Do non-profits encourage compliance? Watershed
Groups and the US Clean Water Act
Laura E Grant and Katherine Grooms∗
September 18, 2012
Abstract: We empirically measure the affect of nonprofit watershed group existence
and spending on enforcement and compliance of the Clean Water Act. We link data on
inspections, violations, SNC Category I violations, enforcement actions and penalties from
the EPA’s ECHO database with data on the watershed organizations’ characteristics and
finances. We estimate the effect of these groups at the state and local level. Our results at
both the state and the local level indicate that states free-ride on these groups by decreasing
inspections as the number of groups increase. However, at the local level we find a decrease
in violations, particularly the most serious classifications. These groups have a real impact
on firm compliance. Yet the effect does not operate through influence on official regulatory
channels, but instead through indirect oversight.
Keywords: Clean Water Act, Watershed groups, Nonprofits, Voluntary enforcement
∗
Grant: University of Wisconsin, Milwaukee, Department of Economics, Northwest Quadrant B 4459,
Milwaukee, WI 53201 (email: grantle@uwm.edu). Grooms: University of California, Santa Barbara, Department of Economics, North Hall, Santa Barbara, CA (email: kimble@econ.ucsb.edu). We appreciate the
extensive research assistance by Benjamin Blair. This draft is preliminary; we welcome general feedback and
notification of possible errors, but please do not cite.
1
1
Introduction
The Clean Water Act (CWA) passed 40 years ago to address concerns with water quality
and firm complacency about pollution discharge. Despite this broad and ambitious federal
push to mitigate water pollution in the United States, noncompliance and other issues persist (GAO, 1996, 2009). The structure of the CWA places much of the responsibility for
enforcement in the hands of the states. Such sweeping federal policies cause tension at the
state level, as states struggle with satisfying local interests while enforcing federal mandates.
Local nonprofits that seek to improve the quality of water resources may step in where state
efforts fail. In the recent decades, thousands of these water-related groups have sprung up
across the US as stewards of local rivers. But little is known regarding how and to what
extent these groups might increase compliance.
Theory and intuition suggest environmental groups are important in providing and protecting environmental amenities. Watershed group oversight often provides an appropriate
scale of factors affecting water, as geographical boundaries define a watershed rather than
arbitrary political ones. Additionally, conflicting state level interests do not directly dictate
the activities of these groups, and thus they may be more immune to these distortions of
enforcement. This research provides a direct, large-scale statistical test of these ideas. We
use data on the existence and spending of watershed groups, linked with firm-level compliance data to assess impacts on compliance and enforcement of the US CWA. Subsequently,
we discuss the possible mechanisms for this effect.
To examine how these groups might induce compliance, we first think about the role of
nonprofits generally. The economy can be divided into three distinct sectors: the for-profit
(market) sector, the government sector, and the nonprofit (independent) sector. This leads
to the three-failures theory; each sector provides something the other two do not (Steinberg,
2006). Nonprofits’ role is often in provision of public goods beyond the level determined by
political consensus.
The watershed groups’ goal is maximizing water quality but they are limited by their
2
budget. Therefore, we proxy for these voluntary efforts in two ways: the number of watershed
groups and the annual revenue to the groups. Throughout we assume watershed groups
proceed with voluntary actions and advocacy taking government actions as given. However,
government oversight is dependent upon the watershed group actions and other factors. The
regulatory agencies wish to minimize expenses subject to meeting CWA standards.
A specific function of these groups is to act as watchdogs, alerting authorities of negligence and violations. If watershed groups operate through this mechanism, the voluntary
efforts should complement and increase the level of regulatory enforcement, also know as
crowding in. However, voluntary groups can also raise community awareness and work with
firms directly to improve compliance. Direct monitoring and actions to improve water quality
include river cleanups, water quality testing, media campaigns, and educational programs.
These actions should decrease the probability of a firm being out of compliance, alleviating
the need for government oversight and decreasing instances of inspections and possibly violations. Langpap (2007) presents an analytical model and predicts this spectrum of possible
outcomes when citizens can sue non-compliers, but does not address the other functions
nonprofits may fill.
While groups should fill a void, anecdotal evidence implies there is little credible evidence that most nonprofit organizations produce any social value.1 The literature assessing
performance of any type of nonprofit is sparse. Rather than measuring the actual costs
of voluntary provision or the beneficial impacts of the groups, most papers on nonprofit
crowd-in/-out measure financial inputs: whether contributions from citizens are complements or substitutes with contributions from government grants (Andreoni, 1993; Andreoni
and Payne, 2011; Heutel, 2009). In a related paper, Monti (2010) assesses how state-level
government budgets spent directly on the environment affects donations to related nonprofit
groups.
1
From the “End of Nonprofits” sourced on September 12, 2012 at http://www.
philasocialinnovations.org/site/index.php?option=com_content&view=article&id=36:
the-end-of-charity-how-to-fix-the-nonprofit-sector-through-effective-social-investing&catid=
20:what-works-and-what-doesnt&Itemid=31
3
Despite the extraordinary increase in environmental groups specifically, there are few empirical investigations of the extent of their role in encouraging regulatory oversight. Previous
literature on federal mandates such as the CWA has addressed issues with state enforcement,
studied enforcement effectiveness, and examined the mechanisms for encouraging compliance,
primarily through regulatory channels (Sigman, 2005; Magat and Viscusi, 1990; Shimshack
and Ward, 2005; Earnhart, 2004a; Gray and Shimshack, 2011). Other work has documented
the failings of enforcement of the CWA due to inconsistent oversight (Flatt, 1997; Grooms,
2012). While earlier work has examined official regulatory intervention and its effect on firm
compliance (Magat and Viscusi, 1990; Shimshack and Ward, 2005; Earnhart, 2004a,b), in
the form of governmental regulatory activities, few papers have specifically accounted for
the role of unofficial nongovernmental monitoring and voluntary enforcement efforts, such as
those undertaken by watershed groups. Most closely aligned with our work, Langpap and
Shimshack (2010) focus on the effects of citizen law suits by environmental advocacy groups
on municipal water treatment plant effluent compliance, finding crowd-in of public monitoring but crowd-out of sanctions. Citizen suits are rare; our work complements their findings
by using comprehensive data regarding nonprofits across the US over many years. Additionally, citizen suits are costly and the legal outcome uncertain, whereas other watershed group
efforts may successfully improve compliance without these caveats.
