Effect of audits on the extent of compliance with wastewater discharge limits

Journal of Environmental Economics and Management 68 (2014) 243–261

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Journal of Environmental Economics and Management journal homepage: www.elsevier.com/locate/jeem

Effect of audits on the extent of compliance with wastewater discharge limits Dietrich Earnhart a,n, Donna Ramirez Harrington b a b

Department of Economics, University of Kansas, 435 Snow Hall, Lawrence, KS 66045, USA University of Vermont, USA

a r t i c l e i n f o

abstract

Article history: Received 26 October 2012 Available online 23 June 2014

This study explores the effect of environmental self-audits (“audits”), which represent an important type of environmental management system practice, on the extent of facilities’ compliance with wastewater discharge limits. Theoretically, audits may (1) improve compliance by enhancing the effectiveness of treatment technologies and pollution prevention methods, (2) undermine compliance by distracting facilities’ personnel with audit-related administrative burdens, or (3) not influence compliance because these effects neutralize each other. By examining the extent of compliance, our study’s results reflect both improvement toward and beyond compliance. By assessing compliance with multiple pollutants separately, our study examines whether audits influence the control of different pollutants uniformly. Lastly, we employ a dynamic panel estimator, which allows us to explore whether facilities adjust their discharges dynamically, while controlling for any inertia in facilities’ pollution control systems. Our study empirically examines the U.S. chemical manufacturing sector between 1999 and 2001 using survey and publicly available EPA data. & 2014 Elsevier Inc. All rights reserved.

Keywords: Audits Wastewater discharges Environmental management Clean Water Act

Introduction Regulatory agencies and industrial experts promote the adoption of Environmental Management System (EMS) practices because these practices presumably help regulated facilities to achieve compliance with environmental regulations. As important, EMS practices may allow facilities to go “beyond compliance”. While EMS practices may directly reduce pollution, they more importantly enhance the effectiveness of other pollution control efforts. However, EMS practices may undermine compliance if EMS practices distract facilities from “meaningful” pollution control by imposing the administrative burden of tracking these process-oriented routines (Anton et al., 2004). EMS practices do not influence compliance if these two effects neutralize each other. These possibilities represent three testable hypotheses: H1. EMS practices improve compliance. H2. EMS practices undermine compliance. H3. EMS practices do not affect compliance. n

Corresponding author. Fax: þ 1 785 864 5270. E-mail address: [email protected] (D. Earnhart).

http://dx.doi.org/10.1016/j.jeem.2014.06.004 0095-0696/& 2014 Elsevier Inc. All rights reserved.

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Environmental regulatory compliance audits, i.e., self-audits, are one important practice within an EMS. This particularly tangible type of management activity represents a systematic, documented, and objective review of a facility’s operations that allows the regulated facility to evaluate its performance relative to audit criteria (ASTM, 2003). The U.S. Environmental Protection Agency (EPA) defines an environmental audit as “a systematic, documented, periodic and objective review by regulated entities of facility operations and practices related to meeting environmental requirements”.1 Thus, the use of audits might improve environmental performance by providing regulated facilities with useful information that should improve their abilities to operate their treatment technologies effectively or run their production processes in ways that prevent pollution. Put differently, audits may allow regulated facilities to learn better ways for improving their environmental performance, such as establishing internal protocols for preventing environmental violations (Evans et al., 2011; Khanna and Widyawati, 2011). Our analysis focuses on audits given their policy importance. Since the mid-1980s, the EPA has been promoting selfaudits as a tool for improving environmental performance especially for increasing environmental compliance (Evans et al., 2011). As evidence of this promotion, the EPA has integrated environmental auditing into its compliance and enforcement strategy; specifically, the EPA began in 1997 to include increased environmental auditing in its strategic plan for improving compliance with environmental regulations (Evans et al., 2011; EPA, 1997a). Despite the EPA’s promotion of environmental auditing, few empirical analyses have provided systematic evidence supporting the notion that audits improve environmental performance. Instead, the EPA’s promotion appears to be based mostly on a single U.S. General Accounting Office (GAO) report on environmental auditing that draws upon anecdotal evidence. More important for our research, the EPA and the U.S. Chemical Manufacturer’s Association (CMA) jointly conducted a survey in the mid-1990s; based on this survey, they concluded that environmental auditing could improve environmental performance (EPA, 1999). Thus, our study’s results should help to assess whether or not environmental auditing can prove to be an effective component of the EPA’s enforcement strategy. Studies of pollution control can measure compliance in various ways. For our empirical analysis, we focus on the extent of compliance, as measured by the ratio of actual pollution to permitted pollution, as reflected in numeric effluent limits. In particular, our empirical analysis examines compliance with Clean Water Act-based effluent limits that constrain wastewater discharges. Throughout this paper, we refer to the ratio of actual discharges to permitted discharges as the “discharge ratio”. In contrast to the simple dichotomy of compliance versus non-compliance, our measure of compliance allows the empirical analysis to examine the extent of compliance, which allows our empirical results to reflect both improvement toward compliance and improvement beyond compliance, the latter component representing our study’s primary contribution. Other studies exploring the link from environmental behavior to performance, especially studies of audits, assess the level of pollution without any reference to limits (Toffel and Short, 2011) or explore only the status of compliance (Evans et al., 2011; Khanna and Widyawati, 2011; Short and Toffel, 2010). With audits serving as our single measure of EMS practices and the discharge ratio serving as our measure of compliance, our empirical analysis seeks to test the three hypotheses identified above by empirically exploring the discharges of multiple pollutants separately. In the process, we assess whether the influence of audits differs across pollutant types, unlike all previous studies, which represents our study’s second contribution. This contribution generalizes to all studies of environmental management with the single exception of Barla (2007), upon which we build by examining the effect of environmental management on compliance and exploring a more specific measure of environmental management. As our third contribution, we employ a dynamic panel estimator, which allows our analysis to explore whether facilities adjust their discharges dynamically while controlling for any inertia in facilities’ pollution control systems. As our fourth contribution, we cover a broader set of facility-conducted self-audits than previous studies. Specifically, our sample of audits is not restricted to those audits connected to the voluntary disclosure of violations discovered through audits within the regulatory context of an audit policy (e.g., Short and Toffel, 2010; Toffel and Short, 2011) or the intent to audit under such a policy (Evans et al., 2011). [Khanna and Widyawati (2011) explore a broader set of audits but only measure the presence of a firm-wide environmental auditing program. Thus, we build upon this previous study by exploring individual facilities’ audits.] As our fifth contribution, we study wastewater discharges controlled by the Clean Water Act, in contrast to preceding studies, which assess air emissions or toxic and hazardous waste. In order to make these contributions, our study empirically examines the U.S. chemical manufacturing sector during the years 1999–2001 using survey data along with publicly available data. Literature review and contributions of current study Our study contributes to a rich empirical literature on environmental behavior and performance. To clarify the distinction, environmental behavior includes audits and other EMS practices, along with other forms of pollution control efforts, such as the use of end-of-pipe treatment technologies. In contrast, environmental performance includes the level of wastewater discharges, along with other forms of pollution outcomes, such as air pollutant emissions and toxic/hazardous waste generation, plus compliance with the related environmental regulations, e.g., actual emissions relative to emission limits. 1

“Interim Guidelines on Environmental Auditing Policy Statement,” 50 FR 46504 (November 8, 1985), Section II.A.

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Several empirical studies examine environmental behavior without assessing its influence on environmental performance (e.g., Harrington et al., 2008; Khanna et al., 2009).2 Other empirical studies examine environmental performance without assessing the role of environmental behavior (e.g., Earnhart, 2004a).3 In this literature review, we focus on empirical studies examining the effect of environmental behavior, especially EMS practices and audits in particular, on environmental performance. Anton et al. (2004) examine the influence of the comprehensiveness of a firm’s EMS, as measured by the count of adopted EMS practices, on four measures of a firm’s toxic emissions per unit of output; the study demonstrates that the extent of EMS adoption has a significant negative impact on the intensity of toxic emissions. Barla (2007) examines the influence of ISO 14001 certification on wastewater discharges from Quebec pulp and paper plants. The study tests whether adopting a ISO 14001-certified environmental management system affects environmental performance; results reveal that adoption decreases BOD discharges but not TSS discharges. Arimura et al. (2008) explore the effect of ISO 14001 implementation on Japanese facilities’ environmental performance as measured by natural resource use, solid waste generation, and wastewater discharge; results demonstrate that ISO 14001 implementation reduces all three impacts. Gangadharan (2006) examines the influence of environmental training on Mexican plants’ environmental compliance. Sam et al. (2009) analyzes the adoption of Total Quality Environmental Management (TQEM) and releases of pollutants covered by the EPA 33/50 Program; they find that TQEM adoption reduces 33/50 releases and that its effect is distinct from the effect of participation in the 33/50 Program.4 As more relevant research, three empirical studies examine the effect of EMS adoption on compliance with environmental regulations. First, Dasgupta et al. (2000) examine the influence of a Mexican company’s environmental management system on the company’s environmental compliance status; the study finds that the adoption of an ISO 14001certified EMS leads to a significant improvement in compliance status. Second, Potoski and Prakash (2005) examine the factors that drive ISO 14001 participation among U.S. facilities and the effects of participation on compliance with Clean Air Act (CAA) regulations. Their results reveal that ISO 14001 participation increases CAA compliance. Third, Sam (2010) analyzes the effects of EMS adoption, along with other pollution prevention activities, on overall environmental compliance; his findings reveal that EMS adoption increases compliance during the early 1990s within the sample period of 1991–2004. As the most relevant studies of environmental behavior and performance, Evans et al. (2011) and Khanna and Widyawati (2011) examine the influence of environmental self-audits on environmental compliance. Evans et al. (2011) examine the effect of auditing, as measured by the filing of an intent to conduct an audit, on Michigan facilities’ compliance with the Resource Conservation and Recovery Act (RCRA). Their results suggest no long-run impact of auditing on compliance with hazardous waste regulations. Khanna and Widyawati (2011) explore the effect of firm-wide environmental auditing programs on facility-level compliance with CAA regulations using a sample of S&P 500 firms. Their results demonstrate that facilities owned by firms that reported the presence of an environmental auditing program in the Investor Research Responsibility Center (IRRC) survey are significantly more likely to be CAA-compliant. Two additional studies – Short and Toffel (2010) and Toffel and Short (2011) – explore the effect of disclosing violations that are identified by self-auditing on compliance status. Toffel and Short (2011) additionally explore this effect on abnormal releases of toxic chemicals. These two studies clearly do not explore the full extent of auditing since the primary regressor is conditioned on the presence of a violation, its detection, and its disclosure. Other studies of audits do not attempt to explore the effect of audits on environmental performance. As one example, Short and Toffel (2008) examine the role of past enforcement actions and the legal environment on audit decisions. As another example, Stafford (2007) analyzes the link from self-audits to future inspection decisions. Representing a broader study, Delmas and Toffel (2008) utilize an institutional framework to analyze the adoption of different environmental management practices, including audits, in response to various external pressures.

