### THE EFFECT OF ELECTRONIC REPORTING OF DISCHARGE MON ITORING 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 Y OUR 

### N AME 

### (

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

.
A BSTRACT 

### Point source dischargers regulated under the Clean Water Act’s National Pollutant Discharge
Elimination System are required to submit a dischar ge monitoring report (DMR) on a periodic
basis, usually every month. Many of these reports a re still submitted on paper forms. However,
over the last decade many states have begun impleme nting 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 ef ficiency gains resulting from electronic
reporting have been documented, whether or not thes e systems have any effect on compliance
rates is an open question. To provide a better sens e of the relationship between electronic
reporting of DMRs and Clean Water Act compliance, t his study analyzes data from Ohio for
fiscal years (FY) 1996 through 2009. Ohio institute d electronic reporting software in FY 2000
and switched to a web-based system in FY 2008. Logi t models were employed that include time
trends and fixed effects for permittees. Results in dicate 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 pro vide 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 wit h GPPI and the U.S. Environmental Protection Agency who provided assistance, including Donna Mor rison, 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 Ag ency for providing essential data on when permittees began reporting electronically.
Last but not least, I’d like to thank my wife, Clai re, for her constant support and companionship.
I am extremely grateful to each of you.

Many thanks, Gregory B. Siedschlag

### iv T ABLE 

### O F 

### C ONTENTS



### 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 Metho ds 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 M inor Permittees 56

Appendix III: Descriptive Statistics for Industrial and Municipal Permittees 57

Appendix IV: Results for OLS Fixed-Effects Model Pr edicting 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 sou rces to hold permits that allow them to
discharge pollutants into surface waters up to pres cribed thresholds. These permits are issued
under the CWA’s permit program, the National Pollut ant Discharge Elimination System
(NPDES), which is typically administered by state ag encies.
NPDES permittees must submit discharge monitoring r eports (DMRs) to their regulating
agency (Case Study: Ohio EPA 2010, 1). DMRs are typ ically submitted once a month. They
contain the results of the permittee’s self-monitor ing of pollutant discharge and are compared to
its permit limits to determine if it is in complian ce (U.S. EPA, Watershed Academy Web,
NPDES Permits: Elements). Many permittees still subm it their DMRs to their regulating agency
by paper, but 24 states have begun allowing permitt ees to report them electronically (U.S. EPA,
NetDMR: Who Can Report?). The U.S. Environmental Pro tection Agency (U.S. EPA) is also
proposing a rule that would require electronic repo rting (e-reporting) of DMRs nationwide.
While e-reporting could be costly to implement in t he 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 le ad to improved compliance rates and less
pollution. But is this actually the case?

1 Point sources are “pipes and other discernible con veyances through which pollutants are added to wate rs 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 se lected 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 pe rmittees are now e-reporting. The dataset
used in my analysis includes 2,575 of the 3,250 Ohi o NPDES permittees active as of October
2010. These permittees were tracked from 1996 to 20 09. My model holds permittee
characteristics such as size and industry fixed. Th e dependent variable is compliance status in a
given quarter. The independent variables of interes t 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, fo rmal enforcement, and the inflation-adjusted
dollar amount of the total penalties assessed by Oh io 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 a ny similar studies be found under other
environmental statutes. Knowing how e-reporting aff ected 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 a ssociated with increased compliance, it may be
able to make a stronger case in the rule’s favor.
The perspectives, information and conclusions conve yed in this thesis convey the
viewpoints of the principal investigator and may no t represent the views and policies of OECA
and EPA. Conclusions drawn by the principal investi gator 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 f or 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 U nited 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 piec e 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 t o specific regions (220). As with its
predecessors, the goal of this statute was to “prev ent impediments to navigation” (Freeman 2000,
170). According to Freeman, “The Refuse Act is a us eful 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 legisl ation, the Refuse Act allowed for exceptions.
For instance, liquid runoff and sewer discharge wer e allowed (Murchison 2005, 530), but all
dischargers were required to hold a permit issued b y the federal government (Freeman 2000,
172). Violators faced monetary penalties and even s hort prison sentences (Murchison 2005, 529).
According to Andreen, this statute actually gave th e federal government the ability to regulate
many different types of water pollution, but the Ar my 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 o f 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 d id 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 Andr een (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 1 955 this shrank to less than $1 million (237-
238).
It appears that the statute had little effect on de creasing 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 onl y 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 c ould 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 g rant program that allowed up to $50 million
in annual spending (Andreen 2003, 240). The 1961 am endments 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 stat ute was the first to make it mandatory for
states to enact water quality standards (Freeman 20 00, 171). However, these standards applied
only to interstate waters (Murchison 2005, 532), an d while enforcement power was strengthened,
there was still too much red tape for it to be effe ctive (Andreen 2003, 249-250).
In spite of these efforts the water pollution situ ation 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, re gardless 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 w ith a groundswell of public support for
environmental protection led to passage of a transf ormative set of FWPCA amendments in 1972.