The outcome variables are measures of enforcement and compliance for point-source pollution under CWA standards, captured by inspections, violations, a measure of Significant
Non Compliance (SNC), enforcement actions and penalties. The CWA requires periodic water pollution reports prepared by industries, municipalities and other facilities discharging
to surface waters, where firms self-disclose water disposal activities. However, regulatory
agencies routinely perform inspections to check compliance, cite violations, and issue enforcement actions. States report compliance data on facilities permitted under the CWA
to the US Environmental Protection Agency (EPA) in two datasets. These are the Permit Compliance System (PCS) and the Integrated Compliance Information System National
4
Pollutant Discharge Elimination System (ICIS-NPDES), both contained in the EPA-ECHO
database. Findings on inspections, violations, and enforcement actions are available at
the facility/permit level from 1976 to 2008, but for the purposes of this work we focus on
1993-2007. We aggregate each outcome variable across facilities in a watershed to study
enforcement and compliance efforts at that spatial level. We observe about 355,228 facilities
in the Permit Compliance System dataset and about 66,885 in the Integrated Compliance
Information System dataset.2
We match the oversight data to information about environmental nonprofits that operate
in the same watershed. US nonprofit groups must register and report finances annually to the
Internal Revenue Service (IRS). There are over 2,600 nonprofit groups classified as working
in the area of “Water Resource, Wetlands Conservation & Management”. The watershed
group data include date of incorporation, location and type of watershed group (and other
characteristics). For larger watershed groups, expenditures and other financial data are
derived from IRS tax return data of nonprofits and are available since 1992. Smaller groups
do not file taxes, so we include an indicator for the existence of low-revenue groups. Over
10,000 group-year observations of financial data and an additional 15,000 binary observations
for the years the groups are operating but not reporting financial data. The median revenue
of the reporting groups over all the years is $117,268.
Our research design addresses several novel issues. First, this research gives evidence
of oversight by voluntary groups. Our analysis assesses changes in firm-level compliance of
water quality standards in all industries through time relative to the existence and activities
of the watershed groups. We establish a general crowding out of governmental regulatory
efforts attributable to the differential impacts of watershed groups across regions. The result
contrasts the previous finding that citizen suits increase inspections. Second, we deal with
potential endogeneity of watershed groups within states that could affect regulatory efforts.
We believe federal-level conservation voting records may be orthogonal to state-level changes
2
Due to a lack of location data on some facilities, our sample is smaller than the entire universe of
available facilities. See the data section.
5
in watershed inspections, (except through the impact on formation of citizen groups) thus,
voting scores may be a valid instrument. Third, we estimate results at a local level, delineated by watersheds, with boundaries that are uncorrelated with political and other social
jurisdictions. Finally, our analysis provides estimates of the benefits of watershed groups
with regard to the CWA, which we compare with the costs of state government monitoring
and enforcing under the CWA.
Specifically we find no evidence that watershed groups have an impact on firm compliance
at the state level. Whereas, these groups have a significant effect on compliance at the local
or watershed level. We see a significant negative effect on the fraction of facilities in violation,
conditional on the fraction of facilities inspected, and a significant negative effect on the most
egregious violations (Significant Non Compliance Category I Effluent), conditional on the
fraction of facilities inspected. This effect appears to operate through alternative channels
rather than regulatory means: an increase in watershed groups significantly reduces the
fraction of facilities inspected but has no effect on official enforcement actions or penalties.
The successes occur regardless of the spending capacity of the watersheds groups, indicating
that the existence rather than size is most influential.
The paper is organized as follows: Section 2 discusses the data we use in our empirical
analysis. Section 3 gives our empirical specification and results. Section 4 concludes. Tables
and Figures are shown in Sections 5 and 6.
2
Data
This section details the data for our empirical analysis.
6
2.1
Compliance Data
Data on facilities permitted under the CWA are included in two datasets which can be accessed through the EPA’s Enforcement and Compliance History Online (ECHO).3 These are
the Permit Compliance System (PCS) and the Integrated Compliance Information System
National Pollutant Discharge Elimination System (ICIS-NPDES).4 These datasets contain
basic information such as facility name, location and industry. In addition to basic identifying
information about each facility, these datasets contain information on the firm’s compliance
record including information on inspections, violations, and enforcement actions.5 Note that
we only observe facilities when they are inspected, in violation or enforced upon; we do not
observe the entire universe of facilities. A separate violations variable identifies facilities
which are in Significant Non Compliance (SNC) for a given quarter of a given year, and
which type of SNC they fall under. This dataset gives a feel for the severity of facility non
compliance, as SNC codes are rank ordered beginning with S (SNC/Category I - an enforcement action has been issued and the facility is not meeting its compliance schedule), E
(SNC/Category I - effluent violations of monthly average limits) and X (SNC/Category I - effluent violations of non-monthly average limits), which are all related to effluent exceedances,
and moving to less severe non compliance, such as failing to report.
These measures can be totaled across facilities to construct measures of total inspections, total violations, total enforcement actions, and total penalties in a state or watershed
(HUC). However, this does not account for variation in the number of facilities generating
3
The unique identifier contained in the datasets is the sNPDES permit number. Some larger facilities
may have more than one NDPES permit, and so they may be listed multiple times for different permits.
The difference between an individual permit and a facility is not relevant for the purposes of this paper. For
simplicity we refer to these as facilities rather than facilities or permits.
