## 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 Pr otection Agency’s Office of Enforcement and Compliance Assurance (OECA) by Jay P. Shimshack.

1AUTHOR 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 Ag ency or any of its employees.

This report reviews research on the current st ate of science in environmental deterrence measurement and develops findings a nd recommendations based upon technical feasibility, without assessing implementation costs . No work has been done to determine the costs or practicality of im plementing the deterrence measures discussed in this report. In other words, this report does not determ ine the costs to the EPA of conducting any specific information gathering, deterrence meas urement, or statistic al 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 Ke n Gigliello, Dinah Koehler, Jon Silberman, and Scott Throwe for sharing their institutional expertise.

2Executive Summary

Regulatory punishment for pollution viola tions is a mainstay of nearly every industrialized nation’s environmental policy, an d a rich theoretical literature examines enforcement in general and environmental enfo rcement 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 reportreviews the policyrelevant environmental compliance literatur e, with emphasis on the growing empirical literature measuring the specific and gene ral 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 de terrence. (3) Environmental monitoring and enforcement activities generate substantia l general deterrence. (4) Environmental monitoring and enforcement activities may ge nerate significant emissions reductions,

even for sector/contaminant combinations where compliance is typically high. (5) Enforcement and monitoring responses ar e 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 conclu sively demonstrated, by the literature include: (1) The regulator reput ation effect underlying genera l deterrence tends to decay. (2) There are limits to the “rea ch” of the reputation effect underlying general deterrence. (3) Deterrence varies across enforcement and monitoring instruments; inspections, intermediate enforcement actions, administ rative 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 aut hor’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 monito ring and enforcement. (2) OECA should consider closely replicating qualitative survey analyses for measuring the specific and general deterrence effects of monitoring and en forcement. (3) OECA should consider expanding existing qualitative survey analyses for measuring th e specific and general deterrence effects of monitoring and enforcement. (4) For both qua ntitative and qualitative methods, studies based upon a universal metric are not a dvised. (5) Specific compliance rates and deterrence metrics should be statistically valid.

31. Purpose & Scope

Regulatory punishment for pollution viola tions is a mainstay of nearly every industrialized nation’s environmental policy, an d a rich theoretical literature examines enforcement in general and environmental enfo rcement 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 reportreviews the policyrelevant environmental compliance literatur e, with emphasis on the growing empirical literature measuring the specific and gene ral deterrence effects of environmental monitoring and enforcement.

The review highlights peer-reviewed resear ch 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 Re sults (STAR) program, this white paper typically considers the ensuing journal artic les rather than the underlying STAR Grant final reports. Particularly important conference or policy papers that have received substantial professional or institutional ve tting are also investigated. The report emphasizes the domestic environmental contex t to reference results most directly applicable to the Agency’s mission. Howeve r, significant papers from other policy settings, like occupational safe ty, nuclear regulation, and tax compliance, are investigated when their methods or conclusions yiel d 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 scien ce are referenced and explored, but less fundamental articles with publication date s 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 contex t. The particularly

interested reader should cons ult Cohen’s [1999] excellent l iterature review or the EPA Compliance Information Project’s [1999] Litera ture 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 curre nt state of science in environmental deterrence measurement and develops findings and recommendations based upon technical feasibility, without assessing im plementation costs . No work has been done to determine the costs or practi cality of implementing the deterrence measures discussed in

4this 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 feas ibility of doing so given the EPA and OECA’s workload, workforce, and budget.

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

factors that influence environmental co mpliance. The discussion corresponds to Statement of Work sub-question 1C, “What confounding variables (var iables other than monitoring and enforcement) impact environmental performance and responsiveness to regulatory actions?” The intent of this secti on is to place the literature examining the deterrence effects of monitoring and enforcement in context.

Sections 3 and 4 explore the deterren ce impacts of compliance monitoring and enforcement in detail. Section 3 highlights th e deterrence effects of regulator actions on the sanctioned or monitored firm, otherwis e known as specific deterrence. Section 4 investigates the mechanisms and magnitude s of general deterrenc e, where regulatory actions directed towards one facility affect th e behavior of other faci lities. 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 enforc ement actions?” Sections 3 and 4 dissect key studies to examine how the authors meas ured deterrence and consequently provide specific examples for OECA to follow.

Section 5 synthesizes research met hodologies and presents frameworks for

deterrence assessment. Additi onally, Section 5 references a technical appendix that provides specific quantitative tools for databa se analyses of deterr ence. This discussion corresponds to Statement of Wo rk sub-question 2, and addr esses “What approaches to deterrence measurement are suggested by the re levant literature?” and “What information currently collected by the EPA could OECA use to measure deterrence?”

Section 6 discusses major findings a nd recommendations. The section first presents conclusions that can be definitivel y supported by the literature, then reviews important findings suggested (but not definite ly confirmed) by the literature, and finally offers ideas for further exploration. Subse quent recommendations follow Statement of Work sub-question 3 and suggest considera tions 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 rationa l profit-maximizing decision-maker. Here,

1 Discussions with personnel at th e Office of Enforcement and Compliance Assurance (OECA) suggest that estimating the costs and benefits of practical implem entation from academic studies presents significant

difficulties.

5compliance factors include traditional regula tory monitoring and enforcement, pollution abatement costs, the ability to influe nce 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 attit udes, and organizational structure.

The goal of this section is to briefly review the literat ure on non-regulatory compliance factors to put later discussions of the deterrence eff ects of monitoring and

enforcement in context. There are two ke y messages. First, many factors influence environmental performance. Second, despite the diverse determinants of environmental behavior, the literatu re indicates that regulation, monitoring, and enforcement have historically been, and remain, critical de terminants of environmental outcomes.

2.1 Factors Predicted by the Standard Economic Model

In the standard economic model, th e facility chooses its environmental performance by balancing the expected costs of polluting with the expected benefits of doing so. This is consistent with the tr aditional law and economic framework that originated with Becker [1968] and Stigler [1970], and progressed as summarized in Polinsky and Shavell [2000]. Russell, Harri ngton, and Vaughn [1986] comprehensively applied this analytical framewor k to environmental behavior.

Perhaps the most natural explanation fo r environmental performance is marginal variable costs of abatement, as determined by technology, industria l characteristics, and other factors. If marginal abatement cost s are small, facilities may maintain low emissions even in the absence of significan t regulations. Unfortunately, published peerreviewed studies examining abatement costs are limited. Relatively recent studies include McClelland and Horowitz [1999] for pulp a nd paper water pollution and Swinton [1998] and Rezek and Campbell [2006] for electricit y air emissions. Results generally suggest significant marginal abatement costs, indicatin g that firms are unlikely to pollute at low levels in the absence of regulations or othe r pressures. Further, a key finding is that

variable costs of contaminan t reductions are extremely heterogeneous across both sources and industrial processes. Uniform regulatory pol icies and actions are therefore unlikely to achieve socially efficient outcomes.

Another important determinant of envir onmental 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 in tuition is straightforward. Firms that expect consumers to lobby for stringent e nvironmental 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 su pporting the influence of preemptive behavior on environmental outcomes is sugge stive, but not yet de finitive. Maxwell,

6Lyon, 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 gr oup membership and th at state’s reported toxic releases was evidence for their regulator y threat theory. However, their sample size was small, it was difficult to disentangle the effects of regulat ory threats from TRI reporting itself, and other mechanisms like c onsumer demand might well explain their empirical regularity.

Community characteristics may influen ce environmental behavior in several ways. For example, community pressure may influence the threat of future regulation as

discussed above. Locational characteristics ma y 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 cons umer purchasing power and preferences, and therefore community composition may influenc e environmental performance (Arora and Gangopadhyay [1995], Kirchoff [2000], and Cavalie re [2000]). Finally, it is possible that pure discrimination influences emissions, a lthough the empirical evidence for this “environmental injustice” hypothesis is de batable (Hamilton [1995] , Wolverton [2002], Becker [2004]). Of course, community charact eristics may also influence the level of monitoring and enforcement directed at a spec ific facility. This indirect effect is subsumed in the specific and general regulat ory deterrence discussed in Sections 3 and 4.

