ROSS SCHOOL OF BUSINESS
CAN PRIVATE WATER COMPANIES DELIVER QUALITY?:
THE ROLE OF SCALE AND CUSTOMER ATTENTIVENESS
Thomas P. Lyon†, A. Wren Montgomery‡, and Dan Zhao§
January 13, 2015
Abstract
We compare the performance of private versus public water systems in terms of their compliance
with water quality and treatment technique standards. We present a simple theoretical model of
multi-task effort allocation by firms under alternative ownership structures. Our dataset covers
52,011 municipal water systems in the U.S., with more than 200,000 observations over the
period 2010-2013. Empirical estimation is conducted using a number of different specifications
of violations by private and public water systems. Private systems are found to underperform
public systems both in terms of procedural and outcome compliance, with private systems
performing particularly poorly in procedural compliance. Performance effects are moderated by
system size, however. Private systems’ likelihood of being in compliance on outcomes and on
procedures improves with size, and may outperform public systems at large scale.
†
Dow Professor, Ross School of Business and School of Natural Resources and Environment, University of
Michigan. [email protected]
‡
School of Business, Queen’s University, Kingston, Ontario. [email protected]
§
Ross School of Business, University of Michigan. [email protected]
I. INTRODUCTION
Businesses and industry associations around the globe are recognizing the fundamental
challenges that growing pressures on water resources present (2030 Water Resource Group, 2009;
CDP and Deloitte, 2011; KPMG, 2012). Yet, despite this growing attention, management
scholarship has lagged behind in studying the business implications of a water-constrained world,
and the stakeholder and institutional forces that businesses face around water (Kurland & Zell,
2010). Moreover, there is little research exploring how alternative approaches to managing
water, e.g. public vs. private ownership, perform. The need to understand these alternatives is
especially urgent in light of recent conflicts over water in Detroit and Ireland. In the summer and
fall of 2014, protests erupted in Detroit over the 30,000 household water shutoffs by the city’s
water department, with banners and chants repeating“Who’s Water? Our Water” and “Who’s on
our side? United Nations. Who’s on their side? Corporations.”1
Whether crucial social systems such as education, electricity, natural gas, health care,
telecommunications, and water---which are fundamental to our quality of life or even to life
itself---should be provided by public or private entities has long been an issue of heated debate,
and there has been plentiful research on this topic. Masten (2010) points out that water and
sewage systems constitute an anomaly, in that they have traditionally been publicly owned while
other utilities have long been privatized. Water supply has emerged into the spotlight in recent
years following the economic crisis as many municipal governments, especially in the Rust Belt,
considered selling off their water systems to cope with fiscal challenges (Food & Water Watch,
2010).
Privatization advocates argue that market forces provide discipline that leads to lower
costs and more efficient service. However, water privatization often faces strong popular
1
From the authors’ own field observations and data collection.
1
opposition from groups worried that privatization amounts to a very expensive loan that will
result in higher water rates and lower quality of service (Bakker, 2010). The controversy
surrounding Detroit’s water shutoffs in 2014 is but one facet of these political battles.2 In
England, Ireland, and Wales, when Thatcher’s government privatized the 10 regional water
authorities in 1989, similar controversies arose and the results have been decidedly mixed. For
example, Saal & Parker (2000) find that the privatization produced no measurable efficiency
improvements.
Most of the existing literature on privatization has taken a cost-benefit perspective
focusing on cost, pricing and efficiency. Scant attention has been paid to the issue of quality of
service, in particular, compliance with quality standards among water systems of different
ownership statuses. In this paper we aim to fill that gap by studying the relative performance of
public and private water systems with respect to violations of EPA’s Safe Drinking Water Act
(SDWA). We develop a simple model inspired by the economics, finance, and strategy
literatures to explain the effect of privatization, taking into account government monitoring
behavior and the presence of consumers with limited attention. We then use data taken primarily
from EPA’s Safe Drinking Water Information System (SDWIS) to identify the relationship
between different kinds of SDWA violations and ownership statuses, size, and a series of other
factors. Our findings suggest that private systems generally perform more poorly than public
systems, both in terms of pollution outcomes and, even more markedly, procedural compliance
with regulation, but that this underperformance gradually disappears with scale.
The remainder of the paper is structured as follows: Section II reviews the existing
literature on privatization. Section III presents background information on water systems and
2
News link: The Guardian: “Detroit's Water War: a tap shut-off that could impact 300,000 people” Jun 25, 2014
http://www.theguardian.com/environment/true-north/2014/jun/25/detroits-water-war-a-tap-shut-off-that-couldimpact-300000-people
2
their monitoring. Section IV lays out a simple theoretical model and derives several testable
predictions. Section V describes our data sources and presents summary statistics. Section VI
reports empirical specifications and regression results, and Section VII concludes.
I.
LITERATURE REVIEW
There is a large literature comparing the merits of public and private ownership. The
findings indicate that there is no universal domination of one mode of governance over the other;
instead, relative performance depends upon a variety of factors such as the complexity of the
technology involved, the predictability of the institutional environment, and the relationship
between cost and quality.
Theoretical work has come from both transaction cost analysis and formal agency theory.
For example, Crocker & Masten (1996) apply a transaction cost approach, starting from the
observation that when relationship-specific investments play a crucial role, the potential for
opportunism may lead to traditional spot markets (which in our case represents private water
systems) being replaced in favor of more structured governance alternatives. When it comes to a
simple market context, the parties may formally assign responsibilities at the very beginning,
creating a long-term relationship administered through contracts. With the complexity of the
market background growing or the cost of negotiation of future duties accumulating, the parties
may choose to implement the exchange through internal administration (public water systems).
Hart, Shleifer & Vishny (1997) derive complementary results using a formal model of
incomplete contracting. They show that public ownership should be preferred when cost
reductions, which are not contractible, may undermine quality to a large extent, when not much
emphasis is needed on quality innovations, and when government procurement suffers from
3
severe corruption. On the contrary, private firms provide a better trade-off when the utilization of
contracts or competition can limit the reduction in quality associated with cost reductions, when
a lot of weight is put on quality innovations, and when the government has a lot of trouble
dealing with patronage and powerful unions.
Within the empirical literature, the bulk of the work has focused on the relative efficiency
of the two alternative governance structures. Megginson and Netter (2001) offer an extensive
review of the empirical literature on privatization. They focus on the methodology and results of
ten prominent recent empirical papers, among which nine find that private enterprises
outperform public ones, while the remaining one finds no significant difference. However, the
authors note that privatization is likely to perform less well in the presence of serious market
failures, and they do not separate out the results for transition economies from results for
developed capitalist economies, nor do they treat separately the privatization of traditionally
monopolistic firms such as public utilities. Thus it is important to delve more deeply into works
that focus on utilities such as electricity, natural gas, telecommunications, and water and sewage.
The 1990 privatization and liberalization of the British electricity industry is assessed by
Newbery and Pollitt (1997), who find significant post-privatization performance improvements.
However, they find that almost all of the financial rewards of this improvement were captured by
the producers and their shareholders, whereas little benefit went to the government or consumers.
Using U.S. data, Koh, Berg, & Kenny (1996) find that municipally owned power plants are more
efficient when output is low, probably due to the more effective public cost control generated by
higher voter attention when jurisdictions are small. Kwoka (2002, 2005) finds that even though
privately-owned electric utilities have an overall cost advantage, publicly-owned utilities offer
significantly lower prices, in part because they perform better in the end-user-oriented
4
distribution function due to quality attributes that are difficult to contract for. Kwoka (2008)
reviews ten studies of the restructuring and privatization of the U.S. electric power industry,
eight of which give electricity restructuring a generally positive appraisal, though most of these
had one or more serious methodological limitations.
A large empirical literature also focuses specifically on water and sewage. Masten (2010)
points out that water and sewage are the only public utilities that are predominantly public in the
U.S. He lays out several possible causes of the historical municipalization of water, and assesses
them using data from the turn of the 19th century. He finds that the simplicity of early water
supply systems made them less vulnerable to poor management by government bureaucrats, and
that the contentiousness of any attempts to raise water prices made private ownership less
attractive. Troesken & Geddes (2003) use a dataset on municipalization of private water
companies from 1897 to 1915 to support a transaction cost theory for public acquisition. They
conclude that since governments cannot credibly commit to eschew opportunistic expropriation
from the private companies once they have made investments, private companies have weak
incentives to make investments and governments accordingly may need to acquire private water
systems in order to induce appropriate investments.
