THE EFFECT OF ELECTRONIC REPORTING OF DISCHARGE MONITORING REPORTS 

ON CLEAN WATER ACT COMPLIANCE IN OHIO 

 

A Thesis 

submitted to the Faculty of the 

Graduate School of Arts and Sciences 

of Georgetown University 

in partial fulfillment of the requirements for the 

degree of 

Master of Public Policy 

in Public Policy 

By 

Gregory B. Siedschlag, B.A. 

Washington, DC 

April 11, 2011
ii 

YOUR NAME (EXACTLY AS SHOWN ABOVE)

THE EFFECT OF ELECTRONIC REPORTING OF DISCHARGE MONITORING REPORTS

ON CLEAN WATER ACT COMPLIANCE IN OHIO 

Gregory B. Siedschlag, B.A. 

Thesis Advisor: Donna R. Morrison, Ph.D. 

ABSTRACT

 Point source dischargers regulated under the Clean Water Act’s National Pollutant Discharge 

Elimination System are required to submit a discharge monitoring report (DMR) on a periodic 

basis, usually every month. Many of these reports are still submitted on paper forms. However, 

over the last decade many states have begun implementing electronic systems for submitting 

DMRs, and the U.S. Environmental Protection Agency is currently writing a rule that would 

require more electronic reporting of DMRs. While efficiency gains resulting from electronic 

reporting have been documented, whether or not these systems have any effect on compliance 

rates is an open question. To provide a better sense of the relationship between electronic 

reporting of DMRs and Clean Water Act compliance, this study analyzes data from Ohio for 

fiscal years (FY) 1996 through 2009. Ohio instituted electronic reporting software in FY 2000 

and switched to a web-based system in FY 2008. Logit models were employed that include time 

trends and fixed effects for permittees. Results indicate a significant negative effect on 

compliance from the first system and a significant positive effect from the second. This paper’s 

findings suggest that electronic reporting may not only help regulatory agencies more accurately 

identify compliance violations, but it may also provide more complete and accurate data to the 

U.S. EPA and the public. 
iii 

The research and writing of this thesis 

is dedicated to everyone who helped along the way. 

I would first like to thank everyone affiliated with GPPI and the U.S. Environmental Protection 

Agency who provided assistance, including Donna Morrison, Eric Gardner, Mike Barker, Harriet 

Komisar, Ann Wolverton, Chris Knopes, Thomas King, Sue Gilbertson, John Dombrowski, 

Daniel Palmer, Jon Silberman, Mike Barrette, James Blodgett, Jeffrey Clark, Steven Rubin, John 

Veresh, Andrew Schulman, and Ron Shadbegian. 

Thanks also to the Ohio Environmental Protection Agency for providing essential data on when 

permittees began reporting electronically. 

Last but not least, I’d like to thank my wife, Claire, for her constant support and companionship. 

I am extremely grateful to each of you. 

Many thanks, 

Gregory B. Siedschlag 
iv 

TABLE OF CONTENTS

Chapter I: Introduction 1

Chapter II: Background on the Clean Water Act and E-Reporting 3

Chapter III: Conceptual Model 15

Chapter IV: Data, Expected Relationships, and Methods 24

Chapter V: Results 39

Chapter VI: Policy Implications 51

Chapter VII: Conclusion 53

Appendix I: Compliance by Quarter 54

Appendix II: Descriptive Statistics for Major and Minor Permittees 56

Appendix III: Descriptive Statistics for Industrial and Municipal Permittees 57

Appendix IV: Results for OLS Fixed-Effects Model Predicting Ordinal Compliance, 

FY 1997-2009 58

Appendix V: Results for Logit Fixed-Effects Models Predicting Binary Compliance, 

FY 1997-2009 59

References 60
1 

Chapter I: Introduction

 

The United States Congress passed the Federal Water Pollution Control Act in 1972 to 

address point source surface water pollution.1

 This statute, better known today as the Clean 

Water Act (CWA), requires a wide range of point sources to hold permits that allow them to 

discharge pollutants into surface waters up to prescribed thresholds. These permits are issued 

under the CWA’s permit program, the National Pollutant Discharge Elimination System 

(NPDES), which is typically administered by state agencies. 

 NPDES permittees must submit discharge monitoring reports (DMRs) to their regulating 

agency (Case Study: Ohio EPA 2010, 1). DMRs are typically submitted once a month. They 

contain the results of the permittee’s self-monitoring of pollutant discharge and are compared to 

its permit limits to determine if it is in compliance (U.S. EPA, Watershed Academy Web, 

NPDES Permits: Elements). Many permittees still submit their DMRs to their regulating agency 

by paper, but 24 states have begun allowing permittees to report them electronically (U.S. EPA, 

NetDMR: Who Can Report?). The U.S. Environmental Protection Agency (U.S. EPA) is also 

proposing a rule that would require electronic reporting (e-reporting) of DMRs nationwide. 

While e-reporting could be costly to implement in the short term, it can lead to long-term savings 

for both government and permittees alike (U.S. EPA, CWA Action Plan 2009, 11). Many 

regulators also believe that DMR e-reporting may lead to improved compliance rates and less 

pollution. But is this actually the case? 

 

1

 Point sources are “pipes and other discernible conveyances through which pollutants are added to waters of the 

United States” (Andreen 2004, 547). 
2 

 This paper will address this question by studying DMR reporting in the state of Ohio, 

which began converting to e-reporting in 2000. I selected Ohio as a case study because of its 

large, diverse permittee universe; it was an early e-reporting adopter, so we can observe an effect 

over many years; and more than 99 percent of its permittees are now e-reporting. The dataset 

used in my analysis includes 2,575 of the 3,250 Ohio NPDES permittees active as of October 

2010. These permittees were tracked from 1996 to 2009. My model holds permittee 

characteristics such as size and industry fixed. The dependent variable is compliance status in a 

given quarter. The independent variables of interest are whether or not the permittee has begun ereporting under one of the two systems used in Ohio during this period. The other independent 

variables are inspections, informal enforcement, formal enforcement, and the inflation-adjusted 

dollar amount of the total penalties assessed by Ohio in each quarter. 

 This study could inform environmental e-reporting policy discussions currently taking 

place at the state and federal levels. It does not appear that the effect of DMR e-reporting on 

compliance has been tested empirically, nor could any similar studies be found under other 

environmental statutes. Knowing how e-reporting affected compliance for the large permittee 

universe in Ohio could help determine the extent to which it can be relied upon to improve 

compliance nationally. This information could also inform U.S. EPA in its e-reporting 

rulemaking process. If e-reporting is found to be associated with increased compliance, it may be 

able to make a stronger case in the rule’s favor. 

The perspectives, information and conclusions conveyed in this thesis convey the 

viewpoints of the principal investigator and may not represent the views and policies of OECA 

and EPA. Conclusions drawn by the principal investigator have not been influenced by EPA.
3 

Chapter II: Background on the Clean Water Act and E-Reporting

 

To gain a better sense of e-reporting’s potential for improving regulation under the CWA, 

it will be helpful to first discuss the history of water protection legislation in the United States, 

and then discuss how e-reporting has contributed to those efforts over the last 10 years. 

A. History of Water Protection Legislation in the United States 

 As noted in Andreen (2003), the Rivers and Harbors Appropriation Act of 1899 or the 

Refuse Act, is “often referred to as the first piece of federal water pollution control legislation.” 

In fact, this law actually “consolidates” four very similar pieces of legislation signed into law 

between 1886 and 1894, some of which only applied to specific regions (220). As with its 

predecessors, the goal of this statute was to “prevent impediments to navigation” (Freeman 2000, 

170). According to Freeman, “The Refuse Act is a useful reminder that at one time, the sheer 

physical volume of waste discharged into waterways — for example, sludge, fiber, and sawdust 

from paper mills and sawmills — threatened to block some rivers and channels” (2000, 170). 

In response, this legislation protected stream flow in navigable waters by making it 

unlawful to dump refuse. However, like later legislation, the Refuse Act allowed for exceptions. 

For instance, liquid runoff and sewer discharge were allowed (Murchison 2005, 530), but all 

dischargers were required to hold a permit issued by the federal government (Freeman 2000, 

172). Violators faced monetary penalties and even short prison sentences (Murchison 2005, 529). 

According to Andreen, this statute actually gave the federal government the ability to regulate 

many different types of water pollution, but the Army Corps of Engineers’ only used the law for 

interferences with navigation (2003, 220-222). 
4 

 It took almost 50 years for the next major federal water pollution legislation to be signed 

into law. The Federal Water Pollution Control Act of 1948 (FWPCA) was the product of a 

decade-long debate over how to address this growing problem, but it did not represent a major 

step forward (Andreen 2003, 235-237). However, it did make a lasting contribution in its 

provision of federal monetary assistance to states for wastewater treatment and water pollution 

prevention (Murchison 2005, 530). According to Andreen (2003), 40 percent of sewage 

discharge at the time was completely untreated, and much of the remainder was only minimally 

treated. But even though the legislation set aside $22.5 million annually in loans for new 

facilities, in 1950 only $3 million was spent. By 1955 this shrank to less than $1 million (237-

238). 

It appears that the statute had little effect on decreasing surface water pollution. It 

delegated responsibility to the states for enacting and enforcing water pollution laws without any 

requirements for them to follow through (Murchison 2005, 530-531). The federal government 

did have enforcement capabilities, but it could only use them if one state’s discharge harmed 

citizens of another state (Andreen 2003, 238). Even in these cases, the federal government had to 

cut through numerous layers of red tape before it could enforce (Andreen 2003, 238-239). 

Several changes to federal water pollution law were made between 1948 and passage of 

the Clean Water Act in 1972. The 1956 amendments to FWPCA changed the loan program for 

sewage treatment authorized by the 1948 bill to a grant program that allowed up to $50 million 

in annual spending (Andreen 2003, 240). The 1961 amendments increased this cap to $100 

million (Andreen 2003, 242). While these amendments did give the federal government slightly 

more enforcement power, major improvements to water protection law were not made until 
5 

passage of the Water Quality Act of 1965. This statute was the first to make it mandatory for 

states to enact water quality standards (Freeman 2000, 171). However, these standards applied 

only to interstate waters (Murchison 2005, 532), and while enforcement power was strengthened, 

there was still too much red tape for it to be effective (Andreen 2003, 249-250). 

 In spite of these efforts the water pollution situation in the U.S. was still grim. According 

to Andreen (2004), “By the end of the 1960s, 80% of the pollution loading to U.S. waters was 

industrial in origin. And of the 22 billion gallons of pollutants that U.S. industry discharged daily 

in 1970, only 29% received any treatment at all, regardless of whether the level of treatment was 

adequate or not” (554). In spite of the mandate in the Water Quality Act of 1965, by 1972 

roughly half the states still lacked water quality standards (Murchison 2005, 534). 

 The ineffectiveness of past legislation combined with a groundswell of public support for 

environmental protection led to passage of a transformative set of FWPCA amendments in 1972. 

These amendments form the core of what is now referred to as the Clean Water Act (CWA).2

The CWA implemented numerous reforms to reduce point-source pollution. First, instead of 

applying only to interstate waters, the CWA applies to all navigable waters (Murchison 2005, 

538). Second, it granted regulatory authority to the recently launched U.S. EPA (Murchison 

2005, 538). Third, instead of leaving states to enact their own standards, the CWA required U.S. 

EPA to set baseline technology-based standards that all regulated entities were required to meet 

(Freeman 2000, 174), while requiring states to adopt water quality standards to ensure that 

 

2

 Murchison (2005) describes the CWA as having three “major” amendments — in 1977, 1981, and 1987 (557). 

“The 1977 Amendments officially acknowledged the ‘Clean Water Act’ label by which the Federal Water Pollution 

Control Act had generally come to be known in the 1970s, adopting it as a second, or alternative, statutory name” 

(Murchison 2005, 557). 
6 

receiving waters support their designated uses (Andreen 2004, 548).3

 Fourth, the CWA set a goal 

of ending all pollutant discharge by 1985 (Freeman 2000, 174). Fifth, and most relevant to this 

study, it instituted the National Pollutant Discharge Elimination System (NPDES) as its means of 

regulating point source pollutant discharges (Andreen 2004, 549). 

B. Point Source Regulation under the National Pollution Discharge Elimination System 

 The CWA effectively bans point source pollutant discharges into surface waters unless 

the discharger possesses an NPDES permit (Andreen 2004, 549). According to the U.S. EPA, 

“NPDES permits cover industrial and municipal discharges, discharges from storm sewer 

systems in larger cities, storm water associated with numerous kinds of industrial activity, runoff 

from construction sites disturbing more than one acre, mining operations, and animal feedlots 

and aquaculture permittees above certain thresholds.” However, some point sources are exempt 

from possessing NPDES permits. U.S. EPA’s examples of such sources include some but not all 

types of abandoned mines, logging road ditches, and agriculture discharge. Also, facilities that 

discharge “indirectly” into a sewer that then flows to a publicly owned treatment works (POTW) 

are regulated under the CWA’s pretreatment program and are exempt from NPDES (Watershed 

Academy Web, Regulation of Point Sources). However, POTWs themselves are required to hold 

NPDES permits. 

 NPDES permits specify the conditions under which a permittee is considered to be in 

compliance with the CWA. U.S. EPA outlines the contents of an NPDES permit, which typically 

includes (but is not limited to) the following items: 

 

3

 According to Freeman (2000), technology-based standards are set according to “what kind of pollution abatement 

equipment [is] available” (174). Examples of designated uses include fishing, recreation, and drinking water supply 

(U.S. EPA, Watershed Academy Web, WQS: Designated Use Categories). 
7 

1. The permittee’s discharge limits. This is the amount of each regulated pollutant 

that is allowed in the permittee’s discharge. The amount is usually defined as either a 

“concentration,” “volume,” or an amount that can be discharged over a certain time 

period. 

2. “Monitoring requirements” that provide instructions for permittees to follow 

when performing their monitoring. 

3. How often the permittee must submit its discharge monitoring reports (DMRs) 

(Watershed Academy Web, NPDES Permits: Elements). 

