Local Response to Water Crisis:
Explaining Variation in Usage Restrictions during a Texas Drought
Megan Mullin
Duke University
megan.mullin@duke.edu
Meghan E. Rubado
Temple University
mrubado@temple.edu
April 10, 2015
Abstract:
What explains local policy response to extreme weather events? This question takes on
growing importance as climate change increases the frequency of droughts, floods, heat
waves, and severe storms. Local governments considering how to respond to these events
may face political opposition to policies that restrict resource use or otherwise limit
personal activities. Using data on the adoption of local water usage restrictions during the
2010-13 Texas drought, we examine how features of a water system and its customer
base as well as severity of the drought influence the timing and stringency of policy
response. We find that problem conditions and institutional capacity of water systems
outweigh political interests in shaping drought emergency response.
As global climate change increases the frequency of extreme weather events such
as droughts, floods, heat waves, and severe storms, local governments come under
growing pressure to implement effective emergency response. Although responsibility for
crisis management often is shared among multiple levels of government, natural hazards
typically are experienced at the local level, and local policy makers are most familiar
with local conditions and citizen preferences. In responding to these events, localities
have a duty to protect the well-being of their residents, but they also want to avoid
overreacting and causing further damage to the local economy. Moreover, they may have
limited capacity to take action, constrained by shortfalls in knowledge about the problem,
organizational weakness, or financial or human resources.
This paper reports findings from research analyzing local government decision
making in response to a climate-change related extreme event. Existing research offers
little evidence comparing the reactions of different localities to a common crisis in order
to unravel how objective conditions related to the crisis itself, political demands from the
community, and previous policy context influence emergency response. Our work is
situated in Texas, which experienced severe drought following an exceptionally
devastating La Niña weather pattern in the winter of 2010-11. By April 2011, the entire
state was experiencing drought conditions, with 68% of the land area designated in one of
the highest two categories of extreme or exceptional drought. Continued warm weather
and lack of rainfall over subsequent months produced the hottest and driest year ever
recorded in Texas. Another La Niña the following winter exacerbated these conditions,
producing a long-term drought from which much of the state still has not recovered.
1
The drought has caused significant economic disruption and losses. In 2011, more
than 31,000 fires occurred, affecting more than four million acres of land and destroying
almost three thousand homes. Through 2013, the drought has cost the state’s farmers and
ranchers an estimated $8 billion and caused $3.4 billion of losses to the timber industry
(House Committee on Natural Resources 2013, 26).
Unsurprisingly, it also has taken a toll on the state’s water resources.
Groundwater-level recorders in the state’s nine major aquifers showed declines in water
levels between 2010 and 2012, with the most dramatic drops observed between 2010 and
2011. The median decline in water level between 2010 and 2011 for 125 recorders in the
nine major aquifers was 4.8 feet, and the median decline between 2011 and 2012 was 0.9
feet, with the Ogallala Aquifer wells in Northwest Texas showing the greatest declines
through 2012 (Texas Water Development Board 2013). Water levels in Texas reservoirs
also showed steep drops during the drought of 2011 and have failed to rebound since that
year. While the median percent of capacity for Texas reservoirs between 1990 and 2013
hovered around 80 percent, the levels in 2013 and early 2014 remained at near-record
lows of 60 to 65 percent (Texas Water Development Board 2014a). Two reservoirs dried
up completely between 2012 and 2013 (Texas Water Development Board 2014b).
Faced with these shortages in supply, public water systems have needed to find
ways to stretch their limited water resources further. Many are pursuing new
infrastructure projects to tap additional supply sources, but these are long-term strategies
that at best will help communities prepare for future drought events. In the short term,
utilities facing water shortfalls have two choices: they can find ways to immediately
boost supply by drilling a new well, extending surface water intakes, or interconnecting
2
with neighboring systems; alternatively, they can encourage customers to change
consumption patterns to live within resource limits. Living within limits long has been a
challenge for Texas water utilities, however, which often have played an important role
in enabling rapid growth that has contributed to groundwater depletion (Perrenod 1984;
Porter, Lin, and Peiser 1987; Thomas and Murray 1991). Texas water systems pursuing
aggressive growth policies sometimes have been blind to resource constraints, ultimately
running out of the water they promised to deliver (Mullin 2009, 107-110). Water officials
seeking to satisfy customers’ demands for plentiful water may postpone setting limits on
water usage as long as possible, jeopardizing reliable delivery of water for essential needs
in order to avoid setting restrictions on non-necessary water usage.