Regulatory context Our empirical analysis examines environmental behavior and performance relating to the U.S. Clean Water Act. We focus on wastewater discharges controlled by the Clean Water Act because, unlike other media, regulators systematically record wastewater discharge limits and actual discharges so that we are able to measure the extent of compliance rather than merely the status of compliance, which masks overcompliance. The Clean Water Act seeks to protect water quality mostly by controlling wastewater discharges from point sources of pollution. To this end, the U.S. Environmental Protection Agency (EPA) constructed the National Pollutant Discharge Elimination System (NPDES). As the system’s primary form of control, government efforts begin with the issuance of facility-specific permits, which identify the pollutant-specific discharge limits imposed on regulated facilities. Permits are issued by the EPA or authorized state regulatory agencies that meet federal criteria through the efforts of permit writers and re-issued generally on a 5-year cycle. 2 Harrington et al. (2008) examine the motivations prompting the adoption of Total Quality Environmental Management (TQEM), while Khanna et al. (2009) examine the link from TQEM to the implementation of pollution prevention practices. 3 Earnhart (2004a) examines the influence of federal and state inspections and enforcement actions on wastewater discharges from municipal wastewater treatment plants. 4 Wang et al. (2003) examine the influence the ratio of pollution control-related operating costs to total operating costs on the degree of Chinese companies’ compliance with wastewater discharge limits. This ratio may represent a highly aggregated form of environmental behavior.

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When establishing the discharge limits embedded within facility-specific permits, permit writers consider two standards: (1) the Effluent Limitation Guideline standard, and (2) the state water quality-based standard. Effluent Limitation Guideline standards are designed to require a minimum level of wastewater treatment for a given industry (i.e., uniform upper bound on limits across the entire United States for a given industry) as derived from available pollution control technologies.5 The state water quality-based standard is designed to ensure that the ambient water quality of the receiving water body meets state-based ambient quality standards. In other words, the discharge limit is set so that the facility’s discharges do not cause the water body’s ambient concentration of the relevant pollutant to exceed the acceptable level, which is designed to assure that the receiving water body can sustain designated use, e.g., fishable, swimmable. Discharge limits identified by state water quality-based standards differ across facilities and time since ambient water quality standards differ across states and water bodies’ capacities to assimilate discharges differ across time and space even in the same state. After a limit is determined under each standard, the more stringent limit is written into the permit. Since the state water quality-based standards may trump the Effluent Limitation Guideline Standards, effluent limits differ across facilities and time even within the same industry at the same moment in time. Thus, our consideration of discharges relative to limits seems strongly meaningful. Regardless of the standard used to determine a limit, the effluent limit represents a performance-based policy. Compliance is based exclusively on discharges. Thus, a facility is allowed to use any available abatement method in order to comply with the imposed limit. All categories of abatement methods are available to facilities: end-of-pipe treatment technologies, which address pollution after it is created, and pollution prevention methods, which by definition address pollution before it is created. EMS practices represent an overarching category of methods since they may help to improve the effectiveness of both treatment and pollution prevention. Audits, in particular, might help to evaluate the effectiveness of EMSs, identify opportunities for discharge reduction, or find ways for increasing compliance or improving environmental performance more broadly (Evans et al., 2011). Since no form of pollution abatement is required, there is no reason to refer to audits or other EMS practices as “voluntary”. In essence, all forms of abatement are voluntary.6 The issued NPDES permits require regulated facilities to monitor and self-report their discharges on a regular, generally monthly, basis by completing and submitting Discharge Monitoring Reports (DMRs) to regulatory agencies. To ensure compliance with the issued permit limits, the EPA and state agencies periodically inspect facilities and take enforcement actions as needed. While the EPA retains authority to monitor and sanction facilities, state agencies are primarily responsible for monitoring and enforcement. Inspections represent the backbone of environmental agencies’ efforts to monitor compliance and collect evidence for enforcement (Wasserman, 1984); inspections also maintain a regulatory presence (EPA, 1990). As for enforcement, agencies use a mixture of informal enforcement actions (e.g., warning letters) and formal enforcement actions, which include both penalties (i.e., fines) and formal enforcement actions that do not represent penalties, e.g., administrative orders. EPA regional offices may initiate an administrative proceeding in order to impose an administrative sanction or may request the Department of Justice (DOJ) to initiate a civil court proceeding in order to impose a civil sanction. Point sources generally divide into two categories: municipal wastewater treatment facilities and industrial sources. Our study focuses on a single sector within the category of industrial sources: chemical manufacturing facilities. This focus on a single sector is consistent with other empirical studies of industrial pollution (e.g., Laplante and Rilstone, 1996; Barla, 2007; Earnhart, 2009). The sector of chemical and allied products serves as an excellent vehicle for examining the influence of corporate environmental behavior, audits in particular, on environmental performance. First, the EPA has demonstrated a strong interest in this sector as evidenced by its study, jointly authored with the Chemical Manufacturing Association (CMA), on the root causes of non-compliance in this sector (EPA, 1999) and its study on the compliance history for this sector (EPA, 1997b). Consistent with this interest, two sub-sectors in the industry, industrial organics and chemical preparations (SIC-codes 2869, 2899), were regarded by the EPA as priority sectors during a portion of the study period. Second, the CMA, which is now known as the American Chemistry Council (ACC), has demonstrated a strong interest in promoting pollution reduction and prevention with its Responsible Care initiative. Third, this sector is expected to display a meaningful variation in environmental performance, involving non-compliance and overcompliance (EPA, 1997b). Fourth, this sector permits the analysis to explore many sub-sectors. Fifth, this sector generates a large amount of wastewater. As evidence, data on wastewater discharged in 2008 that are disaggregated by 4-digit Standard Industrial Classification (SIC) code reveal that four of the 10 most polluting sub-sectors operate in the chemical manufacturing sector (EPA, 2011). Nonetheless, we acknowledge that the chemical industry is not necessarily representative of all industrial sectors.

5 If no industry-specific Effluent Limitation Guideline applies to the particular facility, the permit writer uses his/her Best Professional Judgment, which draws upon all reasonably available and relevant data. In particular, the permit writer evaluates the effect of a permitted discharge limit on the environment. In the studied sample, the role for professional judgement is highly limited since nearly all of the facilities operate in sub-sectors with effluent limitation guidelines. Thus, the scope for negotiation over effluent limit levels, including any reflection of compliance history, is severely restricted in the sample. 6 As a related point, overcompliance is arguably voluntary as long as wastewater discharges are deterministic. Of course, discharges are most likely stochastic to some extent. In this case, overcompliance in general may represent an ex ante decision to lower the likelihood of being non-compliant ex post. Thus, some portion of overcompliance is not truly voluntary either.