 How exactly are a permittee’s limits determined? First, these limits must take into 

account the water quality standards for the receiving water. Water quality standards have several 

components (U.S. EPA, Water Quality Standards). The first two components are designated uses 

and water quality criteria.4

 Water quality criteria is meant to ensure that the water body meets its 

designated uses; they can include numeric, narrative, biological, sediment, and wildlife criteria 

(U.S. EPA, Water Quality Handbook, Chapter 3.5). Antidegradation, the third component, is 

designed to preserve current water quality and designated uses (U.S. EPA, Water Quality 

Handbook, Chapter 4). General policies, the fourth component, include any additional 

clarification or guidance needed for a particular water body (U.S. EPA, General Policies). 

 

4

 Under the 1972 CWA all waters were supposed to have met a “fishable and swimmable” standard by 1983 

(Freeman 1983, 174). This has become the de facto baseline for designated use, but there is some flexibility. If a 

state determines that such a standard is not feasible, with “appropriate documentation” it may be able to proceed 

with a lower standard (U.S. EPA, NPDES Permit Writers’ Manual, 119). 
8 

 With water quality standards in place, NPDES permit writers determine technologybased effluent limits for industrial and municipal dischargers (U.S. EPA, NPDES Permit Writers 

Manual 2010, 68). According to the NPDES Permit Writers Manual, 

[t]echnology-based effluent limitations (TBELs) aim to prevent pollution by requiring a minimum 

level of effluent quality that is attainable using demonstrated technologies for reducing discharges 

of pollutants or pollution into the waters of the United States. TBELs are developed independently 

of the potential impact of a discharge on the receiving water, which is addressed through water 

quality standards and water quality-based effluent limitations (WQBELs). (2010, 68) 

Water quality-based effluent limits (WQBELs) must be determined if TBELs do not adequately 

protect water quality standards (U.S. EPA, NPDES Permit Writers Manual 2010, 117), to be 

followed by final effluent limits (U.S. EPA, NPDES Permit Writers Manual 2010, 159). 

When the CWA was passed, states were given the option of running their own NPDES 

program (Murchison 2005, 550). In the ensuing years 45 states have been granted this authority, 

with the remaining five states leaving NPDES to U.S. EPA (U.S. EPA, State Review 

Framework). The responsibilities of these agencies include setting water quality standards, 

issuing permits that meet those standards, collecting data, conducting inspections, and engaging 

in enforcement. U.S. EPA performs oversight in these 45 states and may also supplement state 

efforts by issuing permits, performing inspections, and taking enforcement action (U.S. EPA, 

Watershed Academy Web, NPDES Permits). 

C. Effectiveness of the Clean Water Act 

 The 1972 CWA had a very lofty — most would say unrealistic — goal of eliminating all 

pollution discharge that it has been unable to live up to. In spite of this, by regulating point 

sources, surface water quality is almost certainly better than it would have been without the 

CWA. Unlike before the CWA, point sources are now less of a problem than non-point sources. 
9 

According to the most recent available study (1998), only 10 percent of the sources of water 

quality impairment were exclusively point sources, 43 percent were exclusively non-point, and 

the remaining 47 percent were both point and non-point (U.S. EPA, Watershed Academy Web, 

Sources of Impairment). However, significant challenges remain. U.S. EPA data shows that of 

the river and stream miles assessed between 2002 and 2010, 49.7 percent supported all their 

designated uses, 49.6 percent did not support at least one designated use, and 0.7 percent 

supported all designated uses but displayed evidence of decline in water quality. For lake, 

reservoir, and pond acreage assessed during this time period, these results showed that only 34 

percent supported all designated uses and 66 percent did not support one or more designated uses 

(U.S. EPA, Watershed Assessment). 

 Andreen (2004) identifies perhaps the major challenge to improving water quality when 

he says that “non-point source pollution, especially from agriculture, is the chief impediment to 

achieving national water quality objectives” (564). According to the U.S. EPA, agriculture is the 

most common contributor to river and stream impairment and the third most common for lakes, 

reservoirs and ponds (Watershed Assessment). While concentrated animal feeding operations are 

regulated under NPDES as point sources (Idaho DEQ), this evidence indicates that current 

regulations are inadequate for addressing agricultural sources. 

 Another impediment to realization of the CWA’s goals has been its federalist approach to 

water protection. Much of the difficulty of this approach stems from inadequate funding for 

water protection at the state level. To provide some historical context, Rechtschaffen (2004) cites 

a study from the late 1990s that discovered the average state had less than 50 percent of the 
10 

funding they needed to run their water programs. This came after a period of growth in 

environmental budgets (788-789). 

Rechtschaffen reports that many of these cash-strapped agencies were not able to keep 

permittees in compliance. Between 1999 and 2001, 81 percent of major permittees had one or 

more violations (782). In 2003, one-quarter of majors were in significant noncompliance, the 

most serious type of compliance violation, “at any given time,” a rate that has not improved since 

1994 (782-783). Funding shortfalls result in understaffing that in turn leads to reduced capacity 

to perform inspections or take enforcement action. Between 1999 and 2001, states and U.S. EPA 

made 8 percent fewer inspections, 11 percent fewer formal enforcement actions, and 50 percent 

fewer informal enforcement actions. As a result, just 24 percent of permittees in significant 

noncompliance in these years had formal enforcement taken against them (783). 

When moving from the aggregate to the individual state level Rechtschaffen uncovers 

specific problems. Between 1990 and 1998, Wisconsin responded with notices of violation (an 

informal enforcement action) for just 10 percent of its significant noncompliance violations 

(784). Minnesota only inspected 32 percent of its NPDES universe in 1995, then just 12 percent 

in 2001 (784-785). And in Louisiana between 1999 and 2001, 26 percent of DMRs were lost or 

never sent to the state agency and 58 percent of its penalties weren’t collected (785). 

Ten years later, the budget situation is even worse. The recent recession left every state in 

the nation with a budget deficit, many of them severe. The Center on Budget and Policy 

Priorities projects modest improvements in FY 2012 compared to FY 2011, but at least 27 states 

still face deficits of 10 percent or more. California, Nevada, New Jersey, Oregon, and Texas 

could all encounter deficits of 25 percent or more in FY 2012 (McNichol et al. 2011). 
11 

Many states are cutting already depleted environmental agency budgets to close part of 

the gap. Pennsylvania’s Department of Environmental Protection (DEP) lost 27 percent of its 

general fund in FY 2010, 5 percent of its staff, and 120 unfilled positions. Out of 319 workers 

the state let go as a result of the FY 2010 cuts, 138 worked in DEP (Mauriello and Hopey 2009). 

Arizona’s Department of Environmental Quality lost 64.5 percent of its general fund between 

2009 and 2011 and suffered additional decreases to staff, which is 18 percent smaller than in 

2002. Staffing cuts have been far more severe in the Water Resources Department, which had 

236 employees in 2009 but just 98 in 2011 as a result of layoffs (McKinnon 2010). The New 

York Department of Environmental Conservation (NYDEC) employs only 2.5 percent of the 

state government’s workers, but it bore 10 percent of the job cuts in 2009 and 2010 (Gordon 

2011). In this time period, NYDEC lost 595 staff and 40 percent of its funding (Citizens 

Campaign). 

A look at more recent national data indicates the challenges these cash-strapped agencies 

face in the coming years. According to U.S. EPA, in 2009, 55 percent of all major NPDES 

permittees had at least one compliance violation, and 23.3 percent had at least one significant 

noncompliance violation. For minor facilities that had data available, 52.5 percent had one or 

more compliance violations in 2009. Only 519 formal enforcement actions were taken against 

majors in 2009, which is little more than one-third of the number of majors with significant 

noncompliance violations (Annual Noncompliance Report). As budget cuts hit more and more 

states, these agencies will likely find it increasingly difficult to adequately staff their NPDES 

programs. Resource-deprived agencies will struggle to carry out as many inspections and 

enforcement actions as before the most recent round of budget cuts. 
12 

As noted earlier, states can rely on U.S. EPA for some assistance in this regard. U.S. EPA 

received a 35.5 percent increase in funding in FY 2010, its largest increase in nearly 25 years, 

bringing its total to $10.3 billion (U.S. EPA, EPA Budget and Spending). However, the agency’s 

budget will likely shrink in the near term as both Republicans and Democrats seek to lower the 

federal deficit (Office of Management and Budget). 

D. Electronic Reporting Under the Clean Water Act 

These budgetary shortfalls point to a need to innovate low-cost methods to improve 

compliance and reduce pollution. Electronic reporting of discharge monitoring reports (DMRs) 

has the potential to achieve these objectives. E-reporting, which is typically done over the web, 

saves work for both agencies and permittees. First of all, it automatically checks for data 

inaccuracies and alerts permittees to possible errors in their reports (Case Study: Ohio EPA 

2010, 3). Second, it makes compliance determinations by checking the pollutant loadings 

reported in the DMR against the permittee’s permit limits (Ohio EPA 2010). According to U.S. 

EPA, with paper reporting, the state or federal agency has to reenter the data from the report into 

their data system, a resource-intensive duplication of effort. With web-based e-reporting the data 

make a seamless transition from the permittee to the state agency. In fact, with NetDMR, U.S. 

EPA’s web-based e-reporting tool, the data can make another seamless jump to the federal level 

(CWA Action Plan 2009, 10-11). 

As of December 2010, 24 states had deployed NPDES e-reporting, and four additional 

states had plans to deploy (U.S. EPA, NetDMR). According to an e-mail to me from U.S. EPA 

management on October 27, 2010, 19 of these states adopted e-reporting between 2006 and 

2010. Of these 24 states, 15 use NetDMR (U.S. EPA, NetDMR). 
13 

Many regulators would like to see e-reporting become the sole means of DMR 

submission. U.S. EPA’s Office of Enforcement and Compliance Assurance (OECA) is currently 

drafting a proposed rule under the Clean Water Act that would require greater use of e-reporting. 

While the date for the proposed rule is unknown at this time, OECA’s Clean Water Act Action 

Plan (2009) offers this elaboration: 

To fully realize the transformation of reporting and data management into the 21st Century, 

OECA will develop a rule to require NPDES permittees to provide DMRs electronically to EPA or 

states, using either NetDMR or an equivalent state electronic DMR system, phasing out paper 

DMR forms. Pilot projects using electronic reporting tools show limited rates of success unless the 

tool is mandated. The full benefits of electronic DMR reporting can only be achieved when 

implementation is close to 100 percent. (11) 

Ohio was the first state to offer DMR e-reporting, with its first permittees making the 

switch in 2000 (U.S. EPA management, October 27, 2010, e-mail message to author). From 2000 

to 2007 the Ohio Environmental Protection Agency (Ohio EPA), the state agency with authority 

over the NPDES program (and independent of U.S. EPA), used software called SWIMware for 

e-reporting. While in many ways a step up from paper, many users encountered difficultly with 

the system (Ohio EPA 2010). Nonetheless, by 2006 approximately 70 percent of Ohio’s NPDES 

universe was using SWIMware (Case Study: Ohio EPA 2010, 1). 

According to the Ohio e-reporting case study, implementation of eDMR, a web-based 

system, began in the first quarter of FY 2008 with a pilot program of SWIMware users. Then 

Ohio EPA phased in eDMR in its five districts, introducing it in one district per month (2010, 2). 

By the end of the first quarter of FY 2008, 232 of the 2,575 permittees in the dataset used for this 

analysis had adopted eDMR. By the end of the fourth quarter of FY 2008, this number had 

increased to 2,017. 
14 

It appears that eDMR has had significant positive returns for both permittees and Ohio 

EPA. According to Ohio EPA personnel, the percentage of permittees reporting their data has 

increased, DMR data entry errors have decreased from an average of 50,000 per month before 

eDMR to 5,000 after (Case Study: Ohio EPA 2010, 3), and staffing needs are down to just onequarter of a full-time equivalency from a high of five just a few years ago (Ohio EPA 2010). 
15 

Chapter III: Conceptual Model

 

The conceptual model for inputs that affect compliance status for CWA-NPDES 

permittees is simplified in two important respects. First, it only considers the most significant 

inputs for affecting compliance. Second, though others may exist, it only maps the most 

important relationships between the included inputs. The narrative below discusses the 

relationships that are most relevant to this study. 

Figure 1: Conceptual Model 

A. Environmental Factors 

The earlier discussion regarding water quality standards helps us understand how 

environmental factors can influence compliance. As an example, let us consider a permittee 
16 

called XYZ Manufacturing. XYZ discharges into the Bluebird River, which has designated uses 

of fishing and swimming. In close proximity to XYZ are three other NPDES permittees. 

Environmental factors will influence XYZ’s ability to stay in compliance. Because XYZ is in an 

area that receives modest amounts of rainfall compared to the rest of the country, XYZ’s 

discharge will compose a larger percentage of the stream flow than it would if it were in a wetter 

area. As a result, it will have a greater impact on water quality, potentially making it more 

difficult for XYZ to stay in compliance. The Bluebird is also home to the purple trout, an 

endangered species whose only known habitat is in one section of this river (a section that is, 

incidentally, just a few miles downstream from XYZ). This means that the Bluebird is closely 

monitored by the state agency, local environmentalists, and the press. In fact, XYZ has been in 

the local news on more than one occasion because of significant noncompliance violations, 

which has placed added pressure on it to come back into compliance. Last but not least, the three 

other NPDES permittees close to XYZ will lower XYZ’s permit limits because of the permittees’ 

combined impact on attaining water quality standards in the Bluebird. 

B. Human Factors 

 The human factors element in the conceptual model refers to aspects of civic engagement 

that can significantly impact compliance. These include a citizenry that is politically engaged and 

protective of their natural environment, the amount of environmental reporting in local media, 

concern about the effect of the environment on public health, and the degree to which local 

tourism is dependent upon natural beauty. The most salient factor for our purposes is the public’s 

use of CWA data to affect change in their state enforcement agencies, the U.S. EPA, and noncompliant permittees. 
17 

 Karkkainen (2001) notes that when citizens act collectively to pressure polluters to 

comply with environmental standards, something that economists at the World Bank refer to as 

“informal regulation” occurs. Public relations campaigns, protests, and boycotts are a few of the 

strategies citizens employ during informal regulation campaigns. These efforts can be a vital 

supplement to formal regulation, particularly when formal regulation is lacking. However, there 

is usually an information asymmetry at work, as companies (and sometimes regulatory agencies) 

have more information about emissions than citizens (316). 