4
The data used in this paper was accessed in early 2010. The EPA has transitioned data from the PCS
to the ICIS-NPDES, data accessed at a different time may contain slightly different variables.
5
Linking all of the compliance indicators discussed above is not straightforward. Because the reported
violations come from both Discharge Monitoring Reports (DMRs) filed by firms, self reported effluent violations, and from inspections, they cannot be linked by date to the inspections data. There is not an indicator
for the source of a violation report. Similarly, there is no variable which links the enforcement actions to
a given violation. Using dates is one approach, however it is unclear if there is a delay between when a
violation is registered and when a subsequent enforcement action is filed.
7
these totals. We create a measure of total facilities that is not time varying due to data
constraints but is the best approximation available for the total number of facilities operating in state/HUC. We then construct per facility measures of our totals. We are concerned
about outliers from these measures driving our results (see Table 1), so in our empirical
specification we use alternative measures. These are constructed as the number of facilities
with at least one inspection, violation, enforcement action or quarter in SNC. Dividing by
the total number of facilities, we generate the fraction of facilities inspected/in violation/in
SNC violation/enforced upon in a given state/year or HUC/year. Descriptive statistics on
our measures of enforcement and compliance are shown in Table 1.
2.2
Watershed Group Data
We link the data on Clean Water Act compliance with information on the activities of
relevant environmental groups. Environmental groups aim to make a difference voluntarily,
by protecting endangered species, mitigating climate change, or improving the quality of
water resources. Nearly 18,000 environmental groups are registered as nonprofits with the
US Internal Revenue Service.6 Unfortunately, other factors influence the outcomes of the
groups’ work, like complex bio-physical systems, community interest, and fluctuating political pressures. The confounding factors create difficulty in measuring whether or not the
voluntary mechanisms improve implementation of environmental regulations. To overcome
these difficulties, we use available data on watershed groups to measure the impact. This
type of group provides a convenient example as the groups are fairly homogenous in mission,
but disaggregated and dispersed throughout the US.
A watershed is geographic concept. Watersheds are topographical, defined by an outlet
point on a river or into a lake/sea and outlined by a ridge of land such that surface water
(rather than ground water) eventually flows through the outlet. The basin sizes vary due
6
Determined using a search in Guidestar, the premier organization that gathers and publicizes information about nonprofit organizations. The query was performed on Oct 25, 2011 with the advanced search
function on guidestar.org using the categories “Conservation and Environmental Education” and “Pollution.”
8
to geography and the choice of outlet point; the choice is somewhat arbitrary. Mississippi
River basin is 1,245,000 mi2 (3,220,000 km2 ) and covers almost 40% of the US mainland.
The basins are further subdivided into smaller and smaller catchments, defined by the forks
of main rivers.
US Geological Survey delineates and defines hydrological unit codes (HUC) to provide a
consistent measurement of all US basins. Large rivers have two-digit HUCs. The Mississippi
River is comprised of six HUC2 regions: the Upper Mississippi, the Lower Mississippi the
Missouri, the Ohio, the Tennessee, and the Arkansas-Red-White. The HUCs are spatially
nested by beginning with the two-digit code and adding two more digits to designate subregions, then two additional digits for smaller sub-basins within the subregions, and so on.
We use the 8-digit HUC (HUC8) designations as our definition for a watershed, also referred
to as cataloging units 7 . There are 2264 HUC8 watersheds in the US that range in size from
a few hundred square miles to a few thousand, depending on regional topography. As an
example of watershed boundaries, see Figure 1 of Iowa.
We use data from the Internal Revenue Service (IRS), which provides information on
nonprofit watershed organizations’ characteristics, locations, and watershed-specific spending
by year. All nonprofits with donation receipts of $25000 or more must file Form 990 annually
disclosing revenue and expenses to the IRS. 8 We have fifteen years of IRS tax filing data, from
1993 to 2007. Watershed-oriented groups are listed under code C32, classified as working
in the area of “Water Resource, Wetlands Conservation & Management.” There are 1333
groups in that are in the data through this time period, with only 185 filing in 1993 and
consistently growing though time. The groups are small on average. Median revenues of
these groups are $117, 268 and median contributions are $86,250, annually.
7
See http://water.usgs.gov/GIS/huc.html
Numerous smaller watershed groups exist and are not included in this figure. We are currently in the
process of collecting information on these groups.
8
9
2.3
Controls
We also collect and include data that reflect state level characteristics that vary annually
and may affect regulatory oversight of the Clean Water Act. We include controls for annual
state budgets for activities related to environmental regulation; budget year to year partially
dictates the number of inspections a state can perform. We proxy for state budgets using
Census of Governments data, from the Census Bureau, with categories for health expenditures, natural resources expenditures, and parks and recreation. We include all of these
measures in per capita terms. These measures may also capture time-varying state preferences which might also affect the outcomes. Additionally, we control for state population
which comes from the U.S. Bureau of Economic Analysis.
2.4
Matching Data
Matching data is an empirical challenge. Data defined by geography, water quality,
politics, and nonprofit activism is often misaligned. Facility compliance reports have either
a geographic point, latitude and longitude, or a zip code, which may overlap with more than
one watershed. Water quality data falls within a watershed but unfortunately each state
uses alternative codes, which need to be matched up with HUCs and watershed nonprofit
groups. Nonprofit groups define their “territory” in varying ways, for which each must be
accounted. We use the best available locators and match at the most specific spatial level.
Additionally, there are numerous watersheds that cross political lines, referred to as
transboundary. While not the focus here, these transboundary watersheds are an empirical
challenge, but also opportunity for further empirical work. For the moment, transboundary
watershed are dropped and the effects ignored.
10
3
Empirical Specification
This section details our empirical approach. We implement fixed effects specifications at
the state and HUC level, as well as an IV specification at the state level.