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


### The CWA implemented numerous reforms to reduce poin t-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 th e recently launched U.S. EPA (Murchison
2005, 538). Third, instead of leaving states to ena ct 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 adop t 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 ‘C lean Water Act’ label by which the Federal Water Po llution Control Act had generally come to be known in the 1 970s, adopting it as a second, or alternative, stat utory name” (Murchison 2005, 557).

### 6

receiving waters support their designated uses (And reen 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 Discharg e Elimination System (NPDES) as its means of
regulating point source pollutant discharges (Andre en 2004, 549).
B. Point Source Regulation under the National Pollu tion Discharge Elimination System The CWA effectively bans point source pollutant di scharges into surface waters unless
the discharger possesses an NPDES permit (Andreen 20 04, 549). According to the U.S. EPA,
“NPDES permits cover industrial and municipal discha rges, discharges from storm sewer
systems in larger cities, storm water associated wi th numerous kinds of industrial activity, runoff
from construction sites disturbing more than one ac re, 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 cont ents of an NPDES permit, which typically
includes (but is not limited to) the following item s:

3 According to Freeman (2000), technology-based stan dards are set according to “what kind of pollution abatement equipment [is] available” (174). Examples of design ated uses include fishing, recreation, and drinking water supply (U.S. EPA, Watershed Academy Web, WQS: Designated U se Categories).

### 7

1. The permittee’s discharge limits. This is the a mount of each regulated pollutant
that is allowed in the permittee’s discharge. The a mount 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 instruct ions for permittees to follow
when performing their monitoring.
3. How often the permittee must submit its dischar ge monitoring reports (DMRs)
(Watershed Academy Web, NPDES Permits: Elements).
How exactly are a permittee’s limits determined? F irst, these limits must take into
account the water quality standards for the receivi ng 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, narrativ e, biological, sediment, and wildlife criteria
(U.S. EPA, Water Quality Handbook, Chapter 3.5). An tidegradation, the third component, is
designed to preserve current water quality and desi gnated uses (U.S. EPA, Water Quality
Handbook, Chapter 4). General policies, the fourth component, include any additional
clarification or guidance needed for a particular w ater body (U.S. EPA, General Policies).

4 Under the 1972 CWA all waters were supposed to hav e met a “fishable and swimmable” standard by 1983 (Freeman 1983, 174). This has become the de facto b aseline for designated use, but there is some flexi bility. If a state determines that such a standard is not feasib le, with “appropriate documentation” it may be able to proceed with a lower standard (U.S. EPA, NPDES Permit Write rs’ Manual, 119).

### 8

With water quality standards in place, NPDES permitwriters determine technologybased effluent limits for industrial and municipal dischargers (U.S. EPA, NPDES Permit Writers
Manual 2010, 68). According to the NPDES Permit Writ ers 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 U nited States. TBELs are developed independently of the potential impact of a discharge on the recei ving water, which is addressed through water quality standards and water quality-based effluent limitations (WQBELs). (2010, 68) 

### Water quality-based effluent limits (WQBELs) must b e determined if TBELs do not adequately
protect water quality standards (U.S. EPA, NPDES Per mit Writers Manual 2010, 117), to be
followed by final effluent limits (U.S. EPA, NPDES P ermit Writers Manual 2010, 159).
When the CWA was passed, states were given the opti on 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, collecti ng data, conducting inspections, and engaging
in enforcement. U.S. EPA performs oversight in thes e 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 unr ealistic — 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 l ess 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-po int (U.S. EPA, Watershed Academy Web,
Sources of Impairment). However, significant challe nges remain. U.S. EPA data shows that of

the river and stream miles assessed between 2002 an d 2010, 49.7 percent supported all their
designated uses, 49.6 percent did not support at le ast one designated use, and 0.7 percent
supported all designated uses but displayed evidenc e of decline in water quality. For lake,
reservoir, and pond acreage assessed during this ti me period, these results showed that only 34
percent supported all designated uses and 66 percen t did not support one or more designated uses
(U.S. EPA, Watershed Assessment).
Andreen (2004) identifies perhaps the major challe nge to improving water quality when
he says that “non-point source pollution, especiall y 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 impairm ent 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 agricultu ral sources.
Another impediment to realization of the CWA’s goa ls has been its federalist approach to
water protection. Much of the difficulty of this ap proach stems from inadequate funding for
water protection at the state level. To provide som e historical context, Rechtschaffen (2004) cites
a study from the late 1990s that discovered the ave rage state had less than 50 percent of the