As for the consequences of water privatization, Bel & Warner (2008) conduct a review of
all published econometric studies of water and waste production since 1970. They do not find
much backing for a connection between privatization and cost savings. For example, no evidence
of cost savings exists in water delivery, and savings are not systematic in waste. In line with this,
Hunt & Lynk (1995) find that the privatization of the UK water industry resulted in significant
efficiency losses due to lost economies of scope. In a rare study of the impact of privatization on
service quality, Troesken (2001) finds African Americans to be a major beneficiary of water
5
municipalization, for it decreases the spread of waterborne-disease among them, however the
effect is much smaller on whites, i.e. public water companies provided blacks with better service
than did private water companies.
Finally, another stream of literature that relates to our study involves the impact of
information on the provision of product quality. Bennear & Olmstead (2008) study the 1996
Amendments to the SDWA, which stipulates that water systems issue annual consumer
confidence reports (CCRs) to their customers and that systems serving over 10,000 people mail
the reports directly to consumers. They conclude that the requirement to mail the CCRs directly
to households significantly decreased violations by water suppliers in Massachusetts. To the
contrary, Johnson (2003) uses an experiment in which customers read different versions of a
water quality report (Consumer Confidence Report) to show that, in contrast to what might be
hoped or feared, the content of the reports per se probably makes little difference to the
consumers.. This finding suggests that consumers of water are not always paying attention to
water quality, even when information is provided directly to them. Together, these papers show
the importance of consumer attention to water quality information, a factor that plays an
important role in our theoretical model.
Overall, the existing literature suggests that privatization may have important impacts on
costs, productivity, prices, and quality. These impacts, however, may depend in subtle ways on
details of the empirical setting, such as scale of operation and demographic factors such as race
or consumer attention to service quality. The effects of private ownership on service quality, in
particular, have received very little empirical attention.
II.
BACKGROUND ON WATER SYSTEMS AND THEIR MONITORING
6
A.
Overview
According to EPA’s 2012 National Public Water Systems Compliance Report, by the end
of December 2012 there were 150,848 active water systems in the U.S., serving over 320 million
consumers. Among these systems, the vast majority are small systems serving fewer than 10,000
customers. The number of systems with violations decreased from 37,631 in 2011 to 36,536 in
2012. Agencies with enforcement authority, such as state governments, reported that roughly 24
percent of all systems in the U.S. had at least one significant violation in 2012; 6 percent of all
systems in the U.S., serving approximately 23.7 million consumers, had violations of healthbased standards, while monitoring and reporting violations made up another 15 percent. In the
same year, 7,809 enforcement actions were initiated in response to drinking water violations at
water systems. The following graph is from EPA’s 2012 National Public Water Systems (PWSs)
Compliance Report.3
Figure 1
B. Water Supply Models – A Public to Private Continuum
Considering the existence of a range of forms of water system management that entail
both public and private participation, it is a simplification to see water supply models in terms of
3
Note that here “public” means serving the public, not publicly owned.
7
a strict dichotomy between public and private. In practice, there is actually a continuum with
purely public on one end and purely private on the other.
Bakker (2003) characterizes these supply models broadly into 3 categories, providing a
useful framework for our discussion. First is the “public utility” municipal model. This was the
dominant form of water provision over the past century. As the name indicates, the systems are
owned and primarily operated by governments. They are generally not-for-profit, and often have
subsidized rates. The second category is the private sector “commercial” model, under which
systems are managed and sometimes owned by private parties. Popular types in this category
include Private Sector Participation (PSP, also termed Public Private Partnerships) having private
companies managing infrastructures usually owned by municipal governments and it has been
gaining popularity in recent years. Private systems are typically for-profit and have a marketoriented pricing scheme. The third model, lying somewhere between the aforementioned two, is
the community ‘cooperative’ model. According to Bakker, a cooperative can be defined as “an
enterprise owned and democratically controlled by the users of the goods and services provided.”
This model is more prevalent in rural areas. According to EPA’s Safe Drinking Water
Information System, in Fiscal Year 2013, there are 1322 Community Water Systems serving
4,736,882 residents. Of these, 1135 are small or very small (less than 3300 people), and only 7
serve more than 100,000 residents.
Despite (or perhaps because of) the complexities pointed out above, the EPA, in its Safe
Drinking Water Information System, categorizes each water system as either public or private,
and we will make use of this categorization in the empirical analysis below.
C. Monitoring Process
8
The monitoring process is based upon the Safe Drinking Water Act (SDWA), the primary
federal law to ensure the quality of Americans' drinking water. Originally passed by Congress in
1974 to protect public health by regulating the nation's public drinking water supply, the law
later was amended in 1986 and 1996 and requires many actions to protect drinking water and its
sources: rivers, lakes, reservoirs, springs, and ground water wells.
The Act requires U.S. EPA to set drinking water standards that public water systems
(PWS) (meaning providing drinking water to the public instead of publicly owned) must meet.
US EPA has set standards for 90 contaminants. Under SDWA, states that meet certain
requirements, including setting regulations that are at least as stringent as US EPA’s, may apply
for, and receive primary enforcement authority, or primacy. Individual water systems submit
samples of their water for laboratory testing to verify that the water they provide to the public
meets all federal and state standards. How often and where samples are taken varies according to
numerous conditions. Monitoring schedules differ according to the type of contaminant, the type
of source water used to produce drinking water, and the population served by the public water
system. Each regulation outlines the requirements that systems must follow. US EPA regulations
specify the methods that must be used to analyze drinking water samples. States or the US EPA
certify the laboratories that conduct the analyses.
The SDWA provides states or EPA the authority to grant “variances” that allow public water
systems to use less costly technology, and exemptions to allow public water systems more time
to comply with a new drinking water regulation. Variances allow eligible systems to provide
drinking water that does not comply with a National Primary Drinking Water Regulation
(NPDWR) on the conditions that the system installs a certain technology and that the quality of
the drinking water is still protective of public health. Likewise, exemptions allow eligible
9
systems additional time to build capacity in order to achieve and maintain regulatory compliance
with newly promulgated NPDWRs, while continuing to provide acceptable levels of public
health protection. Exemptions do not release a water system from complying with NPDWRs;
rather, they allow water systems additional time to comply with NPDWRs.
In addition to monitoring methods and frequency, EPA requires community water
systems to deliver annual drinking water quality reports (Consumer Confidence Report) to their
customers. This information supplements public notification that water systems must provide to
their customers upon discovering any violation of a contaminant standard. Large water systems
with more than 10,000 customers must deliver the water quality reports to their customers, and
take steps to get the information to people who do not receive water bills. Water systems serving
fewer than 10,000 people, on the other hand, may be able to distribute the information through
newspapers or by other means. The largest water systems must post their reports on the Internet,
in addition to other delivery mechanisms, to make the reports easily accessible to all consumers.
D. Violations
Violations are detected by assessment of the above-mentioned sample results or reviews
(including on-site visits). Once detected, violations may lead to legal actions or compliance
orders. They will also be publicized, when required, by public notification. Violations may be
remedied by compliance/ enforcement actions, such as improved filtration techniques or changes
in procedures. Some examples of violations include: Maximum Contaminant Level (MCL)
violations, Treatment Technique (TT) violations, failure to replace lead service lines, monitoring
and reporting violations, and procedural violations. Among these, MCL and TT violations are the
two major types of violations that deserve special notice. They themselves also include a
10
multitude of subcategories of violations and they are what we will be looking at primarily in this
paper. An MCL denotes the highest level of a contaminant that EPA allows in drinking water so
as not to pose either a short-term or long-term health risk. EPA sets MCLs at levels that are
economically and technologically feasible. A TT refers to a required process intended to reduce
the level of a contaminant in drinking water. In the table below we list the key types of violations.