3.1
Fixed Effects Specification
We examine effects at both the state and local level given watershed groups may affect
regulator behavior and firm compliance over various geographic extents. State level measures
of enforcement and compliance capture how the aggregate number of groups operating within
a state impact enforcement within that state. Yet these groups work typically within more
localized areas than states. Their influence may be targeted to the watershed where they
are located. We capture local effects using 8-digit Hydrologic Unit Codes (HUC8) as the
coverage area of each group.
We look at a range of variables as outcomes, from inspections through penalties, also
capturing changes in firm compliance. We undertake a study of the full enforcement process;
beginning with the fraction of facilities inspected, moving to the fraction of facilities enforced
upon and finally looking at total penalties per facility. We also examine measures of firm
compliance given by fraction of facilities in violation, and the fraction of firms with the worst
violations, SNC Category I Effluent Violations. The various steps of regulatory oversight
mostly occur in succession: a firm is inspected, then conditional on a visit from the state
agency, may be found in violation and penalized.9 Unfortunately inspections are not linked
directly with violations. As a proxy we include a variable from the previous step for each
successive step of the analysis: the specification for violations includes fraction of facilities
inspected, with a similar inclusion of violations for the penalties estimation. Including the
preceding dependent variable in the regression also allows an interaction between overall
levels of regulatory oversight – for example a state that conducts many inspections in a year
9
The firm’s Discharge Monitoring Reports (DMR) report violations but our data lacks the reporting
source of the violation.
11
may also levy many additional penalties.
Our outcomes are summed across the facilities in a state/HUC, over the total number
of facilities, or alternatively, the average over facilities in the state/HUC. In the estimation
process, we weight by the number of facilities at the state/HUC level.10
We sequentially estimate effects on inspections, violations, and penalties, first at the state
level then the HUC level using
yit = β1 groupsit + β2 groups revenueit + X’it Π + αi + γt + εit
(1)
where yit are the outcomes variables, groupsit is the number of groups operating in
state/HUC i in year t, groups revenueit is the total revenue of all groups operating in
state/HUC i in year t, Xit is a vector of controls which includes state expenditures and
state population, and αi and γt give state and year fixed effects respectively.
The independent variable of interest is the number of groups operating in a given
state/HUC. States and HUCs may have a similar number of groups but with different levels of spending power and therefore different levels of potential impact. Therefore, we also
include total revenue across groups to give some measure of group size and influence.
Before moving to the full specification, we assess outcomes on the pooled data. At
the state level, watershed groups have a negative effect on inspections, a positive effect on
violations and SNC violations, and a negative effect on penalties. At the HUC level, these
organizations produce a positive effect on violations and SNC violations but no significant
effect on inspections.11 Pooling assumes effects are constant across states and years (by
excluding state/year fixed effects). The pooled result showcases the impact of the number of
groups without allowing states to behave differently. However, there is likely heterogeneity
across states. The results of the fixed effects specification account for this heterogeneity,
10
These analytic weights are appropriate when using aggregated measures like ours, as the larger the
sample over which a given measure is calculated, the closer it approximates the population measure. Thus
we want to give more weight, or rely more heavily, on these more precisely estimated measures.
11
Pooled results available upon request.
12
with clustered standard errors to mitigate heteroskedasticity and serial correlation. These
refinements provide results closer to the causal effect of the number of groups on our outcomes
of interest. The state level results are shown in Table 2.
From the state level results, we see a negative and significant effect of -0.00095 of the
number of groups on the fraction of facilities inspected at least once, from Table 2, Column
1. States decrease the fraction of facilities they inspect as the number of groups increases,
suggesting free riding on the voluntary monitoring provided by watershed groups. The
results on other enforcement and compliance outcomes are not statistically different from
zero, implying that the number of groups at the state level is not affecting violations and
SNC Category I violations.12
At the state level, observing compliance in aggregate, we ignore variation in enforcement
and compliance outcomes at the smaller watershed level. As these groups cover a specific
territory, their impact on firm compliance may be very localized. Thus, we perform the
same analysis at the HUC level, to exploit this additional source of variation. The HUC
level results are shown in Table 4.
A more complete picture of the effect of these groups emerges. The result we saw at the
state level, that the number of groups has a negative and significant effect on the fraction of
facilities inspected, holds with a slightly larger magnitude coefficient of -0.0025. Additionally,
we observe a significant effect of -0.00118 of the number of groups on fraction of facilities in
violation (Table 4, Column 2). We include a control for the fraction of facilities inspected, as
we might be concerned that a drop in inspections would lead to a drop in violations simply
because violations are not then being detected. The results, conditional on inspections,
demonstrate that these groups are having an actual impact on compliance that does not
operate through the inspections channel. Further evidence is provided by Column 3, which
shows the impact of groups on SNC Category I Effluent violations. These are the worst
types of violations and indicate actual overages in pollution discharges. We see groups
12
Using total per facility measures for inspections, violations and enforcement actions yield similar results:
the coefficients are negative and significant until we cluster the standard errors.
13
reduce SNC Category I violations, with a significant negative effect of -0.00038 conditional
on total inspections (Column 3).13 To think about the magnitude of these coefficients, our
results imply that an increase of 10 watershed groups would result in a 2.5 percentage point
decrease in the fraction of facilities inspected, a 1.2 percentage point decrease in the number
of facilities in violation, and a .38 percentage point decrease in significant non compliance.
Taken together, the results on violations and more specifically the result on SNC Category I violations indicate that watershed groups have a strong impact on Clean Water Act
compliance. The effect operates through multiple channels by directly affecting firm behavior rather than encouraging regulatory threat. Additionally, these results suggest that their
impact on violations is not simply on violations associated with correctly filling paperwork
and other non-critical noncompliance, but on the types of violations which contribute most
to serious noncompliance with the CWA and potentially to higher levels of water pollution.