### 10

funding they needed to run their water programs. Th is came after a period of growth in
environmental budgets (788-789).
Rechtschaffen reports that many of these cash-strap ped 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 majo rs 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 unders taffing 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, ju st 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 st ate 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 NP DES universe in 1995, then just 12 percent
in 2001 (784-785). And in Louisiana between 1999 an d 2001, 26 percent of DMRs were lost or
never sent to the state agency and 58 percent of it s 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 seve re. 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. Californ ia, 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 environmen tal 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, an d 120 unfilled positions. Out of 319 workers
the state let go as a result of the FY 2010 cuts, 1 38 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 th e Water Resources Department, which had
236 employees in 2009 but just 98 in 2011 as a resu lt 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 c hallenges 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, a nd 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 Repor t). As budget cuts hit more and more
states, these agencies will likely find it increasi ngly 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 s ome assistance in this regard. U.S. EPA
received a 35.5 percent increase in funding in FY 2 010, 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 innov ate low-cost methods to improve
compliance and reduce pollution. Electronic reporti ng of discharge monitoring reports (DMRs)
has the potential to achieve these objectives. E-re porting, 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 erro rs in their reports (Case Study: Ohio EPA
2010, 3). Second, it makes compliance determination s 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 age ncy 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 th e 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). Accor ding 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, Ne tDMR).

### 13

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

submission. U.S. EPA’s Office of Enforcement and Co mpliance 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 an d data management into the 21st Century, OECA will develop a rule to require NPDES permittee s 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 reportin g tools show limited rates of success unless the tool is mandated. The full benefits of electronic D MR 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, 20 10, e-mail message to author). From 2000
to 2007 the Ohio Environmental Protection Agency (O hio 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 pape r, many users encountered difficultly with
the system (Ohio EPA 2010). Nonetheless, by 2006 app roximately 70 percent of Ohio’s NPDES
universe was using SWIMware (Case Study: Ohio EPA 2 010, 1).
According to the Ohio e-reporting case study, imple mentation 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, intr oducing 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 r eturns for both permittees and Ohio
EPA. According to Ohio EPA personnel, the percentag e of permittees reporting their data has
increased, DMR data entry errors have decreased fro m 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 f ive just a few years ago (Ohio EPA 2010).

### 15



## Chapter III: Conceptual Model

The conceptual model for inputs that affect complia nce status for CWA-NPDES
permittees is simplified in two important respects. First, it only considers the most significant
inputs for affecting compliance. Second, though oth ers 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 stan dards 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 B luebird 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 compa red to the rest of the country, XYZ’s
discharge will compose a larger percentage of the s tream 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 Bluebi rd is also home to the purple trout, an
endangered species whose only known habitat is in o ne 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 environmentali sts, 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 standard s 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 inc lude 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 publ ic 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 col lectively to pressure polluters to
comply with environmental standards, something that economists at the World Bank refer to as
“informal regulation” occurs. Public relations camp aigns, protests, and boycotts are a few of the
strategies citizens employ during informal regulati on 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 com panies (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 d ata is publicly available through U.S.
EPA’s ECHO, which was launched in 2002. ECHO is a w eb-based tool that provides extensive
permittee-level data as well as the ability to obse rve trends at the state level (801-804). But the
most famous example in the U.S. is the Toxics Relea se Inventory (TRI), which began in 1991
(U.S. EPA, Toxics Release Inventory). It requires m any industries to report their toxic emissions
and the data are made available to the public (Kark kainen 2001, 286).
Increased data availability provides the public wi th 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 In ternet, 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 emp owering function of the site. One wrote that “[t]his is a tremendous resource to help us tr ack the actions of potential polluters in our watershed. It helps us identify persistent flaunter s 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 famil y & 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-treatmen t permittees’ statistical information, it will assist countless individuals to make the best choic es for their loved-ones.” (2004, 802-803) 