An empirical literature does indeed detect a significant influence of community

characteristics on environmental behavior. Ar ora and Cason [1999] found that zip-code level TRI-reported toxic releases were correlated with numerous economic and demographic characteristics. Results vari ed significantly across regions, however. Earnhart [2004b] determined that a co mmunity’s income, population density, unemployment rate, and average political affiliation influenced facility-level environmental performance at Kansas wastew ater treatment plants. Becker [2004] found that some ideological and socio-economic factors influenced air pollution abatement expenditures at manufacturing plants. In contra st, however, several no table characteristics like voter turnout, education, a nd race/ethnicity did not yiel d consistent environmental implications. Most recently, Stafford [ 2007] analyzed the impacts of consumer preferences (as proxied by ma rket size and the number of competitors) on hazardous waste violations reported by federally regul ated facilities in the EPA’s Biennual Reporting System. The author hypothesized th at more competitive local markets would be associated with fewer violations because consumers favoring environmental performance have more consumption options. Indeed, results indica ted that increased local market competition induced increased co mpliance 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 statisti cally and economically strong re lationships between the BOD pollution intensity of Indone sian industrial sources a nd income, education, and population density. During the sample period, formal regulations in Indonesia were

7largely absent. In contrast, Dasgupta et al. [2000] established that the degree of direct influence by neighbors and community act ivism 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 traditiona l economic model. For example, subsidiaries may be less

inclined towards emissions reductions since liabi lity rules protect their parent companies. Indeed, Grant and Jones [2001] studied TRI t oxic releases by 2000 chem ical facilities and found that subsidiaries had higher release rate s than their non-subsidia ry counterparts. In contrast, Grant and Jones [2004] found that foreign ownership had no detectable impact on chemical plant releases, presumably b ecause other factors trumped any potential disregard for local communities by overseas parent companies.

In addition to ownership type, ownershi p concentration and influence may affect pollution outcomes. Suppose, for example, that fewer owners are more risk averse because risks are spread out over fewer indi viduals. 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 c oncentration, investor owners may express environmental preferences or concerns over future environmental regulation by decreasing demand for shares. Konar and Cohen [1997] and Kona r and Cohen [2001] studied the relationship between stock pr ices and TRI-reported toxic emissions and

environmental lawsuits. The authors determ ined that poor environmental performance resulted in lower asset values and negativ e stock price changes produced lower future

toxic releases.

2.2. Factors Not Predicted by the Standard Economic Model

Management knowledge, managerial attit udes, and organizational structure also determine compliance and performance. These corporate culture considerations do not fit neatly into the standard law and economi c model of environmental behavior and compliance. Managers’ understa nding of complex le gal requirements partially determines compliance. Dasgupta el al. [2000] conducted a survey of Mexican industrial facilities and found that environmental management stra tegies and training significantly improved compliance with pollution control laws, larg ely because managers were learning about the evolving regulatory struct ure. Winter and May’s [2001] survey of Danish livestock operators determined that farmers with hi gh levels of awarene ss of rules exhibited compliance levels 28 percent higher that farm ers 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

8interpretation. Facil ities with more complex regulati ons (exogenously determined) are more likely to violate RCRA hazardous wa ste requirements, all else equal.

Manager attitudes also affect enviro nmental behavior. Kagan et al. [2003] uncovered both qualitative and quantitative evidence that managerial outlooks significantly affected water pollution emissi ons of 13 pulp and paper mills in the US, Canada, Australia, and New Zealand. In part icular, the authors found that firms with proactive environmental management styles invested in more advanced abatement equipment and more day-to-day environm ental oversight. May [2005] surveyed 421 marinas and boatyards in the we stern US and determined that facility operators’ attitudes towards government strongly influenced th eir deterrence percep tions. ‘Conservative’ boatyard operators were 13 percent more lik ely to have significant concerns about regulatory action and 16 percent less likely to fe el obligated by a sense of duty to comply.

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

structure to understand its role in environmental performan ce. Delmas and Toffel [2005] used survey and archival da ta to study institutional pressure and internal power

distribution at US industrial sources. They found that faci lities with strong market pressures and marketing departments adopted different environmental practices than facilities with influential non-market pressu res and legal departments. For example, facilities that were more receptive to market constituents like customers, suppliers, the media, and community groups were more lik ely to adopt international environmental management systems. In contrast, facilities that were more receptive to non-market constituents like legislators and regulato rs were more likely to adopt governmentsponsored voluntary pollution control progr ams. Howard-Greenville [2006] conducted a detailed ethnographic study of a large computer chip manufacturer, and results indicated

that different sub-cultures framed environmenta l priorities, definitions, and solutions very differently. Therefore, firms with heterogene ous 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 e nvironmental performance reviewed in sub-sections 2.1 and 2.2, the litera ture strongly suggests one re sult 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 regula tion/legislation is a more important determinant of environmenta l 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 manage ment were largely attributable to market factors, first-order practices like environmental staffing, audits, and internal policies were attributable to legal and regul atory factors. Kagan et al. [ 2003] argued that regulation is principally responsible for large environmenta l gains for pulp and paper facilities in the

9US, Canada, Australia, and New Zealand. In a survey of Canadian pulp and paper facilities, Doonan et al. [2005] discovered th at a full 70 percent of managers rated the government as the single most important sour ce of environmental pressure. Similarly, Delmas and Toffel’s [2005] survey of 493 US industrial sources indicated that the influence of regulators and le gislators on environmental performance was considerably stronger than community organizations, act ivist groups, and the media. Finally, May [2005] showed that traditiona l regulation had a considerably stronger influence on marine industry managers’ deterrence viewpoi nts than non-mandatory regulation.

3. Monitoring and Enforcement: Specific Deterrence

Given the critical importance of regu lation, monitoring, and enforcement for environmental performance and compliance, it is important to assess empirical deterrence magnitudes. This section reviews the recent li terature on the specific deterrence effects of monitoring and enforcement. In this context, specific deterrence refers to the effects of regulatory actions on the evalua ted or sanctioned firm itself. General deterrence effects, in which regulatory actions aimed at one f acility impact the environmental performance at other facilities, are cons idered in the next section. 2

The key goal of this section is to diss ect important studies. The examination of each paper’s approach to measuring specif ic deterrence should provide concrete examples for possible replication. Collectivel y, the results of the specific deterrence literature indicate that both qua litative and quantitative methods exist to measure specific deterrence. Further, joint re sults show that significant reductions in non-compliance and

emissions are obtainable with tradit ional monitoring and enforcement. Inspections and enforcement actions consistently produce impr oved future environm ental performance at the evaluated or sanctioned facility . Results hold both hist orically and currently.

3.1 The Specific Deterrence Literature

Table 2 summarizes key empirical studies that measure the specific deterrence

effects of domestic environmental monito ring 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 inspec tions had statistically significant negative impacts on both BOD compliance status and continuous BOD discharges. The results were also economically significant. Lagged inspectio ns 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 inspectio n or enforcement action. For the purp oses 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.

10Other 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 st eel industry. They studi ed the response of plant-closing probabilities to all EPA/stat e 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 poi nt. 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 complia nce of 41 US steel manufacturers for the period 1980-1989. Here, the authors de tected a substantial impact of actual lagged inspections and non-monetary enforcement actio ns on air pollution co mpliance, 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 pollu tion compliance rates for 175 US pulp and paper plants from 1979-1989, Nadeau [1997] sh owed that EPA regulatory activity can significantly affect plants’ duration of non- compliance. In particular, Nadeau [1997] studied the response of time spent in viola tion of air pollution st atutes to EPA/state inspections and enforcement actions. Results indicated that a 10 percent increase in a plant’s predicted threat of enforcement acti ons, including monetary penalties, resulted in a 4 to 5 percent reduction in the violation length. Results fo r predicted inspection activity, however, were largely indeterm inate. A 10 percent increase in a plant’s predicted threat of inspection resulted in a 0 to 4 percen t reduction in the length of non-compliance.