Additional results at the HUC level focus on enforcement actions and penalties which are
levied against violating firms. The number of groups significantly reduces both the fraction
of facilities with at least one enforcement action, as well as total penalties per facility,
conditional on the fraction of facilities with at least one violation (Columns 4, 6). Including
the fraction of facilities in violation but not inspections may cause omitted variable bias in our
estimates, as violations and inspections are very highly correlated. When we add the fraction
of facilities inspected, we no longer see a significant effect of groups on enforcement actions
or penalties (Columns 5, 7). Our interpretation is that states maintain their enforcement
behavior regardless of the presence of watershed groups.
Watershed groups may have differential impacts depending upon whether the state or the
federal government is authorized to implement the CWA program. Over the first decades of
the CWA, most states took responsibility for enforcing the regulation rather than leaving it
to federal implementation. An event analysis, which centers all years around authorization,
13
Total inspections is the relevant measure of inspections in this case as these facilities may be inspected
more often due to existing enforcement actions. The total measure of inspections more accurately captures
the level of scrutiny and potential detection that these facilities are under.
14
uses the time variation in authorization to look at the differential effect of groups in the pre
and post authorization period. However, as our sample covers 1992-2008, we do not have
enough time variation in authorization status to use this strategy.14 Instead, we look at
the subsample of authorized states as a robustness check of differential effects in authorized
states. The findings support the results from the entire sample of states.
An unexpected result emerges from studying each stage of the enforcement process as well
as firm compliance. The number of groups reduces inspections, the monitoring outcome for
which the states are responsible. If watershed group activities encourage compliance without
operating through official channels, the state can free-ride on the voluntary oversight. And
indeed, increases in the number of watershed groups induce higher levels of compliance,
represented by lower violation rates.
The results contrast with an alternative possible mechanism: that these groups seek to
improve compliance through official regulatory channels. For example, groups could influence the state to increase inspection rates and increase punishment for violators through
enforcement actions and penalties. Yet we do not see an effect on punishment for violators
through enforcement actions and penalties, suggesting that states do not become more lax or
more stringent in the presence of these groups. Our results do suggest that watershed groups
play an important role in encouraging firm compliance and decreasing egregious violations.
However, the specific mechanism through which their influence operates is not clear. It is
possible that the presence and implied oversight of these groups is enough to change firm
behavior, even in the face of the decreased oversight by state regulators that follows the
formation of these groups. Our current analysis does not address this mechanism.
3.2
Instrumental Variable Specification
The above analysis assumes watershed existence is exogenous to state level regulation
actions. A concern is that watershed group and governmental regulatory presence are simul14
States were authorized over time beginning in the 1970s. Some states did transition during our study
period, but a large proportion of our sample was previously authorized.
15
taneous. We can disentangle causality with the use of an instrumental variable. We have
identified federal voting records of state representatives as a variable that represents the
proclivity of a state to have voluntary watershed groups but does not directly affect state
regulatory efforts.
The League of Conservations Voters (LCV) calculates scores based on Federal House
or Senate member’s voting record favoring pro-environment policies. From Sigman (2005):
“The LCV score (which ranges from 0 to 100) represents the share of a legislator’s votes on
selected measures that the LCV considers pro-environment.” As these officials are elected in
their home districts to Congressional office, their voting record should be somewhat indicative
of the level of pro-environment sentiment in their home district. However, as these are not
local officials they do not make direct decisions on budget, spending, and enforcement of
environmental policy within the state. As a further support, congressional jurisdictions have
boundaries that are not aligned with state regulatory agencies.
In order to use the LCV score as an instrument or a control which is not captured by
state or year fixed effects, we examine how much time variation there is in this measure.
The correlation between the LCV score and the past year’s LCV score is .9327, which is
quite large. However, the correlation between the current LCV and past LCV decreases
over time. When we take out time trends and state fixed effects, substantial year to year
variation emerges. Thus, we believe there is enough time variation in LCV scores to allow
us to capture annual changes in environmental sentiment in a given state.
The relevant equation for the first stage in two-stage least squares is given by
groupsit = φZit + X’it Γ + θi + ρt + εit
(2)
.
We are most concerned with endogeneity of groupsit , the number of groups operating in
state/HUC i in year t. We look to φ to give an indication that federal voting records affect
how many watershed groups exist in each state. We expect a positive relationship between
16
green support at the federal level and citizen involvement in environment at the local level.
Xit is again the vector of controls and includes state expenditures and state population, and
θi and ρt give state and year fixed effects respectively. Zit is the instrument, the voting
scores.
After the first-stage is run, the remaining variation in the number of watershed groups is
used to explain government oversight, per Equation 1.
The IV regressions parallel the fixed effects models, results shown in Table 3. The
outcomes are also very similar to the fixed effects specifications. The only result worth
noting in contrast to previous is that we lose significance for the effect of watershed groups on
inspections, though the coefficient is still negative. We believe that the state level results are
hiding local level heterogeneity, an issue that would be more pervasive with instrumentation
if some groups act differently than others. We also see from first-stage results that our
instrument choice is weak, given F-statistics in the range of 2 and t-statistics not providing
significance. In results that are unweighted, but not reported here, all coefficients remain
similar but indicate the instrument is stronger, with F-statistics over 10 and t-statistics
greater than 2.
4
Conclusion
We find that at the state level, watershed groups have a significant negative effect on in-
spections but no effect on firm compliance. We investigate this using a fixed effects estimation
strategy and an instrument to address the concern that watershed group and governmental
regulatory presence are simultaneous. We do not find a result in the instrumental variable
specification. However, at the watershed level, we find a significant negative result of the
number of watershed groups on both inspections and violations, including SNC Category I
violations, with no result on enforcement actions or penalties. These results suggest that
watershed groups have a localized effect on regulatory behavior and firm compliance. Our
17
results imply that the states free-ride on these groups by reducing inspections, but without
diminishing the quantity of violations detected or the penalties levied. Additionally, the
groups are having the desired effect of increasing firm compliance, particularly for the most
egregious violations. The spending capacity of the watersheds groups does not seem influential; their successes come even given the limited budgets of these watershed groups, many
of which have few or no paid employees.