### As Karkainnen, Rechtschaffen, and Bennear have note d, the availability of this
information provides significant incentives for per mittees to pollute less and comply more. Many
consumers make decisions based partly on the enviro nmental impact of a product. As a result,
businesses that receive negative publicity due to t heir 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 NPDE S 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 entiti es 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 t o 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 i mprove 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 s tands to reason that e-reporting could result
in improved potential for the public to target regu lated entities for informal enforcement
campaigns.
C. Agency Characteristics Agencies responsible for issuing and enforcing NPDE S permits vary in their ability to
keep permittees in compliance. Much of this variati on 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 h and, these states might have more stringent
water quality standards and effluent limits than th e national baseline. While these more stringent
limits are better for the environment, they are als o more difficult for permittees to meet. If
permittees aren’t able to meet these limits then no n-compliance will increase as a result. On the
other hand, these states may also take stronger act ion against violators. Generally we would
expect states that place a higher value on environm ental protection to have higher rates of
compliance, but the ability to implement more strin gent effluent limits complicates this
relationship.
As noted earlier, funding is a major impediment to successful programs. Underfunded
programs have greater difficulty issuing and reissu ing permits on time. They also tend to have
less ability to conduct on-site inspections. Variat ion 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 wi ll 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, i t is not mandatory for states to pass along
data for minor permittees to U.S. EPA (U.S. EPA, CW A Action Plan 2009, 3). Therefore, while
national data on major permittees are relatively co mplete, minor permittee data are virtually nonexistent for many states. Whether a state chooses t o 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 i nspections. These mechanisms
are used to determine whether a permittee is in com pliance with its NPDES permit. According to
one study that examined monitoring from a cost-bene fit perspective, monitoring only occurs
when “the probability of catching a rule breaker ti mes 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 monit oring, 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 Wat er Act Action Plan (2009) says that “EPA
estimates that conversion from hard copy to electro nically-submitted DMRs may save EPA, states, and the regulated sources more than $50 mil lion 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: Oh io 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 increase d inspections, another monitoring
mechanism. On-site inspections are a vital componen t of any NPDES program. We would expect

### 21

compliance to increase as inspections increase. Han na and Oliva (2010) studied the effect of
inspections on emissions under the Clean Air Act an d found that they decreased emissions by 15
percent on average (3). A similar response under th e 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 det errent effect. Once a permittee is inspected it
may believe it will receive closer scrutiny, thus g iving it added incentive to comply. So
permittees, wary of follow-up inspections or enforc ement, 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 per formed 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 enfo rcement 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 inspection s), 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 $250,0 00 penalty taken against XYZ
Manufacturing that deters XYZ from future noncompli ance. General deterrence refers to the
effect of that same penalty on other permittees. Wh en other dischargers find out about the

### 22

penalty through word-of-mouth or the media, in theo ry 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 stro nger for facilities in close proximity to or in the
same sector as XYZ. Shimshack also notes that the p enalty on XYZ is unlikely to have any effect
on dischargers in other states, and that enforcemen t actions other than penalties have been shown
to have a much smaller deterrent effect than penalt ies (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 home builders’ compliance with building codes, May
found that homebuilders were more responsive to “af firmative” than to “negative” motivations to
comply with regulations. Affirmative motivations in clude “homebuilder's general reputation,
demands for homes without defects, confidence in co des, 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 outco mes. 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 sti gma effect” may be a greater deterrent for
larger entities than for smaller ones (15).

F. Permittee Characteristics We also expect to observe fluctuations in complianc e depending on characteristics unique
to each permittee. For instance, major/minor classi fication may influence compliance because
major permittees have been far more heavily monitor ed and enforced against in recent years.

### 23

Also, the nature of operations at a permittee may a lso 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 compl iance 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, wa ter 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 the re were only a few dischargers nearby.

### 24



## Chapter IV: Data, Expected Relationships, and Metho ds

A. Data The data used for this analysis came from two sour ces. 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 Comp liance 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 t he complete universe of NPDES
permittees active as of October 2010. For each of t he 3,250 permittees, this dataset includes four
variables — the permittee’s name, NPDES identificati on number, the date it submitted its first
DMR using SWIMware, and the date it submitted its f irst DMR using eDMR.

