Policy. 5, Article 21 (2006). 

*S. Stafford, Can Consumers Enforce Environmental Regulations? The Role of the 

Market in Hazardous Waste Compliance, J. Regulatory Econ. 31, 83-107 (2007). 

*State of Oregon Department of Environmental Quality, General Deterrence of 

Environmental Violation: A Peek into the Mind of the Regulated Public (2004). 

*G.J. Stigler, The optimum enforcement of laws, J. Polit. Economy 78, 526-536 (1970). 

*J.R. Swinton, At What Cost Do We Reduce Pollution? Shadow Prices of SO2 

Emissions, Energy J. 19, 63-83 (1998). 

*D. Thornton, N. Gunningham, and R. Kagan, General Deterrence and Corporate 

Environmental Behavior, Law and Policy, 27, 262-288. 

*W. Van de Ven and M. Van Pragg, The Demand for Deductibles in Private Health 

Insurance: A Probit Model with Sample Selection, J. Econometrics, 17, 229-252 

(1981). 

*S. Winter and P. May, Motivation for Compliance with Environmental Regulations, J. 

Policy Analysis Manage. 20, 675-698 (2001). 

*A. Wolverton, Does Race Matter? An Examination of a Polluting Plant’s Location 

Decision, USEPA National Center for Environmental Economics mimeo, (2002). 

36 
Technical Appendix: Methodologies for Quantitatively Assessing Deterrence 

A.1 Continuous Self-Reported Data (Emissions) 

The particular methodological approach to measuring general and specific 

deterrence depends on the data generating process. A relatively simple panel-data 

technique is appropriate for regimes with continuous self-reported emissions data over 

many facilities and several time periods. For example, water pollution data from the 

Permit Compliance System, air pollution data from the Continuous Emissions Monitoring 

System, and toxics data from the Toxic Releases Inventory fit this empirical framework. 

Here, a measure of continuous emissions like discharges as a percent of the standard is 

regressed on lagged firm-specific enforcement and monitoring variables, lagged 

enforcement and monitoring activity at other facilities, seasonality indicators, year 

indicators, and firm-specific fixed effects. The first regressions in Shimshack and Ward 

[2007] illustrate the method. 

The key methodological issue here is the fixed effects specification. By including 

a facility-specific constant in the regression model, fixed effects exploit the panel 

structure of the data. In essence, fixed effects automatically control for all facilityspecific factors that do not significantly vary over time. This reduces the data 

requirements substantially, as common deterrence determinants like industrial 

classification, industrial sub-category, plant-specific technology, most community 

characteristics, ownership factors, management style, and geographic conditions do 

typically change substantively over the medium-run. Many authors have not employed 

fixed effects because they were specifically interested in assessing the impact of these 

time invariant factors on compliance. Such determinants, however, are not necessary if 

the goal is simply to accurately assess the deterrence effects of enforcement and 

monitoring activities. Other techniques, like random effects, exploit the panel structure of 

the data as well, but numerous papers have statistically rejected the random effects 

specification for these purposes. A key additional advantage of the fixed effects model is 

that it prevents bias from an important type of inspection and enforcement targeting. 

Specifically, fixed effects remove any bias associated with enforcement or inspection 

targeting based upon the plant’s overall environmental performance. 

A.2 Discrete Self-Reported Data (Violations) 

The above methodology encounters statistical problems when the dependent 

variable is limited – ie. when the dependent variable is a discrete 0/1 indicator variable 

indicating compliance or non-compliance. Water pollution violation data from the Permit 

Compliance System, air pollution violation data from the Continuous Emissions 

Monitoring System, hazardous waste violation data from the RCRA Biennial Reporting 

System, and air pollution violation data from Compliance Data System fit this empirical 

framework. Here, a discrete measure like a 0/1 violation indicator is regressed in binary 

probit model on lagged firm-specific enforcement and monitoring variables, lagged 

enforcement and monitoring activity at other facilities, seasonality indicators, year 

indicators, state indicators, firm characteristics, and Chamberlain [1980] conditional 

37 
random effects. The regressions in Shimshack and Ward [2005] illustrate the method in 

practice. 