Inspections are costly for the state. In the face of watershed group activities, states
reduce inspections, which reduces state spending on this aspect of enforcement. Thus it
is relevant to investigate the tradeoff between voluntary provision of public goods through
nonprofit spending and the use of tax payer dollars. We can do a back of envelope calculation
of the cost of inspections and monitoring from the state perspective to compare this to the
budgets of watershed groups. The average yearly cost of regulation and monitoring for both
federal and state agencies over 1974 to 2001 was $893 million, split roughly halfway between
regulation and monitoring costs (Johnson, 2004). The bulk of the monitoring cost falls on
states, as they assume the responsibility of carrying out inspections and other activities
after authorization. If we attribute the entire cost of monitoring to inspections and with an
average of 828 yearly inspections across the 50 states in our sample, we can compute a rough
average cost per inspection of $10,785. The average fraction of facilities inspected is 0.084
and watershed group activity reduces this inspection rate by 0.001. Therefore around ten
fewer inspections occur in each state per additional watershed group. With median budgets
for watershed groups of $117,000, they reduce the need for inspections and increase firm
compliance through fewer pollution discharges at a relatively low cost . We suggest that
oversight provided by these groups is at least as efficient and likely more than direct state
enforcement.
We believe these results are an important contribution to both the literature on the
enforcement of the CWA and the effect of nonprofits. We are also extending the analysis.
We are pursuing additional instruments such as watershed group formation, due to weakness
18
in some specifications of our current instrument. One possible option is using water quality
or water flow data from right before our sample period. This variable is likely related to
group formation, but also may affect inspections and violations. If we are able to add earlier
data on watershed group formation, we can exploit the timing of authorization. At this
stage, we have looked at the subsample of only authorized states as a robustness check of
our results.
We wish to understand how the type of watershed group, those that are oriented toward community education versus toward sanctioning firms through litigation, can affect
regulation and compliance. We will focus on two national organizations that franchise organizational models to the local groups. Over 600 groups are affiliated with one organizational
model and 200 others with the alternative. The remaining thousands are independent organizations. Future specifications will make distinctions in watershed groups by interacting
a type variable with the two watershed group variables. The results will give the change in
government oversight due to each group type and the difference that spending by each type
of group. If these factors are significantly different than zero, the direction and magnitude
will inform our understanding of the effectiveness of alternative organizational models for
water management institutions.
The HUC results rely on the orthogonality of watershed boundaries to local political
jurisdictions such as counties, zip codes, and congressional districts. We did not specifically
exploit the fact that US state and watershed boundaries do not always align – we have
dropped all watersheds that cross state lines. These transboundary watersheds are an empirical challenge, but also opportunity for further empirical work. Future research will assess
how watershed groups implement regulatory oversight in neighboring states and mitigate
transboundary water issues between states. The results could provide a nice counterpoint
to Sigman’s work (Sigman, 2005), which finds that states and countries allow poorer water
quality near borders where it will be flushed into neighboring territories.
19
References
Andreoni, James, “An Experimental Test of the Public-Goods Crowding-Out Hypothesis,”
The American Economic Review, 1993, 83 (5), 1317–1327.
and A. Abigail Payne, “Is crowding out due entirely to fundraising? Evidence from
a panel of charities,” Journal of Public Economics, 2011, 95 (56), 334–343. Charitable
Giving and Fundraising Special Issue.
Earnhart, Dietrich, “Panel Data Analysis of Regulatory Factors Shaping Environmental
Performance,” The Review of Economics and Statistics, February 2004, 86 (1), 391–401.
, “Regulatory factors shaping environmental performance at publicly-owned treatment
plants,” Journal of Environmental Economics and Management, July 2004, 48 (1), 655–
681.
Flatt, Victor B., “Dirty River Runs Through It (The Failure of Enforcement in the Clean
Water Act),” Boston College Environmental Affairs Law Review, 1997, 25 (1).
GAO, “Water Pollution: Differences among the States in Issuing Permits Limiting the
Discharge of Pollutants,” 1996. Washington, D.C.: U.S. General Accounting Office.
, “Clean Water Act: Longstanding Issues Impact EPAs and States Enforcement Efforts,”
2009. U.S. Government Accountability Office: Testimony Before the Committee on Transportation and Infrastructure, U.S. House of Representatives.
Gray, Wayne B. and Jay P. Shimshack, “The Effectiveness of Environmental Monitoring and Enforcement: A Review of the Empirical Evidence,” Review of Environmental
Economics and Policy, 2011, 5 (1), 3–24.
Grooms, Katherine, “Enforcing the Clean Water Act: The Effect of State-level Corruption
on Compliance,” 2012. Working Paper.
20
Heutel, Garth, “Crowding Out and Crowding In of Private Donations and Government
Grants,” May 2009. Working Paper 15004 http://www.nber.org/papers/w15004.
Johnson,
Joseph
Water Act,”
M.,
2004.
versity. accessed at:
“The Cost of Regulations:
Working Paper,
Implementing the Clean
Mercatus Center George Mason Uni-
http://mercatus.org/sites/default/files/publication/
cost-regulations-implementing-clean-water-act.pdf.
Langpap, Christian, “Pollution abatement with limited enforcement power and citizen
suits,” Journal of Regulatory Economics, 2007, 31 (1), 5781.
and Jay P. Shimshack, “Private citizen suits and public enforcement: Substitutes or
complements?,” Journal of Environmental Economics and Management, May 2010, 59
(3), 235–249.
Magat, Wesley A. and W. Kip Viscusi, “Effectiveness of the EPAs Regulatory Enforcement: The Case of Industrial Effluent Standards,” Journal of Law and Economics, 1990,
32 (2), 331–360.
Monti, Holly, “Environmental Policy and Giving: Does Government Spending Affect
Charitable Donations?,” November 2010. accessed at http://www.portal.environment.
arizona.edu/files/portal/files/docs/Sp%2011_Monti.pdf.