Table 1
Key Types of Violations
Abbreviations
Arsenic
CCR
FBRR
GWR
I_LT1_ESWTR
LCR
LT2_ESWTR
Misc
Nitrates
Other_IOC
PN_rule
Rads
SOC
St1_DBP
St2_DBP
SWTR
TCR
TTHM_pre-St1
VOC
Full Name
Arsenic
Consumer Confidence Report Rule
Filter Backwash Recycle Rule
Ground Water Rule
Interim Enhanced Surface Water Treatment Rule and the LT1 (future)
Enhanced SWTR
Lead and Copper Rule
LT2 (future) Enhanced SWTR
Miscellaneous
Nitrates
Other Inorganic Chemicals
Public Notification Rule
Radionuclides
Synthetic Organic Chemicals
Stage 1 Disinfectants By-Product Rule
Stage 2 Disinfectants By-Product Rule
Surface Water Treatment Rule
Total Coliform Rule
TTHM Rule violations, which was replaced by the ST1 DBP Rule
Other Volatile Organic Chemicals
11
A number of descriptors are normally used to describe each violation, such as: type, date,
description, severity, and recommended corrective actions. It is worth reiterating that even
though violations are quite prevalent among systems, many should not be a cause of severe
concern since they may not relate directly to water quality and health but are rather monitoring
and reporting, and other minor violations. Nevertheless, when a violation is reported, EPA
records very detailed information about it throughout its course of life and makes the information
publicly available through its Safe Drinking Water Information System (SDWIS) and ECHO
(Enforcement and Compliance History Online) website. Below is a screen capture from the
ECHO website on violations for Barton Hills Ann Arbor, which shows that in addition to
information about the violation itself, enforcement actions to make the system return to
compliance are also recorded.
Figure 2
III.
MODEL AND PREDICTIONS
In this section, we present a simple formal model that will guide our empirical analysis. It
aims to incorporate several factors that are important in our context, including ownership
structure; the distinction between outcome compliance and procedural compliance; and a mix of
customers, some of them attentive and some inattentive to water quality. We were motivated in
part by the findings by Philippe and Durand (2011) that for some types of firms, simply
conforming to the procedural dimensions of environmental norms suffices to earn them a good
12
reputation, but firms in environmentally sensitive industries must conform to both procedural
and goal dimensions of the norms in order to earn a good reputation, because they are subject to
more stringent public monitoring. Our formal modeling approach was motivated in part by the
work of Hart, Shleifer & Vishny (1997), which showed that privately-owned systems may be
able to achieve lower costs but may provide either higher or lower quality compared with public
systems. However our model differs in several important ways from theirs. In particular, we
include the possibility of monitoring by both government and consumers, the possibility that
only a fraction of consumers are attentive to issues of water quality, and the possibility of
compliance with both procedural and outcome requirements.
We assume there are two kinds of effort that a water system can devote to its level of
compliance:
t = effort towards procedural compliance
m = effort towards outcome compliance
These efforts have effects on compliance levels characterized by two dimensions:
outcome compliance level (M), a failure of which is reflected by Maximum Contaminant Level
(MCL) violations, and procedural compliance level (T), a failure of which is reflected by
Treatment Technique (TT) violations:
1
𝑀(𝑚) = 2𝑚2
1
𝑇(𝑡) = 2𝑡 2
Our use of specific functional forms allows us to obtain closed-form solutions but without any
meaningful loss of generality in our hypotheses, which would remain directionally unchanged
for any concave functions ( 𝑀′ (𝑚) > 0, 𝑇 ′ (𝑡) > 0, 𝑀′′ (𝑚) < 0, 𝑇 ′′ (𝑡) < 0).
13
Of course these efforts dedicated to compliance come at a cost. Letting s denote the size
of the system, we represent the system’s costs as
𝜌 2 𝛾 2
𝐶(𝑡, 𝑚, 𝑠) = 𝐶0 + 𝑡 3 + 𝑚3
𝑠
𝑠
1
We assume the cost function 𝐶(𝑡, 𝑚, 𝑠) to be concave due to economies of scale. Here 𝑆
implies that the bigger the size of the system, the more efficient it becomes in applying
compliance effort. Again, our use of specific functional forms is just a particular concave
functional form that is adopted for simplicity in obtaining closed-form solutions.
The system’s compliance levels determine the government monitoring agency’s (EPA’s)
assessments of the systems. Let A denote EPA’s assessment of a water system’s compliance
level, which we will assume takes the additive form:
𝐴(𝑀, 𝑇) = 𝛼𝑀(𝑚) + 𝛽𝑇(𝑡).
This specification captures the notion that government bureaucrats assess the performance of
water systems by assigning weights (𝛼, 𝛽 ∈ 𝑅 + ) to outcome and procedural compliance levels
respectively, i.e. we define their utility by adding up the weighted compliance levels.
Not only will the system’s compliance level determine the government’s assessment,
compliance will also define the water system’s reputation among consumers, investors and the
general public. Focusing on consumers, we assume, following previous work in finance and
accounting (Hirshleifer & Teoh, 2003; Bagnoli & Watts, 2014) that they have limited attention
and information processing power. On this ground we can differentiate between two kinds of
consumers: 1) attentive, relatively rational, Bayesian individuals, and 2) inattentive individuals
who use simple heuristics in their decision making. More specifically, we assume that for every
system, a proportion 𝑝 ∈ [0,1] of its consumers are attentive, and 1-p are not.
14
Finally, we also draw from the law and economics literature on accidents and optimal
precaution, and assume that for a certain level of outcome compliance, there exists some chance
a(M) that a water quality accident leading to a public health incident will occur (Shavell, 1984;
Kolstad et al., 1990). We assume a’(M)= k < 0.
We assume 𝑘, the derivative of the 𝑎(𝑀)
function, to be an unknown constant illustrating the notion that the exact extent to which changes
in compliance level affect occurrence of accidents is unknown to any parties. If such an incident
occurs, some harm H will be done to the water system’s customers and also to its reputation (or
there may be more direct punishments to the system). We assume 𝐻 to be an increasing function
of 𝑠, the size of the system represented by the population served, to convey the notion that the
bigger a system is, the more severe the damage may be. We let H(s) = ℎs for simplicity.
Letting R stand for the water system’s reputation in the eyes of consumers, we have:
𝑅(𝑝, 𝑀, 𝑇, 𝑠) = 𝑝𝐴(𝑀, 𝑇) + (1 − 𝑝)[𝛿𝜔𝑀(𝑚)] − 𝐻(𝑠)𝑎(𝑀).
(1)
The first term on the right-hand side of this expression reflects the evaluations of
attentive consumers, who read the annual Consumer Confidence Reports, check the EPA website,
and may even go and visit the water plants in person; they are assumed to completely observe the
reported compliance levels, just like government officials do. They also evaluate the system’s
reputation in the same manner government bureaucrats assess the water system.
The second term in equation (1) reflects the role of inattentive consumers, who typically
do not raise any concern about how their drinking water is processed and only become informed
when a public notification is issued. Our representation of inattentive consumers makes use of an
interesting pattern in the distribution of enforcement action types for MCL and TT violations. In
our data, around 50 percent of MCL violations have public notifications issued, while only
around 23 percent of TT violations ever have public notifications issued. A bold simplification is
15
made in our model that inattentive customers only pay attention to public notifications about
MCL violations, i.e. outcome compliance. This reflects both the greater likelihood of MCL
violations been announced publicly, as well as the greater import of MCL violations for
customer health. Furthermore, the outcome compliance level they observe (that is, the
proportion of MCL violations) is characterized by 𝜔 < 1. After they observe the outcome
compliance level, they assign weight 𝛿 to this information. We assume that 𝛿 < 𝛼, the weighting
parameter used by bureaucrats and attentive customers, since the inattentive consumers probably
won’t take the observed information as seriously as the bureaucrats or attentive consumers do.
The third term in equation (1) captures the risk of harm due to lack of effort on the
system’s part. It is assumed that both the attentive and inattentive consumers downgrade the
system’s reputation when public health incidents arise. The way inattentive consumers are
modelled reflects the idea that inattentive consumers are not totally passive and will partially
observe compliance levels when that information is specifically brought forward to them, and
punish the water system when a severe accident does occur.
The proportion of attentive consumers, p, can be thought of as a function of two variables.