The key methodological issue here is the Chamberlain conditional random effects 

specification. While standard plant-level fixed effects are desirable, they yield biased 

estimates in binary models like the probit and are therefore no longer appropriate when 

the dependent variable is limited. Chamberlain’s conditional random effects achieve the 

same intuitive outcome as fixed effects for monitoring and enforcement variables. 

Essentially, they remove any bias associated with enforcement or inspection targeting 

based upon the plant’s overall environmental performance. While the theory is beyond 

the scope of this report, practical implementation amounts to including plant-specific 

average inspections, plant-specific average enforcement actions, and plant-specific 

average violations as explanatory variables in the model. Since the model no longer 

contains facility fixed-effects to account for facility-specific time invariant factors, one 

must include available plant data. Particularly desirable control variables include 

industrial classification, industrial sub-category, plant size, and community 

characteristics. 

A.3 An Alternative Specification for Self-Reported Continuous or Discrete Data 

Another approach to analyzing the specific and general deterrence effects is 

illustrated in Gray and Shadbegian’s [2005] compliance-enforcement models (Table 5). 

This is a flexible approach which works for both discrete dependent variables like a 0/1 

compliance indictor and continuous variables like discharges as a percent of the standard. 

Water pollution data from the Permit Compliance System, toxic discharges from the 

Toxic Releases Inventory, air pollution data from the Continuous Emissions Monitoring 

System, hazardous waste violation data from the RCRA Biennial Reporting System, and 

air pollution violation data from the Compliance Data System fit this empirical 

framework. Here, the dependant variable is regressed on a contemporaneous threat of 

enforcement, a contemporaneous threat of inspection, lagged firm-specific actual 

enforcement and monitoring variables, lagged enforcement and monitoring activity at 

other facilities, seasonality indicators, year indicators, state indicators, and firm 

characteristics. 

The key to this specification is the recovery of the threat variables. The goal of 

this process is to remove any bias associated with inspection and enforcement targeting. 

The threat variables are based upon first-stage predictions of facility-specific monitoring 

and enforcement actions. One first-stage analysis regresses inspections on targeting 

factors like the time since last inspection, community characteristics, state indicators, and 

year indicators. First-stage regressions for enforcement actions proceed similarly. 

Importantly, all first-stage regressions must also include a variable that does not directly 

belong in the second-stage emissions or compliance regressions. Such a variable can be 

difficult to identify, but it is necessary for statistical identification. The first stage 

regressions are used to predict probabilities of enforcement for this plant this period. 

These predictions are then included directly in the second-stage emissions or compliance 

regression. Again, since the model does not contain facility fixed-effects to account for 

38 
facility-specific time invariant factors, one must include available facility data in the 

second stage regressions. Particularly desirable control variables include industrial 

classification, industrial sub-category, plant size, and community characteristics. 

A.4 Discrete Data: Compliance Status Only Observed with Inspections 

Unfortunately, none of the above models are appropriate when discrete 0/1 

compliance status is only observed in the presence of an inspection. Most RCRA 

hazardous waste data, and nearly all minor program data, fit this empirical framework. In 

this context, the relevant information is selectively identified, and simply assessing the 

impact of enforcement and monitoring actions on observed compliance will yield 

substantially biased results. The necessary solution is the bivariate probit with selection 

model, also known as the censored probit model. Here, the dependent 0/1 compliance 

variable is regressed on lagged firm-specific actual enforcement and monitoring 

variables, lagged enforcement and monitoring activity at other facilities, seasonality 

indicators, year indicators, state indicators, and firm characteristics. However, this second 

stage regression is only conducted after a first-stage regression corrects for selectivity 

bias. Stafford [2002] illustrates the method in practice. 

The key to the censored bivariate probit is the correction for sample selection. 