 This knowledge gap has closed significantly due to policy innovations over the last 20-

plus years. As noted in Rechtschaffen (2004), CWA data is publicly available through U.S. 

EPA’s ECHO, which was launched in 2002. ECHO is a web-based tool that provides extensive 

permittee-level data as well as the ability to observe trends at the state level (801-804). But the 

most famous example in the U.S. is the Toxics Release Inventory (TRI), which began in 1991 

(U.S. EPA, Toxics Release Inventory). It requires many industries to report their toxic emissions 

and the data are made available to the public (Karkkainen 2001, 286). 

 Increased data availability provides the public with better tools for informal regulation. 

Releases of toxins covered under TRI have decreased by 50 percent since inception (Bennear 

2008, 119). Today, TRI data are available on the Internet, making them more readily accessible 

than in the past. As for ECHO, Rechtschaffen offers anecdotal evidence of its impact: 

Numerous members of the public commented on the empowering function of the site. One wrote 

that “[t]his is a tremendous resource to help us track the actions of potential polluters in our 

watershed. It helps us identify persistent flaunters of environmental laws and, just as importantly, 

lets us know which industries are doing a good job at managing [sic] their pollution.” Another 

commented that “I really appreciate the opportunity to see how well the companies in my 

community are complying with our environmental law. Others noted how ECHO had directly 

helped inform their decisionmaking: “. . . My family & I have already terminated an agreement for 

a home purchase due to the excessive quantities of both lead & arsenic in the home’s drinking 
18 

water. To have access to the various water-treatment permittees’ statistical information, it will 

assist countless individuals to make the best choices for their loved-ones.” (2004, 802-803) 

As Karkainnen, Rechtschaffen, and Bennear have noted, the availability of this 

information provides significant incentives for permittees to pollute less and comply more. Many 

consumers make decisions based partly on the environmental impact of a product. As a result, 

businesses that receive negative publicity due to their negative environmental impacts face losses 

in market share. Let’s pretend that a local Sierra Club chapter uses ECHO and finds that ABC 

Shoe Co., an area manufacturer, exceeded their NPDES mercury limits in each of the last eight 

quarters. As a result they inform the environmental reporter at the local paper, who writes a 

story. This leads to bad publicity, which leads to protests. Soon, ABC notices that sales are 

declining. This is a scenario that regulated entities hope to avoid, and informed citizens now 

have the ability through tools like ECHO and TRI to discover bad actors and call others’ 

attention to them. 

E-reporting systems like NetDMR have the potential to improve the accuracy and 

completeness of data that are made available to the public. In Ohio, eDMR, a product very 

similar to NetDMR, has been shown to significantly improve data quality. And with NetDMR, 

data are much more easily shared between states and U.S. EPA than with paper reporting. With 

U.S. EPA’s ability to share data through ECHO, it stands to reason that e-reporting could result 

in improved potential for the public to target regulated entities for informal enforcement 

campaigns. 

C. Agency Characteristics 

 Agencies responsible for issuing and enforcing NPDES permits vary in their ability to 

keep permittees in compliance. Much of this variation stems from some states valuing 
19 

environmental protection more than others. However, the effect of prioritizing environmental 

protection on compliance can be mixed. On the one hand, these states might have more stringent 

water quality standards and effluent limits than the national baseline. While these more stringent 

limits are better for the environment, they are also more difficult for permittees to meet. If 

permittees aren’t able to meet these limits then non-compliance will increase as a result. On the 

other hand, these states may also take stronger action against violators. Generally we would 

expect states that place a higher value on environmental protection to have higher rates of 

compliance, but the ability to implement more stringent effluent limits complicates this 

relationship. 

 As noted earlier, funding is a major impediment to successful programs. Underfunded 

programs have greater difficulty issuing and reissuing permits on time. They also tend to have 

less ability to conduct on-site inspections. Variation among states in enforcement action is also 

partly a function of funding. Enforcement action is a labor-intensive process, and states that 

cannot fully staff their enforcement departments will not be able to issue as many actions. So we 

would expect compliance rates to increase as agency funding increases. 

 Data quality also varies by state. For instance, it is not mandatory for states to pass along 

data for minor permittees to U.S. EPA (U.S. EPA, CWA Action Plan 2009, 3). Therefore, while 

national data on major permittees are relatively complete, minor permittee data are virtually nonexistent for many states. Whether a state chooses to forward the data is often dependent on 

having staff available to complete this task. When state agencies do not share these data, it 

decreases transparency and accountability, limiting the public’s ability to monitor pollution in 

their locales. 
20 

D. Monitoring 

 Monitoring mechanisms include DMR submission and inspections. These mechanisms 

are used to determine whether a permittee is in compliance with its NPDES permit. According to 

one study that examined monitoring from a cost-benefit perspective, monitoring only occurs 

when “the probability of catching a rule breaker times the benefit of doing so is greater than the 

cost of monitoring” (Walker 2009, 1298). 

 This speaks to the need to lower the cost of monitoring, and e-reporting holds promise in 

this regard. As noted earlier, Ohio’s transition to eDMR resulted in significant efficiency gains 

for Ohio EPA. Furthermore, the U.S. EPA’s Clean Water Act Action Plan (2009) says that “EPA 

estimates that conversion from hard copy to electronically-submitted DMRs may save EPA, 

states, and the regulated sources more than $50 million per year when fully implemented” (11). 

In addition to lower costs, e-reporting under eDMR checks user entries against permit limits and 

notifies users of possible mistakes (Case Study: Ohio EPA 2010, 3). As was the case in Ohio 

under eDMR, nationwide e-reporting adoption should increase data accuracy, decrease the 

amount of time that permittee and agency employees spend on DMRs and data management, and 

result in more complete and accurate national data. 

 The benefits of a system like eDMR feed into other pieces of the conceptual model. First, 

more complete and accurate data improves the public’s ability to target noncompliant entities. 

Second, the data quality and efficiency gains free up resources for an agency to devote to 

keeping permittees in compliance. 

 Some of these resources could be spent on increased inspections, another monitoring 

mechanism. On-site inspections are a vital component of any NPDES program. We would expect 
21 

compliance to increase as inspections increase. Hanna and Oliva (2010) studied the effect of 

inspections on emissions under the Clean Air Act and found that they decreased emissions by 15 

percent on average (3). A similar response under the CWA in Ohio should result in improved 

compliance in quarters following an inspection for at least two reasons. First, inspections are 

supposed to provide permittees with helpful advice on correcting problems that may be causing 

non-compliance. Second, inspections also have a deterrent effect. Once a permittee is inspected it 

may believe it will receive closer scrutiny, thus giving it added incentive to comply. So 

permittees, wary of follow-up inspections or enforcement, should be more likely to take extra 

care to resolve problems. 

E. Enforcement 

Under NPDES, enforcement takes two forms — informal enforcement and formal 

enforcement. Informal enforcement under NPDES is performed through a notice of violation. 

These are letters that are mailed to permittees to inform them that they are in non-compliance. 

These letters also warn permittees that formal enforcement action is the next step if the 

compliance issues are not resolved. There are many different types of formal enforcement 

actions that can be taken under the CWA. Among the most serious of these is a monetary penalty 

against the violator. 

 With regard to enforcement actions (and inspections), Shimshack (2007) contends that 

we can expect two effects to result. He calls these “specific deterrence” and “general deterrence.” 

An example of specific deterrence would be a $250,000 penalty taken against XYZ 

Manufacturing that deters XYZ from future noncompliance. General deterrence refers to the 

effect of that same penalty on other permittees. When other dischargers find out about the 
22 

penalty through word-of-mouth or the media, in theory they will change their behavior to avoid 

any negative publicity and penalties of their own. According to Shimshack’s literature review, 

the general deterrent effect will generally be stronger for facilities in close proximity to or in the

same sector as XYZ. Shimshack also notes that the penalty on XYZ is unlikely to have any effect 

on dischargers in other states, and that enforcement actions other than penalties have been shown 

to have a much smaller deterrent effect than penalties (14-18). 

The strength of the effects of specific and general deterrence resulting from enforcement 

is, of course, open to question. In a study of homebuilders’ compliance with building codes, May 

found that homebuilders were more responsive to “affirmative” than to “negative” motivations to 

comply with regulations. Affirmative motivations include “homebuilder's general reputation, 

demands for homes without defects, confidence in codes, and sense of duty to comply.” Negative 

motivations include “fear of fines or sanctions,” “fear of legal liability,” and “avoidance of 

delays in construction brought about by the need to correct code deficiencies” (56-57). If these 

relationships hold true in the environmental realm, we would expect enforcement to be less 

significant than other factors in influencing outcomes. This indicates that the threat of negative 

publicity may be acting as more of a deterrent than the actual enforcement action. Shimshack 

(2007) echoes this when he notes that a “social stigma effect” may be a greater deterrent for 

larger entities than for smaller ones (15). 

F. Permittee Characteristics 

We also expect to observe fluctuations in compliance depending on characteristics unique 

to each permittee. For instance, major/minor classification may influence compliance because 

major permittees have been far more heavily monitored and enforced against in recent years. 
23 

Also, the nature of operations at a permittee may also play a role — it may be more difficult for a 

coal mine to stay in compliance than, say, a POTW. 

We may also see the permittee’s age influence compliance rates. A new permittee with 

state-of-the-art pollution prevention equipment may have an easier time staying in compliance 

than a 40-year-old permittee. On the other hand, water quality standards can put new permittees 

at a disadvantage. For instance, if a new permittee opens alongside a waterway already heavily 

populated with dischargers, their permit limits may be more difficult to meet than older 

permittees whose limits were set at a time when there were only a few dischargers nearby. 
24 

Chapter IV: Data, Expected Relationships, and Methods 

A. Data 

 The data used for this analysis came from two sources. Data on when permittees began 

using the two e-reporting systems employed in Ohio, SWIMware and eDMR, was provided by 

the Information Resources Management Section in the Division of Surface Water at Ohio EPA. 

All other data were downloaded from the Permit Compliance System (PCS) Data Set available 

on U.S. EPA’s ECHO website, or pulled directly from PCS by U.S. EPA staff. 

 The e-reporting data provided by Ohio EPA covers the complete universe of NPDES 

permittees active as of October 2010. For each of the 3,250 permittees, this dataset includes four 

variables — the permittee’s name, NPDES identification number, the date it submitted its first 

DMR using SWIMware, and the date it submitted its first DMR using eDMR. 

 Four of the PCS datasets downloaded from ECHO were used — permittee information, 

historic compliance, inspections, and enforcement actions and penalties. These are national 

datasets so Ohio data were extracted from these. (When we speak of the PCS datasets going 

forward it will be with regard to Ohio data.) A PCS programming relic required an additional 

dataset provided by U.S. EPA staff. This dataset includes two variables — NPDES identification 

number and the effective date of the discharger’s first NPDES permit. This is essential 

information because the default compliance code in PCS is blank, which indicates compliance. 

Having the effective date of the first permit enables us to differentiate between an entity that 

does not yet have a permit and one that has a permit and is compliant. 
25 

 To derive the dataset used for the analysis, the U.S. EPA datasets were merged with the 

Ohio EPA e-reporting dataset. With each successive merge permittees were removed. These 

permittees were included in Ohio EPA’s dataset, but were not included in one or more of the 

U.S. EPA datasets, thereby preventing their inclusion in the analysis. To ensure that all the 

permittees included in the analysis had at least two quarters of compliance history, I also 

removed any permittees that didn’t come online as NPDES facilities until the fourth quarter of 

FY 2009. As a result, the final dataset includes data for each quarter between FY 1996 and FY 

2009 for 2,575 permittees. The unit of analysis is permittee quarter. With 56 quarters for each 

permittee, there are 144,200 permittee quarters in the dataset. Through independent variables and 

fixed effects I attempted to control for many of the factors discussed in the conceptual model. 

B. Dependent Variable: Compliance 

 The compliance variable was taken from the PCS data described above. In its PCS Data 

Dictionary, U.S. EPA offers the following table to explain the different compliance statuses in 

the variable: 

 
26 

Table 1: U.S. EPA compliance codes

Code Category Ordinal 

Variable

U.S. EPA Official Short 

Description 

U.S. EPA Official Long Description 

S Noncompliant 0 NC-SNC Compliance 

Schedule Violation 

SNC/Category I: An enforcement action has 

been issued, and the facility is not meeting its 

compliance schedule 

E Noncompliant 1 NC-SNC Effluent Violation SNC/Category I: Effluent violations of monthly 

average limits (Technical Review Criteria and 

chronic) 

X Noncompliant 2 NC-SNC Effluent NonMonthly Average SNC/Category I: Effluent violations of nonmonthly average limits (Technical Review 

Criteria and chronic) 

T Noncompliant 3 NC-SNC Compliance 

Schedule Reporting 

SNC/Category I: Compliance Schedule 

Reporting Violation 

D Noncompliant 4 NC-SNC DMR Non-Receipt SNC/Category I: Reporting violation, nonreceipt of DMR 

W Noncompliant 4 [Author note: Same as D] [Author note: Ohio uses W in lieu of D for 

minor permittees] 

N Noncompliant 5 NC-RNC Violations Only RNC/Category II: Reportable non-compliance 

P Compliant 6 Resolved Pending Resolved Pending: An enforcement action has 

been issued, and facility compliance with the 

action is pending final completion 

R Compliant 7 Resolved Resolved: The facility has returned to 

compliance with its permit conditions, either 

with or without issuance of an enforcement 

action 

C Compliant 8 Compliant Not considered in RNC/SNC based on manual 

review of data by state or EPA region 

Blank Compliant 9 Compliant Not considered in RNC/SNC 

Sources: Emails from U.S. EPA personnel to author, PCS data elements included in IDEA Data Download, and 

Data Dictionary for Detailed Permittee Reports, U.S. EPA, http://www.epa-otis.gov/otis/dfr_data_dictionary.html

Table 1 shows us the “order of precedence from most to least important” for compliance 

statuses (U.S. EPA, Data Dictionary). This information allows us to construct two variables — 

an ordinal variable that ranks compliance in order from best to worst, and a binary variable 

coded 0 for noncompliance and 1 for compliance. 