One is the level of local educational attainment. As people become better educated, they tend to
care more about their health and quality of life, and thus the quality of the water they drink.
Similarly, the poverty rate can also play a role. As more residents fall below the poverty line,
fewer of them are inclined to pay attention to their water quality, since they have more urgent
matters to attend to. (The poverty rate may also influence the compliance level indirectly
through the financial conditions of the municipal governments and thus funding in maintaining
16
and upgrading the equipment of the water systems.)4 Letting 𝐸 denote educational attainment
and 𝑃 the poverty rate, mathematically we can write:
𝑝 = 𝑝(𝐸, 𝑃),
with
𝜕𝑝
> 0,
𝜕𝐸
𝜕𝑝
< 0.
𝜕𝑃
Combining the foregoing model elements, we characterize the water system’s total utility
as a function of the government’s assessment of its performance, its reputation, and its costs. We
let 𝜆(𝑂) ∈ [0,1] represent the relative weight given to the government’s assessment, which we
assume is a function of the ownership status O of the system. We assume O to be a continuous
variable which takes the value of 0 when a system is completely public and 1 when the system is
completely private. This takes into consideration the fact that the notion of a simple dichotomy
of ownership status is a simplification, and we can actually view the water systems’ ownership
status as ranging from absolute public to public/private cooperation to absolute private
ownership. This representation reflects the notion that the publicly owned systems who answer
to bureaucrats likely have limited incentive to respond to public opinion on their performance.
As for private systems, even though they are also subject to some bureaucratic regulation, they
also place substantial weight on their public reputation, especially since for-profit provision of
water is often viewed with suspicion by the public and water privatization has been a topic of
public debate in recent years. So 𝜆 is close to 1 when the system is public and close to 0 when
𝜕𝜆
the system is private, with 𝜕𝑂 < 0.
Thus, we can ultimately write the system’s total utility U as:
4
If we consider 𝑝 from the viewpoint of a water system, we might also hypothesize that as the system size increases,
the water system has a “stronger presence” in town and draws more attention from the public, so 𝑝 for its pool of
consumers may increase.
17
𝑈(𝐴, 𝑅, 𝐶, 𝜆) = 𝜆𝐴(𝑀, 𝑇) + (1 − 𝜆)𝑅(𝑝, 𝑀, 𝑇, 𝑠) − 𝐶(𝑡, 𝑚, 𝑠).
Substituting in, we have:
𝑈(𝐴, 𝑅, 𝐶, 𝜆) = 𝜆[𝛼𝑀(𝑚) + 𝛽𝑇(𝑡)] + (1 − 𝜆)[𝑝𝐴(𝑀, 𝑇) + (1 − 𝑝)[𝛿𝜔𝑀(𝑚)] − 𝐻(𝑠)𝑎(𝑀)]
𝜌 2 𝛾 2
− (𝐶0 + 𝑡 3 + 𝑚3 )
𝑠
𝑠
or, in terms of the underlying variables,
1
1
𝑈(𝑡, 𝑚, 𝜆, 𝑝, 𝑠) = 𝜆 (𝛼2𝑚2 + 𝛽2𝑡 2 )
1
1
1
+ (1 − 𝜆) [𝑝 (𝛼2𝑚2 + 𝛽2𝑡 2 ) + (1 − 𝑝) (𝛿𝜔2𝑚2 ) − ℎ𝑠𝑎(𝑀)]
𝜌 2 𝛾 2
−(𝐶0 + 𝑡 3 + 𝑚3 )
𝑠
𝑠
Now a water system solves the following maximization problem:
1
1
1
1
1
max {𝜆 (𝛼2𝑚2 + 𝛽2𝑡 2 ) + (1 − 𝜆) [𝑝 (𝛼2𝑚2 + 𝛽2𝑡 2 ) + (1 − 𝑝) (𝛿𝜔2𝑚2 ) − ℎ𝑠𝑎(𝑀)] − (𝐶0
𝑡,𝑚
𝜌 2 𝛾 2
+ 𝑡 3 + 𝑚3 )}
𝑠
𝑠
Taking the first-order conditions and solving for the optimal effort levels (𝑚∗ , 𝑡∗ ), we obtain
729𝛽 6 𝑠 6 (𝑝𝜆 − 𝑝 − 𝜆)6
𝑡∗ =
64𝜌6
(2)
and
𝑚∗ =
729𝑠 6 (ℎ𝑠𝜆𝑘 − ℎ𝑘𝑠 + 𝛼𝑝 − 𝛼𝑝𝜆 + 𝛼𝜆)6
.
64𝛾 6
(3)
Differentiating these expressions with respect to size and customer attentiveness, we obtain
the following comparative statics results:
𝜕𝑡∗ 2187𝛽 6 𝑠 5 (𝑝(𝜆 − 1) − 𝜆)6
=
>0
𝜕𝑠
32𝜌6
18
𝜕𝑡∗ 2187𝛽 6 𝑠 6 (𝜆 − 1)(𝑝(𝜆 − 1) − 𝜆)5
=
>0
𝜕𝑝
32𝜌6
5
𝜕𝑚∗ 2187𝑠 5 (ℎ𝑘𝑠(𝜆 − 1) − 𝛼(𝑝(𝜆 − 1) − 𝜆)) (2ℎ𝑘𝑠(𝜆 − 1) − 𝛼(𝑝(𝜆 − 1) − 𝜆))
=
>0
𝜕𝑠
32𝛾 6
5
2187𝛼𝑠 6 (𝜆 − 1)(ℎ𝑘𝑠(𝜆 − 1) − 𝛼(𝑝 (𝜆 − 1) − 𝜆))
𝜕𝑚∗
=−
> 0.
𝜕𝑝
32𝛾 6
These results imply that as the size of systems increase, they improve on both procedural
and outcome compliance. In addition, since
𝜕𝑡∗
𝜕𝑝
> 0 and
𝜕𝑚∗
𝜕𝑝
> 0, and
𝜕𝑝
𝜕𝐸
> 0,
𝜕𝑝
𝜕𝑃
< 0, the
model predicts that both procedural and outcome compliance are growing as local educational
attainment and poverty conditions improve. Thus, we have the following hypothesis:
Hypothesis 1: Both procedural compliance and outcome compliance are higher for large systems,
and systems serving communities with higher levels of local educational attainment and lower
rates of poverty.
Turning now to our primary interest, the role of ownership status, we find that
𝜕𝑡∗ 2187𝛽 6 (𝑝 − 1)𝑠 6 (𝑝(𝜆 − 1) − 𝜆)5
=
>0
𝜕𝜆
32𝜌6
𝜕𝑚∗ 2187𝑠 6 (𝛼 + ℎ𝑘𝑠 − 𝛼𝑝)(ℎ𝑘𝑠(𝜆 − 1) + 𝛼(𝑝(−𝜆) + 𝑝 + 𝜆))5
=
𝜕𝜆
32𝛾 6
>
<
0.
The first of these expressions shows that publicly-owned systems tend to have higher rates of
procedural compliance, which we state explicitly as
Hypothesis 2: Procedural compliance is higher for publicly-owned water systems.
19
Interestingly, however, unlike our prediction for procedural compliance, the sign of
is undetermined in general. When 𝛼(1 − 𝑝) > −ℎ𝑠𝑘,
𝜕𝑚∗
𝜕𝜆
𝜕𝑚∗
𝜕𝜆
𝜕𝑚∗
𝜕𝜆
> 0, and when 𝛼(1 − 𝑝) < −ℎ𝑠𝑘,
< 0. Intuitively this means when the proportion of inattentive consumers is high enough and
the size of the system is small enough (such that the product of it and the weight attentive
consumers assign to outcome compliance is larger than the harm of an accident multiplied by the
negative marginal likelihood of an accident), public systems will have a higher outcome
compliance level. On the other hand, when enough consumers are attentive and the water system
is big enough, private systems may outperform public ones in outcome compliance. Thus, we
have our final hypothesis:
Hypothesis 3: Outcome compliance is higher under public ownership for small systems with a
relatively high proportion of inattentive customers. Outcome compliance is higher under private
ownership for large systems with a relatively high proportion of attentive customers.