Four of the PCS datasets downloaded from ECHO were used — permittee information,
historic compliance, inspections, and enforcement a ctions and penalties. These are national
datasets so Ohio data were extracted from these. (W hen 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 in cludes two variables — NPDES identification
number and the effective date of the discharger’s f irst 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 enabl es us to differentiate between an entity that
does not yet have a permit and one that has a permi t 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 inclusi on in the analysis. To ensure that all the
permittees included in the analysis had at least tw o quarters of compliance history, I also
removed any permittees that didn’t come online as NP DES facilities until the fourth quarter of
FY 2009. As a result, the final dataset includes da ta 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 th e factors discussed in the conceptual model.
B. Dependent Variable: Compliance The compliance variable was taken from the PCS dat a 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: Effluen t 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: Reportabl e non-compliance P Compliant 6 Resolved Pending Resolved Pending: A n 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 manu al 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, a nd Data Dictionary for Detailed Permittee Reports, U.S . EPA, http://www.epa-otis.gov/otis/dfr_data_dictionary.ht ml 

### Table 1 shows us the “order of precedence from most to least important” for compliance
statuses (U.S. EPA, Data Dictionary). This informat ion 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 app roaches. 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 exhib it substantial volatility from quarter to
quarter (see Appendix I). According to U.S. EPA sta ff, this can result from changes at the state
level, such as a lapse in data entry or a policy ch ange 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 c ategories 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 bin ary variable as the dependent variable.
The compliance variable has been recoded due to wha t appears to be a data quality issue.
In the first quarter of FY 2005 there were 862 W-co de observations compared to zero
observations in every preceding quarter. After fall ing to 12 in second quarter and 3 in the third,
143 and 257 observations were recorded in fourth qu arter 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 neigh bors. But to be on the safe side, any W
observations for these three quarters were coded mi ssing (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-v alues. There were more noticeable differences
in the “noncompliant” regression, with the magnitud e of the SWIMware and eDMR coefficients

### 28

increasing slightly and a handful of other variable s that were statistically significant in the other
regressions becoming insignificant at the 95 percen t 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 act ive as of October 2010 began DMR
reporting with SWIMware and eDMR. Thus, there are t wo 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 us ing 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 per mittee has not begun using eDMR and 1 if it
has. The reference category is paper reporting.
Table 2: DMR reporting by fiscal year for permittee s 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 wh en permittees begin e-reporting.
However, the relationship is complicated by the poo r data quality that came out of SWIMware.
By Ohio EPA’s own account, SWIMware data quality wa s perhaps even worse than paper. This
may be at least partially due to the difficulty use rs encountered with the software (Ohio EPA
2010). The fact that estimated monthly DMR errors d ecreased from 50,000 to 5,000 after eDMR
implementation (Case Study: Ohio EPA 2010, 3) attes ts to the magnitude of the problem. As a
result, the expected effect of SWIMware on complian ce is hard to determine.
On the other hand, I expect an improvement in compl iance 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 durin g the SWIMware period were likely the
result of data errors. If this is the case, more ac curate data from eDMR should increase

### 30

compliance. But there is also the chance of additio nal effects, as the response of regulated
permittees to e-reporting may also influence result s. Improved data quality provides interested
parties — such as regulatory agencies, environmenta l groups, and the public — with a clearer
sense of which permittees have significant violatio ns. 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 th e extent that permittees sense the impact of
improved data, this serves as an additional incenti ve to comply.
These factors could affect individual permittees, b ut 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 maj or distraction to Ohio EPA. With this
burden drastically reduced, Ohio EPA personnel note d that they were finding it much easier to
target true violators (Ohio EPA 2010). These are no t the only gains for the agency as a result of
eDMR. As noted earlier, the agency once had five fu ll-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 t o six, with more than three inspections in a
quarter being extremely uncommon. Given the small n umber of EPA inspections, it was not
judged appropriate to separate state and EPA inspec tions.

### 31

Table 3: Total inspections, enforcement actions, an d 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 o n an inspection up to three quarters later. The
number of lagged quarters for this variable and oth ers were chosen based on trial and error after
running numerous regressions. For the reasons discu ssed 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 t hese data, separate variables were

created for the number of informal and formal actio ns 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 enou gh EPA actions to warrant separate variables

for state and EPA actions.