 There are advantages and disadvantages to both approaches. The advantage of the ordinal 

variable — which is coded from 0 to 9, with 0 being “S” and 9 being “blank” — is a higher 

degree of precision. There are at least a couple of disadvantages to this approach however. First 

is its arbitrariness. Can we really say that there is an equal one-unit difference between each of 
27 

these variables? Second, the compliance codes exhibit substantial volatility from quarter to 

quarter (see Appendix I). According to U.S. EPA staff, this can result from changes at the state 

level, such as a lapse in data entry or a policy change in how certain violations are coded. With a 

binary compliance variable these policy changes are less of a worry, as it is less likely that such 

changes would result in a swing from non-compliance to compliance, or vice versa. Instead, 

these changes are more likely to occur within the categories of non-compliance or compliance 

themselves. So, while the binary measure is not as precise, it should be more accurate. As a 

result most of the models in this study use the binary variable as the dependent variable. 

The compliance variable has been recoded due to what appears to be a data quality issue. 

In the first quarter of FY 2005 there were 862 W-code observations compared to zero 

observations in every preceding quarter. After falling to 12 in second quarter and 3 in the third, 

143 and 257 observations were recorded in fourth quarter FY 2005 and first quarter FY 2006 

respectively. As a result, compliance during these quarters is the lowest during the period 

studied, especially for first quarter FY 2005 (see Figure 2). When observing trends, it appears 

that most or all of these permittees were actually compliant — recoding them as compliant 

brings these quarters back in line with their neighbors. But to be on the safe side, any W 

observations for these three quarters were coded missing (Figure 2 reflects this change). 

Fixed effect logit regressions were run with these observations coded as noncompliant, 

compliant, and missing. Results for the “compliant” and “missing” regressions were very similar, 

with negligible changes to the coefficients and p-values. There were more noticeable differences 

in the “noncompliant” regression, with the magnitude of the SWIMware and eDMR coefficients 
28 

increasing slightly and a handful of other variables that were statistically significant in the other 

regressions becoming insignificant at the 95 percent confidence level (p<.12). 

Figure 2: Ohio compliance by quarter 

C. Independent Variables 

1. Electronic Reporting Status

 Data on permittees’ electronic reporting status is taken from the dataset provided by Ohio 

EPA that reports the dates that NPDES permittees active as of October 2010 began DMR 

reporting with SWIMware and eDMR. Thus, there are two binary variables for electronic 

reporting status in the dataset. The first is 0 if a permittee either has not begun or has stopped 

using SWIMware, and 1 if the permittee has begun using SWIMware but not yet switched to 

20%

30%

40%

50%

60%

70%

80%

90%

1996119971199811999120001200112002120031200412005120061200712008120091
29 

eDMR. The second e-reporting status is 0 if the permittee has not begun using eDMR and 1 if it 

has. The reference category is paper reporting.

Table 2: DMR reporting by fiscal year for permittees included in dataset 

FY SWIM % SWIM eDMR % eDMR paper % paper total 

1997 0 0.0% 0 0.0% 1,643 100.0% 1,643

1998 0 0.0% 0 0.0% 1,706 100.0% 1,706

1999 0 0.0% 0 0.0% 1,748 100.0% 1,748

2000 761 40.7% 0 0.0% 1,109 59.3% 1,870

2001 945 47.9% 0 0.0% 1,029 52.1% 1,974

2002 1,207 57.6% 0 0.0% 890 42.4% 2,097

2003 1,455 66.2% 0 0.0% 743 33.8% 2,198

2004 1,655 72.2% 0 0.0% 638 27.8% 2,293

2005 1,777 75.3% 0 0.0% 582 24.7% 2,359

2006 1,894 77.8% 0 0.0% 541 22.2% 2,435

2007 1,976 79.0% 0 0.0% 525 21.0% 2,501

2008 158 6.2% 2,017 79.2% 373 14.6% 2,548

2009 19 0.7% 2,488 96.6% 68 2.6% 2,575

I expect compliance status to improve on average when permittees begin e-reporting. 

However, the relationship is complicated by the poor data quality that came out of SWIMware. 

By Ohio EPA’s own account, SWIMware data quality was perhaps even worse than paper. This 

may be at least partially due to the difficulty users encountered with the software (Ohio EPA 

2010). The fact that estimated monthly DMR errors decreased from 50,000 to 5,000 after eDMR 

implementation (Case Study: Ohio EPA 2010, 3) attests to the magnitude of the problem. As a 

result, the expected effect of SWIMware on compliance is hard to determine. 

On the other hand, I expect an improvement in compliance to result from eDMR 

adoption. If Ohio EPA wasn’t able to address all of the 50,000 errors generated by SWIMware 

each month, some of the compliance violations during the SWIMware period were likely the 

result of data errors. If this is the case, more accurate data from eDMR should increase 
30 

compliance. But there is also the chance of additional effects, as the response of regulated 

permittees to e-reporting may also influence results. Improved data quality provides interested 

parties — such as regulatory agencies, environmental groups, and the public — with a clearer 

sense of which permittees have significant violations. As noted earlier, previous studies place a 

high degree of importance on informal regulation — the public’s ability to pressure permittees to 

comply. With more accurate data at their disposal, the public has a more reliable tool for 

targeting permittees for informal regulation. To the extent that permittees sense the impact of 

improved data, this serves as an additional incentive to comply. 

These factors could affect individual permittees, but what about the regulated universe as 

a whole? Once eDMR was fully implemented, Ohio had much less noise in their data. The 

50,000 errors each month during SWIMware were a major distraction to Ohio EPA. With this 

burden drastically reduced, Ohio EPA personnel noted that they were finding it much easier to 

target true violators (Ohio EPA 2010). These are not the only gains for the agency as a result of 

eDMR. As noted earlier, the agency once had five full-time employees dedicated to DMRs. They 

now use just one-quarter of a full-time equivalency, which is used primarily to facilitate eDMR 

usage. Two employees have been reassigned to other positions in the surface water division, 

while the remaining employees retired and were not replaced (Ohio EPA 2010). 

2. Inspections 

 A variable was coded from PCS inspection data for the number of inspections a permittee 

had in each quarter. This number ranges from zero to six, with more than three inspections in a 

quarter being extremely uncommon. Given the small number of EPA inspections, it was not 

judged appropriate to separate state and EPA inspections. 
31 

Table 3: Total inspections, enforcement actions, and penalties per fiscal year 

FY Inspections Informal 

actions

Formal 

actions

Penalties 

(real $)

Penalties 

(2009 $)

1996 513 92 36 $93,000 $127,163

1997 522 166 37 $70,600 $94,370

1998 421 110 16 $115,000 $151,360

1999 319 51 28 $0 $0

2000 309 24 32 $0 $0

2001 405 9 25 $0 $0

2002 393 17 20 $368,000 $438,853

2003 528 30 23 $151,500 $176,644

2004 549 20 27 $874,550 $993,242

2005 805 2 40 $405,000 $444,892

2006 1,245 12 20 $92,902 $98,864

2007 1,338 194 18 $556,000 $575,293

2008 1,346 648 17 $210,000 $209,252

2009 1,325 703 25 $930,800 $930,800

Total 10,392 2,252 368 $3,867,352 $4,240,733

Source for inflation-adjusted penalties: Bureau of Labor Statistics Inflation Calculator, 

http://www.bls.gov/data/inflation_calculator.htm

Lagged variables are included to gauge the effect on an inspection up to three quarters later. The 

number of lagged quarters for this variable and others were chosen based on trial and error after 

running numerous regressions. For the reasons discussed in the monitoring section of the 

conceptual model, I expect inspections to increase compliance. 

3. Informal and formal enforcement actions 

 Enforcement action data also came from PCS. From these data, separate variables were 

created for the number of informal and formal actions taken against a given permittee in a given 

quarter. The number of informal actions range from zero to six in a quarter, and formal actions 

range from zero to three. Again, there weren’t enough EPA actions to warrant separate variables 

for state and EPA actions. 
32 

 The expected relationship of enforcement to compliance was touched on in the 

conceptual model, but a brief elaboration is warranted. I expect compliance to improve after 

either type of enforcement action, though the effect may be small for the reasons cited earlier. 

Enforcement is typically a last-resort approach to dealing with non-compliance, as it consumes 

significant resources for agencies and permittees alike. Following Walker’s (2009) cost-benefit 

logic regarding monitoring cited earlier, a permittee that has been through a formal enforcement 

action is unlikely to repeat it if the remedies needed to return to compliance are less expensive 

than the costs of the action. One such cost of enforcement — albeit indirect — occurs when a 

permittee receives negative publicity. As noted in Karkkainen (2001), Shimshack (2007), and 

May (2004), this provides additional incentives to avoid future actions. 

 Two quarters of lagged variables for informal enforcement and three quarters for formal 

enforcement are included in the models to gauge the longer-term effects of enforcement actions. 

4. Compliance in previous quarters 

 We would expect the best predictor of current permittee compliance to be past 

compliance. Permittees with excellent compliance records tend to stay in compliance, and those 

with quarter after quarter of noncompliance find it difficult to reverse the trend. For that reason, I

have included four quarters of lagged variables for compliance as a way to capture past 

performance in the model. 

5. Sum of total monetary penalties in a quarter 

 In order to account for some degree of general deterrence, I have included two variables 

that capture the total amount of penalties assessed in the Ohio NPDES program in a given 

quarter. One variable is the logarithmic function of the total amount of penalties taken in each 
33 

quarter, the other is the square of the logarithmic function. These two variables will give us a 

sense of whether there is a different effect from large penalties than from small ones. 

Because there were only 48 penalties taken in Ohio during this period, there aren’t 

enough to include it as a separate specific deterrent variable. Therefore individual penalties are 

counted as formal enforcement actions and their effect is part of the total effect for that variable. 

However, short of prosecution, monetary penalties are the most severe action an agency can take 

against a permittee. As such, they are also the strongest general deterrent available to us. By 

adding up the total for each quarter, logging it, then lagging it over three quarters, we should get 

some sense of how much of a general deterrent effect exists in this universe. 

 I expect compliance to increase as the size of the total penalty amount increases. 

However, given the small number of penalties taken in Ohio over this period, the magnitude of 

the effect is difficult to predict, and may be quite small. 

6. Quarter 

 In addition to fixed effects for individual permittees, the models also include a time trend. 

This is achieved by including a variable for quarter. 

D. Limitations 

 Data quality in the form of missing or inaccurate data is our first concern. For instance, it 

is possible that we are missing some inspection and enforcement data due to it not being entered 

into the PCS datasets. In a 2002 audit of inspection data for major permittees in PCS, U.S. EPA’s 

Office of Compliance found that 0.06 percent of these permittees were “false positives,” 

meaning they had inspections reported with no proof of an actual inspection, and 5.4 percent 

were “false negatives,” having had inspections that were not reported (U.S. EPA, Inspection 
34 

Data Audit). The 2005 Office of Compliance study of PCS enforcement actions at major 

permittees raises greater concern. Out of an estimated 563 enforcement actions, it identified 26 

false positives, which is only 0.4 percent of the PCS universe, but 4.6 percent of the total number 

of enforcement actions reported. Even more alarming is the number of false negatives, which 

U.S. EPA estimated at 143, which is 25 percent of the total number of reported enforcement 

actions (U.S. EPA, Enforcement Action Audit). Given that states are required to report data for 

major permittees to U.S. EPA but are not required to for minor permittees, minor source data 

completeness is likely to fall below these numbers.

However, I expect the rates of missing data in Ohio to be relatively low for a couple of 

reasons. First of all, in fiscal years 2008 and 2009, Ohio tracked 100 percent and 98 percent of its 

minor sources for violations, marks that most other states did not match (U.S. EPA, Annual 

Noncompliance Report). Second, Ohio issued no corrections or comments to its 2008 or 2009 

data, indicating that the state felt their data were sound (U.S. EPA, State Comments). 

Unfortunately, while the data completeness in this period appears to be solid, data accuracy and 

completeness in prior years is more difficult to determine. 

A second limitation in the data is created by the “C” code. Violations that do not rise to 

the level of RNC or SNC are typically coded with the C code, which is the manual override code 

indicating “compliance.” As a result, a binary version of this variable only captures whether a 

permittee was in RNC or SNC versus neither. The ordinal measure is not a huge improvement in 

this regard, as there is only a one-unit separation from true compliance (0) and the C code (1). 

Capturing all violations would require creating a dependent variable from the violation-specific 

data in PCS. However, the degree of difficulty here is much higher and the time-sensitive nature 
35 

of this study did not allow for this. Other concerns are captured in the earlier discussion of this 

variable. Nonetheless, this study should capture e-reporting’s effect on reducing the most serious 

types of noncompliance. 

 A third limitation is created by the nature of the e-reporting data. Ohio EPA only 

provided data on when permittees began e-reporting under SWIMware and eDMR. During the 

SWIMware period it was still possible for permittees that had adopted SWIMware to report by 

paper. Given the degree of difficulty that users encountered with SWIMware, some may have 

chosen to do this. However, in an email to the author, one Ohio EPA official said “I don’t think 

that was common.” Furthermore, cumbersome as SWIMware was, Ohio EPA staff stated that 

most users preferred it over paper forms (Ohio EPA 2010). Based on this information I don’t 

believe that enough SWIMware permittees moved back to paper to affect the results in a 

significant way. 

 A fourth limitation is created by the time period. While data were available from 1996 to 

2010, the model examines compliance from 1997 to 2009. Because of the lagged variables, it 

was necessary to have a full year worth of compliance, inspection and enforcement data before 

the analysis of the dependent variable, hence the reason for beginning in 1997. The decision to 

end the analysis in 2009 was made because it did not appear that the inspection and enforcement 

data for 2010 were complete at the time the dataset was assembled. Including this year in the 

analysis, if it were possible, would have given us an even better sense of eDMR’s effect. 