In sum, the model predicts that private systems underperform public systems in terms of
procedural compliance, but which kind of system has a higher outcome compliance level varies
depending on the proportion of consumers who are attentive to water quality and the size of the
water system. In addition, as the size of water systems increases, they tend to do better both in
terms of procedural and outcome compliance. Finally, both compliance levels are higher for
systems in areas with higher education attainment levels and lower poverty rates.
IV.
DATA SOURCES AND SUMMARY STATISTICS
Data Description
20
The primary source of data used in this study is the Environmental Protection Agency’s
official website, and specifically the Government Performance and Results Act (GPRA) pivot
tables in the Drinking Water Data from the Safe Drinking Water Information System Federal
(SDWISFED). The water systems in the datasets are classified into seven ownership categories.
They are: Federal government, State government, Local government, Mixed public/private,
Native American, Private, and not specified. In the regressions we perform in the following
section, we only consider the three government and one private ownership categories, leaving
out the other types in order to sharpen our empirical analysis.
The pivot tables contain data regarding water systems that have reported GPRA
violations. Violations are counted towards GPRA if they are health-based violations at
community water systems that are open5 during the fiscal year (FY) 2010, 2011, 2012, or 2013.
Our unit of observation is a pair: water system ID# - Fiscal Year. The oldest violations that
remained open in our dataset were first reported in fiscal year 1993. Health-based violations
include MCL, MRDL and TT violations. MCL refers to Maximum Contaminant Level while
MRDL and TT refer to Maximum Residual Disinfectant Level and Treatment Technique,
respectively. (No MRDL violations are recorded for the water systems we look at.) For every
violation, additional information is provided including the ID and name of the public water
system it is associated with, which state and county the system is in, its source of water (surface
or ground), its size as measured by population served, the system’s first and last reported date, its
ownership status, the violation’s ID, violation and contaminant type and name, date a violation
was first reported to SDWIS-Fed, its enforcement action date and type, number of system and
5
Violations are considered open during (some or all of) FY 2013 if the calendar date of the beginning of a
monitoring period in which a public water system was determined to be in violation of a primary drinking water
regulation is earlier than or equal to 6/30/2013 and the calendar date of the end of a monitoring period in which a
public water system was determined to be in violation of a primary drinking water regulation is later than or equal to
7/1/2012. The same is true for FY 2010 to 2012.
21
population in violation, total number of systems and population, and finally, fiscal year. In
addition to this main data set, the study also uses per capita personal income by county from the
CA1-3 Personal Income Summary in U.S. Bureau of Economic Analysis (BEA) Regional Data,
GDP & Personal Income. Finally, these data sets are supplemented by information from the
Missouri Census Data Center on the percentage of people with a Bachelor’s degree or higher, the
poverty rate, and the unemployment rate at the county level.
A. Summary Statistics
Over the four years 2010-2013, we have data on 52,011 public water systems, with
200,055 water system-FY observations, and 39,386 violation records. Thus, the vast majority of
water systems had zero violations in any given year. The following table presents some basic
statistics about the size of all the systems in the data set by ownership status.
Table 2
To illustrate the structure of the industry, the distribution of the size categories (retail population
served), the water source type (ground or surface water or unknown) of the water systems, as
well as the ownership status of water systems in fiscal year 2013 are listed below.
Table 3
Distribution of Size Categories in FY 2013
Public
Size Categories
Frequency
Percentage
Cumulative
Frequency
Private
Percentage
Cumulative
a <=100
2,021
6.71
6.71
12,692
44.66
44.66
b 101-500
6,907
22.93
29.64
11,267
39.64
84.3
c 501-1,000
4,296
14.26
43.9
1,831
6.44
90.75
d 1,001-3,300
7,647
25.39
69.29
1,576
5.55
96.29
e 3,301-10,000
5,009
16.63
85.92
555
1.95
98.24
22
f 10,001-50,000
3,276
10.88
96.8
377
1.33
99.57
g 50,001-100,000
566
1.88
98.68
62
0.22
99.79
h 100,001-250,000
281
0.93
99.61
43
0.15
99.94
i 250,001-500,000
63
0.21
99.82
9
0.03
99.97
j 500,001-1,000,000
35
0.12
99.93
7
0.02
100
k >1,000,000
20
0.07
100
1
0
100
30,121
100
28,420
100
Total
Table 4
Distribution of Water Source Types in FY 2013
Type
Unknown
Ground Water
Surface Water
Total
Frequency
Percentage
128
43,968
14,445
58,541
Cumulative
0.22
75.11
24.68
100
0.22
75.32
100
Table 5
Distribution of Ownership Status in FY 2013
Owner
Federal
Government
Local
Government
Private
State
Government
Total
V.
Frequency Percentage Cumulative
484
0.83
0.83
29,021
28,420
49.57
48.55
50.4
98.95
616
58,541
1.05
100
100
EMPIRICAL SPECIFICATION AND REGRESSION RESULTS
We applied multiple methods to estimate the effects of ownership status, as well as a
series of other factors, on violations of water systems. We used two main specifications. The first
is a test for the presence of a non-zero number of violations on a system in a given year, which is
a binary variable. As mentioned earlier, the vast majority of systems have zero violations in any
given year, so we use a logit specification to assess the determinants of a system being in
violation at least once.
23
Our second specification probes more deeply into variation amongst violating firms,
using as a dependent variable total violation duration per system, which involves adding up the
duration of all violations associated with a water system in each year. We define the violation
duration as the year compliance period ended6 minus the first year a violation was reported + 1.
This variable is intended to capture both the number and the duration of violations at a water
system, and serves as a composite estimate of occurrence of violations. A histogram of this
variable is presented in Figure 3. See appendix A for histograms of MCL and TT violation
durations.
Figure 3
Each of these approaches is applied separately to total violations of all types, Maximum
Contaminant Level (MCL) violations, and Treatment Technique (TT) violations. Our basic
specification is as follows, where subscripts it denotes water system i’s record in fiscal year t,
since our unit of observation is water system – fiscal year.
6
This refers to the end of a monitoring period in which a public water system was determined to be in violation of a
primary drinking water regulation
24
𝑉𝑖𝑜𝑙𝑎𝑡𝑖𝑜𝑛𝑠𝑖𝑡 = 𝛽0 + 𝛽1 𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑆𝑦𝑠𝑡𝑒𝑚𝑖𝑡 + 𝛽2 𝑅𝑒𝑡𝑎𝑖𝑙 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑆𝑒𝑟𝑣𝑒𝑑𝑖𝑡
+ 𝛽3 𝑈𝑠𝑒𝑠 𝐺𝑟𝑜𝑢𝑛𝑑 𝑊𝑎𝑡𝑒𝑟𝑖𝑡 + 𝛽4 𝑂𝑙𝑑 𝑆𝑦𝑠𝑡𝑒𝑚𝑖𝑡 + 𝛽5 𝑃𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎 𝐼𝑛𝑐𝑜𝑚𝑒𝑖𝑡
+ 𝛽5 𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑅𝑎𝑡𝑒𝑖𝑡 + 𝛽6 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 𝑅𝑎𝑡𝑒𝑖𝑡
+ 𝛽7 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝐴𝑡𝑡𝑎𝑖𝑛𝑚𝑒𝑛𝑡𝑖𝑡
+ 𝛽8 𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑆𝑦𝑠𝑡𝑒𝑚 × 𝑅𝑒𝑡𝑎𝑖𝑙 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑆𝑒𝑟𝑣𝑒𝑑𝑖𝑡 + 𝑆𝑡𝑎𝑡𝑒 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠𝑖𝑡
+ 𝜀𝑖𝑡
We thus regress our dependent variables on the indicator variable for whether the system
is private or not, along with a series of control variables including: its size characterized as
average daily population served at a water system; whether its primary source of water is
groundwater or surface water; whether the system is considered old (33 years or older)7; per
capita income in the county; percentage of unemployed residents in the county; poverty rate in
the county; and percentage of people with Bachelor’s degree or higher in the county. We also
include state dummies to control for state fixed effects. Lastly we include an interaction term
between ownership status and size in order to test our Hypothesis 3.
Table 6 presents a correlation matrix for our four county-level control variables.