More recently, Earnhart [2004a] and Earnhart [2004c] investigated the relationship between regulator activity and se lf-reported BOD discha rges from 40 Kansas wastewater treatment facilities for the period 1990-1998. The key metric in both studies was the response of BOD discharges to EP A/state enforcement and monitoring actions. The most consistent result across the analys es was that lagged actual inspections and lagged actual enforcement actions caused reduc tions in BOD discharges (as a percentage of permitted levels). Interestingly, the author detected no deterrence difference between actual EPA inspections and actual local author ity inspections. Other findings, including deterrence effects of the threat of inspec tion and the threat of enforcement were inconsistent across papers and statistical specifications. As Earnhart [2004c] highlights, these papers also suggest that empirical test s of specific deterrence may be sensitive to statistical specification. In ot her words, the same data can sometimes yield different

conclusions when assessed via alternative sta tistical methods.

May [2005] analyzed survey results fr om approximately 200 marine facilities (marinas and boatyards) in Washington and California. In a study of how managers’ reported deterrence fears responde d 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.

11found that inspections significantly contribu ted to managers’ deterrence viewpoints. Facilities inspected in the last 5 years had higher deterrent fears than those not inspected. In contrast, past sanctions fo r water quality violations were not associated with higher deterrence fears. Keohane, Mansur, and V oynov [2006] studied the impact of new source review (NSR) enforcement lawsuits on the su lfur dioxide and utilization rates of 249 electric power plants for th e period 1996-2000. The authors dete rmined that the threat of a NSR lawsuit, as primarily predicted by la gged 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 author s 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 specifi c deterrence for water discharg es in the chemical industry. The statistical database analysis exam ined how plants’ composite BOD and TSS discharges responded to EPA/state enforcem ent 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 approximat ely 87 percent reported that inspections definitely or probably effectively induced water pollution compliance. Similarly, the statistical results indicated that both insp ections and enforcement actions resulted in decreased aggregate conventiona l water pollution emissions. Th e authors further explored

the relative contribution of state actions vs . federal actions as well as administrative actions vs. judicial actions, but results interp reted broadly failed to reveal consistent

patterns.

A particularly notable recent paper is Gr ay and Shadbegian [2005]. In addition to measuring specific deterrence in a framework c onsistent with earlier studies, the authors

also considered factors that influenced the responsivenes s 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, th e 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 we re less sensitive to inspections, but more sensitive to enforcement actions, than plants owned by smaller parent firms.

Specific deterrence effects are not re stricted to domestic environmental institutions. For example, Laplante and Rilstone [1996] explored the impact of inspections and the predicte d threat of inspection on th e biochemical oxygen demand (BOD) and total suspended solid (TSS) discha rges of 47 Canadian pulp and paper mills.

12The authors found inspections and their associ ated threat reduced mills’ absolute BOD discharges by approximately 28 percent. Si milarly, Dasgupta et al. [2001] demonstrated that inspections on manufact uring 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 ar ena, Gray and Mendelo ff [2005] found that OSHA inspections reduced wo rkplace injuries by approxi mately 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 th an inspections without penalties.

3.2 Specific Deterrence: Repeat Offenders

The empirical literature on repeat of fenders is very limited. Miller [2005] explored the effect of admini strative, 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 regulat ory actions. The analysis reve aled that both civil and administrative penalties reduced repeat offe nses. However, both did so about equally, despite the fact that administrative penaltie s are constrained to be lower on average. Criminal enforcement appeared to domina te other enforcement actions, and results suggested that criminal sanctions significan tly reduced repeat offenses. Also, there was some suggestive evidence that criminal fine s that increased in the number of offenses were particularly effective for reducing reci divism. 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 th at 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 Cl ean 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 vi olation now may not only trigger a penalty but may also enhance penalties in the fu ture. Guttel and Harel [2005] extended the traditional model to incorporate behavioral economic considerations. They also concluded that penalties should be in creasing in the number of violations. 4

However, economic support for increased penalties for repeat environmental offenders rests on one key assumption. The assump tion is that it must be socially optimal to deter violations from the recidivating fac ility. It is possible th at 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 expendi ng 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 extremel y large relative to the sanctioned firm’s wealth and that increasing penalties in some period inhere ntly implies lower penalties in another period.

13society is receiving in benefits from these marginal reductions. Further, given limited agency enforcement budgets, significantly great er abatement and environmental benefits may be achieved by targeting resources to wards 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 wh en applied to facilities with numerous repeat offenses driven by extreme abatement costs.

4. Monitoring and Enforcement: General Deterrence

Law enforcement agencies and lega l scholars have long postulated that monitoring and enforcement spills over to deter violations at facilities beyond the sanctioned entity. If this is true, this regul ator reputation-building ‘general deterrence’ effect has critical implications. Notably, focu sing on deterrence effects at the sanctioned facility alone may seriously underestimate th e efficacy of fines and other sanctions.

This section reviews the literature on th e 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 deterr ence should provide specific examples for possible replication. Collectively, the results of the general de terrence literature indicate that both qualitative and quantitative met hods exist to measure general deterrence. Further, joint results show that environmenta l facilities learn from the experiences of their neighbors, and this lear ning impacts compliance behavior. Inspections and enforcement actions consistently produce sign ificant spillover effe cts on non-sanctioned facilities. Consequently, a s ubstantial 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 facil ities 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, St afford’s complementary 2003 study found no

consistent effect of state-le vel strict liability rules on h azardous waste violations. This more recent study, however, did find that incr eased state-level environmental spending decreased Class I pollution violations. While these fi ndings 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 incr eased spending reduced violations, it is also possible that increased spending increased in stances where firms cheat by no longer selfreporting violations. Collectively, Stafford [2002] and Stafford [2003] considered the impact of rule and budget changes that may ha ve enhanced the regulator’s reputation for

14toughness. Therefore, these papers importan tly 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 envi ronmental monitoring and enforcement. In companion studies, Thorton, Gunningham, a nd 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 environmenta l responses to knowledge of enforcement actions levied against other facilities. Gunningham et al. [ 2005] used interview evidence from 35 chemical and electro-plating fac ilities to explore managers’ reasons for environmental performance and their stated responses to enforcement actions levied against other faciliti es in their industry. The authors found that 89 percent of survey respondents could recall some en forcement actions imposed on other firms, 63 percent of survey respondents took an environmental act ion 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 deterr ence, interpreted broadly, is a significant factor for

environmental behavior.

Thorton, Gunningham, and Kagan [2005] and Gunningham, T horton, and Kagan [2005] also explored the specific mechanisms that might drive general deterrence. The authors detected that respondent s 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 num ber of interview respondents reacted to knowledge of penalties at other facilities by performing a sp ecific environmental action to immediately avoid a similar infraction. Ye t, many officials indicated that learning

about someone else’s fine focused their atten tion on environmental issues or altered their long-term compliance motivations. Collectivel y, the companion studies concluded that general deterrence may operate more as focu sing, reminder, or reassurance mechanism than a short-run fear-generating mechanism. Th e 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 da ta on BOD and TSS discharges from 217 pulp and paper mills to explore the regulat or reputation-building effect of general deterrence. In particular, the authors statis tically analyzed the re sponse of plants’ BOD and TSS discharges to lagged enforcement and monitoring activity at both the sanctioned plant and other plants in the same regulat ory 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 av erage deterrence impact on each of the other plants in a state was almost as strong as th e impact on the sanctione d facility. The authors also showed that general deterrence was incr easing in the amount of the penalty. In other

15words, larger fines induced fewer subsequent vi olations by other plants in the state. Other results included the finding that non-monetary enforcement responses like notices of

violation alone had little influe nce 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 re duction in statewide vi olation rates in the subsequent year, this regulat or-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 regard ing regulatory stringency.