E. Methods 

 The models in this study include fixed effects for permittees and a time trend. This was 

achieved by using the tsset command in Stata and including the variables for NPDES 
36 

identification number and quarter. All models presented in the results section utilize binary 

compliance as the dependent variable (another in the appendix uses ordinal compliance as a 

means of comparison). For the models with the binary dependent variable I employed a fixed 

effects logit model. These models take the following form: 

	 
  
         ! 

"!  #!$  %&!  '&!  
(&!$  

 


  
$  
)  
 *  
"* 


#*$ + 
%+,--  
'*	  (*	  
*	$ 

Table 4: Explanation of variables in model 

Abbreviation Description Usage 

BC Binary compliance (equal to 0 for 

noncompliance and 1 for compliance) 

Dependent variable and 

independent variable with four 

quarters of lags 

SW SWIMware usage (equal to 0 if facility had 

not yet adopted or if it had adopted and 

changed to eDMR, otherwise 1) 

Independent variable of interest 

eDMR eDMR (equal to 0 if facility had not yet 

adopted, 1 if it had adopted) 

Independent variable of interest 

Insp Inspections (number of inspections in the 

quarter) 

Independent variable, two 

quarters of lags 

IE Informal enforcement (number of informal 

actions in the quarter) 

Independent variable, three 

quarters of lags 

FE Formal enforcement (number of formal 

actions in the quarter) 

Independent variable, three 

quarters of lags 

lnPen Log of total amount of penalties, adjusted for 

inflation, assessed in Ohio during a given 

quarter 

Independent variable, three 

quarters of lags 

lnPen2Log squared of total amount of penalties, 

adjusted for inflation, assessed in Ohio during 

a given quarter 

Independent variable, three 

quarters of lags 

Quarter Quarter (ranging from 1 for first quarter FY 

1996 to 56 for fourth quarter FY 2009) 

Independent variable 
37 

The most immediate concern with the above model results from the inclusion of the 

informal and formal enforcement variables. First of all, we suspect a high degree of 

multicollinearity between these two variables, as formal enforcement is typically preceded by 

informal enforcement. Secondly, there is an obvious endogeneity concern — not only do we 

expect these variables to influence future compliance, but we also expect past compliance to 

influence the likelihood of an enforcement action. To a lesser degree, similar concerns exist with 

regard to inspections, as some are targeted based on past permittee performance. 

 One way of dealing with this is to run a two-stage least squares regression that includes 

past quarters of compliance as instrumental variables. When this was attempted the first-stage 

regression coefficients were insignificant, rendering the second-stage regression moot. The 

alternative to this approach is to include four lagged variables for compliance in the fixed-effects 

logit model. Given that the dataset includes 2,575 permittees tracked over 56 quarters, this 

enables us to at least partially (though not completely) address the endogeneity and 

multicollinearity concerns. 

 In addition to looking at the permittee universe in its entirety, I have also run separate 

models for majors and minors and for municipal and industrial permittees to gauge the effects of 

electronic reporting on these sub-universes. 

 
38 

F. Descriptive Statistics

Table 5: Descriptive statistics 

Mean SD Min Max

Binary compliance 0.726 0.446 0 1

Ordinal compliance 6.817 3.359 0 9

SWIMware status 0.397 0.489 0 1

eDMR status 0.130 0.336 0 1

E-reporting status 0.526 0.499 0 1

Informal enforcement action 0.020 0.163 0 6

Formal enforcement action 0.004 0.069 0 3

Inspections 0.100 0.351 0 6

Quarter 28.5 16.163 1 56

Penalties (all quarters) $35 $3,201 $0 $700,000

Penalties (for quarters with penalty) $85,987 $133,650 $5,668 $700,000

Log of total penalties in quarter 4.133 5.593 0 13.679

Log2 of total penalties in quarter 49.321 68.228 0 187.125

 Table 5 above provides descriptive statistics for all permittees in the dataset. The mean 

for binary compliance indicates that permittees were in compliance 72.6 percent of the time, 

whereas the mean for ordinal compliance also indicates that the average permittee is in 

compliance. Permittees reported their DMRs electronically for 52.6 percent of the quarters 

between FY 1996 and 2009, reporting 39.7 percent of the time with SWIMware and 13 percent 

with eDMR. The average value in a given permittee quarter for informal enforcement was 0.02, 

which is somewhat inflated by permittees that had two or more actions taken in a given quarter. 

The mean for formal enforcement is even lower at 0.004. Inspections are more common, with an 

average value of 0.1. Penalties in a given permittee quarter averaged $35. The value of the 

average penalty was $85,987. The average for the logged total value of all penalties taken in a 

given quarter is 4.1, and the mean of the squared log of total penalties is 49.3.

 
39 

Chapter V: Results 

A. Model results and discussion 

 To begin this discussion I have included results in Table 6 from a logit model that 

includes fixed effects for permittees. The model examines the effect of the independent variables 

on binary compliance beginning in first quarter FY 1997 and ending in fourth quarter FY 2009. 

The effects of our variables of interest aren’t too surprising given what we know about 

SWIMware and eDMR. SWIMware is highly statistically significant (p<.001) and negative. The 

odds ratio indicates that permittees reporting with SWIMware have 19 percent lower odds of 

being in compliance than those reporting with paper. Meanwhile the effect of eDMR is also 

highly significant (p<.001) and positive, with the odds ratio showing a 43.6 percent greater odds 

of being compliance for eDMR reporters versus paper reporters. 

The effect of compliance in the previous quarter dwarfs everything else, but all four 

quarters of lags are highly significant (p<.001). A permittee that was in compliance in the 

previous quarter has 1,532 percent greater odds of being compliant in the current quarter than a 

permittee that was in noncompliance. The effect is much smaller in earlier quarters but still 

substantial, with odds ratios ranging from 1.37 to 1.87. 

Some of the other explanatory variables also have the expected effect. For instance, 

informal enforcement taken in the previous quarter is significant (p=.047). Each informal action 

increases the odds of compliance in the following quarter by about 14 percent. The effect two 

quarters prior is insignificant. For inspections, the effect takes longer. Inspections in one quarter 
40 

have no effect (p=.922) on compliance in the following quarter, whereas inspections two and 

three quarters prior have a positive and highly significant effect. 

Table 6: Results for fixed effects logit model predicting binary compliance 

from FY 1997-2009 

Coeff std error odds ratio odds ratio 

95% CI 

significance 

level 

SWIMware -0.210 0.044 0.810 0.744 - 0.883 *** 

eDMR 0.362 0.064 1.436 1.266 – 1.629 *** 

Informal enforcement 

In previous quarter 0.131 0.066 1.140 1.002 – 1.297 * 

Two quarters ago -0.074 0.067 0.929 0.814 – 1.060 

Formal enforcement 

In previous quarter -0.087 0.141 0.917 0.695 – 1.209 

Two quarters ago -0.146 0.141 0.864 0.656 – 1.139 

Three quarters ago -0.191 0.139 0.826 0.630 – 1.084 

Inspections 

In previous quarter 0.003 0.033 1.003 0.941 – 1.070 

Two quarters ago 0.107 0.033 1.113 1.044 – 1.187 ** 

Three quarters ago 0.076 0.032 1.079 1.013 – 1.150 * 

Compliance 

In previous quarter 2.793 0.029 16.322 15.430 – 17.266 *** 

Two quarters ago 0.318 0.036 1.374 1.282 – 1.474 *** 

Three quarters ago 0.624 0.038 1.866 1.732 – 2.011 *** 

Four quarters ago 0.591 0.035 1.805 1.686 – 1.934 *** 

Log of total penalties in quarter 

In previous quarter -0.132 0.017 0.876 0.848 – 0.905 *** 

Two quarters ago -0.062 0.018 0.940 0.908 – 0.974 ** 

Three quarters ago -0.169 0.019 0.844 0.813 – 0.877 *** 

Squared log of total penalties in quarter 

In previous quarter 0.010 0.001 1.010 1.007 – 1.013 *** 

Two quarters ago 0.005 0.001 1.005 1.002 – 1.007 **

Three quarters ago 0.012 0.002 1.012 1.009 – 1.015 *** 

Quarter 0.003 0.001 1.003 1.000 – 1.006 * 

* p < .05 ** p < .01 *** p < .001 

Notes: Number of observations = 81,432. Number of groups = 1,946. Log likelihood = -20,822.17. Chi-squared = 29,871.98. 

 

 The biggest surprises come from the effect of the formal enforcement and penalty 

variables. None of the lagged formal enforcement variables are statistically significant, which 

could be at least partially due to the small number of formal actions taken. What’s even more 
41 

surprising is the effect of the first-power penalty variables, which are consistently negative and 

highly statistically significant (p≤.001). Contrast this effect with the squared penalty variables, 

also highly significant (p≤.002) but positive. Given the range of values for these variables (see 

Table 5), the odds ratios indicate a very large effect for both. However, due to the fact that all 

permittees share the same values for these variables, there is a tendency for their effects to be 

overestimated. 

Considering both effects together, it would appear that large penalties encourage 

compliance, but small ones actually discourage compliance. If permittees hear of violators 

receiving small penalties — and only nine of the 48 penalties taken were $100,000 or greater — 

they may decide that noncompliance is less expensive than compliance. In the case of larger 

penalties, permittees appear to be arriving at the opposite conclusion. However, the net effect of 

the penalty variables is negative in 14 out of the 20 quarters in which penalties were recorded.5

In six of the 20 quarters, the net effect of the second quarter lag is positive — the effect flips 

when $298,134 was assessed in fourth quarter FY 2002. The net effect for the first quarter lag is 

positive in just two quarters. Its effect flips in first quarter FY 2004 when $544,633 was 

assessed. All net effects were negative for the third quarter lag. This indicates that for penalties 

to serve as a true deterrent, the total dollar amount assessed in a given quarter should be 

relatively large. 

 

5

 This is determined for each quarter by: 1. Multiplying the log value of the penalty by the logit coefficient for firstpower logged total penalties; 2. Multiplying the log-squared value of the penalty by the logit coefficient for secondpower logged total penalties; 3. Adding these two values to determine the net effect of total penalties on compliance 

one quarter later; 4. Repeating steps 1-3 to determine the effect two and three quarters later. Inflation adjusted values 

were used to calculate the log values. 
42 

Table 7 displays the results of two models similar to the previous one, the difference 

being that one includes only major permittees and the other only minors. The results for the 

major model are quite different than the model in Table 6. In this model SWIMware’s effect is 

statistically insignificant (p=.346). The magnitude of the eDMR effect is only slightly smaller 

than in the Table 6 model (odds ratio=1.43) and remains significant (p=.012). Informal 

enforcement is insignificant in the previous quarter. However, the second lag is significant 

(p=.038) and negative, with permittees receiving an action having 16 percent lower odds of being 

in compliance two quarters later than those that do not. The effect of inspections is statistically 

significant (p=.029) and negative (odds ratio=0.92) in the first lagged quarter, and is insignificant 

in the other two quarters. Binary compliance remains highly significant across all quarters and 

continues to have a disproportionately large effect in the first quarter. However, the magnitude of 

the effect is noticeably smaller than for the universe as a whole. Finally, none of the penalty 

variables are statistically significant at conventional confidence levels, although the second 

quarter lags are marginally significant (p=.109 for first-power penalties and p=.054 for squared 

penalties). 

 
43 

Table 7: Results for fixed-effects logit models predicting binary compliance for majors 

versus minors, 1997 through 2009

Model 1 

Majors

Model 2 

Minors 

Coeff Std 

error 

odds 

ratio 

odds ratio 

95% CI 

Sig. 

level 

Coeff Std 

error 

odds 

ratio 

odds ratio 

95% CI 

Sig. 

level 

SWIMware -0.079 0.084 0.924 0.785 – 1.089 -0.249 0.052 0.779 0.704 – 0.863 *** 

eDMR 0.356 0.142 1.427 1.080 – 1.886 * 0.342 0.075 1.408 1.216 – 1.631 *** 

Informal enforcement 

In previous quarter 0.074 0.077 1.077 0.925 – 1.252 0.128 0.102 1.137 0.931 – 1.388

Two quarters ago -0.160 0.077 0.852 0.733 – 0.991 * 0.148 0.106 1.160 0.942 – 1.429

Formal enforcement 

In previous quarter -0.075 0.146 0.928 0.698 – 1.234 -0.236 0.280 0.790 0.456 – 1.366

Two quarters ago -0.147 0.146 0.863 0.649 – 1.150 -0.129 0.262 0.879 0.526 – 1.470

Three quarters ago -0.134 0.145 0.874 0.658 – 1.161 -0.360 0.252 0.698 0.426 – 1.143

Inspections 

In previous quarter -0.083 0.038 0.920 0.854 – 0.992 * 0.222 0.051 1.249 1.131 – 1.379 *** 

Two quarters ago -0.029 0.038 0.971 0.901 – 1.047 0.329 0.051 1.390 1.259 – 1.534 *** 

Three quarters ago 0.049 0.038 1.051 0.975 – 1.133 0.133 0.051 1.142 1.034 – 1.261 ** 

Compliance 

In previous quarter 1.864 0.054 6.452 5.807 – 7.167 *** 3.069 0.035 21.523 20.108 – 23.038 *** 

Two quarters ago 0.258 0.062 1.294 1.147 – 1.460 *** 0.228 0.044 1.256 1.153 – 1.368 *** 

Three quarters ago 0.135 0.064 1.145 1.010 – 1.298 * 0.799 0.047 2.224 2.027 – 2.441 *** 

Four quarters ago 0.326 0.061 1.385 1.230 – 1.560 *** 0.567 0.043 1.763 1.619 – 1.918 *** 

Log of total penalties 

in quarter 

In previous quarter 0.018 0.042 1.018 0.938 – 1.105 -0.175 0.019 0.840 0.809 – 0.871 *** 

Two quarters ago -0.071 0.044 0.932 0.854 – 1.016 -0.061 0.020 0.941 0.904 – 0.979 ** 

Three quarters ago -0.061 0.046 0.941 0.861 – 1.029 -0.217 0.022 0.805 0.771 – 0.841 *** 

Squared log of total 

penalties in quarter 

In previous quarter 0.000 0.003 1.000 0.994 – 1.007 0.013 0.002 1.013 1.010 – 1.016 *** 

Two quarters ago 0.007 0.004 1.007 1.000 – 1.014 0.004 0.002 1.004 1.001 – 1.008 ** 

Three quarters ago 0.004 0.004 1.004 0.997 – 1.012 0.016 0.002 1.016 1.012 – 1.020 *** 

Quarter 0.006 0.003 1.006 1.000 – 1.011 -0.000 0.002 1.000 0.996 – 1.003

* p < .05 ** p < .01 *** p < .001 

Model 1: Number of observations = 13,821. Number of groups = 268. Log likelihood = -5,008.48. Chi-squared = 2,133.34. 