### 32

The expected relationship of enforcement to compli ance was touched on in the
conceptual model, but a brief elaboration is warran ted. I expect compliance to improve after
either type of enforcement action, though the effec t 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 a like. Following Walker’s (2009) cost-benefit
logic regarding monitoring cited earlier, a permitt ee that has been through a formal enforcement
action is unlikely to repeat it if the remedies nee ded to return to compliance are less expensive

than the costs of the action. One such cost of enfo rcement — 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 enfo rcement 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 perm ittee compliance to be past
compliance. Permittees with excellent compliance re cords 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 det errence, 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 o f 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 l arge 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 deterre nt variable. Therefore individual penalties are

counted as formal enforcement actions and their eff ect is part of the total effect for that variable.
However, short of prosecution, monetary penalties a re the most severe action an agency can take
against a permittee. As such, they are also the str ongest general deterrent available to us. By
adding up the total for each quarter, logging it, t hen lagging it over three quarters, we should get
some sense of how much of a general deterrent effec t 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 quit e small.
6. Quarter In addition to fixed effects for individual permit tees, the models also include a time trend.
This is achieved by including a variable for quarte r.

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 inspectio n data for major permittees in PCS, U.S. EPA’s
Office of Compliance found that 0.06 percent of the se 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 estima ted 563 enforcement actions, it identified 26
false positives, which is only 0.4 percent of the P CS 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 t he 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 t o 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 200 9, 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 correc tions or comments to its 2008 or 2009
data, indicating that the state felt their data wer e 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 de termine.
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 vers ion of this variable only captures whether a
permittee was in RNC or SNC versus neither. The ordin al 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 d ependent 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 concern s are captured in the earlier discussion of this
variable. Nonetheless, this study should capture e-r eporting’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 permittee s that had adopted SWIMware to report by
paper. Given the degree of difficulty that users en countered with SWIMware, some may have
chosen to do this. However, in an email to the auth or, one Ohio EPA official said “I don’t think
that was common.” Furthermore, cumbersome as SWIMwa re 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 20 09. Because of the lagged variables, it
was necessary to have a full year worth of complian ce, inspection and enforcement data before
the analysis of the dependent variable, hence the r eason for beginning in 1997. The decision to
end the analysis in 2009 was made because it did no t 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 in cluding the variables for NPDES

### 36

identification number and quarter. All models prese nted in the results section utilize binary
compliance as the dependent variable (another in th e appendix uses ordinal compliance as a
means of comparison). For the models with the binar y dependent variable I employed a fixed
effects logit model. These models take the followin g 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 fo r

inflation, assessed in Ohio during a given quarter Independent variable, three quarters of lags

lnPen 2 

### Log 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 F Y

1996 to 56 for fourth quarter FY 2009) Independent variable

### 37

The most immediate concern with the above model res ults 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 f ormal enforcement is typically preceded by
informal enforcement. Secondly, there is an obvious endogeneity concern — not only do we
expect these variables to influence future complian ce, 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 o n 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 variabl es. When this was attempted the first-stage
regression coefficients were insignificant, renderi ng the second-stage regression moot. The
alternative to this approach is to include four lag ged 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 comple tely) address the endogeneity and
multicollinearity concerns.
In addition to looking at the permittee universe i n 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 Log 2 

### 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 wer e in compliance 72.6 percent of the time,
whereas the mean for ordinal compliance also indica tes that the average permittee is in
compliance. Permittees reported their DMRs electron ically 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 q uarter for informal enforcement was 0.02,
which is somewhat inflated by permittees that had t wo 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 permitte e quarter averaged $35. The value of the average penalty was $85,987. The average for the lo gged total value of all penalties taken in a
given quarter is 4.1, and the mean of the squared l og of total penalties is 49.3.

### 39



## Chapter V: Results

A. Model results and discussion To begin this discussion I have included results i n Table 6 from a logit model that
includes fixed effects for permittees. The model ex amines 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 dw arfs 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 t he 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 q uarter by about 14 percent. The effect two
quarters prior is insignificant. For inspections, t he effect takes longer. Inspections in one quarter

### 40

have no effect (p=.922) on compliance in the follow ing quarter, whereas inspections two and
three quarters prior have a positive and highly sig nificant effect.
Table 6: Results for fixed effects logit model pred icting 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 – 1.20 9 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 gro ups = 1,946. Log likelihood = -20,822.17. Chi-squar ed = 29,871.98.