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Lastly, since effluent limits are pollutant-specific, our analysis must focus on certain pollutants in order to assess the extent of compliance. Our study focuses on the two pollutants most common to the sampled facilities – total suspended solids (TSS) and biological oxygen demand (BOD). [Respectively, TSS and BOD are the first and second most prevalent pollutant in our sample.] As important, these pollutants represent two of the five EPA conventional pollutants, which are the focus of EPA efforts. As further support, all previous studies of wastewater discharges examine only BOD, only TSS, or both BOD and TSS (e.g., Earnhart, 2004a, b, 2009; Laplante and Rilstone, 1996). Conceptual explanation In this section we intuitively explain an individual facility’s chosen extent of compliance with its wastewater discharge limit. Based on this intuition, we derive the hypotheses identified in Section 1.7,8 In each time period, a facility faces a discharge limit and chooses its absolute level of wastewater discharges in order to maximize its expected profits. Essentially, the facility chooses the extent of compliance, as measured by the ratio of absolute discharges to discharge limit level (“discharge ratio”). Expected profits equal revenues minus the usual costs of production, along with the sum of compliance costs (i.e., abatement costs) and expected non-compliance costs (i.e., sanctions). We posit that abatement costs depend on the facility’s discharge ratio and its use of audits, along with other factors, such as facility and firm characteristics and end-of-pipe treatment technologies. Abatement costs rise as the discharge ratio falls, i.e., marginal abatement costs are positive. In contrast, abatement costs fall as the use of audits grows. More important, marginal abatement costs drop as use of audits rises, i.e., lowering the discharge ratio increases abatement costs more slowly when audit use is greater. In the end, the facility maximizes its expected profits by minimizing the noted sum of costs, which is achieved by adjusting the facility’s discharge ratio until the marginal costs of compliance equal the expected marginal costs of non-compliance. If audits are effective at improving compliance, they should lower marginal abatement costs by improving the effectiveness of the existing abatement methods. If true, an increase in audits leads to a lower discharge ratio. However, facilities may expend effort on pollution control practices other than audits that should also improve compliance (hereafter “non-audit practices”). Similar to audits, as the use of non-audit practices grows, both total and marginal abatement costs fall. Yet facilities may be constrained by a fixed supply of managerial and engineering effort available for audit and non-audit practices. One might hope that audits and non-audit practices are complements. However, given a limited quantity of managerial and engineering attention available for audit and non-audit practices, greater efforts expended on audits must lower the amount of managerial and engineering effort devoted to non-audit practices. [See Gabel and Sinclair-DesGagné (1993) and Earnhart and Lizal (2010) for analysis on this type of constraint. Sam (2010) and Carrión-Flores and Innes (2010) also discuss this type of constraint.9] Therefore, any increase in audits lowers non-audit practices, causing an ambiguous influence on marginal abatement costs and the profit-maximizing discharge ratio. Intuitively, if audits lower marginal abatement costs more strongly than do non-audit practices, then an increase in audits lead to a lower discharge ratio (Hypothesis H1). Conversely, if audits lower marginal abatement costs less strongly than do non-audit practices then an increase in audits actually leads to a higher discharge ratio by distracting managerial and wastewater engineers’ attention away from the more effective non-audit practices (Hypothesis H2). If the two types of efforts are equally effective, then audits does not affect the chosen discharge ratio (Hypothesis H3). As the last factor, we claim that the lagged discharge ratio influences the profit-maximizing contemporaneous discharge ratio through two channels. As the first channel, the lagged discharge ratio influences the costs of non-compliance, which stem from sanctions. In practice, enforcement authorities consider a facility’s compliance history when exploring sanctions for non-compliance, an approach consistent with EPA guidelines. Given this practice, the probability of sanction is driven by both the contemporaneous and the lagged discharge ratios. In particular, the higher was the discharge ratio in the past, the greater is the impact of an increased current discharge ratio on the threat of sanction.10 Given this connection, a higher lagged discharge ratio leads to a greater marginal cost of non-compliance, prompting the facility to lower its currently chosen discharge ratio; the converse also holds. We identify this connection as the “adjustment effect”. As the second channel, inertia connects the facility’s lagged discharge ratio to the facility’s current choice of discharge ratio. We interpret inertia as influencing abatement costs. If the discharge ratio was high in the preceding period, then 7

A supplemental document that provides a formal, yet simple, conceptual framework is posted on the journal’s website. Previous theoretical research on audits focuses on the role of audits for discovering violations, which then may be disclosed to regulatory agencies (Friesen, 2006; Pfaff and Sanchirico, 2000; Mishra et al., 1997; Stafford, 2007). Consistent with this focus, these studies explore the EPA’s Audit Policy, which encourages audits and disclosure by offering reduced penalties on disclosed violations when certain eligibility requirements are met. Thus, this theoretical research focuses on voluntary self-reporting. Other theoretical studies explore the role of voluntary self-reporting more generally (Innes, 1999, 2001; Evans et al., 2009). As noted above, in the regulatory context explored for our empirical analysis, regulated facilities are required to self-report, which eliminates the role of audits for disclosure and use of the EPA’s audit policy (Stafford, 2007; Friesen, 2006). Consequently, we do not draw on this previous audit-related theoretical research when constructing our intuitive explanation. 9 Sam (2010, p. 271) derives the following hypothesis regarding the effect of pollution prevention (P2) practices on environmental compliance: “The adoption of more P2 practices yields a redirection of resources away from compliance activities and, therefore, leads to more environmental violations.” 10 Alternatively, the lagged discharge ratio might also influence the sanction size or only the sanction size instead of the sanction probability. These alternative approaches generate insight identical to the insight provided in the text. 8

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inertia implies that the facility faces higher abatement costs in the current period, ceteris paribus, and vice versa. From this perspective, the lagged discharge ratio affects the costs of compliance. From a more narrow perspective, this connection stems from the intangible aspects of environmental management that spill over from one period to the next. From a broader perspective, factors affecting the costs of environmental management vary over time yet follow a temporal pattern so that they persist from one period to the next, perhaps due to technical constraints. If these factors led to higher marginal abatement costs in the preceding period, prompting a greater discharge ratio, then these factors’ persistence leads to higher marginal abatement costs in the current period, prompting again a greater discharge level. Given this connection, it follows that an increase in the lagged discharge ratio leads to a higher current discharge ratio, which we label as the “inertia effect”.11 In sum, the lagged discharge ratio generates two countervailing effects. Since both effects may co-exist, we are only able to identify the dominant effect (or net effect), which may be positive or negative. If the two effects are sufficiently comparable in strength, our empirical analysis is only able to observe a “zero” net effect.

Data In order to explore the influence of audits on Clean Water Act-regulated facilities’ wastewater discharge ratios, we explore data drawn from a survey of regulated facilities and an EPA database.

Survey of regulated entities This first sub-section describes the set of facilities sampled by the survey and the information extracted by the survey. Our survey was administered to a sample of U.S. chemical manufacturing facilities whose wastewater discharges were regulated by effluent limits imposed within permits issued as part of the National Pollutant Discharge Elimination System (NPDES) in 2001. The population of CWA-regulated facilities in the chemical manufacturing industry as of September 2001, was extracted from the EPA’s Permit Compliance System (PCS) database, which records information on facilities permitted within the NPDES system. This extract included 2596 chemical facilities. The population held both “minor facilities” and “major facilities” as classified by the EPA for the NPDES system; in general, minor facilities are smaller and major facilities are larger.12 We further limited the survey sample to those facilities that met the following criteria: (1) possessed a NPDES permit; (2) faced restrictions on their wastewater discharges, (3) discharged regulated pollutants into surface water bodies, (4) were operating as of 2002, and (5) contact information was available from either the EPA or alternative sources, e.g., phone books. These criteria identified 1003 facilities to contact. Of those facilities contacted between April of 2002 and March of 2003, 736 refused to participate in the survey, while 267 facilities completed at least 90% of the survey, implying a 27% response rate. This rate is comparable to previous large-scale surveys of industrial sectors (e.g., Arimura et al., 2008, 2011; Nakamura et al., 2001) and lies above the average response rate of 21% as identified by a review of 183 studies based on business surveys published in academic journals (Paxson, 1992). Given the survey’s non-response rate of 73%, the potential for sample selection bias is a valid concern. As the initial assessment of this concern, we compare the original sample of 1003 potentially eligible facilities to the 267 facilities that actually completed the survey. Based on this comparison, we find no systematic state or regional bias in survey participation. For example, only the Midwest region is slightly over-represented in the response group, and only the Northeast region is slightly under-represented. These differences, however, are small. In addition, across most of the states, the difference between representation in the original sample and representation in the response group averages less than 2%. In contrast, our initial assessment reveals some difference in the participation of major facilities versus minor facilities. In the original sample, 69% of facilities are minor facilities and 31% are major facilities. In the group of survey respondents, major facilities are slightly over-represented at 39%. This difference proves statistically significant. As a stronger assessment, we test for sample selection bias by assessing whether any relevant factors appear to affect a facility’s decision to complete our survey once it is contacted. We use a probit model to estimate a functional relationship between the binary decision to complete or not our survey and a set of relevant factors, including major versus minor status, inspections, enforcement actions, and EPA region. This assessment reveals a bias in a single dimension: major facilities were more likely to respond to the survey than were minor facilities. Put differently, the analysis indicates that only the distinction between minor and major facilities proves important for explaining whether or not a contacted facility completed the administered survey. This single distinction proves irrelevant for our final sample of analysis, which includes only major facilities.13 11 These points notwithstanding, we acknowledge that inertia is difficult to assess and the lagged discharge ratio represents only a crude tool for capturing inertia. As the most important concern, the “inertia effect” does not reflect a causal relationship: a higher lagged discharge ratio does not cause a higher current discharge ratio. Instead, the “inertia effect” stems from a temporal correlation between the two periods involving abatement cost factors. 12 For the classification of each regulated facility, the EPA calculates a major rating with points assigned on the basis of toxic pollution potential, flow type, conventional pollutant load, public health impact, and water quality impact; the EPA classifies any discharger with a point total of 80 or more as a “major facility”. 13 The results of our analysis reveal that neither the preceding history of inspections nor the preceding enforcement actions against a particular facility appear to explain whether or not a contacted facility responded to the survey. Moreover, our results demonstrate that the decision to respond is not explained by the EPA region in which a particular facility resides.