State of Oregon Department of Envi ronmental Quality [2004] conducted an extensive survey of 450 regulated compan ies in Oregon for one month in 2002. The sample was selected primarily from high prio rity industries, and thus results are not representative for less visibl e industrial sectors. Key metr ics included the response of managers’ self-reported environmental beha vior 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 anot her 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 es timated that inspections and penalties directly generated an average of 1.6 envir onmental 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, concer ns about penalty actions de pended 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 enforcem ent. In particular, small companies were significantly less likely to respond to gene ral deterrence, in part because these organizations were considerably less likely to be aware of regulato ry activities devoted towards other facilities. Further, a substantia l 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 di oxide, and toxics emissions from 521 US manufacturing plants located w ithin 50 miles of three major cities, Gray and Shadbegian [2007] analyzed the response of toxic and conventional ai r pollution violations and emissions to EPA/state inspections and en forcement actions. The authors found evidence for both specific and general deterrence. Inspections increased compliance with regulations for both the inspected facility a nd for nearby plants. The specific and general deterrence effects of inspections were found to be approximately the same magnitude.

16Interestingly, the authors also determined that general deterrence was restricted to states, suggesting that regulatory juri sdictions may be important dete rminants of the “reach” of the regulator reputa tion effect underlying general deterrence.

Spillover effects of environmental enfo rcement not only affect compliance, but beyond compliance behavior as well. For example, Shimshack and Ward [2007] examined the impact of enforcement- induced regulator re putation 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 facili ties 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 si gnificant 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 ye ar 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 demons trated how these empi rical regularities can be rationalized by economic theories of discharge randomness and discharge jointness. Plants with partially random discha rges may have some possibility of a fine from accidental releases over the permitte d 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-produce d, pollutant. If such a plant perceives an increased regulatory threat by observing fine s on other facilities, it may reduce the pollutant with the binding limit and corre spondingly 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 near ly $17 billion in increased tax revenues. Nearly all of these increased collections st emmed from general dete rrence, 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 percepti ons every two to three years. Finally, the author found suggestive evidence that medi a information dissemination enhanced deterrence spillovers.

17General deterrence effects beyond tax and the environmental settings are more uncertain. Feinstein (1989) found no evidence fo r either specific or general deterrence effects from safety sanctions on 17 nuclear pow er generators. However, this institutional context was quite unique, as each facility had a resident inspector who has primary responsibility for plant safety. Additional insp ectors 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 se veral definitive findings arise from the existing literature, care should be exercised before extrapolating more nuanced results beyond the relevant institutional settings. C onsequently, replication for new sectors, contaminants, and time periods could importan tly contribute to the state of knowledge on deterrence. This section discusses replic ation considerations, examines studies particularly promising for replication, and reviews the strengths and weaknesses of the diverse approaches to compliance and deterren ce measurement. All discussions are based upon the author’s subjective assessment of th e related literature and the state of knowledge.

Quantitative Database Analyses

Theoretically, the quantitative database an alysis approach could be replicated for other sectors by OECA. This approach is most directly applicable to regulations and subsamples with continuous self-reported emi ssions data over many facilities and several time periods. Such comprehensive data allo w for month-by-month, qua rter-by-quarter, or year-by-year compliance determination a nd emissions assessment for all analyzed facilities. For example, major facilities’ water pollution data from the Permit Compliance, air pollution data from the Continuous Emi ssions Monitoring System, and toxics data from the Toxic Releases Inventory fit this fra mework. The approach is also applicable to regulations and sub-samples with comprehe nsive compliance indicators over several periods. Major facilities’ water pollution vi olation 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 Complia nce Data System fit this framework.

Quantitative database analyses are particularly powerful for individual sectors with census or near census monitoring cove rage. Sector-specific analyses of common discharges or violations from CWA major s ources are a notable example. The technical appendix summarizes rigorous sta tistical procedures for implementation in such contexts. Strengths of the quantitative approach incl ude 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 c onfounding factors. Further, database analysis reveals how regulated faciliti es actually responded to monitoring and

18enforcement, rather than how these facilities say they responded to monitoring and enforcement. In other words, quantitative an alyses avoid survey response bias, recall bias, and strategic reporting bias. This latter concern, where respondents may have

incentives not to reveal certain information tr uthfully in a survey, may be particularly crucial for surveys conducted by the regulatory agency itself. Quantitative database analyses tie monitoring and enforcement acti ons to actual measures of compliance and emissions, which are better connected to e nvironmental quality than the intermediate factors like process and management modi fications that are typically measured by qualitative methods. Finally, qua ntitative analyses do not re quire 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 Shim shack 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 gene ral deterrence. The study’s key metric is the response of plants’ BOD and T SS 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 explanator y variable is a 0-1 i ndicator 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 to tal 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 i ndicators to control fo r seasonal variation, and plant-specific indicators to control for time invariant confounding factors like community characteristics, industrial sub-se ctor, and basic contro l technology. Techical appendix A.1 discusses statistical methods for Shimshack and Ward [2007] type analyses.

In Gray and Shadbegian [2005], the ke y question is ‘What is the empirical relationship between a plan t’s pollution discharges and lagged enforcement and monitoring actions directed at that facility?’ An importa nt 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 respons e 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 pl ant was in compliance with air pollution regulations throughout the year. The key explanatory variable s are predictions of total enforcement actions and total inspections dir ected towards a plant in the given year. To measure the sensitivity of deterrence to othe r factors, the key expl anatory variables are the predictions interacted w ith plant-specific characteristic s like industrial sub-sector (pulp/paper in their analysis), plant age, and plant size. Since Gray and Shadbegian [2005] do not use plant-specifi c indicator variables to control for time invariant confounding factors, the study al so includes detailed plant and firm characteristics. Technical appendix A.3 discusses statistical methods for Gray and Shadbegian [2005] type analyses. Confounding vari able data is typically not collected by the EPA itself and

19must be obtained externally. This may be a disadvantage of this specific quantitative analysis approach for OECA.

Note that the metrics in both Shim shack and Ward [2007] and Gray and Shadbegian [2005] are sector-s pecific (both pulp and paper) and regulation-specific (the former analyzes conventional water pollutant s regulated under the Clean Water Act and the latter analyzes crit eria air pollutants regu lated under the Clear Air Act). These metrics

are similar to those used in other quantitative database analysis studies, including Earnhart [2004a], Earnhart [2004c], Shimsh ack and Ward [2005], and Keohane et al. [2006]. 5 While these metrics are narrowly defi ned, 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 agen cy’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 uni versal or output-oriented metrics mask important heterogeneity across sectors and are therefore more likely to obscure, rather than illuminate, our understa nding of specific and general deterrence.

Another notable feature of most quan titative database analyses, including Earnhart [2004a], Earnhart [2004c], Gray and Shadbegian [2005], and Shimshack and Ward [2007], is lagged monitoring and enforc ement variables. Lags serve two important purposes. First, lags reduce statistical si multaneity (endogeneity) and help isolate the direction of causality. If contemporaneous monitoring and enforcement variables are included in the analysis, any st atistically detected correlations between these factors and compliance or emissions may reflect the cau sal effect of compliance and emissions on monitoring and enforcement actions due to re gulator targeting. This reverse causation influence is likely to understat e true deterrence effects. S econd, lags allow time for firms

to alter their environmental behavior in re sponse to monitoring a nd enforcement actions.

Qualitative Survey Analysis

Qualitative deterrence assessments have si gnificantly contributed to the state of science. Key advantages of the survey approach include the ability to examine organizational culture issues, knowledge of enforcement and mon itoring activity across facilities, and the interactions between polic ies in greater depth. An additional advantage is that deterrence and compliance metrics from survey methods can be defined more broadly than metrics for quantitative anal ysis. 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 statis tical 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.