### The biggest surprises come from the effect of the formal enforcement and penalty
variables. None of the lagged formal enforcement var iables 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. Give n the range of values for these variables (see
Table 5), the odds ratios indicate a very large eff ect for both. However, due to the fact that all
permittees share the same values for these variable s, 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 comp liance. 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 expensiv e 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 se cond quarter lag is positive — the effect flips when $298,134 was assessed in fourth quarter FY 200 2. 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 thi rd quarter lag. This indicates that for penalties
to serve as a true deterrent, the total dollar amou nt assessed in a given quarter should be
relatively large.

5 This is determined for each quarter by: 1. Multipl ying the log value of the penalty by the logit coefficient for firstpower logged total penalties; 2. Multiplying the lo g-squared value of the penalty by the logit coeffic ient for secondpower logged total penalties; 3. Adding these two v alues to determine the net effect of total penaltie s on compliance one quarter later; 4. Repeating steps 1-3 to determ ine the effect two and three quarters later. Inflat ion 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 t he other only minors. The results for the
major model are quite different than the model in T able 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 rem ains significant (p=.012). Informal
enforcement is insignificant in the previous quarte r. 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 remain s 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 unive rse as a whole. Finally, none of the penalty
variables are statistically significant at conventi onal 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 pre dicting 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-squa red = 2,133.34. Model 2: Number of observations = 67,611. Number of groups = 1,678. Log likelihood = -15,396.22. Chi-s quared = 28,573.57.



### The results for minors are quite different than tho se for majors. SWIMware’s effect is
once again significant (p<.001), with these permitt ees having 22.1 percent lower odds of being in
compliance than paper reporters. eDMR is also highl y significant (p<.001) but its effect is

### 44

slightly smaller than for majors. Past compliance i n these models has a much larger effect on
current compliance for minor permittees than for ma jors, and inspections for minors appear to
have a sizeable deterrent effect at least three qua rters into the future. However, of the
enforcement variables, only those for total penalti es 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 resu lts could be due to the size of the universes.
There are 268 permittees in the major regression ve rsus 1,678 in the minor regression, so there
are fewer opportunities to establish significant re lationships in the major permittee universe. 6


### However, there may be different motivations within these two groups. For instance, large
penalties are correlated with substantial improveme nts in compliance for minors, whereas this
was not the case for majors. These penalties may ha ve a larger deterrent effect for minors
because they are less able to withstand the financi al blow of a large fine. Also of interest is the
difference in significance levels for SWIMware betw een the two groups. The reason for this is
unclear. One obvious explanation is that there were n’t enough observations to establish an effect.
It is also possible that major permittees had perso nnel who were better at navigating the software
than minor permittees, and this higher competency l ed to fewer coding errors, which in turn led

to fewer violations.

6 In logit regressions for fixed effects models, Sta ta removes facilities that have “all positive or al l negative outcomes.”

### 45

Continuing with logit fixed-effects with binary com pliance 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 e ffects of SWIMware and eDMR are significant
in both groups, with SWIMware having a larger effec t for municipal permittees (25.3 percent
lower odds of compliance compared to paper) and eDM R having a large effect for industrial
permittees (46.7 percent higher odds compared to pa per). Informal and formal enforcement were
not correlated with improved compliance in either s ector. In fact, for municipal facilities,
informal enforcement was correlated with 22.7 perce nt lower odds of compliance two quarters
later. The effect of inspections on industrial perm ittees 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 descriptiv e statistics for the major, minor,
municipal, and industrial sub-universes. In Appendi x IV I have included results from an OLS

fixed effects model that estimates the effect on or dinal 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 pre dicting binary compliance for municipal versus industrial permittees, FY 1997 through FY 20 09
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-s quared = 16,585.97. Model 2: Number of observations = 36,156. Number of groups = 785. Log likelihood = -10,232.23. Chi-squ ared = 12,901.84.



### B. Overarching conclusions based on results When considering all of the results together, I ha ve drawn the following conclusions
about the effect of the independent variables on co mpliance in Ohio during this period:

### 47

1. SWIMware appears to have had a negative effect comp ared to paper and eDMR. For the most part, SWIMware has a negative and high ly statistically significant effect on
compliance. The first model showed a 19 percent low er odds of compliance versus paper
reporting. Based on what we know about SWIMware, th is could be due to its low data
quality, which could have resulted in a larger numb er of erroneous compliance violations
compared to paper reporting. However, the insignifi cant result for the major sub-universe
indicates that this effect may not have been univer sal.