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As one last form of sample selection assessment, we incorporate information on discharges and effluent limits, for which data are publicly available only for major facilities, for both survey respondents and non-respondents. Consistent with our final sample of analysis, our last form of assessment focuses exclusively on major facilities. Using Two-Sample Means T-tests, we demonstrate that the sample of survey respondents and the sample of survey non-respondent facilities generated extremely similar discharge-to-limit ratios (i.e., actual discharges divided by permitted limits) for both TSS and BOD discharges over the time period covered by the survey instrument: January 1999 to March 2003. For the TSS discharge ratio, both of the sample means equal 0.267 and the t-test p-value is 0.969. For the BOD discharge ratio, the two-sample means are nearly identical – 0.261 and 0.256 – and the t-test p-value is 0.616. For all these reasons, our study does not correct for any potential sample selection bias. This lack of correction is consistent with recent prominently published studies of environmental management practices (Anton et al., 2004; Arimura et al., 2008). When administering the survey, we first contacted those individuals responsible for signing their respective facilities’ wastewater discharge monitoring reports, which facilities are required to submit to the EPA on a regular basis, generally monthly. This selection of survey participants allows our survey to exploit the insight of those individuals most knowledgeable about their facilities’ wastewater operations. The survey gathered various data elements. Most important, it gathered data on environmental management practices employed by individual facilities, especially the count of wastewater-related self-audits conducted per year.14 The survey gathered annual data for the years 1999, 2000, and 2001. The survey also gathered data on characteristics of the facilities, such as facility age, and characteristics of the firms that own these facilities, such as ownership structure and the number of firm-level environmental employees and total employees.15 In addition, the survey posed questions on the facility’s relationship with their regulatory authorities and local communities. In particular, one survey question asks respondents to characterize the prevalence of their regulators’ fair treatment: always unfair, sometimes fair/sometimes unfair, or always fair. Another survey question asked respondents to assess their perceived need to respond to local community pressure. Publicly available data on regulated entities To complement the data gathered by our survey, we also collected information from the EPA Permit Compliance System (PCS) database. This database provides information on each facility’s (1) location, (2) NPDES major or minor classification, (3) four-digit standard industrial classification [SIC] code, (4) monthly limit levels of permitted discharges, (5) monthly levels of actual discharges, (6) inspections performed by federal and state regulators, (7) formal enforcement orders imposed by federal administrative and civil courts, and (8) informal enforcement actions issued by federal enforcement agencies.16 From these data, we calculate the monthly discharge ratio for TSS and BOD discharges.17 Of the monthly observations relevant to TSS discharge limits, 99.5% also provide data on actual TSS discharges. Of the monthly observations relevant to BOD discharge limits, 99.7% also provide data on actual BOD discharges. This nearly universal reporting of wastewater discharges is quite reassuring and practically eliminates the need to consider strategic non-reporting of discharges. To match the annual data on audits for the years 1999–2001, we aggregate the monthly data on discharge ratios to an annual basis by identifying the year-specific median ratio among the 12 monthly ratios. (Use of the year-specific mean ratio generates highly similar estimation results.) The PCS database in general systematically records wastewater discharges and discharge limits only for major facilities. Not surprisingly, our extract from the PCS database contains no information on discharge limits and measurements for any of the 164 survey respondent minor facilities. Of the 103 survey respondent major facilities, the PCS database contains records that potentially provide data on discharge limits and wastewater discharges for 97 major facilities. For all of our analysis, we focus exclusively on these 97 major facilities for which we possess both survey data on environmental management practices and EPA data on limits and discharges. Our exclusive focus on major facilities clearly limits the generalizability of our results. Nevertheless, our analysis remains policy relevant since the EPA focuses its regulatory efforts on major facilities (Earnhart, 2004a, b, 2009; Earnhart and Segerson, 2012). Moreover, major facilities represented 21% of the 2481 chemical manufacturing facilities in the NPDES system in 2001. Given their size, we suspect that major facilities were responsible for the bulk of wastewater discharges from this sector during the sample period. 14 Our analysis focuses exclusively on environmental regulatory compliance audits regarding a single environmental aspect: wastewater discharges. An environmental regulatory compliance audit represents a systematic, documented, and objective review of a facility’s operations, which allows the regulated facility to evaluate its performance relative to audit criteria (ASTM, 2003). 15 To validate the self-reported data on firm ownership structure, our study draws upon publicly available data. The EPA Toxic Release Inventory (TRI) database provides annual information on a facility’s parent company. The Business and Company Resource Center database and Compustat/Research Insight database provide annual data on a parent company’s ownership structure. 16 The analysis aggregates the four-digit SIC codes into three broader sectoral categories: organic chemicals, inorganic chemicals, and “other” chemicals. The broad category of organic chemicals includes the following four-digit SIC codes: 2821, 2823, 2824, 2843, 2865, 2869, 2891, and 2899. The broad category of inorganic chemicals includes the following four-digit SIC codes: 2812, 2813, 2816, 2819, 2873, and 2874. 17 Discharge limits constrain wastewater measured as a quantity, e.g., pounds per day, or as a concentration, e.g., milligrams per liter of wastewater. By calculating discharge ratios, we avoid the complication of combining these two disparate forms of measurement.

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In sum, the sample represents a panel of 97 facilities with annual data for the years 1999, 2000, and 2001. Thus, the unit of observation is an individual facility discharging in a given year. However, not all 97 facilities discharge TSS or BOD in all 3 years so the regression sample is smaller. Further, by first-differencing our variables, which is required by our chosen estimation procedure (as described in the next section), we effectively use only 2 years of data. In the end, the TSS regression sample includes 144 observations reflecting 73 individual facilities, while the BOD regression sample includes 123 observations reflecting 63 individual facilities.

Econometric framework In this section, we construct an econometric framework that parallels the conceptual framework, while incorporating additional factors. The facility’s chosen current discharge ratio represents the dependent variable. Our two primary independent variables are audits and the lagged discharge ratio. We measure the use of audits by counting the number conducted by an individual facility within a single calendar year. An increase in the use of audits can lower, raise, or not affect the chosen discharge ratio. We focus on the frequency of audits, rather than the mere presence or absence of an audit program, for three reasons. First, according to Ebihara and Irminger (2005), most guidelines recommend that environmental self-audits be conducted at least annually. Therefore, many facilities may conduct at least one audit per year. Second, according to Wilson and Thomas (1998), regularly scheduled self-audits might contribute to both stronger overall environmental management and to more consistent compliance with relevant regulations. Given this insight, if inspections are helpful, then the frequency of audits is arguably more important than merely the presence of an audit. Third, compliance audits of NPDES-permitted facilities most likely assess equipment and treatment process operations. This assessment of actual operations stands in contrast to other types of environmental self-audits, such as those of hazardous waste programs, which focus heavily on the review of regulations and documents (Ebihara and Irminger, 2005). While regulations and documents may not change much within a single year, equipment and operations are likely to vary within a single year so more frequent audits are more likely to catch changes in equipment and operations sooner. Conditional on audits being helpful, an increased ability to catch changes sooner allows facilities to control their wastewater discharges more effectively. If audits are not helpful, neither audit frequency nor audit presence should influence discharges. If audits distract management efforts, then clearly as audit frequency rises, the extent of distraction grows, leading to a more detrimental impact on environmental performance.18 As our second primary independent variable, the lagged discharge ratio may influence the currently chosen discharge ratio positively or negatively. To identify the remaining independent variables, we draw upon the factors relating to the marginal costs of compliance and non-compliance. As we identify these independent variables, we also describe the a priori expectations regarding the relationship between each independent variable and the dependent variable. The first set of remaining independent variables relate to the marginal costs of compliance, i.e., marginal costs of abatement. First, abatement costs depend on abatement methods, which we measure as the presence or absence of an endof-pipe treatment technology. Presence of end-of-pipe treatment is expected to lower the chosen discharge ratio. Second, facility and firm characteristics influence abatement costs. The age of a facility represents a proxy for the vintage of production capital. Older facilities are expected to discharge more due to higher abatement costs. In addition, the type of production process as proxied by broad sectoral classification – “organic chemicals” versus “inorganic chemicals” versus “other chemicals” – influences abatement costs but without a priori expectations regarding the effect of production process on the chosen discharge ratio. Abatement costs also depend on ownership structure, which we measure by contrasting publicly held structures from all other ownership structure. Facilities owned by publicly held firms are expected to discharge less (i.e., lower discharge ratio) because publicly held firms enjoy greater access to external financing. Abatement costs additionally depend on the firm-level ratio of environmental employees to total employees, which we interpret as a proxy for firm-level commitment to environmental protection or at least the quantity of resources available from corporate staff. Facilities owned by firms with more environmental employees are expected to discharge less (i.e., lower discharge ratio). The second set of remaining independent variables relate to the marginal costs of non-compliance. The first variables reflect regulatory pressure, which we divide into monitoring and regulatory enforcement. We measure regulatory monitoring based on the prevalence of inspections. Consistent with the conceptual framework, we construct a variable that measures the ex ante likelihood or threat of an inspection, which arguably approximates the likelihood of a sanction. We assess inspections conducted at the individual facility in the preceding calendar year, consistent with several previous empirical studies (Magat and Viscusi, 1990; Helland, 1998a, b; Earnhart, 2004a, b, 2009), which treat these lagged inspections as an exogenous regressor. Given the discrete nature of monitoring, we simply count the inspections conducted. Based on work by Earnhart (2004a), we derive two separate measures for state inspections and federal inspections. These measures serve collectively as a proxy of the ex ante threat of inspections based on the facility’s own experiences. Obviously, greater monitoring is expected to lower the chosen discharge ratio. 18 The influence of audit frequency may be non-linear. Inclusion of a squared audit term may capture this potential non-linearity. However, this term never proves statistically significant in alternative specifications explored.