20and 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, fr equency and kinds of process modifications made because of environmental issues, most important factors stimulating compliance, and stated perceptions of regulatory risk. Questions sp ecifically targeting general deterrence examined awareness of inspections and enforcement actions at other facilities and company response to awareness of insp ections 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 othe r 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 percen t. 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 heari ng about a fine or prison sent ence at another company in their industry induced them to (1) review their environmental programs, (2) change their management plans, (3) change their envir onmental 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 re gulated facilities and same regulations. Implementation of any technique is costly. Un fortunately, the literat ure does not examine how frequently replication should occur. Th e key consideration is likely the internal

benefits and costs of assessmen t, although this white paper does not attempt to determine the costs or practicality of implementing any specific info rmation gathering, deterrence measurement, or statistical analysis. My subj ective assessment is that broad conclusions are likely applicable for 10-20 years for sector s/regulations in which technical change is modest, regulations are fairly static, and ma nagerial attitudes are not evolving rapidly. More detailed results, including the specific magnitudes of deterrence effects, probably have a lifespan of less than 10 years fo r most industry/regulation combinations.

It is not the case, however, that rapi dly decaying deterrence effects over time render any given study obsolete. The key c onsideration is whether the underlying decision-making process has importantly change d, on average, for the regulated facilities. For most regulated industries, average devi ations from organizational structure and managerial attitude trends remain relative ly small over multiple year periods. Suppose,

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

21for example, a quantitative data base study finds that an add itional fine reduces aggregate discharges in a state by 8 per cent but that this effect is no longer significant after one year. These findings suggest that, X years fr om now, firms in the studied industry are expected to pollute baseline le vels (after controlling for tre nds) 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 st ill expected to reduc e pollution by 8 percent below baseline levels (X years from now) unl ess dramatic organizati onal, managerial, or technical changes have o ccurred in the industry.

How should samples be collected?

Sample selection is an important com ponent of both quantita tive 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 yi elds 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 limite d number of sectors, and most study few regulations for few industries (see, for exampl e, Earnhart [2004a], Gray and Shadbegian [2005], Shimshack and Ward [2005], Thort on 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 iden tify sectors, contaminants, and even characteristics (‘majors’) based upon agency priorities. Such priorities may include environmental impacts, traditional compliance ra tes, or assessment costs. For quantitative analyses of specific deterrence, a random sa mple 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 majo r facilities in much of the PCS, CEMS, CDS, TRI, and the RCRA Biennial Reporting Syst ems. For qualitative survey approaches, random samples should be obtained. Note that the originally sele cted survey sample should be considerably larger than necessary for statistical validity, as response rates will be significantly less than 100 percent. In a ll cases, replications should explain their sampling procedure in detail and should ac knowledge that individual generated results are most useful for assessing compliance and deterrence effects in the context of this

sector/contaminant/characteris tic 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 viola tions is a mainstay of nearly every industrialized nation’s environmental pol icy. This report reviews the relevant environmental compliance literature, with emphasis on measuring the specific and general deterrence effects of environmental re gulatory activity. This section first presents

conclusions that can be conclu sively supported by the literature. The major criterion for this categorization is broad- based support across several stud ies in several sectors. The section then reviews important findings suggest ed (but not definitely confirmed) by the

22literature. The major criterion for this categor ization is support by three or fewer notable studies and the absence of strong contradict ory evidence. This section concludes with

recommendations for OECA consideration. A ll findings and recommendations are based upon the author’s subjective assessment of th e related literature and the state of knowledge.

Major Findings Strongly Support ed by the Related Literature

• Regulation, monitoring, and enforcement ha ve historically been, and remain,

critical determinants of environmental behavior.

As demonstrated in Section 2 of this pa per, many factors influence environmental compliance. However, every reviewed st udy that compared traditional regulation to other compliance determinants indicated that regulation/legi slation is a more important determinant of environmental be havior than any ot her 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 influentia l. For example, Doonan et al. [2005] found that a full 70 percent of pulp and pa per 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 th e impacts of domestic environmental monitoring and enforcement actions on the inspected or sanctioned facility found at least some evidence for specific dete rrence. This includes Magat and Viscusi [1990], Deily and Gray [1991], Gray and Deily [1996], Nadeau [1997], Earnhart [2004a], Earnhart [2004c], May [2005], Gr ay and Shadbegian [2005], Keohane et al. [2006], and Glicksman and Earnhart [2007]. In many cases, the specific deterrence effects were large. For exam ple, Gray and Shadbegian (2005) found

that the threat of an addi tional enforcement action or inspection increased future compliance with air pollution regulations in the pulp and paper sector by 10

percent.

Inspections and enforcement activities di rectly 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 th at 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 th e 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 Wa rd [2007]. In many cases, general deterrence effects are pract ically 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 follo wing a fine. Failure to account for such general deterrence may significantly underestimate the efficacy of fines, inspections, and other regulatory actions.

• Environmental monitoring and enforcemen t activities may generate significant emissions reductions, even for sector/contaminant combinations where compliance is typically high.

Reviewed studies consistently find speci fic and general dete rrence effects of environmental monitoring and enforcement, even in industries with relatively high compliance rates. Earnhart [2004a] and Earnhart [2004c] find substantial specific deterrence from m onitoring and enforcement actions in the Kansas wastewater industry where average conve ntional water pollution discharges are 45 percent of permitted levels. Shimshack and Ward [2005] find significant general deterrence for conve ntional water pollutant fi nes 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 th at 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 we ll 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 sugges t 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 Wa rd [2005], and Gray and Shadbegian

24[2007]) finds significantly di fferent deterrence magnitude s for different sectors, different time periods, different enfor cement actions, and different monitoring instruments. Factors like facility-specifi c abatement costs, industrial sub-sector, and facility size determine responses to regulatory activity. A few studies examine heterogeneity in more detail. For exam ple, 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 th at deterrence was a smaller overall concern to larger facilities.

• Quantitative databases and statistical met hods exist for deterrence measurement.

Sections 3 and 4 of this white paper discuss more than 15 recent papers that quantitatively measure the specific an d/or general dete rrence 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 databa se analyses (see, for example, Earnhart [2004a], Gray and Shadbegian [2005], Sh imshack and Ward [2005], and Gray and Shadbegian [2007]) is that the key metric s are restricted to specific regulations and specific populations. 7

• Qualitative survey methods exist for deterrence measurement.

Sections 3 and 4 of this wh ite 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 assessm ent 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 ar e typically restricted to specific populations (or a small number of specific populations), but not specific regulations. State of Ore gon DEQ [2004] represents a notable exception, where

metrics are defined for several re gulations in eight industries.

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

• The regulator reputation effect underlyi ng general deterrence tends to decay.

The results of the general deterrence literature indicate that environmental

facilities learn from the e xperiences 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.

25and the sensitivity analyses of Shimshack and Ward [2007] also show that facilities regularly update their beliefs about regulatory stringen cy. At least for the studied pulp and paper sector, regulator re putation begins to decay within a year after a fine for water pollution viola tion. 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 spi llover effects may operate through “implicit general deterrence,” where the perceived threat of legal sanctions is a function of enforcement history and the mere existen ce of regulations. Enforcement history is itself significantly determined by rela tively recent regu lator behavior.

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

deterrence.

Theory suggests that plants learn primar ily from the experiences of their most similar neighbors (Sah [1991]). Indeed, empirical evidence suggests that the “reach” of the regulator reputation eff ect underlying general deterrence is restricted to a single regula tory entity. For example, Gray and Shadbegian [2005] found that plants seem inclined to res pond to general deterrence created by the experiences of facilities in the same state, but not in neighboring states.

• Deterrence varies across enforcement a nd monitoring instruments; inspections, intermediate enforcement actions, administ rative fines, and criminal actions have different compliance impacts.

Inspections, intermediate en forcement actions, administrative fines, and criminal actions have different compliance imp acts. Unfortunately, relative deterrence

effects seem somewhat sensitive to contex t. 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 environmenta l behavior more significantly than administrative or civil actions. See, for example, Miller [200 5] and Cohen [1999]. Second, most evidence suggests no detect able consistent difference between

deterrence associated with state actions and federal actions (Earnhart [2004c], Glicksman and Earnhart [2007]).

• Sanctions should generally increase for re peat 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 evid ence 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. Targ eting these facilities for stringent

26sanctions may be socially wasteful and may achieve significantly less environmental benefits than an equivale nt resource allocation towards facilities that are able to reduce emissions more cost effectively.

Recommendations

All recommendations are based upon th e author’s subjective professional

assessment of the related literat ure and the state of knowledge.