Shimshack, Jay P. and Michael B. Ward, “Regulator reputation, enforcement, and
environmental compliance,” Journal of Environmental Economics and Management, 2005,
50, 519–540.
Sigman, Hilary, “Transboundary spillovers and decentralization of environmental policies,”
Journal of Environmental Economics and Management, July 2005, 50 (1), 81–101.
21
Steinberg, Richard, “Economic Theories of Nonprofit Organization,” in Walter W. Powell
and Richard Steinberg, eds., The Nonprofit Sector: A Research Handbook, Second Ed.,
New Haven: Yale University Press, 2006.
22
5
Tables
23
Table 1: Descriptive Statistics, Enforcement and Compliance
Mean
State Level
SD
Min
Total Inspections per facility
0.159
0.228
Total Violations per facility
0.376
0.650
Total Enforcement Actions per facility
0.032
0.060
Total penalties per facility
17.376
44.642
Total Facilities 8180.360 10052.830
Fraction of facilities inspected
0.084
0.064
Fraction of facilities in violation
0.049
0.067
Fraction of facilities in SNC Category I Effluent
0.020
0.030
Fraction of facilities enforced upon
0.018
0.023
Note: This table gives descriptive statistics at the state and HUC level.
Max
0.002
2.272
0.000
7.193
0.000
0.724
0.000
404.088
323.000 53779.000
0.002
0.337
0.000
0.539
0.000
0.171
0.000
0.246
Mean
HUC Level
SD
Min
Max
0.075
0.147 0.000
3.583
0.203
0.707 0.000
32.125
0.021
0.069 0.000
2.000
7.298 88.727 0.000 9615.385
196.506 371.012 1.000 4322.000
0.044
0.066 0.000
1.000
0.027
0.056 0.000
0.699
0.011
0.026 0.000
0.290
0.012
0.028 0.000
0.667
24
Table 2: State Level
Number of groups, State level
Total revenue across groups, State level
LCV average (year/state)
State health expenditures (per capita)
State natural resources expenditures (per capita)
State parks and recreation (per capita)
State population
(1)
Fraction of
Facilities
Inspected
(2)
Fraction of
Facilities
In violation
(3)
SNC
Category I
Effluent
(4)
Fraction of
Facilities
Enforced upon
(5)
Fraction of
Facilities
Enforced upon
(6)
Total
Penalties
per facility
(7)
Total
Penalties
per facility
-0.00095***
(0.00032)
-0.00000
(0.00000)
-0.00007
(0.00014)
0.00003
(0.00006)
-0.00009
(0.00009)
0.00053
(0.00032)
0.00000
(0.00000)
-0.00044
(0.00053)
0.00000**
(0.00000)
-0.00003
(0.00029)
0.00029**
(0.00012)
-0.00038**
(0.00017)
-0.00011
(0.00029)
-0.00000
(0.00000)
0.17930
(0.15471)
-0.00001
(0.00023)
0.00000
(0.00000)
-0.00006
(0.00013)
0.00003
(0.00003)
-0.00005
(0.00004)
-0.00013
(0.00011)
-0.00000
(0.00000)
-0.00037
(0.00044)
-0.00000
(0.00000)
-0.00011
(0.00010)
-0.00003
(0.00003)
0.00012*
(0.00006)
0.00013
(0.00008)
-0.00000
(0.00000)
-0.00010
(0.00037)
0.00000
(0.00000)
-0.00009
(0.00008)
-0.00003
(0.00003)
0.00014*
(0.00007)
-0.00003
(0.00010)
-0.00000
(0.00000)
0.29907***
(0.07755)
-0.45516
(0.35768)
-0.00000
(0.00000)
0.07485
(0.17629)
-0.07267
(0.04379)
0.05996
(0.04871)
0.19836
(0.27091)
0.00000
(0.00000)
-0.07420
(0.36452)
-0.00000
(0.00000)
0.10172
(0.17582)
-0.07826*
(0.04439)
0.08655
(0.06182)
-0.02349
(0.26950)
0.00000
(0.00000)
418.78995**
(194.52410)
0.00151
(0.02211)
-0.02035
(0.02585)
78.89399
(56.28862)
48.28707
(45.28952)
0.454
0.516
0.422
0.478
Fraction of facilities Inspected
Inspections per facility
0.01110
(0.01179)
Fraction of facilities in Violation
25
R-squared
0.819
0.598
0.726
Note: Fraction of facilities inspected/in violation/enforced upon is defined as the fraction of facilities in a given state that had at least one inspection/violation/enforcement
action in a given year/state. SNC Category 1 Effluent violations are the most serious type of violation involving effluent overages. This measure is denoted as the fraction
of facilities in SNC category 1 violation at least one quarter of a given year. The measure of total facilities is not time varying. Total penalties is only given for some
facilities, and only the in the PCS dataset. Total penalties per facility is defined as the total penalties per facility, over all facilities. All regressions have 738 observations
and include state and year fixed effects and standard errors clustered at the state level. All regressions are weighted by total number of facilities in a given state. This
measure is not time varying due to data constraints. Significance is denoted by *** p<0.01, ** p<0.05, * p<0.1.