Model 2: Number of observations = 67,611. Number of groups = 1,678. Log likelihood = -15,396.22. Chi-squared = 28,573.57. 

The results for minors are quite different than those for majors. SWIMware’s effect is 

once again significant (p<.001), with these permittees having 22.1 percent lower odds of being in 

compliance than paper reporters. eDMR is also highly significant (p<.001) but its effect is 
44 

slightly smaller than for majors. Past compliance in these models has a much larger effect on 

current compliance for minor permittees than for majors, and inspections for minors appear to 

have a sizeable deterrent effect at least three quarters into the future. However, of the 

enforcement variables, only those for total penalties are statistically significant, with the effects 

roughly mirroring those in the Table 6 model. 

What could be causing these differences between the major and minor sub-universes? 

Some of the differences in the major and minor results could be due to the size of the universes. 

There are 268 permittees in the major regression versus 1,678 in the minor regression, so there 

are fewer opportunities to establish significant relationships in the major permittee universe.6

However, there may be different motivations within these two groups. For instance, large 

penalties are correlated with substantial improvements in compliance for minors, whereas this 

was not the case for majors. These penalties may have a larger deterrent effect for minors 

because they are less able to withstand the financial blow of a large fine. Also of interest is the 

difference in significance levels for SWIMware between the two groups. The reason for this is 

unclear. One obvious explanation is that there weren’t enough observations to establish an effect. 

It is also possible that major permittees had personnel who were better at navigating the software 

than minor permittees, and this higher competency led to fewer coding errors, which in turn led 

to fewer violations. 

 

6

 In logit regressions for fixed effects models, Stata removes facilities that have “all positive or all negative 

outcomes.” 
45 

Continuing with logit fixed-effects with binary compliance as the dependent variable, 

Table 8 breaks industrial and municipal permittees apart.7

 Compared to major and minors, there 

are fewer notable differences in the results. The effects of SWIMware and eDMR are significant 

in both groups, with SWIMware having a larger effect for municipal permittees (25.3 percent 

lower odds of compliance compared to paper) and eDMR having a large effect for industrial 

permittees (46.7 percent higher odds compared to paper). Informal and formal enforcement were 

not correlated with improved compliance in either sector. In fact, for municipal facilities, 

informal enforcement was correlated with 22.7 percent lower odds of compliance two quarters 

later. The effect of inspections on industrial permittees is positive and statistically significant in

the second and third quarter lagged variables, and large penalties appear to have had a major 

deterrent effect in both sectors. 

In Appendices II and III I have included descriptive statistics for the major, minor, 

municipal, and industrial sub-universes. In Appendix IV I have included results from an OLS 

fixed effects model that estimates the effect on ordinal compliance. The signs of the coefficients 

are consistent with the logit model in Table 6, and the significance level changes in only a couple 

of instances. 

 

 

7

 Municipal facilities are most often POTWs but also include other publicly owned entities. 
46 

Table 8: Results for fixed-effects logit models predicting binary compliance for municipal 

versus industrial permittees, FY 1997 through FY 2009

Model 1 

Industrial

Model 2 

Municipal 

Coeff Std 

error

odds 

ratio

odds ratio 

95% CI

Sig. 

level

Coeff Std 

error

odds 

ratio

odds ratio 

95% CI

Sig. 

level 

SWIMware -0.141 0.063 0.868 0.767 – 0.983 * -0.292 0.062 0.747 0.662 – 0.843 *** 

eDMR 0.383 0.090 1.467 1.231 – 1.750 *** 0.296 0.094 1.344 1.118 – 1.617 ** 

Informal enforcement 

In previous quarter 0.125 0.103 1.133 0.927 – 1.386 0.118 0.086 1.126 0.950 – 1.333

Two quarters ago 0.204 0.106 1.226 0.995 – 1.510 -0.257 0.086 0.773 0.653 – 0.915 ** 

Formal enforcement 

In previous quarter -0.102 0.274 0.903 0.528 – 1.544 -0.088 0.164 0.916 0.664 – 1.263

Two quarters ago -0.112 0.262 0.894 0.535 – 1.492 -0.162 0.166 0.850 0.614 – 1.177

Three quarters ago -0.249 0.248 0.779 0.480 – 1.270 -0.174 0.166 0.840 0.607 – 1.163

Inspections 

In previous quarter 0.050 0.062 1.052 0.931 – 1.188 -0.020 0.038 0.980 0.909 – 1.056

Two quarters ago 0.296 0.061 1.345 1.193 – 1.516 *** 0.035 0.039 1.035 0.960 – 1.116

Three quarters ago 0.144 0.061 1.155 1.026 – 1.301 * 0.046 0.038 1.047 0.972 – 1.128

Compliance 

In previous quarter 2.932 0.042 18.764 17.292 – 20.362 *** 2.664 0.040 14.353 13.262 – 15.534 *** 

Two quarters ago 0.154 0.052 1.166 1.053 – 1.292 ** 0.454 0.049 1.575 1.429 – 1.735 *** 

Three quarters ago 0.831 0.055 2.295 2.059 – 2.558 *** 0.441 0.053 1.554 1.400 – 1.724 *** 

Four quarters ago 0.630 0.051 1.878 1.699 – 2.078 *** 0.538 0.049 1.713 1.558 – 1.884 *** 

Log of total penalties 

in quarter 

In previous quarter -0.117 0.023 0.890 0.850 – 0.931 *** -0.148 0.025 0.863 0.822 – 0.906 *** 

Two quarters ago -0.052 0.025 0.949 0.904 – 1.000 * -0.073 0.026 0.930 0.883 – 0.979 ** 

Three quarters ago -0.096 0.027 0.908 0.861 – 0.958 *** -0.241 0.028 0.786 0.744 – 0.831 *** 

Squared log of total 

penalties in quarter 

In previous quarter 0.008 0.002 1.008 1.004 – 1.012 *** 0.012 0.002 1.012 1.008 – 1.016 *** 

Two quarters ago 0.004 0.002 1.004 1.000 – 1.008 0.006 0.002 1.006 1.001 – 1.010 ** 

Three quarters ago 0.006 0.002 1.006 1.002 – 1.011 ** 0.018 0.002 1.020 1.014 – 1.023 *** 

Quarter 0.002 0.002 1.002 1.000 – 1.006 0.004 0.002 1.004 1.000 – 1.009 * 

* p < .05 ** p < .01 *** p < .001 

Model 1: Number of observations = 43,771. Number of groups = 1,128. Log likelihood = -10,249.27. Chi-squared = 16,585.97. 

Model 2: Number of observations = 36,156. Number of groups = 785. Log likelihood = -10,232.23. Chi-squared = 12,901.84. 

 

B. Overarching conclusions based on results 

 When considering all of the results together, I have drawn the following conclusions 

about the effect of the independent variables on compliance in Ohio during this period: 
47 

1. SWIMware appears to have had a negative effect compared to paper and eDMR. 

For the most part, SWIMware has a negative and highly statistically significant effect on 

compliance. The first model showed a 19 percent lower odds of compliance versus paper 

reporting. Based on what we know about SWIMware, this could be due to its low data 

quality, which could have resulted in a larger number of erroneous compliance violations 

compared to paper reporting. However, the insignificant result for the major sub-universe 

indicates that this effect may not have been universal. 

2. eDMR appears to have had a positive effect compared to paper and SWIMware. 

The eDMR coefficient is statistically significant and positive in every model included in 

this paper (with the exception of Model 3, which is included in Appendix V and analyzes 

compliance from FY 2008-2009). This is probably partially due to vastly improved data 

quality under eDMR. However, there may be other factors contributing to the effect as 

well. For instance, it’s possible that improved data quality benefited both formal and 

informal regulatory efforts. Better data would make it easier for both Ohio EPA and U.S. 

EPA to pinpoint the permittees with the most serious violations. It may also have allowed 

U.S. EPA to provide more accurate CWA data on Ohio to the public, which in turn would 

be able to more effectively target permittees for informal regulation. 

3. The effects of SWIMware and eDMR may have been largest at the beginning of 

their implementation. Appendix V provides results from three different models, each 

employing logit fixed effects with binary compliance as the dependent variable. Each 

model examines compliance during three different time periods. The first model covers 

FY 1997 through the second quarter of FY 2000. The second model covers the 
48 

SWIMware period before eDMR introduction, which ran from third quarter FY 2000 

through FY 2007. The third model covers eDMR introduction, FY 2008 through FY 

2009. Interestingly, none of the e-reporting coefficients in these models are statistically 

significant. When comparing these with our other models it appears that SWIMware and 

eDMR had their largest impacts on compliance in the first few quarters of their 

introduction. The models cited earlier capture this effect because they compare the 

periods before these systems were introduced to the periods when these systems were in 

use, whereas the models in the appendix only capture changes that occurred within the 

periods of use. However, there are limitations to this approach that make it more difficult 

to find a significant result. For example, the predictive power of the effect of eDMR 

between FY 2008 and FY 2009 alone is quite low. eDMR was implemented very rapidly, 

with nearly every permittee in the state having adopted it by the last quarter of FY 2009. 

Therefore, in addition to looking at a relatively short time period, there may not be 

enough variation within the eDMR variable by the end of the period to capture an effect. 

4. The effect of inspections may vary depending on the sub-universe. Inspections had no 

significant effect on compliance for municipal permittees, whereas for majors they had a 

significant negative effect on compliance one quarter later. For industrial and minor 

permittees the effect was generally positive and significant. Overall (Table 6), the effect 

was significant and positive two and three quarters later. The lack of a first quarter effect 

could be the result of post-inspection improvements at some facilities being offset by 

compliance violations found during inspections at others. The positive effect in the 

second and third quarters could stem from a combination of these compliance violations 
49 

being resolved, a deterrent effect of inspections, and facilities following 

recommendations offered during inspections.

5. The effect of informal enforcement is ambiguous. In the macro model (Table 6), 

informal enforcement has a positive and significant effect on compliance one quarter 

later. However, it does not have a positive and significant effect in any of the subuniverses analyzed. In fact, for municipal and major permittees it actually has a 

significant negative effect two quarters later. The effect emerging two quarters later may 

result from informal actions not being followed by formal ones — permittees may make 

the calculation that no formal action is forthcoming. The macro model, with the higher 

predictive power gained from including all permittees, does indicate that for a substantial 

number of permittees informal enforcement does serve as a deterrent. On the other hand, 

larger permittees may view the action as a slap on the wrist and may actually place less of 

a priority on compliance if the action stops there.

6. Formal enforcement actions appear to have been ineffective at improving 

compliance. None of the coefficients on formal enforcement were significant in any of 

the models. However, there were only 368 formal actions taken between FY 1997-2009, 

and never more than 40 in a quarter. Therefore, the predictive power is quite low. 

7. Noncompliance appears to be encouraged when the total dollar value of penalties in 

a quarter is low, and discouraged when the value is high. With the exception of 

majors, where these variables had no significant effect, the first-power penalty variables 

were always significant and negative, whereas the squared penalty variables were almost 

always significant and positive. Aside from the obvious deterrent effect that large fines 
50 

appear to have on facilities, part of this effect may be due to larger penalties attracting 

more media coverage than smaller ones. As for the negative effects for small penalties, it 

could be that these have a similar explanation to the one offered with regard to major 

permittees and informal enforcement. If permittees see a small fine as a worst-case 

scenario, it may encourage laxity. 

The sizes of these effects varied somewhat across groups but were always 

substantial. In fact, the squared penalty variables are far more likely to be positive and 

significant than the other enforcement variables. While the effect may be overestimated 

due to the lack of variation, it appears that the general deterrent effect from large 

penalties overwhelms the effects of any other type of enforcement action. 

8. Compliance in one quarter is a very strong predictor of compliance in the next, but 

its reliability is greatly diminished going back further. The odds ratios for the first 

quarter lag were up to eight times higher than the second quarter lag. Still, the magnitude 

of the odds ratios in the second through fourth quarters is still quite a bit larger than those 

of most of the other variables in the model. 

 
51 

Chapter VI: Policy Implications

 

If these results are an accurate reflection of the dynamics at work in Ohio over this time 

period, then the policy implications with regard to e-reporting are fairly straightforward. First of 

all, there are clear financial incentives for both governments and regulated entities alike to adopt 

e-reporting. But if adopting e-reporting also improves compliance rates, as would appear to be 

the case with eDMR, this is yet another reason to make the switch. 

 However, Ohio’s experience with SWIMware illustrates that some e-reporting systems 

are better than others. The system was difficult for the regulated universe to use and imposed a 

major burden on Ohio EPA in both helping permittees use the product and sorting through the 

errors it generated each month. Nonetheless, while its benefits are less well documented than 

those of eDMR, Ohio EPA personnel did note that SWIMware permittees generally preferred it 

to paper reporting (Ohio EPA 2010). 

Given the number of states that have recently adopted e-reporting, as well as U.S. EPA’s 

e-reporting rule, it is probably just a matter of time before all NPDES permittees report 

electronically. If properly implemented, this should be a step forward for the NPDES program. 