2. eDMR appears to have had a positive effect compare d to paper and SWIMware. The eDMR coefficient is statistically significant a nd 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 par tially due to vastly improved data
quality under eDMR. However, there may be other fac tors contributing to the effect as
well. For instance, it’s possible that improved dat a 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 seriou s 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 i nformal regulation.

3. The effects of SWIMware and eDMR may have been larg est at the beginning of
their implementation. Appendix V provides results from three different mo dels, each
employing logit fixed effects with binary complianc e as the dependent variable. Each
model examines compliance during three different ti me 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 introd uction, FY 2008 through FY
2009. Interestingly, none of the e-reporting coeffi cients in these models are statistically
significant. When comparing these with our other mo dels 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 captur e changes that occurred within the
periods of use. However, there are limitations to t his approach that make it more difficult
to find a significant result. For example, the pred ictive power of the effect of eDMR
between FY 2008 and FY 2009 alone is quite low. eDM R was implemented very rapidly,
with nearly every permittee in the state having ado pted it by the last quarter of FY 2009.
Therefore, in addition to looking at a relatively s hort time period, there may not be
enough variation within the eDMR variable by the en d 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 perm ittees, whereas for majors they had a
significant negative effect on compliance one quart er later. For industrial and minor
permittees the effect was generally positive and si gnificant. 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 o thers. The positive effect in the
second and third quarters could stem from a combina tion 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 sig nificant effect in any of the subuniverses analyzed. In fact, for municipal and majo r 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 forthcomin g. The macro model, with the higher
predictive power gained from including all permitte es, does indicate that for a substantial
number of permittees informal enforcement does serv e 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 inef fective at improving
compliance. None of the coefficients on formal enforcement were significant in any of
the models. However, there were only 368 formal act ions 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 tot al 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 ef fect, the first-power penalty variables
were always significant and negative, whereas the s quared penalty variables were almost
always significant and positive. Aside from the obv ious deterrent effect that large fines

### 50

appear to have on facilities, part of this effect m ay be due to larger penalties attracting
more media coverage than smaller ones. As for the n egative effects for small penalties, it
could be that these have a similar explanation to t he 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 g roups but were always
substantial. In fact, the squared penalty variables are far more likely to be positive and
significant than the other enforcement variables. W hile the effect may be overestimated
due to the lack of variation, it appears that the g eneral deterrent effect from large
penalties overwhelms the effects of any other type of enforcement action.

8. Compliance in one quarter is a very strong predicto r of compliance in the next, but
its reliability is greatly diminished going back fu rther. 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 qua rters 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 impro ves compliance rates, as would appear to be
the case with eDMR, this is yet another reason to m ake the switch.
However, Ohio’s experience with SWIMware illustrat es that some e-reporting systems
are better than others. The system was difficult fo r 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 i ts benefits are less well documented than
those of eDMR, Ohio EPA personnel did note that SWI Mware permittees generally preferred it
to paper reporting (Ohio EPA 2010).
Given the number of states that have recently adopt ed e-reporting, as well as U.S. EPA’s
e-reporting rule, it is probably just a matter of t ime before all NPDES permittees report
electronically. If properly implemented, this shoul d be a step forward for the NPDES program.
Systems like eDMR and NetDMR have the potential to i mprove data quality, lower costs for
both regulating agencies and permittees, and even i mprove compliance rates. However, the
SWIMware example should illustrate to both states a nd U.S. EPA the importance of
implementing e-reporting systems that are fully aut omated, web-based, and user friendly.
With or without e-reporting, inspections and enfor cement are essential methods of
promoting compliance. In recent years Ohio has begu n 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 inspecti ons than in 2000. Informal actions, which
were a rarity for most of the decade, came back int o 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 st ill 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 perm ittees discern that enforcement begins and
ends with informal action, then it too will begin t o lose its deterrent effect over time. And in fact,
this does appear to be happening with majors and mu nicipal permittees in Ohio, as informal
enforcement action taken two quarters prior actuall y had a negative and significant effect on
compliance. My results also indicate that if Ohio t ook a larger number of high-dollar penalties,
compliance rates should improve, as this had a sign ificant deterrent effect for all but major
permittees.

### 53



## Chapter VII: Conclusion

This analysis indicates that e-reporting has produc ed 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. Add itional 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 contr ol for endogeneity.
Implementation of eDMR has led to efficiency and da ta 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 w hile at least holding other factors constant
should give them greater ability to perform their j obs 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 exp erience 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 M inor 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 Log 2 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 Log 2 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 Pr edicting 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. R 2 = 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



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