Table 3: 2SLS Regression, Instrumenting for number of groups with state/year average LCV score
Number of groups, HUC level
Total revenue across groups, HUC level
State health expenditures (per capita)
State natural resources expenditures (per capita)
State parks and recreation (per capita)
State population
Fraction of facilities Inspected
Inspections per facility
Fraction of facilities in Violation
(1)
Fraction of
Facilities
Inspected
(2)
Fraction of
Facilities
In violation
(3)
SNC
Category I
Effluent
(4)
Fraction of
Facilities
Enforced upon
(5)
Fraction of
Facilities
Enforced upon
(6)
Total
Penalties
per facility
(7)
Total
Penalties
per facility
-0.002981
(0.003)
0.000000
(0.000)
0.000070
(0.000)
-0.000137
(0.000)
0.000542***
(0.000)
0.000000
(0.000)
-0.001598
(0.006)
0.000000
(0.000)
0.000308***
(0.000)
-0.000408***
(0.000)
-0.000082
(0.000)
0.000000
(0.000)
0.132628
(0.234)
-0.001629
(0.002)
0.000000
(0.000)
0.000059
(0.000)
-0.000091
(0.000)
-0.000101
(0.000)
0.000000
(0.000)
-0.003718
(0.003)
0.000000
(0.000)
0.000039
(0.000)
0.000032
(0.000)
0.000151
(0.000)
0.000000
(0.000)
-0.003204
(0.003)
0.000000
(0.000)
0.000029
(0.000)
0.000053
(0.000)
0.000054
(0.000)
0.000000
(0.000)
0.178411
(0.121)
1.840971
(4.127)
-0.000001
(0.000)
-0.119855
(0.095)
0.120237
(0.129)
0.183549
(0.205)
-0.000004
(0.000)
3.438906
(4.824)
-0.000001
(0.000)
-0.149722
(0.110)
0.184179
(0.157)
-0.117602
(0.264)
-0.000008
(0.000)
555.19473***
(205.513)
-0.034986
(0.043)
-0.044198
(0.036)
103.916625
(77.465)
75.250819
(73.999)
-0.005608
(0.026)
26
Note: Fraction of facilities inspected/in violation/enforced upon is defined as the fraction of facilities in a given state that had at least one inspection/violation/enforcement action
in a given year/state. SNC Category 1 Effluent violations are the most serious type of violation involving effluent overages. This measure is denoted as the fraction of facilities in
SNC category 1 violation at least one quarter of a given year. The measure of total facilities is not time varying. Total penalties is only given for some facilities, and only the in
the PCS dataset. Total penalties per facility is defined as the total penalties per facility, over all facilities. All regressions have 22,729 observations and include state and year fixed
effects and robust standard errors. All regressions are weighted by total number of facilities in a given state. This measure is not time varying due to data constraints. Significance
is denoted by *** p<0.01, ** p<0.05, * p<0.1.
Table 4: HUC Level
Number of groups, HUC level
Total revenue across groups, HUC level
LCV average (year/state)
State health expenditures (per capita)
State natural resources expenditures (per capita)
State parks and recreation (per capita)
State population
(1)
Fraction of
Facilities
Inspected
(2)
Fraction of
Facilities
In violation
(3)
SNC
Category I
Effluent
(4)
Fraction of
Facilities
Enforced upon
(5)
Fraction of
Facilities
Enforced upon
(6)
Total
Penalties
per facility
(7)
Total
Penalties
per facility
-0.00247*
(0.00137)
-0.00000
(0.00000)
-0.00022*
(0.00012)
0.00006
(0.00006)
-0.00018
(0.00014)
0.00056*
(0.00030)
-0.00000
(0.00000)
-0.00118*
(0.00063)
-0.00000*
(0.00000)
-0.00026
(0.00029)
0.00025**
(0.00010)
-0.00016
(0.00016)
-0.00018
(0.00023)
-0.00000
(0.00000)
0.20756***
(0.07379)
-0.00038*
(0.00022)
-0.00000
(0.00000)
-0.00009
(0.00010)
0.00002
(0.00003)
-0.00002
(0.00005)
-0.00004
(0.00010)
-0.00000
(0.00000)
-0.00062**
(0.00027)
0.00000
(0.00000)
-0.00007
(0.00009)
-0.00002
(0.00002)
0.00010
(0.00008)
0.00008
(0.00008)
-0.00000
(0.00000)
-0.00030
(0.00025)
0.00000
(0.00000)
-0.00005
(0.00008)
-0.00002
(0.00002)
0.00012
(0.00008)
-0.00001
(0.00008)
-0.00000
(0.00000)
0.14840***
(0.03844)
-1.16071*
(0.67456)
-0.00000
(0.00000)
-0.04175
(0.23796)
-0.04537
(0.03540)
0.01782
(0.06664)
0.57311**
(0.21583)
-0.00000
(0.00000)
-1.03385
(0.73000)
-0.00000
(0.00000)
-0.03220
(0.23751)
-0.04607
(0.03549)
0.02613
(0.06662)
0.53940**
(0.20778)
-0.00000
(0.00000)
58.88942**
(24.04331)
0.07407***
(0.02576)
0.04653**
(0.02206)
92.68071
(62.07663)
81.74871
(64.01746)
0.306
0.350
0.041
0.042
Fraction of facilities Inspected
Inspections per facility
0.01894**
(0.00724)
Fraction of facilities in Violation
27
R-squared
0.500
0.451
0.550
Note: Fraction of facilities inspected/in violation/enforced upon is defined as the fraction of facilities in a given HUC that had at least one inspection/violation/enforcenent
action in a given year/HUC. SNC Category 1 Effluent violations are the most serious type of violation involving effluent overages. This measure is denoted as the fraction
of facilities in SNC category 1 violation at least one quarter of a given year. The measure of total facilities is not time varying. Total penalties is only given for
some facilities, and only the in the PCS dataset. Total penalties per facility is defined as the total penalties per facility, over all facilities. All regressions have 22,729
observations and include state and year fixed effects and standard errors clustered at the state level. All regressions are weighted by total number of facilities in a given
HUC. This measure is not time varying due to data constraints. Significance is denoted by *** p<0.01, ** p<0.05, * p<0.1.
6
Figures
Figure 1: Example HUC Boundaries, Iowa
Note: An example of HUC8 boundaries in Iowa. Watershed boundaries, which are irregular shapes following topographical contours, do not
overlay with political/social jurisdictions like counties and zip codes, which tend to be rectangular and grid-oriented. Sourced from
http://www.soc.iastate.edu/extension/watershed/watershed.html and accessed September 1, 2012.
28