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

• OECA should consider closely replica ting statistical database analyses for measuring the specific and general de terrence effects of monitoring and

enforcement.

Replication of existing quantitative studi es 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 exam ples for replication. Part icular 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 statisti cally trained agency personnel. Perhaps a small pilot study for a sector with a relatively small number of similar major facilities could be pe rformed to gauge in ternal feasibility.

• OECA should consider closely replic ating qualitative survey analyses for measuring the specific and general de terrence 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 analys is studies in Section 4 and Table 3 are concrete examples for rep lication. Particular notable studies for consideration include Oregon DEQ [2004] and Thorton et al. [2005]. These studies themselves, coupled with the act ual 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 existi ng qualitative survey analyses for measuring specific and general deterrence effects.

Replication of Oregon DEQ [2004] and Thor ton et al. [2005] to other sectors and time periods could importantly contribute to the state of knowledge on deterrence, and general deterrence in particular. Howe ver, it is also possible that modestly expanding such surveys may significantly increase the state of knowledge about

27the “reach” and decay of reputation eff ects underlying general deterrence. OECA might consider asking respondents: (1 ) How long do deterrence fears from regulatory activity last? (2) If the re gulated plant observed a significant

enforcement action on another facility in the same stat e and industry last year, would that plant change its processes or environmental behavior? (3) If the regulated plant observed a significant en forcement action on another facility in the same industry but in a di fferent state, would that plant change its processes or environmental behavior? (4) If the regulated plant obs erved a significant

enforcement action on another f acility in a different industr y but in the same state, would that plant change its processes or environmental behavior? (5) If the regulated plant observed a significant en forcement action on another facility 2, 3, 4, etc. years ago, would that plant ch ange its processes or environmental behavior? (6) If the regulated plant did not observe a significant enforcement action on another facility la st year (or 2, 3, etc. year s ago), would that plant change its processes or environmental be havior? (7) If the regulated plant was aware of enforcement and monitoring actio ns at other faciliti es, 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 me thods, studies based upon a universal

metric are not advised.

No comprehensive assessment of an enti re regulated universe is recommended, as implementation costs for a credible anal ysis 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 st udy 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. Fi nally, 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 metric s for major facilities is technically possible for those sector/contaminant combin ations 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, ra ndomly selected samples would yield statistically valid results. Qualitative surv ey results with credible sector-specific random sampling include Oregon DEQ [ 2004], Gunningham et al. [2005], and May [2005].

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

• OECA should consider utilizing the exte nsive data already available to the EPA for quantitative compliance and deterrence evaluations.

Extensive Permit Compliance System wate r pollution discharges and violations data, Continuous Emissions Monitoring Sy stem air pollution discharges and violations data, Toxic Releases Invent ory toxics data, RCRA Biennial Reporting System hazardous waste violations da ta, and Compliance Data System air pollution violations data are available fo r analysis. In many cases, near-censuses of major facilities can be obtained. See, for example, Shimshack and Ward [2005], Shimshack and Ward [2007], a nd Glicksman and Earnhart [2007].

Published studies have examined a rela tively small number of sectors for few periods, and considerable insight would be gained by further an alysis. 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 so me 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 m odest 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 de terrence and deterrence assessment?

• OECA should consider important confoundi ng factors when assessing deterrence.

The literature suggests that plant characteristics like si ze, industrial sub-category, age, and abatement technology may infl uence compliance decisions. Similarly, firm characteristics like managerial att itudes, organizationa l structure, and ownership may influence environmenta l 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 controlli ng for these covariates when analyzing survey results. In quantitative settings, two possible methods exist. First, panel data techniques can control for factors th at don’t vary much over time (and most

of those listed above are rela tively static over multiple year periods). With these techniques, the researcher does not n eed 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

29briefly discuss such methods for the inte rested reader. Alternatively, quantitative analyses can include observat ions on plant, firm, and community characteristics. Some of this data is collected by the EP A, but some is not. This may represent a disadvantage of this particular quantit ative approach for OECA replication.

Other recommendations

• If universal compliance rates must be c onstructed, 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 shoul d be constructed to overweight

sector/contaminant combinat ions that have particular ly important environmental benefits. Priorities can be determined, for example, by a panel of internal and external experts that rank sector/con taminant 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 vi gorously publicizing its actions.

General deterrence requires that f acilities know about monitoring and enforcement actions at other regulate d entities (Oregon DEQ [2004], Gunningham et al. [2005], Thorton et al. [2005], Shim shack and Ward [2005], Shimshack and Ward [2007]). Enforcement and complianc e alerts are infrequent and highly aggregated, suggesting that plants may not be sufficien tly informed of monitoring

and enforcement at facilities like th eirs. The qualitative general deterrence literature somewhat supports this hypothe sis, as many compa ny 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 sect or-specific monitoring and enforcement

details. Modern communications technol ogy suggests that the costs of such information dissemination may be decreas ing. Of course, it is possible that facilities actually overestimate their perc eived risk of sanction. Therefore, the effects of such pilot programs should be carefully monitored.

• The agency should examine the net social be nefits 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 deterren ce literature. See, for exam ple, Helland [1998]. At the industry level, inspection ta rgeting is primarily a func tion of national priorities. Resource devotion to these priorities, however, significantly cannibalizes

30resources from core monitoring activities . At least for major programs like air, water, and hazardous waste, facility-speci fic 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 ta rgeting and core programs may induce many facilities that can easily reduce emi ssions to fail to do so. These entities simply do not expect frequent evaluations since targeting is devoted to priority sectors and previously identified “bad” actor s. While targeting pr iority 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, rand omness may not necessarily achieve large reductions at any given facility, but it is qui te likely to induce reductions at a very large number of facilities due to general deterrence. If randomne ss 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 conclu sion is borne out in the tax literature. See, for example, Andreoni et al. [1998], for a survey.

• OECA or related offices should cons ider modestly funding new academic

research.

As reviewed in the findings section of this document, many conclusions are definitively supported by the existing lit erature on domestic environmental deterrence. However, many insights coul d be gained by funding further external research. Studies examining commonly ove r-looked features of the enforcement process, like enforcement-induced be yond compliance behavior and general deterrence mechanisms, may fill pa rticularly import knowledge gaps.

Important unresolved questions include: (1 ) How, exactly, do firms learn about regulator reputation and how do they update their threat perc eptions? (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 fr om their peers? (5) What do differences between typical facilities and recidi vists imply for optimal targeting and sanctioning? (6) How does general deterr ence interact with other compliance factors? (7) What does an optimal insp ection targeting regime look like for domestic environmental performance?

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36Technical 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 pr ocess. A relatively simple panel-data technique is appropriate for regimes with c ontinuous self-reported emissions data over many facilities and several time periods. Fo r example, water pollution data from the Permit Compliance System, air pollution data from the Continuous Emissions Monitoring System, and toxics data from the Toxic Rele ases Inventory fit this empirical framework. Here, a measure of continuous emissions like di scharges as a percent of the standard is regressed on lagged firm-specific enfor cement 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 regre ssion model, fixed effects exploit the panel structure of the data. In essence, fixed e ffects automatically control for all facilityspecific factors that do not significantly vary over time. This reduces the data requirements substantially, as common dete rrence determinants like industrial classification, industrial s ub-category, plant-specific technology, most community characteristics, ownership factors, manage ment 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 assessi ng 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 tec hniques, like random ef fects, exploit the panel structure of the data as well, but numerous papers have statistically rejected the random effects specification for these purposes. A key additiona l advantage of the fixed effects model is that it prevents bias from an important t ype 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 statis tical problems when the dependent variable is limited – ie. when the dependent va riable is a discrete 0/1 indicator variable indicating compliance or non-compliance. Wate r pollution violation data from the Permit Compliance System, air pollution violati on data from the Continuous Emissions Monitoring System, hazardous waste violati on 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 charact eristics, and Chamberlain [1980] conditional

37random effects. The regressions in Shimshack and Ward [2005] illustrate the method in practice.