Table 6
Correlation Matrix
(obs=185735)
Per Capita
Income
Per Capita Income
Unemployment Rate
Educational Attainment
Poverty Rate
Unemployment Educational
Rate
Attainment
1
-0.4269
0.6738
-0.6201
1
-0.308
0.5439
7
1
-0.4811
Poverty
Rate
1
There is a reason for using this indicator variable for old instead of reported age. Since a lot of systems have been
established for a long time but only started reporting to the EPA when the Safe Drinking Water Information System
began to collect their information back in the 70s, their reported age may not necessarily be their real age, which can
be much longer. Using a cutoff point of 33 years to generate the dummy simplifies the problem.
25
As mentioned above, we begin our estimations with logit regressions testing for the
presence of one or more violations on a given system in a given year. Table 7 presents the
results for the sample as a whole. Results for our control variables were as expected: systems
using ground water were significantly less likely to be in violation, as were systems serving more
highly educated customers. Systems serving areas with a higher poverty rate were more likely to
be in violation.
Table 7
Logit Regressions on Existence of Violations
VARIABLES
Private System
Size Categories
Large System (Over 10000 people)
Uses Ground Water
Old System (>=33 years)
Integrity Score
Unemployment Rate
Percent of People With Bachelor's or Higher Degrees
Poverty Rate
Interaction Between Private and Size Categories
Constant
Observations
Robust standard errors in parentheses
26
(1)
Combined
(2)
MCL
(3)
TT
0.544***
(0.114)
0.0511
(0.0445)
-0.164*
(0.0906)
-0.281***
(0.108)
0.0106
(0.0541)
-0.00215
(0.00642)
0.0134
(0.0235)
-0.0121***
(0.00427)
0.0230***
(0.00844)
-0.313***
(0.0566)
-2.793***
(0.349)
0.479***
(0.106)
0.104***
(0.0394)
-0.227**
(0.0890)
-0.0636
(0.130)
-0.0377
(0.0503)
-0.0503***
(0.00839)
0.00607
(0.0264)
-0.0148***
(0.00475)
0.0219*
(0.0113)
-0.290***
(0.0597)
-0.508
(0.429)
0.806***
(0.283)
-0.111
(0.147)
0.0320
(0.298)
-0.981***
(0.212)
0.189
(0.142)
0.0670***
(0.00601)
0.0634***
(0.0238)
0.000304
(0.00644)
0.0283***
(0.00814)
-0.411***
(0.135)
-8.277***
(0.616)
165,514
165,514
165,514
*** p<0.01, ** p<0.05, * p<0.1
Turning to the role of ownership structure, we find that private systems are significantly
more likely to be in violation, especially for TT violations. Large systems serving over 100,000
people are less likely to be in violation of MCL limits, but are not significantly different on TT
violations. Interestingly, the performance of private systems is particularly sensitive to size, with
larger private systems significantly less likely to be in violation.
In order to explore the impact of size more precisely, we divided the sample into two
groups based on system size. Small and medium systems are those with fewer than 10,000
customers, while large systems are those with 10,000 or more customers.
Table 8
Logit Regressions on Existence of Violations for Small and Medium Systems
VARIABLES
Private System
Size Categories
Uses Ground Water
Old System (>=33 years)
Integrity Score
Unemployment Rate
Percent of People With Bachelor's or Higher Degrees
Poverty Rate
Interaction Between Private and Size Categories
Constant
Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
27
(1)
Combined
(2)
MCL
(3)
TT
0.595***
(0.117)
0.0699
(0.0480)
-0.340***
(0.105)
0.0255
(0.0584)
-0.00633
(0.00660)
0.0127
(0.0235)
-0.0110**
(0.00439)
0.0262***
(0.00885)
-0.340***
(0.0584)
-2.600***
(0.365)
0.531***
(0.103)
0.126***
(0.0438)
-0.136
(0.130)
-0.0245
(0.0539)
-0.0587***
(0.00865)
0.00275
(0.0273)
-0.0141***
(0.00495)
0.0258**
(0.0119)
-0.315***
(0.0594)
-0.0732
(0.447)
0.878***
(0.323)
-0.0940
(0.148)
-0.977***
(0.217)
0.205
(0.149)
0.0668***
(0.00612)
0.0732***
(0.0237)
0.00274
(0.00612)
0.0285***
(0.00793)
-0.476***
(0.181)
-8.373***
(0.624)
150,288
150,288
149,875
Table 8 presents the results for small and medium-sized systems. Once again we find
that private systems are significantly more likely to be in violation, both for MCL limits and TT
requirements. And once again, we find that there is an important interaction effect between
ownership status and size, with large private systems performing much better than smaller ones.
Table 9 repeats the estimation for the subsample of large systems. A striking difference
emerges compared to the previous estimates. Private systems are estimated to have fewer
violations than public systems, although the difference is not statistically significant. Large
systems of both types perform better than small systems on MCL violations, but are not
significantly different from small systems on TT violations. In addition, systems serving more
highly educated customers perform better, at least for TT violations.
Table 9
Logit Regressions on Existence of Violations for Large Systems
VARIABLES
Private System
Size Categories
Uses Ground Water
Old System (>=33 years)
Integrity Score
Unemployment Rate
Percent of People With Bachelor's or Higher Degrees
Poverty Rate
Interaction Between Private and Size Categories
Constant
28
(1)
Combined
(2)
MCL
(3)
TT
-2.313
(2.286)
-0.681***
(0.226)
0.0171
(0.151)
-0.115
(0.0828)
0.219***
(0.0453)
0.00827
(0.0291)
-0.0175**
(0.00786)
0.00642
(0.0104)
0.415
(0.535)
-14.84***
(3.449)
-2.617
(2.855)
-0.866***
(0.276)
0.262*
(0.149)
-0.138
(0.0916)
0.159***
(0.0499)
0.0284
(0.0337)
-0.0146
(0.00905)
0.000280
(0.0101)
0.506
(0.685)
-10.19***
(3.796)
-1.219
(4.199)
-0.278
(0.320)
-1.275***
(0.392)
0.0139
(0.200)
0.162***
(0.00864)
-0.138**
(0.0674)
-0.0308**
(0.0145)
0.0458**
(0.0229)
0.152
(0.990)
-10.75***
(1.750)
Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
14,728
14,688
12,673
Our findings thus far are strongly supportive of our first two hypotheses. As predicted by
Hypothesis 1, larger systems and systems with more attentive customers (as proxied by high
levels of education or low poverty rates) tend to have higher levels of compliance with MCL
outcomes and TT procedures. As predicted by Hypothesis 2, public systems outperform private
ones on procedural compliance, although the evidence for this is less persuasive for large private
firms. Support for Hypothesis 3 is more nuanced. Small and medium-sized private systems
definitely improve more with scale than do public ones, and private systems appear to perform
equivalently to public systems at large scale. However, contrary to Hypothesis 3, we do not
observe private systems actually outperforming public systems, even at scale.
We turn next to our analyses of total violation duration. There are numerous empirical
methods for performing these regressions. The simplest and most familiar is ordinary least
squares, which we include as a reference point. We also run a negative binomial model, as well
as the Tobit model, which takes the zero values for the violations as being censored. However,
considering the large number of observations with zero violations, we also use a zero-inflated
binomial regression (zinb). This is consistent with the fact that the data is over dispersed (i.e.
variance is much larger than the mean). This model deals with the issue of “excess” zeros
because in a zero-inflated model, there are two different processes that generate every
observation. A logit model, like that presented above, is used to determine which of the two
processes is applied. As a result, one process leads to certain zeros while the other process leads
to a count variable which can either be zero or positive. For the count variable, the negative
29
binomial model is used. In our model, the state dummies are used as inflators, considering the
possibility that state fixed effects may be driving the occurrences of zeros.
Table 10 presents the results for our regressions on total violation duration. Once again
we find that private systems perform worse; in this case that means they have more violations of
longer duration, than public systems. Scale economies of performance are again found, with
larger systems having lower violation durations. Systems sourced with ground water perform
better, while old systems perform worse. Systems in areas with high poverty rates or low levels
of education tend to perform worse, though significance varies across different estimating
techniques. The interaction between size and private ownership is not as marked in our violation
regressions, and is only significant for the OLS estimations.