Systems like eDMR and NetDMR have the potential to improve data quality, lower costs for 

both regulating agencies and permittees, and even improve compliance rates. However, the 

SWIMware example should illustrate to both states and U.S. EPA the importance of 

implementing e-reporting systems that are fully automated, web-based, and user friendly. 

 With or without e-reporting, inspections and enforcement are essential methods of 

promoting compliance. In recent years Ohio has begun making a great many more inspections 
52 

and taking many more informal enforcement actions. In each of the four years between 2006 and 

2009 Ohio made at least four times as many inspections than in 2000. Informal actions, which 

were a rarity for most of the decade, came back into vogue in 2007, when 194 were taken, 

followed by 648 in 2008 and 703 in 2009 (this from a low of two in 2005). While penalties have 

also increased somewhat in this period, they are still very rare, and formal enforcement on the 

whole has remained relatively constant. 

Shimshack and Ward’s (2008) findings indicate that if Ohio took more formal actions 

and penalties, not only would these actions be more effective, but the informal actions would 

become more effective as well. If noncompliant permittees discern that enforcement begins and 

ends with informal action, then it too will begin to lose its deterrent effect over time. And in fact,

this does appear to be happening with majors and municipal permittees in Ohio, as informal 

enforcement action taken two quarters prior actually had a negative and significant effect on 

compliance. My results also indicate that if Ohio took a larger number of high-dollar penalties, 

compliance rates should improve, as this had a significant deterrent effect for all but major 

permittees. 

 
53 

Chapter VII: Conclusion

 

This analysis indicates that e-reporting has produced a net benefit to compliance in Ohio 

since eDMR was launched. As such, it makes the case even more compelling to adopt ereporting on a national scale. However, given that this is the first study of its kind, as well as the

concerns expressed regarding data completeness and quality, it should not be assumed that ereporting will always improve compliance rates. Additional research would more clearly 

establish the effect of e-reporting on compliance. For instance, constructing a dependent variable 

from violation data rather than the PCS compliance codes would capture all violations rather 

than just those that rise to the level of RNC or SNC. Future research could also experiment with 

other models or add other variables to better control for endogeneity. 

Implementation of eDMR has led to efficiency and data quality gains that have had major 

benefits for Ohio EPA. This could not have come at a better time. State budgets across the nation 

face severe cuts, and environmental agencies often bear a disproportionate share of the burden. 

Despite initial startup costs, e-reporting can be a major cost- and time-saver in the long run. 

Anything that saves these agencies time and money while at least holding other factors constant 

should give them greater ability to perform their jobs as protectors of the environment. 

However, more changes are needed in order to bring all waters up to the “fishable and 

swimmable” standard set by the Clean Water Act. As long as non-point source pollution is not 

adequately addressed, the U.S. will continue to experience widespread water pollution. More 

effective regulation of point sources will also be necessary. Addressing these challenges will 

likely require new legislation and rulemaking, and more resources devoted to water protection. 
54 

Appendix I: Compliance by Quarter 

Quarter Blank C R P N D W T X E S Total

19961 970 250 0 32 83 0 0 0 0 1 176 1,512

19962 975 247 1 30 83 0 0 0 0 1 180 1,517

19963 1,009 243 0 31 82 0 0 0 0 1 181 1,547

19964 1,018 198 1 33 91 0 0 0 0 6 211 1,558

19971 1,087 177 2 35 87 0 0 1 5 18 210 1,622

19972 1,049 199 2 30 124 0 0 1 6 17 194 1,622

19973 1,034 219 49 29 85 0 0 1 2 12 198 1,629

19974 1,146 123 3 42 94 0 0 2 26 22 185 1,643

19981 1,152 148 3 60 94 0 0 0 11 13 184 1,665

19982 1,165 123 4 57 109 2 0 5 2 17 198 1,682

19983 1,192 131 2 58 89 5 0 4 11 19 188 1,699

19984 1,193 107 4 60 103 2 0 3 26 20 188 1,706

19991 1,214 120 25 50 84 2 0 0 24 21 181 1,721

19992 1,253 129 8 44 87 2 0 1 7 16 190 1,737

19993 1,173 128 48 43 112 17 0 4 8 19 189 1,741

19994 1,219 136 2 42 131 2 0 1 13 14 188 1,748

20001 1,262 165 2 42 91 2 0 1 13 9 190 1,777

20002 1,273 160 2 51 118 2 0 1 1 4 186 1,798

20003 1,342 1 12 44 219 2 0 7 11 9 204 1,851

20004 1,341 0 23 42 201 2 0 4 21 14 222 1,870

20011 1,162 182 97 42 107 25 0 3 15 15 235 1,883

20012 1,318 119 11 39 156 8 0 1 6 16 239 1,913

20013 1,368 61 26 39 156 3 0 2 10 18 262 1,945

20014 1,343 58 61 37 161 3 0 2 20 20 269 1,974

20021 1,358 113 21 36 182 3 0 1 1 6 283 2,004

20022 1,378 107 19 35 188 3 0 0 2 9 297 2,038

20023 1,420 57 4 35 218 3 0 0 4 9 322 2,072

20024 1,413 40 5 37 234 3 0 1 11 7 346 2,097

20031 971 541 45 22 1 0 0 1 117 122 308 2,128

20032 980 677 63 30 1 0 0 2 24 142 233 2,152

20033 1,082 439 192 30 1 0 0 2 22 149 258 2,175

20034 1,178 297 147 36 2 0 0 2 84 170 282 2,198

20041 1,189 14 144 36 226 0 0 2 83 151 380 2,225

20042 1,232 31 140 34 252 0 0 2 29 183 339 2,242

20043 1,243 2 65 28 315 0 0 3 75 194 355 2,280

20044 1,246 13 68 32 281 0 0 2 99 180 372 2,293

20051 468 7 45 5 283 0 862 2 76 160 398 2,306

20052 1,213 2 166 33 264 1 12 3 32 202 401 2,329

20053 1,339 2 72 30 239 0 3 2 57 207 395 2,346

20054 1,121 2 70 25 304 4 143 2 106 184 398 2,359

20061 966 2 84 26 398 6 257 1 78 169 387 2,374

20062 1,364 0 186 38 244 0 1 1 30 172 359 2,395
55 

Quarter Blank C R P N D W T X E S Total

20063 1,493 1 68 37 225 0 1 2 72 199 325 2,423

20064 1,459 2 92 39 218 0 1 1 105 205 313 2,435

20071 1,513 0 104 44 216 0 1 2 91 163 318 2,452

20072 1,574 2 102 44 215 0 1 2 34 200 293 2,467

20073 1,591 4 64 41 204 0 1 1 73 214 291 2,484

20074 1,560 26 105 38 187 0 1 0 113 198 273 2,501

20081 1,518 1 237 48 192 0 1 0 87 183 247 2,514

20082 1,638 5 122 44 189 0 1 1 44 240 241 2,525

20083 1,642 4 108 45 187 0 1 0 52 254 245 2,538

20084 1,651 4 128 43 169 0 1 1 95 205 251 2,548

20091 1,706 18 122 47 173 1 4 0 68 178 240 2,557

20092 1,781 14 119 54 154 0 1 1 25 211 205 2,565

20093 1,815 5 84 53 141 0 0 0 46 206 222 2,572

20094 1,791 4 103 54 136 0 0 0 89 184 214 2,575

Total 72,151 5,860 3,482 2,191 8,986 103 1,293 84 2,162 5,578 14,639 116,529

% 61.92% 5.03% 2.99% 1.88% 7.71% 0.09% 1.11% 0.07% 1.86% 4.79% 12.56% 100.00%
56 

Appendix II: Descriptive Statistics for Major and Minor Permittees 

Majors Minors 

Mean SD Min Max Mean SD Min Max

Binary compliance 0.772 0.419 0 1 0.719 0.449 0 1

Ordinal compliance 7.131 2.776 0 9 6.770 3.435 0 9

SWIMware status 0.434 0.496 0 1 0.391 0.488 0 1

eDMR status 0.113 0.317 0 1 0.132 0.339 0 1

E-reporting status 0.547 0.498 0 1 0.523 0.499 0 1

Informal 

enforcement 0.064 0.316 0 6 0.014 0.124 0 3

Formal 

enforcement 0.017 0.149 0 3 0.002 0.047 0 3

Inspections 0.323 0.643 0 6 0.066 0.267 0 5

Penalties (all 

quarters) $208 $8,367 $0 $700,000 $7.69 $1,016.44 $0 $275,000

Penalties (for 

quarters with 

penalty) $125,780 $166,474 $8,500 $700,000 $42,733 $64,589 $5,668 $275,000

Log of total 

penalties in quarter 4.133 5.593 0 13.679 Same as for majors 

Log2 of total 

penalties in quarter 49.321 68.228 0 187.125 Same as for majors 
57 

Appendix III: Descriptive Statistics for Industrial and Municipal Permittees 

Industrial Municipal 

Mean SD Min Max Mean SD Min Max

Binary compliance 0.764 0.425 0 1 0.658 0.474 0 1

Ordinal compliance 7.107 3.265 0 9 6.291 3.475 0 9

SWIMware status 0.388 0.487 0 1 0.411 0.492 0 1

eDMR status 0.137 0.344 0 1 0.119 0.324 0 1

E-reporting status 0.525 0.499 0 1 0.530 0.499 0 1

Informal 

enforcement 0.018 0.145 0 6 0.025 0.191 0 3

Formal 

enforcement 0.002 0.052 0 3 0.006 0.092 0 2

Inspections 0.064 0.263 0 6 0.159 0.455 0 5

Penalties (all 

quarters) $24 $2,449 $0 $459,550 $55 $4,190 $0 $700,000

Penalties (for 

quarters with 

penalty) $89,303 $121,577 $5,668 $459,550 $89,303 $83,814 $5,668 $275,000

Log of total 

penalties in quarter 4.133 5.593 0 13.679 Same as for industrial 

Log2 of total 

penalties in quarter 49.321 68.228 0 187.125 Same as for industrial 

 
58 

Appendix IV: Results for OLS Fixed-Effects Model Predicting Ordinal 

Compliance, FY 1997-2009 

Coeff. std error 95% CI Significance 

level 

SWIMware -0.159 0.021 -0.201 -0.118 *** 

eDMR 0.232 0.031 0.171 0.293 *** 

Informal enforcement 

In previous quarter 0.052 0.039 -0.026 0.129 

Two quarters ago -0.040 0.041 -0.120 0.040 

Formal enforcement 

In previous quarter -0.038 0.087 -0.210 0.133 

Two quarters ago -0.136 0.084 -0.300 0.029 

Three quarters ago -0.145 0.084 -0.309 0.019 

Inspections 

In previous quarter 0.000 0.018 -0.036 0.036 

Two quarters ago 0.060 0.019 0.024 0.096 ** 

Three quarters ago 0.045 0.018 0.008 0.080 * 

Compliance 

In previous quarter 3.893 0.023 3.848 3.937 *** 

Two quarters ago 0.534 0.026 0.483 0.584 *** 

Three quarters ago 0.645 0.026 0.594 0.695 *** 

Four quarters ago 0.699 0.023 0.654 0.744 *** 

Log of total penalties in quarter 

In previous quarter -0.132 0.009 -0.149 -0.115 *** 

Two quarters ago -0.064 0.009 -0.082 -0.046 *** 

Three quarters ago -0.030 0.009 -0.050 -0.011 ** 

Squared log of total penalties in quarter 

In previous quarter 0.009 0.001 0.008 0.011 *** 

Two quarters ago 0.004 0.001 0.003 0.006 *** 

Three quarters ago 0.001 0.001 -0.001 0.002 

Quarter 0.009 0.001 0.007 0.010 *** 

Constant 2.567 0.024 2.520 2.614 *** 

* p < .05 ** p < .01 *** p < .001 

Number of observations = 101,738. Number of groups = 2,548. R2

 = 0.645. 

 
59 

Appendix V: Results for Logit Fixed-Effects Models Predicting Binary 

Compliance, FY 1997-2009 

Model 1 — No e-reporting 

1997-1Q thru 2000-2Q

Model 2 — SWIMware 

2000-3Q thru 2007-4Q

Model 3 — eDMR 

2008-1Q thru 2009-4Q

Coeff s.e. o.r. Sig 

level

Coeff s.e. o.r. Sig 

level

Coeff. s.e. o.r. Sig 

level

SWIMware 0.051 0.064 1.053 0.053 0.185 1.054

eDMR -0.215 0.172 0.807

Informal enforcement 

In previous quarter 0.199 0.106 1.220 0.501 0.188 1.650 ** 0.036 0.102 1.037

Two quarters ago 0.075 0.103 1.078 -0.574 0.187 0.563 ** 0.120 0.105 1.127

Formal enforcement 

In previous quarter 0.168 0.247 1.183 -0.186 0.173 0.830 -0.002 0.592 0.998

Two quarters ago 0.000 0.234 1.000 -0.211 0.174 0.810 0.830 0.510 2.293

Three quarters ago 0.115 0.229 1.122 -0.344 0.176 0.709 * 0.232 0.485 1.261

Inspections 

In previous quarter -0.156 0.065 0.855 * 0.110 0.044 1.116 * -0.058 0.082 0.944

Two quarters ago -0.121 0.063 0.886 0.186 0.044 1.204 *** 0.088 0.083 1.092

Three quarters ago -0.039 0.061 0.962 0.156 0.045 1.169 ** 0.004 0.077 1.004

Compliance 

In previous quarter 1.060 0.089 2.888 *** 2.632 0.036 13.90

1

*** 1.358 0.065 3.888 ***

Two quarters ago 0.295 0.097 1.344 ** 0.142 0.044 1.152 ** -0.585 0.080 0.557 ***

Three quarters ago 0.140 0.106 1.151 0.582 0.047 1.790 *** -0.138 0.081 0.871

Four quarters ago 0.342 0.106 1.408 ** 0.361 0.044 1.434 *** 0.100 0.081 1.105

Log of total penalties in 

quarter 

In previous quarter 0.461 0.483 1.585 0.278 0.026 1.028 -0.098 0.029 0.907 **

Two quarters ago -1.336 0.442 0.263 ** -0.011 0.028 0.989 -0.084 0.035 0.919 *

Three quarters ago 1.805 0.442 6.078 *** -0.329 0.029 0.720 *** 0.033 0.035 1.033

Squared log of total 

penalties in quarter 

In previous quarter -0.036 0.041 0.965 -0.003 0.002 0.997 0.007 0.002 1.007 **

Two quarters ago 0.114 0.038 1.121 ** 0.002 0.002 1.002 0.005 0.002 1.005 *

Three quarters ago -0.156 0.038 0.855 *** 0.024 0.002 1.024 *** -0.004 0.003 0.996

Quarter 0.017 0.011 1.017 0.003 0.002 1.003 0.183 0.035 1.201 ***

* p < .05 ** p < .01 *** p < .001 

Model 1: Number of observations = 4,215. Number of groups = 302. Log likelihood = -1,517.16. Chi-squared = 

269.51. 