Since targeting of inspections occurs, the compliance status among those that are 

inspected will not represent the compliance status of the underlying population as a 

whole. Therefore, a correction is necessary so that the measured effect of enforcement on 

compliance reflects the impact for the entire sample rather than the biased impact for the 

observed sample. Van de Ven and Van Pragg [1981] develop the theoretical 

underpinnings of the bivariate probit with selection model, but the details are beyond the 

scope of this report. Implementation, however, is relatively straightforward with modern 

statistical packages like STATATM (where the command is ‘heckprob’). The basic 

intuition is that a first-stage probit on the entire sample (not just inspection/compliance 

status violations) predicts inspections. A second stage probit uses this prediction to 

control for sample selection when estimating the desired relationships between 

compliance and enforcement actions. Importantly, the model allows for correlations 

between the two-stages as well. A practical concern, similar to the one for the regulatory 

threat model discussed in Section A.3, is that the first-stage selection regression must 

include a variable that does not directly belong in the second-stage emissions or 

compliance regression. Also, the second-stage regression must include a variable that 

does not directly belong in the first-stage regression. Such variable can be difficult to 

identify, but they are necessary for statistical identification. 

39 
Table 1: 

Studies Comparing Regulatory Factors to Other Compliance Dete

Author(s) Outlet Title Sector(s) Key Metric Methodology 

Khanna Corporate 

Environmental 

What is Driving Corporate 

156 S&P 500 firms across 

Firms’ adoption of environmental 

Quantitative Analysis 

First-opractic

Anton Strategy 2002 Environmentalism: 

Opportunity or Threat? 

multiple industrial sectors 

management practices, including environmental staffing, audits, internal policies, TQEM, and public environmental reporting. 

staffingpolicieby reguother fenvironTQEM reportiby mar

Kagan Law and Society 

Review 2003 

Explaining Corporate 

14 pulp and paper mills in 

Managers’ environmental attitudes and behavior, 

Qualitative Survey / 

Regularespon

Gunninham Environmental Performance: How 

Does Regulation 

AUS, NZ, CAN, WA, and GA 

and self-reported BOD, TSS, and AOX water pollution discharges. 

Quantitative Analysis 

environpaper fdecade

Thorton Matter? pressur

observenvironfacilitie

Doonan Ecological Determinants of 86 Canadian Managers’ environmental Qualitative 70 perc

Economics Environmental pulp and paper attitudes and behavior, as Survey the gov

Lanoie 2005 Performance in the Canadian Pulp and mills determined by: 1. self- rating of environmental most im

deterre

Paper Industry: An pressures, 2. integration of govern

LaPlante Assessment from management strategies, 3. not fin

Inside the Industry resources devoted to market

environmental determmanagement. behavi

40 
Table 1 Continued: 

Studies Comparing Regulatory Factors to Other Compliance Dete

Author(s) Outlet Title Sector(s) Key Metric Methodology 

Delmas UC Berkeley, 

Center for 

Institutional Pressures and 

493 US industrial 

Firms’ adoption of two environmental 

Qualitative Survey / 

The inflegislat

Toffel Responsible Business, 

Working Paper 2005 

Environmental Strategies 

sources across several sectors that report TRI releases 

management practices: the international ISO14001 standard and US voluntary environmental programs. 

Quantitative Analysis 

institutfirms’ performorganizimportresponpressur

May Public 

Administrative Review 2005 

Regulation and Compliance Motivation: Examining Different Approaches 

144 marinas and 61 boatyards in CA and WA 

Managers’ commitment to address water quality problems, as determined by: 1. presence of best management practices, 2. self-rating of environmental priorities, and 3. self-rating of environmental actions. 