Table 10
Regressions on Total Violation Duration
(1)
(2)
VARIABLES
Private System
Retail Population Served
Uses Ground Water
Old System
Per Capita Income
Unemployment Rate
Poverty Rate
Educational Attainment
Interaction Between Private and Population
Constant
Ordinary
Least
Squares
Negative
Binomial
(3)
Tobit
0.136**
0.276***
1.651***
(0.0535)
(0.0902)
(0.465)
-4.57e-07*** -3.67e-06*** -2.40e-05*
(1.67e-07)
(1.13e-06)
(1.35e-05)
-0.302***
-0.550***
-3.516***
(0.0770)
(0.0925)
(0.626)
0.110**
0.221**
1.849***
(0.0440)
(0.0871)
(0.441)
3.46e-06
1.59e-05**
4.98e-05
(3.60e-06)
(6.64e-06)
(3.33e-05)
0.0120
0.0215
-0.153*
(0.00973)
(0.0172)
(0.0890)
0.00306
0.0431***
0.306***
(0.00477)
(0.00960)
(0.0511)
-0.00600**
-0.0116**
-0.136***
(0.00243)
(0.00593)
(0.0303)
-7.95e-07*
(4.77e-07)
0.812
30
4.86e-07
(2.77e-06)
-2.285***
1.53e-06
(3.15e-05)
-29.92***
(4)
Zeroinflated
Negative
Binomial
0.241**
(0.0957)
-2.51e-06*
(1.36e-06)
-0.499***
(0.0939)
0.193**
(0.0939)
5.36e-06
(6.05e-06)
0.0517***
(0.0148)
-0.0109
(0.00919)
-0.00315
(0.00524)
-1.14e-06
(2.44e-06)
-0.367
Observations
R-squared
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(0.536)
(0.514)
(3.907)
(0.343)
47,101
0.011
47,101
47,101
47,101
Overall, we find that private systems perform worse, on both MCL and TT violations, but
private systems do especially badly on TT violations (the magnitudes of the TT coefficients are
generally significantly larger than MCL coefficients, and their significance is higher). Also, as
the size of water systems increases, private systems often improve their performance relative to
public systems; in particular they tend to exceed public systems by a lesser amount in terms of
MCL violations in most cases but the interaction terms often show mixed signs for TT violations
(sometimes there may even be more TT violations as size gets bigger). Furthermore, larger
systems are less likely to have violations, even though sometimes the effect is weak, and the
effect on MCL violations is slightly stronger than that on TT violations.8
The predictions of our model are largely confirmed. In particular, both the model and
empirical results show that private systems underperform public systems in terms of procedural
compliance, but which kind of systems has a higher outcome compliance level is undetermined
in general, since the interaction terms for MCL violations tend to be more significant than those
for TT violations and they have a negative sign. However, even though the model predicts that
this undetermined nature comes from the proportion of attentive consumers and size of the water
system, in the empirics it seems to be size that is primarily driving the variation.
8
We also conducted a number of robustness checks, some of which are presented in Appendix B. We estimated the
number of violations per system, as shown in Tables 11-13 for total violations, MCL violations, and TT violations
respectively. Although space limitations preclude us from presenting them here, we also estimated the number of
violations per capita, and total duration for MCL violations and TT violations separately. Throughout, we generally
find that private systems do not perform as well as public systems, especially on TT violations. Large private
systems do proportionately better on MCL violations, though not necessarily on TT violations.
31
VI.
DISCUSSIONS AND CONCLUSION
Given the large literature on utility privatization, there is surprisingly little research on
how privatization affects quality of service, especially water quality. This paper expands our
knowledge by adopting a unique perspective on water privatization to look at compliance
behavior with regard to water quality and treatment technique standards for public and private
water systems. Using a sample of 52,011 water systems from across the U.S., with 200,055 water
system-Fiscal Year units of observations, and 39,386 violation records, we found that private
systems underperform public systems both in terms of procedural and outcome compliance, and
that private systems do particularly poorly in procedural compliance. Performance effects are
moderated by system size, however. Larger systems generally have a higher compliance level,
particularly in terms of outcome compliance. Furthermore, private systems’ relative outcome
compliance improves with size and can even outperform public systems when a system has a
very large size.
These findings offer valuable implications for future privatization policy. They suggest
strongly that we should not only consider the efficiency side of privatization, but also give the
quality effects serious attention. In particular, regulators should bear in mind the tendency for
private systems to underperform when crafting policies to monitor and improve water system
performance, especially for small to medium-sized systems.
There remain a number of interesting questions for further investigation. For example,
one factor likely to affect the water quality of a plant that we have yet to consider is
ambient/source water quality. A possible source of information for this is EPA’s Toxics Release
Inventory (TRI) Program, as well as the National Water Information System, which provides
water-resources data collected at approximately 1.5 million sites in all 50 States, the District of
32
Columbia etc. Using this information, rating counties by total TRI pollution should be possible.
Perhaps more importantly, we would also like to investigate the effects of privatization on water
rates. The American Water Works Association (AWWA)’s biennial surveys of water and
wastewater charges for US utilities since 2002 are the only public source of data on this, but it
only covers about 300 water and wastewater utilities. A more extensive sample would have to
be constructed by hand. Nevertheless, this is an important task. Even though small private water
systems provide lower quality than public systems, they might still be preferred if they offered
substantially lower rates.
33
VII.
BIBLIOGRAPHY
2030 Water Resource Group. (2009). Charting our Water Future: Economic Frameworks to
Inform Decision-Making. McKinsey & Company.
Bagnoli, M., & Watts, S. G. (2014). Voluntary Assurance of Voluntary CSR
Disclosure. Available at SSRN 2465400.
Bakker, K. J. (2003). An uncooperative commodity: Privatizing water in England and Wales.
Oxford University Press.
Bakker, K. (2003). Good governance in restructuring water supply: A handbook. Federation of
Canadian Municipalities.
Bakker, K. (2010). Privatizing water: governance failure and the world's urban water crisis.
Cornell University Press.
Bel, G., & Warner, M. (2008). Does privatization of solid waste and water services reduce costs?
A review of empirical studies. Resources, Conservation and Recycling, 52(12), 1337-1348.
Bennear, L. S., & Olmstead, S. M. (2008). The impacts of the “right to know”: Information
disclosure and the violation of drinking water standards. Journal of Environmental Economics
and Management, 56(2), 117-130.
CDP and Deloitte. (2011). CDP Water Disclosure Global Report 2011. Carbon Disclosure
Project. Carbon Disclousre Project.
Crocker, K. J., & Masten, S. E. (1996). Regulation and administered contracts revisited: Lessons
from transaction-cost economics for public utility regulation. Journal of Regulatory
Economics, 9(1), 5-39.
Food and Water Watch. (2010). Trends in Water Privatization. Washington, DC.
Hart, O., Shleifer, A., & Vishny, R. W. (1997). The proper scope of government: theory and an
application to prisons. The Quarterly Journal of Economics, 112: 1127-1161.
Hirshleifer, D., & Teoh, S. H. (2003). Limited attention, information disclosure, and financial
reporting. Journal of Accounting and Economics, 36(1), 337-386.
Hunt, L. C., & Lynk, E. L. (1995). Privatisation and efficiency in the UK water industry: An
empirical analysis. Oxford Bulletin of Economics and Statistics,57(3), 371-388.
Johnson, B. B. (2003). Do reports on drinking water quality affect customers' concerns?
Experiments in report content. Risk Analysis, 23(5), 985-998.
34
Koh, D. S., Berg, S. V., & Kenny, L. W. (1996). A comparison of costs in privately owned and
publicly owned electric utilities: the role of scale. Land Economics, 56-65.
Kolstad, C. D., Ulen, T. S., & Johnson, G. V. (1990). Ex post liability for harm vs. ex ante safety
regulation: substitutes or complements?. The American Economic Review, 888-901.
KPMG. (2012). Expect the Unexpected: Building Business Value in a Changing World. KPMG
International.
Kurland, N. B., & Zell, D. (2010). Water and business: A taxonomy and review of the research.
Organization and Environment , 23 (3), 316-353.
Kwoka Jr, J. E. (2002). Governance alternatives and pricing in the US electric power
industry. Journal of Law, Economics, and Organization, 18(1), 278-294.