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We measure regulatory enforcement based on the strength of enforcement. Specifically, we construct two variables that collectively measure the ex ante strength of enforcement. As with inspections, we assess enforcement actions taken against the individual facility in the preceding calendar year, consistent with previous empirical studies (Earnhart, 2004a, b, 2009), which treat these lagged enforcement actions as an exogenous regressor. As with inspections, we count the enforcement actions taken. We derive two separate measures for informal enforcement and formal enforcement. These measures collectively serve as a proxy of the ex ante strength of enforcement based on the facility’s own experiences. Similar to monitoring, greater enforcement is expected to lower the chosen discharge ratio. As an additional regulatory factor, we incorporate the flexibility of regulatory treatment, as measured by the perceived treatment on the part of the regulated facilities. We distinguish between treatment that is perceived as “always fair” and treatment that is perceived as less than “always fair”. Greater flexibility may prompt a lower discharge ratio if flexibility promotes a cooperative relationship between the agency and the regulated facility. However, a coercive relationship may be needed to induce greater compliance.19 As other regulatory control factors, we incorporate individual EPA region indicators and individual calendar year indicators, which contain no obvious a priori expectations. Lastly, we incorporate two additional forms of external pressure. Specifically, we incorporate local community pressure, as measured by the facility’s perceived need to respond to local community pressure. We distinguish between a “strong need to respond” and a less than “strong need to respond”. Facilities facing a greater need to respond to local community pressure are expected to discharge less (i.e., lower discharge ratio). And we allow ownership structure to control for both variation in abatements costs, as discussed above, and variation in investor pressure. Facilities owned by publicly held firms are expected to discharge less because publicly held firms face greater pressure from investors for good environmental performance. These two additional forms of external pressure need not be constrained to inducing compliance; instead, they might prompt overcompliance.20,21 Table 1 statistically summarizes the dependent variables and the various independent variables. Information on the dependent variables is most interesting. Based on the average TSS discharge ratio of 0.232, the average facility discharges TSS at levels 77% below its limits [(1 0.232)  100¼76.8%]. Based on the average BOD discharge ratio of 0.230, the average facility discharges BOD at levels 77% below its limits. Clearly, many facilities are choosing to overcomply. Table 2 tabulates the frequency distribution of the audit count. Table 2a displays the TSS sample; Table 2b displays the BOD sample. As shown, regardless of the pollutant-specific sample, a majority of the observations reveal the absence of any audit. A count of one audit represents the next most common category. Yet, nearly 25% of the facilities conduct multiple audits in a given calendar year. Econometric analysis This next section describes the econometric analysis used to explore the gathered data. The econometric analysis alternately excludes and includes the lagged dependent variable as a regressor. Since the inclusion of this factor demands special attention, we describe its inclusion in a separate sub-section. Exclusion of lagged dependent variable from the regressor set This first sub-section depicts the econometric analysis that excludes the lagged dependent variable as a regressor. We first address the panel structure of our data by employing a fixed effects estimator.22 Fixed effects F-test statistics confirm that this estimator dominates a pooled OLS estimator regardless of the specification, as shown at the bottom of Tables 2 and 3. Thus, identification in our estimations stem exclusively from intra-facility variation. By construction, all timeinvariant factors are subsumed into the facility-specific fixed effects. Nevertheless, we are able to provide consistent coefficient estimates of these time-invariant factors by implementing a pooled OLS estimation (Oaxaca and Geisler, 2003).23 We next address the potential endogeneity of the audit-related regressor. We address this potential concern by employing an instrumental variable (IV) estimator. To implement this IV estimator, we must identify at least one instrument for this potentially endogenous regressor. In order to assess instrument validity, we identify multiple instruments. 19 This factor may be endogenous. However, exogeneity tests fail to reject the null hypothesis of exogeneity. The instruments used for this testing are twice-lagged values (in natural logs) of inspections conducted at and enforcement actions taken against other similar facilities. Details are available on request. 20 The economics literature identifies various factors that may prompt overcompliance. As noted, local community pressure may prompt overcompliance. On this point, Henriques and Sadorsky (1996) explore the effect of self-reported community pressure on Canadian firms’ decisions to adopt an environmental plan and Dasgupta et al. (2000) explore the effect of self-reported community pressure on Mexican firms’ decisions to adopt certain environmental management practices. Moreover, a company may wish to present a “green” image to investors and customers. On this point of corporate image, Downing and Kimball (1982) assess management’s concerns over corporate image and Arora and Cason (1996) explore firms’ desire to present a “green” image to consumers. 21 In contrast to Anton et al. (2004), [1] we do not include independent variables to measure state-level variation in liability laws for contamination because these laws are not meaningfully relevant for wastewater discharges, [2] we do not use firm-level financial data in order to expand our ability to control for investor pressure because financial data for non-publicly traded firms are not available, and [3] we do not interpret our sub-sector indicators as proxies of consumer pressure since differences in production processes most likely dominate. 22 We do not employ a random effects estimator since Hausman Tests of Random Effects do not indicate that the random effects estimates are consistent. 23 We thank an anonymous referee for identifying this method for providing consistent estimates.

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Table 1 Descriptive statistics of variables: means, standard deviations (in parentheses). Variable

Variable description [Units]

TSS sample

BOD sample

Count of audits

Number of audits conducted per year [count per year]

Discharge ratio

Ratio of measured discharges to permitted discharges [ratio]

One-year lagged discharge ratio Two-year lagged discharge ratio Informal enforcement (lagged) Formal enforcement (lagged) Federal inspections (lagged) State inspections (lagged)

One year lagged discharge ratio for TSS, BOD [ratio]

2.764 (8.936) 0.232 (0.183) 0.234 (0.181) 0.228 (0.172) 0.299 (1.258) 0.118 (1.014) 0.083 (0.345) 1.750 (2.431) 0.625 (0.486) 0.222 (0.417) 0.920 (0.202) 0.042

3.195 (9.651) 0.230 (0.396) 0.227 (0.370) 0.274 (0.824) 0.350 (1.355) 0.138 (1.096) 0.065 (0.279) 1.886 (2.574) 0.683 (0.467) 0.146 (0.355) 0.935 (0.212) 0.023

0.917 (0.277) 0.929 (0.258) 0.838 (0.370) 0.083 (0.277) 0.111 (0.315) 0.347 (0.478) 0.083 (0.277) 0.347 (0.478) 0.014 (0.117) 0.507 (0.502)

0.935 (0.248) 0.966 (0.181) 0.901 (0.300) 0.098 (0.298) 0.163 (0.371) 0.309 (0.464) 0.098 (0.298) 0.317 (0.467) 0.000 0.000 0.504 (0.502)

144

123

Organic chemical subsector Inorganic chemical subsector Treatment technology Firm environmental employees Firm ownership structure

Two year lagged discharge ratio for TSS, BOD [ratio] Count of informal enforcement actions in preceding year Count of formal enforcement actions in preceding year Count of federal inspections in preceding year Count of state inspections in preceding year ¼1 if facility in organic chemical sub-sector; ¼ 0 otherwise (benchmark: “other chemical”) [dummy indicator] ¼1 if facility in inorganic chemical sub-sector; ¼ 0 otherwise (benchmark: “other chemical”) [dummy indicator] Treatment technology presence (vs absence): Average between TSS and BOD technology [dummy indicator avg] Ratio of firm-level environmental employees to all firm-level employees [ratio] ¼1 if firm is publicly held, ¼ 0 otherwise [dummy indicator]

Local community pressure Facility’s perceived need to respond local community concerns: ¼ 1 if quite bit/great deal, ¼0 if little/some [dummy indicator] Regulator’s fairness Facility’s perceived fairness of regulator’s treatment: ¼1 if always fair; ¼0 if sometimes fair/unfair [dummy indicator] EPA Region 2 ¼1 if facility in EPA Region 2; ¼ 0 otherwise (benchmark: Regions 1, 8, 9, 10) [dummy indicator] EPA Region 3

¼1 if facility in EPA Region 3; ¼ 0 otherwise (benchmark: Regions 1, 8, 9, 10) [dummy indicator]

EPA Region 4

¼1 if facility in EPA Region 4; ¼ 0 otherwise (benchmark: Regions 1, 8, 9, 10) [dummy indicator]

EPA Region 5

¼1 if facility in EPA Region 5; ¼ 0 otherwise (benchmark: Regions 1, 8, 9, 10) [dummy indicator]

EPA Region 6

¼1 if facility in EPA Region 6; ¼ 0 otherwise (benchmark: Regions 1, 8, 9, 10) [dummy indicator]

EPA Region 7

¼1 if facility in EPA Region 7; ¼ 0 otherwise (benchmark: Regions 1, 8, 9, 10) [dummy indicator]

Year 2001

¼1 if year is 2001; ¼ 0 if year is 2000 [dummy indicator]

No. of observations

Consistent with Barla (2007), we consider the use of audits by other similar facilities in order to generate instruments. In particular, we calculate the number of audits conducted by other NPDES-permitted chemical manufacturing facilities who responded to our survey operating in the same EPA region as the individual facility and then divided this region-specific audit count by the number of other similar facilities. We restrict the geographical scope of this measure because facilities may not be aware of audits conducted by other facilities located far away. As important, the geographical restriction generates additional variation in the measure; without this restriction, the measure relies exclusively on temporal variation. From these calculations, we construct both a contemporaneous measure and lagged measure of the audits conducted by other similar facilities. By using both measures as instruments, we allow individual facilities to react to other facilities’ use of audits with and without a delay. Barla (2007) uses a contemporaneous measure. We argue that a lagged measure may also be important, if not more important, since an audit program most likely takes several months to organize and implement. According to Barla (2007), the extent of audit adoption by rival facilities influences the individual facility’s extent of audit adoption by affecting the marginal benefits and costs of adoption. As more rivals adopt audits, the marginal benefits fall because early adopters enjoy more meaningful benefits, yet the marginal costs of adoption also fall since the marginal adopter learns from the preceding adopters’ successes and failures. As the next step in our instrumental variables estimation, we assess both the relevance and validity of our instruments. Effective instrumental variables must satisfy two requirements: they must be correlated with the included (potentially) endogenous variable, i.e., relevance, and they must be orthogonal to the error process, i.e., validity (Baum et al., 2003). To

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Table 2 Frequency distribution of audit count. Audit Count

Frequency

Percent

Cumulative (%)