Qualitative Survey 

Traditiconsidon manviewpoapproadeterreobligatcomplifactors

41 
Table 2: Studies Measuring the Specific Deterrence of Domestic Env

Monitoring and Enforcement 

Author(s) Outlet(s) Title(s) Sector(s) Key Metric Methodology 

Magat Journal of Law Effectiveness of 77 major US Response of BOD Quantitative Signifi

and Economics the EPA’s pulp and paper discharges and BOD Analysis of from in

Viscusi 1990 Regulatory plants, 1982- compliance with CWA Permit all enfo

Enforcement: The 1985 NPDES permits to Compliance Lagged 

Case of Industrial EPA/state inspections. System (PCS) statistic

Effluent Standards database. impact 

continu

Deily Journal of Enforcement of 49 US steel Response of plant-closing Quantitative Signifi

Environmental Pollution plants, 1976- probabilities to all Analysis of enforce

Gray Economics and Management Regulations in a Declining Industry 1986 EPA/state enforcement actions. Permit Compliance percen

probab

1991 System (PCS) enforce

database. sample 

likelihoby 1 pe

Gray Journal of Compliance and 41 US steel Response of criteria air Quantitative Signifi

Environmental Enforcement: Air plants, 1980- pollution compliance to Analysis of from in

Deily Economics and Management Pollution Regulation in the 1989 EPA/state inspections and aggregate enforcement Compliance Data System (CDS) enforce

lagged 

1996 US Steel Industry actions. database. enforce

increascomplithreat oenforcedid not a statisfashion

42 
Table 2 Continued: Studies Measuring the Specific Deterrence of 

Environmental Monitoring and Enforcement 

Author(s) Outlet(s) Title(s) Sector(s) Key Metric Methodology 

Nadeau Journal of 

Environmental Economics and Management 1997 

EPA Effectiveness at Reducing the Duration of PlantLevel Noncompliance 

175 US pulp and paper plants, 1979-1989 

Response of time spent in violation of air pollution statutes to EPA/state inspections and enforcement actions. 

Quantitative Analysis of Compliance Data System (CDS) database. 

Signififrom enpercenpredictactions percenpollutiopredicthowevduratiostatistic

Earnhart Journal of 

Environmental Economics and Management 2004 and Review of Economics and Statistics 2004 

Regulatory Factors Shaping Environmental Performance on Corporate Environmental Performance and Panel Data Analysis of Regulatory Factors Shaping Environmental Performance 

40 Kansas wastewater treatment facilities, 1990-1998 

Response of BOD discharges to EPA/state inspections and enforcement actions. 

Quantitative Analysis of Permit Compliance System (PCS) database, with supplemental data from EPA and Kansas enforcement docket databases. 

Signififrom imenforceinspectactions decreapredictand enhowevinfluenstatistic

43 
Table 2 Continued: Studies Measuring the Specific Deterrence of 

Environmental Monitoring and Enforcement 

Author(s) Outlet(s) Title(s) Sector(s) Key Metric Methodology 

May Public 

Administrative Review 2005 

Regulation and Compliance Motivation: Examining Different Approaches 

144 marinas and 61 boatyards in CA and WA, 2002 

Response of managers’ deterrence fears to regulator inspections and sanctions. 

Qualitative Survey 

Signififrom ininspecthigher not inssanctioviolatiowith hi

Gray Law and Policy 

2005 

When and Why Do Plants 

116 US pulp and paper 

Response of air pollution compliance to EPA/state 

Quantitative Analysis of 

Signifispecifi

Shadbegian Comply? Paper Mills in the 1980s mills, 1979- 1990 inspections and enforcement activities. 

They also examined the factors that influence responsiveness to monitoring and enforcement activities. 

Compliance Data System (CDS) database, with substantial firm characteristic data from other sources. 

inspectactionspredictinspectapproxincreascompli

Keohane Yale University 

Working Paper 

Determinants of Environmental 

249 US electric power 

Response of plants’ sulfur dioxide emissions and 

Quantitative Analysis of DOJ 

Signififrom N

Mansur 2006 Performance in the Canadian Pulp and 

Paper Industry: An 

plants, 1996-2000 

utilization rates to threat and imposition of federal New Source Review 

and EPA suit databases, with substantial firm 

of an Ndecreaemissio

Voynov Assessment from 

Inside the Industry 

lawsuits. characteristic 

data from other sources. 