The key methodological issue here is th e Chamberlain conditional random effects specification. While standard plant-level fixe d effects are desirable, they yield biased estimates in binary models like the probit and are therefore no l onger 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 implem entation amounts to including plant-specific average inspections, plant-specific averag e enforcement actions, and plant-specific average violations as explan atory variables in the mode l. Since the model no longer contains facility fixed-effects to account for facility-specific time invariant factors, one must include available plant data. Particul arly desirable control variables include industrial classification, industrial sub-category, pl ant size, and community

characteristics.

A.3 An Alternative Specification for Sel f-Reported Continuous or Discrete Data

Another approach to analyzing the speci fic 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 lik e 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 Co mpliance 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, lagge d enforcement and monitoring activity at other facilities, seasonality indicators, year indicators, state indicators, and firm

characteristics.

The key to this specification is the recove ry of the threat variables. The goal of this process is to remove any bias associat ed 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, comm unity 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 comp liance regressions. Such a variable can be difficult to identify, but it is necessary for statistical identification. The first stage regressions are used to pred ict probabilities of enforcemen t for this plant this period. These predictions are then incl uded directly in the second- stage emissions or compliance regression. Again, since the model does not cont ain facility fixed-effects to account for

38facility-specific time invariant factors, one mu st include available facility data in the second stage regressions. Part icularly desirable control variables include industrial classification, industrial sub- category, plant size, and co mmunity 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 progr am data, fit this empirical framework. In this context, the relevant information is se lectively identified, and simply assessing the impact of enforcement and monitoring ac tions on observed compliance will yield substantially biased results. The necessary so lution is the bivariate probit with selection model, also known as the censored probit m odel. Here, the dependent 0/1 compliance variable is regressed on lagged firm-sp ecific actual enforcement and monitoring variables, lagged enforcement and monitoring activity at ot her facilities, seasonality indicators, year indicators, state indicators, and firm characteristics. However, this second stage regression is only conducted after a fi rst-stage regression co rrects for selectivity bias. Stafford [2002] illustra tes the method in practice.

The key to the censored bivariate probit is the correction for sample selection. Since targeting of inspections occurs, th e 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 samp le 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 se lection model, but th e details are beyond the scope of this report. Implementation, however, is relatively straightforward with modern statistical packages like STATATM (whe re 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 esti mating 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 th at the first-stage sel ection 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.