(a) TSS sample 0 1 2 3 4 5 10 12 412

79 33 9 1 9 3 1 4 5

54.86 22.92 6.25 0.69 6.25 2.08 0.69 2.78 3.47

54.86 77.78 84.03 84.72 90.97 93.06 93.75 96.53 100.00

(b) BOD sample 0 1 2 3 4 5 10 12 412

67 26 8 1 7 3 1 5 5

54.47 21.14 6.50 0.81 5.69 2.44 0.81 4.07 4.06

54.47 75.61 82.11 82.93 88.62 91.06 91.87 95.93 100.00

assess relevance, we first calculate the partial F-test statistic, which tests whether the multiple instruments are jointly significant in the first stage of estimation. For both the TSS and BOD regression samples, these statistics reveal meaningful relevance given p-values of 0.001 and 0.001, respectively. We next test under-identification in the first stage. Based on both the Angrist–Pischke χ2 Test statistics and the Anderson Canonical Correlation Lagrange Multiplier Test statistics, we strongly reject the null hypothesis of under-identification given p-values of 0.001 and 0.001, respectively, for the TSS regression sample and p-values of 0.001 and 0.001, respectively, for the BOD regression sample. (Table A1 in the Appendix A displays the first-stage estimation results.) To assess the validity of our instruments, we primarily employ the Sargan–Hansen Test of Overidentifying Restrictions. For both the TSS and BOD regression samples, the Sargan–Hansen Test statistics strongly fail to reject the null hypothesis of valid orthogonality conditions given p-values of 0.825 and 0.945, respectively. [Technically, this test assesses the joint hypothesis of correct model specification and valid orthogonality conditions; see Baum et al. (2003) on this point.] As further evidence, we employ weak instrument robust inference tests: Anderson–Rubin Traditional F-Test, Anderson–Rubin Wald Test, and the Stock–Wright Lagrange Multiplier Test. For both regression samples, regardless of the test, the statistics strongly fail to reject the null hypothesis of valid orthogonality conditions with p-values ranging between 0.622 and 0.939. Based on our assessment, the instruments appear both relevant and not invalid. Therefore, we proceed to testing whether the potentially endogenous regressor of audit count is exogenous using the Differences in Sargan Test. For the TSS and BOD regression samples, the test statistics fail to reject the null hypothesis of exogeneity given p-values of 0.303 and 0.942, respectively. These statistics indicate that the audit regressor does not appear endogenous.24 Performing IV estimation when the regressors are uncorrelated with the error process involves an important cost – “the asymptotic variance of the IV estimator is always larger, and sometimes much larger, than the asymptotic variance of the OLS estimator” (Wooldridge, 2003, p. 490). Therefore, we proceed with the standard fixed effects estimator when estimating the functional relationship between the discharge ratio and a regressor set that excludes the lagged dependent variable.25 Inclusion of lagged dependent variable in the regressor set This next sub-section depicts the econometric analysis that includes the lagged dependent variable in the regressor set. Since the exogeneity tests fail to reject the exogeneity of the audit variable, this sub-section focuses on the remaining econometric issue: inclusion of the lagged dependent variable as a regressor in a panel data framework. In the presence of a lagged dependent variable, a fixed effects estimator generates inconsistent estimates because the differenced lagged dependent variable is correlated with the error term (Anderson and Hsiao, 1982). To address this complication, we employ the two-stage Anderson–Hsiao estimator (Anderson and Hsiao, 1982) as applied by Jaffe and Stavins (1995) and Harrington (2014). In the first stage, we first-difference all of the variables and estimate the 24 The use of alternative instruments generates similar test results, demonstrating that the alternative instruments are also relevant and not invalid and that the audit regressor is exogenous in both samples. 25 Table 2 reveals that five observations reflect an annual audit count greater than 12. Exclusion of these five observations from our statistical analysis does not influence any of the conclusions drawn regarding the relevance and validity of our constructed instruments and the exogeneity of the audit count regressor. Thus, our conclusions are not driven by these higher audit counts.

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Table 3 Estimation of TSS discharge ratio: fixed effects, Anderson–Hsiao. Variables

Fixed effects Model A1

Count of audits

 0.003 (0.013)

nn

Anderson–Hsiao Model A2  0.003 (0.003)

nnn

Model A3  0.003 (0.006)

Lagged TSS discharge ratio Informal enforcement (lagged) Formal enforcement (lagged) Federal inspections (lagged) State inspections (lagged) Organic chemical sub-sector Inorganic chemical sub-sector Treatment technology Firm environmental employees Firm ownership structure Local community pressure Regulator’s fairness EPA Region 2 EPA Region 3 EPA Region 4 EPA Region 5 EPA Region 6 EPA Region 7

0.044 (0.341) 0.003 (0.947) 0.166nn (0.034)  0.535nnn (0.002)  0.011 (0.820)  0.089 (0.164) 0.106nnn (0.001) 0.207n (0.062) 0.323nnn (0.003) 0.329nnn (0.002) 0.222nn (0.044) 0.329nnn (0.001) 0.184 (0.215)

Year 2001 # of observations Number of facilities R-squared F-test: fixed effects nnn nn

,

, and

n

144 73 0.094 11.02nnn

0.044 (0.341) 0.004 (0.939) 0.166nn (0.033)  0.534nnn (0.002)  0.010 (0.834)  0.089 (0.167) 0.106nnn (0.001) 0.206n (0.064) 0.322nnn (0.003) 0.329nnn (0.002) 0.220nn (0.047) 0.328nnn (0.001) 0.184 (0.215)  0.022n (0.097) 144 73 0.128 11.33nnn

Model B0

nnn

 0.016 (0.312)  0.068nnn 0.000  0.004 (0.908) 0.022 (0.296) 0.027 (0.569)  0.015 (0.770) 0.169nn (0.032)  0.530nnn (0.002)  0.004 (0.947)  0.086 (0.181) 0.104nnn (0.002) 0.181 (0.122) 0.281nnn (0.013) 0.304nnn (0.005) 0.173 (0.134) 0.328nnn (0.003) 0.178 (0.231)  0.026n (0.053) 144 73 0.203 11.53nnn

Model B1

Model B2

nn

nn

0.086 (0.644)

 0.002 (0.044) 0.060 (0.745)

0.072n (0.089) 0.020 (0.669) 0.114n (0.098)  0.543nnn (0.001) 0.031 (0.443)  0.152 (0.008) 0.089nnn (0.003) 0.150 (0.138) 0.254nnn (0.008) 0.262nnn (0.006) 0.161n (0.099) 0.265nnn (0.005) 0.123 (0.360)

0.073n (0.093) 0.020 (0.675) 0.118n (0.094)  0.556nnn (0.001) 0.031 (0.450)  0.153nnn (0.009) 0.091nnn (0.003) 0.155 (0.136) 0.262nnn (0.008) 0.270nnn (0.005) 0.166n (0.098) 0.273nnn (0.004) 0.128 (0.353)

144

144

 0.003 (0.016) 0.049 (0.786)

0.074n (0.094) 0.020 (0.678) 0.119n (0.093)  0.561nnn (0.001) 0.031 (0.453)  0.154nnn (0.009) 0.092nnn (0.003) 0.157 (0.135) 0.265nnn (0.008) 0.274nnn (0.005) 0.168n (0.097) 0.276nnn (0.004) 0.130 (0.350)  0.019 (0.392) 144

Model B3  0.002nn (0.039) 0.080 (0.702)  0.020n (0.059)  0.032n (0.074) 0.022 (0.636)  0.024 (0.328) 0.072n (0.090) 0.020 (0.670) 0.114n (0.097)  0.546nnn (0.001) 0.031 (0.444)  0.152nnn (0.008) 0.090nnn (0.003) 0.151 (0.138) 0.256nnn (0.008) 0.264nnn (0.006) 0.162n (0.098) 0.267nnn (0.005) 0.124 (0.358)  0.016 (0.472) 144

signify statistical significance at the 1 %, 5 %, and 10 % levels, respectively.

first-differenced model in order to generate coefficients for the time-variant regressors. In the second stage, we recover coefficients for the time-invariant regressors in three steps. As the first step, we multiply each coefficient derived in the first stage with the facility mean of that same variable and then sum these products. As the second step, we subtract the sum from the mean of the dependent variable measured in levels rather than differences, generating the necessary “residuals”. As the final step, we use OLS to estimate a functional relationship between the residuals and the time-invariant factors in order to generate the relevant coefficients. Given the concern over the correlation between the lagged dependent variable and the error term, we instrument for the first-differenced one-year-lagged dependent variable using a two-year-lagged level of the dependent variable, as suggested by Anderson and Hsiao (1982) and employed by Jaffe and Stavins (1995) and Harrington (2013). For the construction of this instrument, we additionally draw upon data on discharge limits and measurements for the years 1997 and 1998. As with the model set excluding the lagged dependent variable as a regressor, we again assess the exogeneity of the audit variable. In this case, we test whether the lagged dependent variable and the audit regressor are jointly endogenous using a Differences in Hansen–Sargan Exogeneity Test.26 The calculated test statistics reject the null hypothesis of exogeneity

26 This test assesses two Hansen–Sargen statistics: one statistic from a model that uses instruments to address the suspected endogenous variable and the other statistic from a model that treats the variable as exogenous. Under conditional homoskedasticity, the Differences in Hansen–Sargan test statistic is numerically equivalent to a Hausman test statistic (Hayashi, 2000).