howevfirm’s suggesavert etime, bcontribimprov

44 
Table 2 Continued: Studies Measuring the Specific Deterrence of 

Environmental Monitoring and Enforcement 

Author(s) Outlet(s) Title(s) Sector(s) Key Metric Methodology 

Glicksman Stanford 

Environmental 

The Comparative Effectiveness of 

Qualitative: 267 US 

Response of plants’ composite BOD and TSS 

Qualitative Survey / 

Signifi75 perc

Earnhart Law Journal 

2007 

Government Interventions on Environmental Performance in the Chemical Industry 

chemical plants Quantitative: 499 major US chemical plants, 1995 to 2001 

discharges to EPA/state inspections and enforcement actions. 

Quantitative Analysis of PCS database. 

responwere an deterrereportedefiniteways to the statinspectactions aggregemissio

45 
Table 3: Studies Measuring the General Deterrence of Domestic Env

Monitoring and Enforcement 

Author(s) Outlet(s) Title(s) Sector(s) Key Metric Methodology 

Thorton Law and Policy 

2005 

General Deterrence and 

233 US firms across 8 

Managers’ knowledge of important ‘signal cases’ of 

Qualitative Survey 

Signififrom en

Gunningham Corporate 

Environmental 

industries, sometime 

enforcement, and selfreported responses to percen

could r

Kagan Behavior between 2001 

and 2004 

knowledge of enforcement actions levied against other firms. 

actions percenenvironresponactions 

Gunningham Law and Policy 

2005 

Motivating Management: 

35 chemical and 

Managers’ responses to knowledge of 

Qualitative Survey 

Signififrom en

Thorton Corporate Compliance and 

Environmental 

electroplating companies in WA and OH, 

enforcement actions levied against other firms, and managers’ interview 

overall enforceimport

Kagan Protection sometime 

between 2001 and 2004 

responses to questions about reasons for environmental performance. 

Learninfacilitieon envaltered compli

Shimshack Journal of 

Environmental 

Regulator Reputation, 

217 major US pulp, paper, or 

Response of plants’ BOD and TSS compliance 

Quantitative Analysis of 

Signifideterre

Ward Economics and Management 

2005 

Enforcement, and Environmental Compliance 

paperboard mills, 1990-1996 

decisions to lagged enforcement and monitoring activity. 

Permit Compliance System (PCS) database. 

additio2/3 redconvenviolatiofollowenforcehad littcompli

46 
Table 3 continued: Studies Measuring the General Deterrence of D

Environmental Monitoring and Enforcement 

Author(s) Outlet(s) Title(s) Sector(s) Key Metric Methodology 

State of Oregon State of Oregon General 450 regulated Response of managers’ Qualitative Signifi

Department of Department of Deterrence of companies in self-reported Survey deterre

Environmental Environmental Environmental several environmental behavior to penalti

Quality Quality 2004 Violation: A Peek 

into the Mind of the Regulated Public 

industries in OR, 2002 

hearing about inspections and sanctions at other facilites. 

averagchangeadditiocompaafter leaction. greater penalti

Gray Journal of The 521 US Response of toxic and Quantitative Signifi

Regional Environmental manufacturing conventional air pollution Analysis of deterre

Shadbegian Science 2007 Performance of Polluting Plants: A plants, located within 50 compliance and emissions to EPA/state inspections IDEA and Emissions signific

compli

Spatial Analysis miles of 3 and enforcement Inventory (but no

major US activities. Databases. for bot

cities, 1997 for nea

deterrestate bo

Shimshack Journal of 

Environmental 

Enforcement and Over-Compliance 

251 major US pulp, paper, 

Response of plants’ BOD and TSS discharges to 

Quantitative Analysis of 

Signififrom fi

Ward Economics and Management 

forthcoming, 2007 

and paperboard mills, 1990-2004 

lagged enforcement and monitoring activity. 

Permit Compliance System (PCS) database. 

TSS diapproxyear fothe redgoing fcomplia reduc

47