Kwoka, J. (2008). Restructuring the US electric power sector: A review of recent studies. Review
of Industrial Organization, 32(3-4), 165-196.
Kwoka, J. E. (2005). The comparative advantage of public ownership: evidence from US electric
utilities. Canadian Journal of Economics/Revue canadienne d'économique, 38(2), 622-640.
Masten, S. E. (2010). Public utility ownership in 19th-century America: the “aberrant” case of
water." Journal of Law, Economics, and Organization, 27: 604-654.
Megginson, W. L., & Netter, J. M. (2001). From state to market: A survey of empirical studies
on privatization. Journal of Economic Literature, 39: 321-389.
Newbery, D. M., & Pollitt, M. G. (1997). The restructuring and privatisation of Britain's
CEGB—was it worth it?. The Journal of Industrial Economics, 45(3), 269-303.
Philippe, D., & Durand, R. (2011). The impact of norm‐conforming behaviors on firm
reputation. Strategic Management Journal, 32(9), 969-993.
Saal, D. S., & Parker, D. (2000). The impact of privatization and regulation on the water and
sewerage industry in England and Wales: a translog cost function model. Managerial and
Decision Economics, 21(6), 253-268.
Shavell, S. (1984). A model of the optimal use of liability and safety regulation. The Rand
Journal of Economics, 15(2), 271-280.
Troesken, W., & Geddes, R. (2003). Municipalizing American Waterworks, 1897–1915. Journal
of Law, Economics, and Organization, 19(2), 373-400.
Troesken, W. (2001). Race, disease, and the provision of water in American cities, 1889–
1921. The Journal of Economic History, 61(03), 750-776.
35
VIII.
APPENDIX A
Figure 4
Figure 5
36
IX.
APPENDIX B
Table 11
Regressions on Total Number of Violations
VARIABLES
(1)
Ordinary Least
Squares
Private System
(2)
Negative
Binomial
(3)
Tobit
(4)
Probit on
Existence
(5)
Truncated Negative Binomial
on Total Number
-0.155**
0.0211**
0.107**
0.337***
0.0845***
(0.00905)
(0.0485)
(0.0841)
(0.0196)
(0.0699)
-1.53e-07**
-2.05e-06
-2.32e-06
-3.83e-07
-9.17e-06**
(6.13e-08)
(1.38e-06)
(1.84e-06)
(3.82e-07)
(4.22e-06)
-0.102***
-0.440***
-0.768***
-0.151***
-0.510***
(0.0128)
(0.0505)
(0.0942)
(0.0211)
(0.0779)
0.0319***
0.141***
0.399***
0.0952***
-0.123*
(0.00877)
(0.0454)
(0.0828)
(0.0193)
(0.0653)
2.10e-06**
1.23e-05***
8.17e-06
7.13e-07
1.96e-05***
(8.41e-07)
(3.84e-06)
(7.16e-06)
(1.63e-06)
(5.04e-06)
Unemployment Rate
-0.00618***
-0.0351***
-0.0482**
-0.00947**
-0.0258*
(0.00232)
(0.0103)
(0.0190)
(0.00452)
(0.0145)
Poverty Rate
0.00879***
0.0446***
0.0738***
0.0152***
0.0331***
(0.00119)
(0.00539)
(0.0103)
(0.00241)
(0.00782)
Educational Attainment
-0.00455***
-0.0217***
-0.0373***
-0.00758***
-0.0141***
(0.000610)
(0.00321)
(0.00587)
(0.00137)
(0.00463)
Retail Population Served
Uses Ground Water
Old System
Per Capita Income
Interaction Between Private and
Population
-2.63e-07*
-2.19e-06
-3.69e-06
-8.73e-07
1.75e-06
(1.45e-07)
(8.77e-06)
(7.44e-06)
(1.74e-06)
(1.39e-05)
0.301***
-2.282***
-6.038***
-1.321***
-1.994***
(0.0862)
(0.297)
(0.592)
(0.137)
(0.475)
Observations
47,101
47,101
47,101
47,099
4,528
R-squared
0.023
Constant
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
37
Table 12
Regressions on Total Number of MCL Violations
VARIABLES
(1)
Ordinary Least
Squares
(2)
Negative
Binomial
Private System
0.00477
Retail Population Served
Uses Ground Water
Old System
(3)
Tobit
(4)
Probit on
Existence
(5)
Truncated Negative Binomial on
Total Number
0.0405
0.213**
0.0535**
-0.115*
(0.00796)
(0.0528)
(0.0213)
(0.0654)
-1.24e-07**
-5.73e-06**
(0.0967)
-8.82e06***
-1.66e-06**
-7.22e-06
(5.30e-08)
(2.37e-06)
(3.28e-06)
(6.73e-07)
(5.34e-06)
-0.0416***
-0.341***
-0.459***
-0.0785***
-0.351***
(0.0101)
(0.0575)
(0.107)
(0.0232)
(0.0743)
0.0197**
0.101**
0.275***
0.0573***
-0.00788
(0.00783)
(0.0500)
(0.0947)
(0.0208)
(0.0591)
Per Capita Income
1.84e-06**
1.25e-05***
5.74e-06
-8.58e-08
2.22e-05***
(7.84e-07)
(4.21e-06)
(8.33e-06)
(1.79e-06)
(4.68e-06)
Unemployment Rate
-0.00571***
-0.0337***
-0.0533**
-0.0102**
-0.0230*
(0.00211)
(0.0114)
(0.0217)
(0.00480)
(0.0136)
0.00736***
0.0428***
0.0717***
0.0138***
0.0351***
(0.00105)
(0.00596)
(0.00257)
(0.00756)
-0.00389***
-0.0234***
(0.0116)
0.0411***
-0.00800***
-0.0140***
(0.000546)
(0.00678)
-8.17e05***
(0.00149)
(0.00454)
-2.26e-07**
(0.00355)
-4.25e05***
-1.82e-05***
6.82e-06
(1.02e-07)
(1.18e-05)
(2.18e-05)
(4.91e-06)
(2.48e-05)
0.245***
-2.389***
-6.488***
-1.313***
-1.899***
(0.0794)
(0.325)
(0.666)
(0.144)
(0.487)
Observations
47,101
47,101
47,101
47,099
3,550
R-squared
0.022
Poverty Rate
Educational Attainment
Interaction Between Private and
Population
Constant
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
38
Table 13
Regressions on Total Number of TT Violations
VARIABLES
(1)
Ordinary Least
Squares
(2)
Negative
Binomial
Tobit
(4)
Zero-inflated Negative
Binomial
Private System
0.0162***
0.417***
0.679***
0.448***
(0.00383)
(0.1000)
(0.128)
(0.103)
Retail Population Served
-2.88e-08*
-4.88e-07
-9.78e-08
-4.91e-07
(1.56e-08)
(6.81e-07)
(5.83e-07)
(5.68e-07)
Uses Ground Water
-0.0604***
-1.434***
-1.729***
-1.223***
(0.00663)
(0.102)
(0.155)
(0.0984)
0.0120***
0.337***
0.680***
0.294***
(0.00337)
(0.100)
(0.122)
(0.0989)
Old System
Per Capita Income
Unemployment Rate
Poverty Rate
Educational Attainment
(3)
2.54e-07
5.17e-06
2.24e-06
1.28e-06
(2.69e-07)
(9.36e-06)
(9.85e-06)
(7.96e-06)
-0.000480
-0.00319
-0.00753
-0.0206
(0.000814)
(0.0210)
(0.0282)
(0.0186)
0.00143***
0.0388***
0.0565***
0.0378***
(0.000455)
(0.0114)
(0.0154)
(0.0106)
-0.000658***
-0.0110*
-0.00849
-0.00813
(0.000225)
(0.00612)
(0.00809)
(0.00512)
-3.68e-08
5.22e-08
1.20e-07
-3.66e-07
(7.65e-08)
(2.17e-06)
(3.08e-06)
(1.43e-06)
0.0564*
-4.075***
-8.467***
-1.607***
(0.0300)
(0.666)
(0.946)
(0.446)
Observations
47,101
47,101
47,101
47,101
R-squared
0.020
Interaction Between Private and
Population
Constant
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
39