We examine how conditions related to the event as well as characteristics of local
water utilities and the communities they serve influenced short-term crisis management
decision making during the Texas drought. Extreme weather events place considerable
stress on local officials who may be uncertain about the extent and duration of the
problem and may face political resistance to any policies that restrict resource use or
otherwise limit personal activities. Research on local policy response to water shortages
and other weather-related emergencies largely has focused on individual cases, because
data are rarely available documenting the actions of a large number of communities to a
common crisis. To fill this gap, we take advantage of data collected by the Texas
Commission on Environmental Quality (TCEQ) on local adoptions of voluntary and
mandatory water usage restrictions over the period 2010 to 2013. Using Geographic
Information Systems (GIS) techniques to combine these data with information about
demographic characteristics of water system customers and the spatial spread of the
3
drought, we test how problem conditions and internal water system characteristics and
constituencies contributed to systems’ willingness to adopt usage restrictions during the
drought. Our results indicate that problem conditions and institutional capacity of water
systems outweigh political interests in shaping drought emergency response.
Local Decision Making in the Context of Climate Change
Climate change is a global problem with effects that are most visible at a local
scale. The nature and severity of its impacts vary across communities in ways that are
attributable to regional patterns of risk as well as to the condition of a community’s built
infrastructure. Local governments are responsible for building and maintaining much of
that infrastructure that can help minimize—or amplify—climate change risks.
Local governments seem to recognize their critical role in planning for climate
change. Although any individual locality acting to mitigate climate change will have
negligible effect on the problem, thousands of cities worldwide nonetheless have pledged
to reduce their greenhouse gas emissions (Zahran et al. 2008; Krause 2010; 2011; Sharp
et al. 2011). These commitments often are part of a broader effort to enhance local
sustainability (Portney 2003; Lubell et al. 2009). Local governments also are taking
action to adapt to the consequences of climate change, but these efforts have received less
attention in the literature (Betsill and Bulkeley 2007; Bulkeley 2010). One reason for the
relative lack of attention is a measurement problem: because local governments provide
much of the critical infrastructure that can help protect communities or put them at risk
from climate-related events, nearly all decision making about how to manage that
infrastructure becomes a form of adaptation planning. Whether or not local officials
4
perceive their actions as related to climate change, their decisions about infrastructure
investment and land use planning affect the vulnerability of local populations to
intensifying hazards. Relative to mitigation efforts, then, adaptation planning produces
more direct benefits for the immediate community.
Response planning for extreme events is a form of climate change adaptation.
Managing impacts from natural hazards such as drought entails both long-term strategies
to reduce risk by integrating hazards into overall comprehensive planning processes and
short-term strategies to respond to crisis (Wilhite 2000). Our focus in this paper is on
crisis response. The literatures in planning and climate adaption advocate for increased
emphasis on risk management and resilience, but localities thus far have failed to heed
that call. A recent study of comprehensive planning processes in 81 of the fastest growing
U.S. counties found that they generally do not address drought risk (Fu and Tang 2013).
The literature on disaster planning more generally indicates wide variability in local
governments’ preparation for hazards. Although some localities respond to crisis events
such as the terrorist attacks on September 11, 2001 by making plans to prepare for
potential future events, many do not (Krane 2002). Cities’ planning processes may
overlook important sources of risk, as when the 1999 New Orleans comprehensive plan
ignored the extreme flood hazard facing the city (Burby 2006).
In the absence of advance preparation, how a community responds to emergency
conditions takes on greater importance. Our emphasis here is on decisions by local water
utilities to enact restrictions on water usage by their customers. Usage restrictions are a
blunt instrument for promoting water conservation, but in the case of extreme water
shortages they could make the difference about whether a community runs out of water.
5
A study of the effects of various usage restrictions imposed by eight water providers
during a 2002 drought in Colorado found that mandatory restrictions were effective at
reducing water use, while voluntary restrictions were not (Kenney et al. 2004; Kenney et
al. 2008). Under emergency conditions, other demand management strategies such as
pricing and incentives are unlikely to produce the immediate reductions in usage that may
be needed to conserve dwindling water supplies. Understanding disaster response also
may provide insight about long-term drought planning, because localities that are slow to
respond to crisis conditions may be less likely to enact policies to manage water demand
in the long term as well.
Texas state law requires all wholesale and retail public water suppliers to prepare
drought contingency plans that outline a set of temporary supply and demand
management responses to be introduced during water supply shortages. Revised plans
must be submitted to the TCEQ every five years. Drought plans are distinct from the
water conservation plans that most Texas water systems also must submit, in that drought
plans outline short-term response measures for use during emergency conditions, while
water conservation plans focus on producing lasting improvements in water use
efficiency. In practice, achieving long-term reductions in water usage can make drought
planning more difficult because less water is dedicated to nonessential uses and therefore
available for short-term reductions.