Model 2: Number of observations = 44,401. Number of groups = 1,727. Log likelihood = 12,447.21. Chi-squared = 

13,367.33. 

Model 3: Number of observations = 7,022. Number of groups = 890. Log likelihood = -2,453.60. Chi-squared = 

667.79. 

 
60 

References

Andreen, William L. 2003. The evolution of water pollution control in the United States—state, 

local, and federal efforts, 1789-1972: Part II. Stanford Environmental Law Journal 22: 

215-286. 

http://heinonline.org/HOL/Page?handle=hein.journals/staev22&div=8&g_sent=1&collect

ion=journals (accessed March 29, 2011). 

Andreen, William L. 2004. Has the Clean Water Act been a success? Alabama Law Review 55: 

537-593. 

http://www.law.ua.edu/lawreview/articles/Volume%2055/Issue%203/Andreen.pdf

(accessed March 29, 2011). 

Baron, David S. 1995. Water quality standards for rivers and lakes: Emerging issues. Arizona 

State Law Journal 27: 559-597. 

http://heinonline.org/HOL/Page?handle=hein.journals/arzjl27&div=27&g_sent=1&collec

tion=journals (accessed March 29, 2011). 

Bennear, Lori S., and Sheila M. Olmstead. 2008. The impacts of the ‘right to know:’ Information 

disclosure and the violation of drinking water standards. Journal of Environmental 

Economics and Management 56: 117-130. http://0-

www.sciencedirect.com.library.lausys.georgetown.edu/science?_ob=MImg&_imagekey=

B6WJ6-4SH6B81-1-

5&_cdi=6870&_user=655954&_pii=S0095069608000399&_origin=gateway&_coverDa

te=09%2F30%2F2008&_sk=999439997&view=c&wchp=dGLbVzzzSkzV&md5=44de8ce0cbf1c3f067cafd28c2480f8e&ie=/sdarticle.pdf (accessed March 

29, 2011). 

Case study: Ohio Environmental Protection Agency’s Electronic Discharge Monitoring Report 

(eDMR) system changes the nature of electronic reporting. 2010. 

http://www.google.com/url?q=http://eitlc.rossassoc.net/images/4/4c/Ohio_eDMRs_Case_Study_04_30_10_FINAL.doc&sa=U&ei=17

7zTOmFOIO8lQeNs8S1Dw&ved=0CBQQFjAA&usg=AFQjCNGDeI61imZkGBJf3u0zXFh7QpLNg (accessed March 10, 2011). 

Citizens Campaign for the Environment. Environmental funding: New York. 

http://www.citizenscampaign.org/campaigns/epf.asp (accessed March 11, 2011). 

Freeman III, A. Myrick. 2000. Water pollution policy. In Public Policies for Environmental 

Protection, ed. Paul R. Portney and Robert N. Stavins, 169-213. Washington, D.C.: 

Resources for the Future. 
61 

Gordon, Jim. 2011. New DEC chief deals with layoffs, budget cuts. Westfair Online. January 7. 

http://westfaironline.com/2011/10054-new-dec-chief-deals-with-layoffs-cuts/ (accessed 

March 11, 2011). 

Hanna, Rema Nadeem, and Paulina Oliva. 2010. The impact of inspections on plant-level air 

emissions. The B.E. Journal of Economic Analysis & Policy 10: Article 19. 

http://www.bepress.com/bejeap/vol10/iss1/art19/ (accessed March 15, 2011). 

Idaho Department of Environmental Quality. Water quality in Idaho: Concentrated animal 

feeding operations (CAFOs). 

http://www.deq.idaho.gov/water/prog_issues/agriculture/cafos.cfm (accessed April 8, 

2011). 

Karkkainen, Bradley C. 2001. Information as environmental regulation: TRI and performance 

benchmarking, precursor to a new paradigm? Georgetown Law Journal 89: 259-367. 

http://0-

vnweb.hwwilsonweb.com.library.lausys.georgetown.edu/hww/results/external_link_main

contentframe.jhtml?_DARGS=/hww/results/results_common.jhtml.44 (accessed March 

29, 2011). 

Kovski, Alan. 2011. EPA among targets as House Republicans plan for hearings, consider 

spending cuts. BNA Daily Environment Report. January 11. 

http://news.bna.com/deln/DELNWB/split_display.adp?fedfid=18941754&vname=dennot

allissues&fn=18941754&jd=a0c5w3y0c6&split=0 (accessed March 29, 2011). 

Mauriello, Tracie, and Don Hopey. 2009. Pennsylvania to lay off 319 workers. Pittsburgh PostGazette. November 17. http://www.post-gazette.com/pg/09321/1014044-454.stm

(accessed March 11, 2011). 

May, Peter J. 2004. Compliance motivations: Affirmative and negative bases. Law and Society 

Review 38: 41-64. http://0-

www.jstor.org.library.lausys.georgetown.edu/stable/1555112?seq=2 (accessed March 29, 

2011). 

McKinnon, Shaun. 2010. Arizona budget cuts hurting water and air agencies. Arizona Republic. 

May 4. 

http://www.azcentral.com/arizonarepublic/news/articles/2010/05/04/20100504arizonabudget-cuts-hurting-water-and-agencies.html (accessed March 11, 2011). 

McNichol, Elizabeth, Phil Oliff, and Nicholas Johnson. 2011. States continue to feel recession’s 

impact. March 9. http://www.cbpp.org/cms/?fa=view&id=711 (accessed March 29, 

2011). 
62 

Murchison, Kenneth M. 2005. Learning from more than five-and-a-half decades of federal water 

pollution control legislation: Twenty lessons for the future. Boston College 

Environmental Affairs Law Review 55: 527-598. 

http://lawdigitalcommons.bc.edu/cgi/viewcontent.cgi?article=1124&context=ealr&seiredir=1#search="Learning+from+more+than+five-and-ahalf+decades+of+federal+water+pollution+control+legislation:+Twenty+lessons+for+the

+future" (accessed March 29, 2011). 

Office of Management and Budget. Department of Environmental Protection Agency, the federal 

budget, fiscal year 2011. http://www.whitehouse.gov/omb/factsheet_department_epa/

(accessed March 11, 2011). 

Ohio EPA personnel. 2010. Interview by A.J. Dronkers. March 9. Sound recording. From U.S. 

Environmental Protection Agency. WMA file. 

Rechtschaffen, Clifford. 2004. Enforcing the Clean Water Act in the 21st century: Harnessing 

the power of the public spotlight. Alabama Law Review 55: 775-814. 

http://www.law.ua.edu/lawreview/articles/Volume%2055/Issue%203/Rechtschaffen.pdf

(accessed March 29, 2011). 

Rechtschaffen, Clifford. 2007. Shining the spotlight on European Union environmental 

compliance. Pace Environmental Law Review 24: 161-186. 

http://digitalcommons.pace.edu/cgi/viewcontent.cgi?article=1057&context=pelr&seiredir=1#search="Shining+the+spotlight+on+European+Union+environmental+complian

ce" (accessed March 29, 2011). 

Shimshack, Jay P. 2007. Monitoring, enforcement, & environmental compliance: Understanding 

specific & general deterrence. 

http://www.epa.gov/compliance/resources/reports/compliance/research/meecwhitepaper.pdf (accessed March 10, 2011). 

Shimshack, Jay P., and Michael B. Ward. 2008. Enforcement and over-compliance. Journal of 

Environmental Economics and Management 55: 90-105. 

http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WJ6-4PN05JS-2-

8&_cdi=6870&_user=14684&_pii=S0095069607000800&_origin=gateway&_coverDate

=01%2F31%2F2008&_sk=999449998&view=c&wchp=dGLbVzbzSkWA&md5=33b3fc395c07774fb4543dfd09c39bf4&ie=/sdarticle.pdf (accessed March 

29, 2011). 

U.S. Environmental Protection Agency. 2009 state summary data for Clean Water Act National 

Pollutant Discharge Elimination System majors. 

http://www.epa.gov/compliance/resources/reports/performance/cwa/cwa-npdes-majors2009.pdf (accessed March 27, 2011). 
63 

U.S. Environmental Protection Agency. 2009. Clean Water Act action plan. 

http://www.epa.gov/oecaerth/resources/policies/civil/cwa/actionplan101409.pdf

(accessed March 10, 2011). 

U.S. Environmental Protection Agency. Clean Water Act Annual Noncompliance Report 

(ANCR) – 2008 and 2009 data for non-major permittees. http://www.epaecho.gov/echo/ancr/us/#map (accessed March 29, 2011). 

U.S. Environmental Protection Agency. EPA budget and spending. 

http://www.epa.gov/planandbudget/budget.html (accessed March 12, 2011). 

U.S. Environmental Protection Agency. NetDMR: Who can report? 

http://www.epa.gov/netdmr/report/index.html (accessed March 11, 2011). 

U.S. Environmental Protection Agency. Nonpoint source pollution: The nation's largest water 

quality problem. http://water.epa.gov/polwaste/nps/outreach/point1.cfm (accessed March 

27, 2011). 

U.S. Environmental Protection Agency. OTIS permittee reports — Data dictionary for detailed 

permittee reports. http://www.epa-otis.gov/otis/dfr_data_dictionary.html (accessed March 

10, 2011). 

U.S. Environmental Protection Agency. 2010. NPDES permit writer’s manual. 

http://www.epa.gov/npdes/pubs/pwm_2010.pdf (accessed March 12, 2011). 

U.S. Environmental Protection Agency. 2002. Results of the random audit of FY 01 inspection 

data. http://www.epa-echo.gov/echo/images/results_of_inspection_audit.pdf (accessed 

March 10, 2011). 

U.S. Environmental Protection Agency. 2005. Results of the random audit of FY 2003 

enforcement action data. http://www.epaecho.gov/echo/images/results_of_enforcement_action_audit.pdf (accessed March 10, 

2011). 

U.S. Environmental Protection Agency. State comments on frozen data. http://www.epaecho.gov/echo/trends/state_data_corrections.html (accessed March 10, 2011). 

U.S. Environmental Protection Agency. State Review Framework. 

http://www.epa.gov/compliance/state/srf/ (accessed March 10, 2011). 

U.S. Environmental Protection Agency. Toxics Release Inventory: Frequent questions. 

http://tri.supportportal.com/ics/support/default.asp?deptID=23021 (accessed March 15, 

2011). 
64 

U.S. Environmental Protection Agency. Water quality standards. 

http://water.epa.gov/scitech/swguidance/standards/about_index.cfm (accessed March 26, 

2011). 

U.S. Environmental Protection Agency. Water quality standards handbook: Second edition. 

http://water.epa.gov/scitech/swguidance/waterquality/standards/handbook/index.cfm

(accessed March 11, 2011). 

U.S. Environmental Protection Agency. Watershed academy web: Introduction to the Clean 

Water Act. http://www.epa.gov/owow/watershed/wacademy/acad2000/cwa/index.htm

(accessed March 10, 2011). 

U.S. Environmental Protection Agency. Watershed academy web: Introduction to the Clean 

Water Act. NPDES: Enforcement. 

http://www.epa.gov/owow/watershed/wacademy/acad2000/cwa/cwa51.htm (accessed 

March 19, 2011). 

U.S. Environmental Protection Agency. Watershed academy web: Introduction to the Clean 

Water Act. NPDES permits. 

http://www.epa.gov/owow/watershed/wacademy/acad2000/cwa/cwa38.htm (accessed 

March 19, 2011). 

U.S. Environmental Protection Agency. Watershed academy web: Introduction to the Clean 

Water Act. NPDES permits: Elements. 

http://www.epa.gov/owow/watershed/wacademy/acad2000/cwa/cwa40.htm (accessed 

March 19, 2011). 

U.S. Environmental Protection Agency. Watershed academy web: Introduction to the Clean 

Water Act. Regulation of point sources. 

http://www.epa.gov/owow/watershed/wacademy/acad2000/cwa/cwa37a.htm (accessed 

March 19, 2011). 

U.S. Environmental Protection Agency. Watershed academy web: Introduction to the Clean 

Water Act. Sources of impairment by category from the 1998 303(d) list. 

http://www.epa.gov/owow/watershed/wacademy/acad2000/cwa/sources.htm (accessed 

March 19, 2011). 

U.S. Environmental Protection Agency. Watershed academy web: Introduction to the Clean 

Water Act. WQS: Designated use categories. 

http://www.epa.gov/owow/watershed/wacademy/acad2000/cwa/cwa6.htm (accessed 

March 19, 2011). 
65 

U.S. Environmental Protection Agency. Watershed assessment, tracking & environmental 

results: National summary of state information. 

http://iaspub.epa.gov/waters10/attains_nation_cy.control#status_of_data (accessed March 

27, 2011). 

U.S. Environmental Protection Agency. What are water quality standards? General policies. 

http://water.epa.gov/scitech/swguidance/standards/pol.cfm (accessed March 27, 2011). 

Walker, Kendra L. 2009. Protected-area monitoring dilemmas: A new tool to assess success. 

Conservation Biology 23: 1294-1303. 

http://deepblue.lib.umich.edu/bitstream/2027.42/73781/3/j.1523-1739.2009.01203.x.pdf

(accessed March 29, 2011).