39Table 1:Studies Comparing Regulatory Factors to Other Compliance DeterminantsAuthor(s)OutletTitleSector(s)Key MetricMethodologyKey ResultsKhannaCorporateEnvironmentalWhat is DrivingCorporate156 S&P 500firms acrossFirms’ adoption ofenvironmentalQuantitativeAnalysisFirst-order environmentalpractices like environmentalAntonStrategy2002Environmentalism:Opportunity orThreat?multipleindustrialsectorsmanagement practices,including environmentalstaffing, audits, internalpolicies, TQEM, andpublic environmentalreporting.staffing, audits, and internalpolicies were more motivatedby regulatory pressures thanother factors. Less significantenvironmental practices, likeTQEM and environmentalreporting, were more influencedby market pressures.KaganLaw and SocietyReview2003ExplainingCorporate14 pulp andpaper mills inManagers’ environmentalattitudes and behavior,QualitativeSurvey /Regulation is primarilyresponsible for largeGunninhamEnvironmentalPerformance: HowDoes RegulationAUS, NZ,CAN, WA,and GAand self-reported BOD,TSS, and AOX waterpollution discharges.QuantitativeAnalysisenvironmental gains at pulp andpaper facilities over the past 3decades. However, communityThortonMatter?pressure is also responsible forobserved differences inenvironmental behavior acrossfacilities.DoonanEcologicalDeterminants of86 CanadianManagers’ environmentalQualitative70 percent of managers ratedEconomicsEnvironmentalpulp and paperattitudes and behavior, asSurveythe government as the singleLanoie2005Performance in theCanadian Pulp andmillsdetermined by: 1. self-rating of environmentalmost important source ofdeterrence pressure. ThePaper Industry: Anpressures, 2. integration ofgovernment and the public, butLaPlanteAssessment frommanagement strategies, 3.not financial and consumerInside the Industryresources devoted tomarkets, are the most importantenvironmentaldeterminants of environmentalmanagement.behavior.40Table 1 Continued:Studies Comparing Regulatory Factors to Other Compliance DeterminantsAuthor(s)OutletTitleSector(s)Key MetricMethodologyKey ResultsDelmasUC Berkeley,Center forInstitutionalPressures and493 USindustrialFirms’ adoption of twoenvironmentalQualitativeSurvey /The influence of regulators andlegislators was the leadingToffelResponsibleBusiness,Working Paper2005EnvironmentalStrategiessources acrossseveral sectorsthat report TRIreleasesmanagement practices: theinternational ISO14001standard and US voluntaryenvironmental programs.QuantitativeAnalysisinstitutional pressure affectingfirms’ environmentalperformance. Corporateorganization structure explainsimportant differences in firmsrespond to external and internalpressure.MayPublicAdministrativeReview2005Regulation andComplianceMotivation:ExaminingDifferentApproaches144 marinasand 61boatyards inCA and WAManagers’ commitment toaddress water qualityproblems, as determinedby: 1. presence of bestmanagement practices, 2.self-rating ofenvironmental priorities,and 3. self-rating ofenvironmental actions.QualitativeSurveyTraditional regulation had aconsiderably stronger influenceon managers’ deterrenceviewpoints than voluntaryapproaches. The “duality” ofdeterrent fears and civicobligations motivatedcompliance more than otherfactors.41Table 2: Studies Measuring the Specific Deterrence of Domestic EnvironmentalMonitoring and EnforcementAuthor(s)Outlet(s)Title(s)Sector(s)Key MetricMethodologyKey ResultsMagatJournal of LawEffectiveness of77 major USResponse of BODQuantitativeSignificant specific deterrenceand Economicsthe EPA’spulp and paperdischarges and BODAnalysis offrom inspections (as a proxy forViscusi1990Regulatoryplants, 1982-compliance with CWAPermitall enforcement activity).Enforcement: The1985NPDES permits toComplianceLagged inspections hadCase of IndustrialEPA/state inspections.System (PCS)statistically significant negativeEffluent Standardsdatabase.impact on BOD compliance andcontinuous BOD emissions.DeilyJournal ofEnforcement of49 US steelResponse of plant-closingQuantitativeSignificant impacts ofEnvironmentalPollutionplants, 1976-probabilities to allAnalysis ofenforcement actions. A 12GrayEconomics andManagementRegulations in aDeclining Industry1986EPA/state enforcementactions.PermitCompliancepercent increase in the predictedprobability of EPA/state1991System (PCS)enforcement over the entiredatabase.sample period increased thelikelihood that a plant will closeby 1 percent.GrayJournal ofCompliance and41 US steelResponse of criteria airQuantitativeSignificant specific deterrenceEnvironmentalEnforcement: Airplants, 1980-pollution compliance toAnalysis offrom inspections andDeilyEconomics andManagementPollutionRegulation in the1989EPA/state inspections andaggregate enforcementCompliance DataSystem (CDS)enforcement actions. Actuallagged inspections and1996US Steel Industryactions.database.enforcement actions directlyincreased criteria air pollutioncompliance. The predictedthreat of inspections andenforcement actions, however,did not influence compliance ina statistically significantfashion.42Table 2 Continued: Studies Measuring the Specific Deterrence of DomesticEnvironmental Monitoring and EnforcementAuthor(s)Outlet(s)Title(s)Sector(s)Key MetricMethodologyKey ResultsNadeauJournal ofEnvironmentalEconomics andManagement1997EPA Effectivenessat Reducing theDuration of PlantLevelNoncompliance175 US pulpand paperplants, 1979-1989Response of time spent inviolation of air pollutionstatutes to EPA/stateinspections andenforcement actions.QuantitativeAnalysis ofCompliance DataSystem (CDS)database.Significant specific deterrencefrom enforcement actions. A 10percent increase in a plant’spredicted threat of enforcementactions resulted in a 4 to 5percent reduction in the airpollution violation length. Thepredicted threat of inspections,however, did not influence theduration of noncompliance in astatistically significant fashion.EarnhartJournal ofEnvironmentalEconomics andManagement2004andReview ofEconomics andStatistics2004Regulatory FactorsShapingEnvironmentalPerformance onCorporateEnvironmentalPerformanceandPanel DataAnalysis ofRegulatory FactorsShapingEnvironmentalPerformance40 Kansaswastewatertreatmentfacilities,1990-1998Response of BODdischarges to EPA/stateinspections andenforcement actions.QuantitativeAnalysis ofPermitComplianceSystem (PCS)database, withsupplementaldata from EPAand Kansasenforcementdocket databases.Significant specific deterrencefrom imposed inspections andenforcement actions. Actualinspections and enforcementactions were associated withdecreased BOD discharges. Thepredicted threat of inspectionsand enforcement actions,however, did not consistentlyinfluence discharges in astatistically significant fashion.43Table 2 Continued: Studies Measuring the Specific Deterrence of DomesticEnvironmental Monitoring and EnforcementAuthor(s)Outlet(s)Title(s)Sector(s)Key MetricMethodologyKey ResultsMayPublicAdministrativeReview2005Regulation andComplianceMotivation:ExaminingDifferentApproaches144 marinasand 61boatyards inCA and WA,2002Response of managers’deterrence fears toregulator inspections andsanctions.QualitativeSurveySignificant specific deterrencefrom inspections. Facilitiesinspected in the last 5 years hadhigher deterrent fears than thosenot inspected. However, pastsanctions for water qualityviolations were not associatedwith higher deterrence fears.GrayLaw and Policy2005When and WhyDo Plants116 US pulpand paperResponse of air pollutioncompliance to EPA/stateQuantitativeAnalysis ofSignificant, but heterogeneous,specific deterrence fromShadbegianComply? PaperMills in the 1980smills, 1979-1990inspections andenforcement activities.They also examined thefactors that influenceresponsiveness tomonitoring andenforcement activities.Compliance DataSystem (CDS)database, withsubstantial firmcharacteristicdata from othersources.inspections and enforcementactions. An additional laggedpredicted enforcement action orinspections results in anapproximately 10 percentincrease in air pollutioncompliance.KeohaneYale UniversityWorking PaperDeterminants ofEnvironmental249 USelectric powerResponse of plants’ sulfurdioxide emissions andQuantitativeAnalysis of DOJSignificant specific deterrencefrom NSR lawsuits. The threatMansur2006Performance in theCanadian Pulp andPaper Industry: Anplants, 1996-2000utilization rates to threatand imposition of federalNew Source Reviewand EPA suitdatabases, withsubstantial firmof an NSR lawsuit significantlydecreased air pollutionemissions. Actually levied suits,VoynovAssessment fromInside the Industrylawsuits.characteristicdata from othersources.however, had little effect onfirm’s air emissions. Findingssuggest that firms sought toavert enforcement ahead oftime, but actual suits did notcontribute to any furtherimprovements in air quality44Table 2 Continued: Studies Measuring the Specific Deterrence of DomesticEnvironmental Monitoring and EnforcementAuthor(s)Outlet(s)Title(s)Sector(s)Key MetricMethodologyKey ResultsGlicksmanStanfordEnvironmentalThe ComparativeEffectiveness ofQualitative:267 USResponse of plants’composite BOD and TSSQualitativeSurvey /Significant specific deterrence.75 percent of surveyEarnhartLaw Journal2007GovernmentInterventions onEnvironmentalPerformance in theChemical IndustrychemicalplantsQuantitative:499 major USchemicalplants, 1995 to2001discharges to EPA/stateinspections andenforcement actions.QuantitativeAnalysis of PCSdatabase.respondents reported that fineswere an effective environmentaldeterrent and 87 percentreported that inspections weredefinitely or probably effectiveways to induce compliance. Inthe statistical analysis, bothinspections and enforcementactions result in decreasedaggregate BOD and TSSemissions.45Table 3: Studies Measuring the General Deterrence of Domestic EnvironmentalMonitoring and EnforcementAuthor(s)Outlet(s)Title(s)Sector(s)Key MetricMethodologyKey ResultsThortonLaw and Policy2005GeneralDeterrence and233 US firmsacross 8Managers’ knowledge ofimportant ‘signal cases’ ofQualitativeSurveySignificant general deterrencefrom enforcement actions. 89GunninghamCorporateEnvironmentalindustries,sometimeenforcement, and selfreported responses topercent of survey respondentscould recall some enforcementKaganBehaviorbetween 2001and 2004knowledge ofenforcement actionslevied against other firms.actions at other firms, and 63percent reported taking anenvironmental action inresponse to learning about suchactions at other firms.GunninghamLaw and Policy2005MotivatingManagement:35 chemicalandManagers’ responses toknowledge ofQualitativeSurveySignificant general deterrencefrom enforcement actions. TheThortonCorporateCompliance andEnvironmentalelectroplatingcompanies inWA and OH,enforcement actionslevied against other firms,and managers’ interviewoverall effect of sustainedenforcement actions wasimportant for deterrence.KaganProtectionsometimebetween 2001and 2004responses to questionsabout reasons forenvironmentalperformance.Learning about fines at otherfacilities focused firms attentionon environmental issues oraltered their long-runcompliance motivations.ShimshackJournal ofEnvironmentalRegulatorReputation,217 major USpulp, paper, orResponse of plants’ BODand TSS complianceQuantitativeAnalysis ofSignificant specific and generaldeterrence from fines. AnWardEconomics andManagement2005Enforcement, andEnvironmentalCompliancepaperboardmills, 1990-1996decisions to laggedenforcement andmonitoring activity.PermitComplianceSystem (PCS)database.additional fine induced about a2/3 reduction in the statewideconventional water pollutionviolation rate in the yearfollowing a fine. Non-monetaryenforcement actions, however,had little influence oncompliance.46Table 3 continued: Studies Measuring the General Deterrence of DomesticEnvironmental Monitoring and EnforcementAuthor(s)Outlet(s)Title(s)Sector(s)Key MetricMethodologyKey ResultsState of OregonState of OregonGeneral450 regulatedResponse of managers’QualitativeSignificant specific and generalDepartment ofDepartment ofDeterrence ofcompanies inself-reportedSurveydeterrence. Inspections andEnvironmentalEnvironmentalEnvironmentalseveralenvironmental behavior topenalties directly generated anQualityQuality 2004Violation: A Peekinto the Mind ofthe RegulatedPublicindustries inOR, 2002hearing about inspectionsand sanctions at otherfacilites.average of 1.6 environmentalchanges per company. 2.6additional changes percompanies at other facilitiesafter learning of regulatoryaction. Inspections inducedgreater deterrence thanpenalties.GrayJournal ofThe521 USResponse of toxic andQuantitativeSignificant specific and generalRegionalEnvironmentalmanufacturingconventional air pollutionAnalysis ofdeterrence. InspectionsShadbegianScience2007Performance ofPolluting Plants: Aplants, locatedwithin 50compliance and emissionsto EPA/state inspectionsIDEA andEmissionssignificantly increasedcompliance with regulationsSpatial Analysismiles of 3and enforcementInventory(but not continuous emissions)major USactivities.Databases.for both the inspected plant andcities, 1997for nearby plants. Generaldeterrence restricted to withinstate borders.ShimshackJournal ofEnvironmentalEnforcement andOver-Compliance251 major USpulp, paper,Response of plants’ BODand TSS discharges toQuantitativeAnalysis ofSignificant general deterrencefrom fines. Statewide BOD andWardEconomics andManagementforthcoming,2007andpaperboardmills, 1990-2004lagged enforcement andmonitoring activity.PermitComplianceSystem (PCS)database.TSS discharges fellapproximately 7 percent in theyear following a fine. Most ofthe reduction was due to plantsgoing further beyondcompliance, rather than simplya reduction in violations.47