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for both the TSS sample, given a statistic of 11.12 and p-value of 0.001, and for the BOD sample, given a statistic of 15.17 and p-value of 0.045. Despite these results, we do not address the endogeneity of the audit regressor because we surmise that the endogeneity of the lagged dependent variable is driving the joint test result since the parallel test results provided in the preceding sub-section indicate that the audit regressor appears exogenous. Assessment of robustness In order to assess the robustness of our empirical results linking audits and the lagged discharge ratio to the chosen discharge ratio, we consider a variety of regressor sets, i.e., models. The first group of regressor sets excludes the lagged dependent variable (Group A). Model A1 includes only the audit regressor. Model A2 adds the time-invariant factors, such as EPA regional indicators, along with the year indicator. Model A3 also adds the inspection and enforcement regressors. The second group of regressors sets include the lagged dependent variable (Group B). Since the Anderson–Hsiao estimator generates efficient coefficient estimates of the time-invariant factors, each of the models in the second group includes these factors. To this set of factors, Model B0 adds only the lagged dependent variable. Model B1 adds the lagged dependent variable and the audit regressor. Relative to Model B1, Model B2 also adds the year indicator, while Model B3 also adds the inspection and enforcement regressors, along with the year indicator. For the sake of improving our estimation’s fit of the data, we take logs of certain regressors: informal enforcement, formal non-penalty enforcement, penalty enforcement, state inspections, federal inspections, and the firm-level ratio of environmental employees to total employees. When using the Anderson–Hsiao estimator, we lose observations in the year of 1999 because we do not possess data on audits in 1998 and the estimator requires first-differences. For comparability across the two groups of regressor sets, we restrict our sample period to the years of 2000 and 2001.

Estimation results This section reports and interprets the estimation results. Table 3 displays the fixed effects estimates and Anderson– Hsiao estimates based on the TSS sample; Table 4 displays these estimates based on the BOD sample. In both tables, the reported estimates reflect robust standard errors. TSS discharge ratio We first interpret the results generated by the estimation of TSS discharge ratios. As shown in Table 3, the effect of audits on discharge ratios is negative and statistically significant regardless of the regressor set. In particular, this result is robust to the exclusion and inclusion of the lagged dependent variable. The negative coefficient indicates that an increase in the count of audits lowers the discharge ratio, indicating that audits effectively control wastewater pollution. The estimation results reveal other relationships. The estimates indicate that formal enforcement negatively influences the discharge ratio regardless of the estimator. This negative coefficient indicates that increased enforcement lowers the discharge ratio. Similarly informal enforcement negatively influences the discharge ratio. This effect is statistically significant based on the Anderson–Hsiao estimates. Moreover, the estimates demonstrate that enforcement, but not monitoring in the form of inspections, proves effective. In keeping with the effectiveness of enforcement, estimates reveal that the degree of regulatory flexibility positively influences the discharge ratio. This positive coefficient implies that “less fair treatment” leads to better environmental performance, supporting the importance of a coercive relationship between the regulatory agency and the regulated community for improving compliance. This conclusion is robust across the estimators and models. As another form of external pressure, the negative coefficient on the community pressure regressor indicates that a greater need to respond to local community pressure appears to lower discharge ratios. This effect is statistically significant based on the Anderson–Hsiao estimates for models that include the audit count regressor, i.e., Models B1–B3. Regardless of the model, the Anderson–Hsiao estimates reveal that the type of production process, as proxied by sectoral classification, influences the discharge ratio. Facilities manufacturing organic chemicals generate a higher discharge ratio than facilities manufacturing “other chemicals”. Estimates also reveal that the firm-level ratio of environmental employees to total employees negatively influences the discharge ratio. This link implies that a firm-level commitment to environmental management resources appears to improve environmental performance at the facility level. This result is robust across the estimators and models. In contrast to this commitment of environmental employees, the presence of a treatment technology surprisingly increases the discharge ratio. We strongly suspect that this result is driven by the greater need to install a treatment technology when discharges are higher in general. Based on the coefficient estimates for the EPA regional indicators, discharge ratios vary across space. This conclusion is robust to the choice of estimator and model. Moreover, the discharge ratios vary over time. Based on the fixed effects estimates, the discharge ratio was higher in 2001.

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Table 4 Estimation of BOD discharge ratio: fixed effects, Anderson–Hsiao. Variables

Fixed effects

Count of audits

Anderson–Hsiao

Model A1

Model A2

Model A3

 0.001 (0.331)

 0.001 (0.319)

0.000 (0.753)

Lagged BOD discharge ratio Informal enforcement (lagged) Formal enforcement (lagged) Federal inspections (lagged) State inspections (lagged) Organic chemical sub-sector Inorganic chemical sub-sector Treatment technology Firm environmental employees Firm ownership structure Local community pressure Regulator’s fairness EPA Region 2 EPA Region 3 EPA Region 4 EPA Region 5 EPA Region 6

0.103 (0.616) 0.107 (0.704) 0.209 (0.617) 0.426 (0.865) 0.292 (0.341) 0.173 (0.709) 0.381n (0.072) 0.084 (0.880)  0.036 (0.950)  0.044 (0.933) 0.045 (0.939) 0.501 (0.325)

Year 2001 # of Observations Number of facilities R-squared F-test: fixed effects nnn nn

,

, and

n

123 63 0.014 59.260nnn

0.103 (0.620) 0.107 (0.704) 0.210 (0.617) 0.427 (0.865) 0.293 (0.343) 0.175 (0.708) 0.382n (0.073) 0.084 (0.882)  0.038 (0.948)  0.046 (0.932) 0.043 (0.942) 0.500 (0.328)  0.004 (0.774) 123 63 0.016 58.340nnn

 0.012 (0.264)  0.050nn (0.013) 0.038 (0.171) 0.037 (0.251) 0.103 (0.627) 0.137 (0.634) 0.165 (0.700) 0.450 (0.862) 0.143 (0.736) 0.243 (0.611) 0.405n (0.064) 0.217 (0.735) 0.051 (0.940) 0.082 (0.895) 0.126 (0.855) 0.706 (0.247)  0.006 (0.710) 123 63 0.104 59.330nnn

Model B0

Model B1

Model B2

Model B3

 0.060nnn 0.000

 0.001 (0.528)  0.060nnn 0.000

 0.001 (0.596)  0.060nnn 0.000

0.138 (0.590) 0.143 (0.676) 0.219 (0.671) 1.141 (0.713) 0.421 (0.274) 0.127 (0.821) 0.533nn (0.041) 0.002 (0.998)  0.124 (0.864)  0.146 (0.827)  0.067 (0.927) 0.580 (0.368)

0.139 (0.590) 0.143 (0.676) 0.219 (0.671) 1.141 (0.713) 0.421 (0.274) 0.127 (0.821) 0.533nn (0.041) 0.002 (0.998)  0.124 (0.864)  0.146 (0.827)  0.067 (0.927) 0.580 (0.368)

123

123

0.138 (0.590) 0.143 (0.676) 0.219 (0.671) 1.141 (0.713) 0.421 (0.274) 0.127 (0.821) 0.533nn (0.041) 0.002 (0.998)  0.124 (0.864)  0.146 (0.827)  0.067 (0.927) 0.580 (0.368) 0.004 (0.833) 123

 0.0005 (0.956)  0.059nnn 0.000  0.007 (0.463)  0.023nn (0.042) 0.051n (0.063)  0.003 (0.861) 0.138 (0.590) 0.143 (0.676) 0.219 (0.671) 1.140 (0.713) 0.421 (0.274) 0.127 (0.822) 0.533nn (0.041) 0.002 (0.998)  0.124 (0.864)  0.146 (0.827)  0.067 (0.927) 0.580 (0.368) 0.004 (0.819) 123

signify statistical significance at the 1 %, 5 %, and 10 % levels, respectively.

Lastly, regardless of the regressor set, the estimates demonstrate that the lagged dependent variable does not influence the current discharge ratio. This result indicates that either (1) facilities do not adjust their current discharges in response to lagged discharges and inertia does not influence abatement costs, or (2) facilities make adjustments and inertia proves important but these two countervailing effects effectively neutralize each other. Certainly, the former conclusion is the safer one to draw.

BOD discharge ratio We next interpret the results generated by the estimation of BOD discharge ratios. As shown in Table 4, the effect of audits on discharge ratios proves negative but statistically insignificant regardless of the estimator and regressor set. The insignificant coefficient indicates that an increase in the count of audits does not influence the discharge ratio, indicating that audits prove ineffective for controlling biological oxygen demand discharges. In contrast, the BOD estimates reveal that the lagged dependent variable negatively and significantly influences the current BOD discharge ratio. This result is robust across the relevant models. This set of results indicates that facilities’ adjustment of their current discharges in response to lagged discharges dominates whatever inertia effect

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may be present. Regardless, the results indicate that those facilities who complied better in the past (i.e., lower lagged discharge ratio) are expected to comply worse in the present (i.e., higher current discharge ratio). The greater importance of the response effect may be surprising. Some previous studies of pollution provide evidence of an inertia effect (i.e., path dependence) and no evidence of an adjustment effect (Harrington, 2013; Harrington et al., 2014; Earnhart, 2004a). However, inertia is arguably more prevalent when exploring the absolute levels of pollution. Exploration of the discharge ratio, which compares the absolute levels to a legal limit, allows more room for facilities to adjust their operations. For example, a larger facility generating much pollution most likely struggles to lower its absolute pollution to levels comparable to smaller facilities, yet this same larger facility may be well able to lower its absolute pollution to levels comparable to its legal limit. BOD estimates also reveal that formal enforcement negatively influences the discharge ratio, similar to the TSS results. This conclusion is robust to the choice of estimator. Yet, in contrast to the TSS results, informal enforcement does not significantly affect the BOD discharge ratio. Nevertheless, in keeping with the effectiveness of formal enforcement, the degree of regulatory flexibility positively influences the discharge ratio, similar to the TSS results. Again, this negative coefficient implies that “less fair treatment” leads to better environmental performance. This conclusion is robust across the estimators and models. Collectively, these BOD results appear to reveal that BOD discharge ratios are influenced by only two factors other than the lagged dependent variable. Given the strength of the relationship between lagged and current discharge ratios, we wonder whether this connection eats up the explanatory power available in the overall relationship connecting the current discharge ratio to a set of factors; however, we can provide no evidence to support this point.

Comparison of TSS and BOD Results We next compare and contrast the TSS and BOD results. Most important, audits influence the TSS discharge ratio but not the BOD discharge ratio. This contrast affirms our choice to consider separately multiple pollutants when assessing the effect of audits on the extent of compliance with wastewater discharge limits. Consideration of a single pollutant would fail to reveal this contrast, while consideration of a multi-pollutant composite would mask this contrast. These results indicate that audits do not effectively control all forms of water pollution. Thus, any enthusiasm about audits must be qualified. In hindsight, we should not be surprised to learn that audits influence the two pollutants differently. The two pollutants reflect different by-products of the production processes. TSS measures the amount of suspended particles (i.e., solids), while BOD measures organic content, i.e., biodegradable waste. As long as audits provide insight to regulated facilities on how better to operate their production processes in order minimize the generation of byproducts, differences in the type of by-product might lead to differences between the effect of audits on pollutantspecific discharges. As important, different pollutants involve different treatment and management options. Regarding treatment technologies, facilities can generally employ a physical treatment process to remove TSS, with the exact physical process depending on the particle size of the substances present (American Water Works Association, 2005). In contrast, to remove the soluble portion of BOD, facilities must employ a process that uses biological treatment (or adsorption) or chemical treatment that tries to precipitate out the soluble BOD (American Water Works Association, 2005).27 As long as audits provide insight to regulated facilities on how better to operate pollutant-specific treatment technologies, differences in the technologies alone might lead to differences between the effect of audits on pollutantspecific discharges.28 Moreover, a lagged discharge ratio influences the current BOD discharge ratio but not the current TSS discharge ratio. Again, this contrast affirms our choice to consider separately multiple pollutants. These results indicate that the absolute and relative importance of the adjustment effect and inertia effect differ across pollutants. Again, different pollutants represent different by-products and involve different treatment options. Thus, we should not be surprised to learn that the two pollutants do not share the same relationship connecting past and current discharge ratios. Collectively, these two contrasts remove the possibility of a counterproductive reaction between lagged audits and current discharge ratios for a given pollutant. Neither pollutant involves both a negative relationship between audits and the current discharge ratio and a negative relationship between lagged and current discharge ratios. If both conditions were 27 Specific to our analysis, the surveyed facilities report that they employed the following types of TSS treatment technologies: coagulation plus sedimentation, sedimentation in effluent pond, sedimentation in final clarifier, granular media filtration, and membrane filtration. In contrast, the surveyed facilities report that they employed the following types of BOD treatment technologies: solids removal, stabilization pond treatment, trickling filter biological treatment, activated sludge biological treatment, chemical oxidation, and carbon adsorption. These two lists share no technologies in common. 28 As noted in Section 2, Barla (2007) reveals that ISO 14001 certification decreases BOD discharges but not TSS discharges from Quebec pulp and paper plants. In contrast, we reveal that audits decrease the TSS discharge ratio but not the BOD discharge ratio. We offer two reasons for this difference. First, Barla (2007) examines ISO 14001 certification, which includes audits and other environmental management practices. If audits and non-audit ISO 14001 practices influence wastewater discharges differently, then our results might be consistent with the results of Barla (2007). Second, Barla (2007) examines the pulp and paper sector, while our study examines the chemical manufacturing sector. Even if audits and non-audit ISO 14001 practices influence wastewater discharges similarly, the effect of audits might differ between the two sectors.

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true, an increase in the count of audits would improve compliance in the current year but undermine compliance in the following year, i.e., the use of audits may prompt facilities to modify their future pollution control efforts in response to the current effectiveness of audits at lowering discharges. Alternative measure of audits: presence vs absence In Section 6, we argue that the count of audits represents a better measure of audits than the mere presence or absence of an audit program. As shown above, the count of audits significantly (negatively) affects the TSS discharge ratio but not the BOD discharge ratio. To assess our claim further, we also construct an audit regressor that reflects the presence of at least one audit in a given calendar versus the absence of any audit.29 Use of this alternative audit-related regressor, in lieu of the audit count regressor, generates a statistically insignificant coefficient for the audit presence regressor regardless of the pollutant-specific sample, estimator, and model specification.30 Thus, use of this alternative regressor is not able to establish a meaningful link from audits to environmental performance even though use of the audit count regressor effectively establishes such a link at least in the case of TSS discharge ratios. (In the case of BOD discharge ratios, use of the audit presence regressor generates estimates that reinforce the original conclusion of no meaningful link from audits to environmental performance.) Consequently, we maintain our focus on audit count as our primary regressor.

Economic assessment Lastly, we assess the economic importance of our primary results considering each pollutant separately. For TSS discharge ratios, we assess the economic importance of audits. For this assessment, we first identify the TSS-specific mean discharge ratio (M); the value is shown in Table 1. Then we compare the audit-related coefficient (F) to the mean discharge ratio: F versus M. A one unit increase in the audit count prompts the TSS discharge ratio to fall by 0.003; relative to a mean TSS discharge ratio of 0.232, this drop represents a 1.29% reduction. For BOD discharge ratios, we assess the economic importance of the lagged discharge ratio. For this assessment, we first identify the BOD-specific mean discharge ratio (M) and its one standard deviation magnitude (V); these values are shown in Table 1. Next, we multiply the lagged discharge ratio coefficient (F) by the one standard deviation: P¼ F  V. Lastly, we compare this product to the mean discharge ratio: P versus M. A one standard deviation increase in the lagged BOD discharge ratio of 0.37 prompts the current BOD discharge ratio to fall by 0.022; relative to a mean BOD discharge ratio of 0.230, this drop reflects a 10% reduction. These calculations demonstrate that the influences of audits and the lagged discharge ratio are both economically important.

Conclusions, policy implications, and future research In this last section, we draw overall conclusions, discuss policy implications of our research results, and provide guidance on future research. Based on our empirical results, we draw the following conclusions. First, audits improve compliance with effluent limits for one but not both pollutants. Second, facilities adjust their discharges dynamically in the case of one pollutant but not both pollutants. Third, audits may not effectively improve compliance when facilities respond to lagged discharges. These three primary results possess policy implications. First, water quality protection policies should induce regulated facilities to audit in order to improve compliance. However, environmental agencies should not expect progress with all regulated pollutants. Instead, agencies may wish to condition their inducement on whether audits are effective at decreasing discharges of the pollutants that are proving problematic for a specific facility, i.e., facility’s discharges for the particular pollutant are near or above the discharge limit level. Second, regulatory agencies should recognize the possibility of an oscillating pattern of discharges for certain wastewater pollutants driven by facilities’ dynamic management of pollution control. Third, agencies may wish to avoid inducing the use of audits when the problematic pollutants are managed dynamically. Finally, we encourage future research to explore additional sectors beyond the chemical manufacturing sector, to explore a broader set of wastewater pollutants beyond TSS and BOD, and to explore pollution in other media, such as air pollution and toxic and hazardous waste generation. Disclaimer This manuscript was partially developed under a STAR Research Assistance Agreement No. R-82882801-0 awarded by the U.S. Environmental Protection Agency. It has not been formally reviewed by the EPA. The views expressed in this 29 As with the audit count regressor, we assess the exogeneity of this alternative audit-related regressor. Again, we demonstrate that our constructed instruments appear relevant and not invalid and fail to reject the null hypothesis of exogeneity for the alternative audit regressor. 30 The tabulated results are available upon request from the authors.

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document are solely those of the authors. The EPA does not endorse any products or commercial services mentioned in this manuscript.

Acknowledgments We thank Dylan Rassier and Trisha Shrum for their excellent research assistance.

Appendix A See Table A1.

Table A1 First stage regressions. Variables

TSS

Peer audit Lagged peer audit Informal enforcement Formal non-penalty enforcement Federal inspections State inspections Organic chemical sub-sector Inorganic chemical sub-sector Treatment technology Firm environmental employees Firm ownership structure Local community pressure Regulator’s fairness EPA Region 2 EPA Region 3 EPA Region 4 EPA Region 5 EPA Region 6 EPA Region 7 Year 2002 Constant

nnn nn

,

, and

n

BOD nnn

 1.19 (0.000)  4.37nnn (0.000) 3.01 (0.224)  1.77 (0.621)  1.60 (0.625) 0.08 (0.944) 1.61 (0.471)  0.96 (0.701)  5.11 (0.171) 4.17 (0.602)  0.19 (0.950) 2.72 (0.376)  2.07 (0.180) 6.32 (0.258) 48.74nnn (0.000) 10.65nn (0.043) 67.26nnn (0.000) 22.74nnn (0.000) 2.32 (0.740)  5.05nnn (0.005) 5.08 (0.438)

signify statistical significance at the 1 %, 5 %, and 10 % levels, respectively.

 1.07nnn (0.004)  3.95nnn (0.000)  0.59 (0.868)  0.92 (0.823)  1.53 (0.769)  0.37 (0.776) 2.30 (0.313)  1.34 (0.663)  6.91 (0.116)  33.83 (0.218)  7.90 (0.144)  0.20 (0.971)  4.33n (0.060) 12.48 (0.102) 53.08nnn (0.000) 17.97 (0.012) 70.48nnn (0.000) 27.26nnn (0.000)

 6.32nnn (0.004) 12.75 (0.186)

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Appendix B. Supplementary material Supplementary data associated with this article can be found in the online version at: http://dx.doi.org/10.1016/j.jeem. 2014.06.004.

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