Drought contingency plans prepared by local water suppliers identify best
management practices for reducing water use in a series of successive stages according to
the severity of water shortage conditions. Each plan must include a set of triggering
criteria specifying when a response stage should begin or end, targets for water use
6
reductions within each stage, and a list of supply and demand management measures
designed to produce the target reductions and better manage available supply. To the
extent possible, triggering criteria are supposed to be expressed in quantitative terms, and
they typically take the form of expectations about how long existing water supplies will
last given operators’ assumptions about demand conditions and replenishment of
supplies. Water systems choose how supply levels relate to drought stage triggers,
however, so that a six-month supply would qualify as a shortage condition in one
community but not another. Moreover, the state recommends that local plans provide
discretion to system operators in deciding if and when to initiate or terminate a response
stage, advising the operator to “weigh the risks of delay against the potential public
relations problems caused by ‘false alarms’” (TCEQ 2005, 9).
The decision to implement usage restrictions is likely to invite public resistance
and political controversy. Rules that limit outdoor watering to certain days or ban
activities such as car washing or hosing down pavement often seem arbitrary and overly
restrictive to a public that has short time horizons and little knowledge about water
supply conditions, even during a period of drought. If neighboring communities have not
enacted similar restrictions, residents may perceive that costs of the drought are not being
shared equally. Water-intensive industry and commercial businesses such as golf courses,
car washes, and landscaping companies could suffer significant economic losses from the
enactment of usage restrictions. Thus local water officials may face competing pressures
in deciding whether and when to implement usage restrictions. On the one hand, they
want to ensure reliable water supply for essential uses throughout the uncertain duration
of the drought event, and they may be under pressure from other water agencies in the
7
region to reduce consumption from a shared supply source. On the other hand, to limit
the water use of their own customers counters the goals of most water utility
professionals, who traditionally have aimed to satisfy customers’ demands for water even
in the presence of variability in supply (Lach, Rayner, and Ingram 2005). Many local
water agencies also have institutional features such as systems of political incentives and
rules for participation that can interfere with strong policy action (Brown 2004; Hughes
and Mullin n.d.). Prior research has shown that even where a local agency has clear
authority to address sustainability, it can be difficult to integrate sustainability
considerations with other responsibilities that are perceived as more central to the
agency’s mission—especially if environmental goals are perceived to threaten economic
goals (Bulkeley and Betsill 2003; 2005). Facing resistance to usage restrictions among
their customers, water managers may anticipate that the state or neighboring localities
would bail them out if they were to drain their own water supplies.
We assess how water system features, demographic characteristics of the system’s
customer base, and local drought severity influence how local officials balance these
competing pressures. In developing our hypotheses, we draw on the policy diffusion
literature, which has emphasized the importance of accounting for internal determinants
of policy adoption as well as external diffusion mechanisms (Berry and Berry 1990;
1999; Mintrom 1997; Daley 2007). While there have been studies of environmental
policy adoption that account for internal and external conditions at the state level (Lowry
1992; 2005; Daley 2007), diffusion studies of local environmental policy adoption have
been lacking. In part, this may be due to the difficulty of controlling for internal
determinants at this level of analysis. The data leveraged in this study provide a unique
8
opportunity to account for factors internal to water systems and their political
jurisdictions that may influence policy makers’ approach to usage restrictions as well as
addressing problem conditions that are shared across jurisdictions yet differentially affect
each of them. Because we have data not only on whether water systems enacted usage
restrictions but also when, we are able to evaluate how these factors relate to the speed
with which a system acts to request or require its customers to limit water usage in order
to preserve existing supplies.
Hypotheses
Our hypotheses capture different potential sources of influence on water system
decision making: objective problem conditions, institutional capacity, and political
interests. With respect to problem conditions, we expect as drought conditions become
more severe, the amount of time a system can resist imposing usage restrictions will be
reduced. A water system typically draws its supply from a river or groundwater aquifer
located within or near its service area, so more severe drought in its area is likely to
indicate scarcity of supply. Local drought severity also may increase public awareness
and willingness to accept limitations on water use. H1: Water systems will adopt usage
restrictions more quickly as drought conditions become more severe.
Our conception of institutional capacity relates to the organizational resources
available to water managers that may provide the information and discretion needed to
implement usage restrictions, as well as features of the water system itself that influence
its ability to withstand severe drought without restricting use. Previous case study-based
research indicates that management capacity is an important predictor of local
9
governments’ ability to plan for and adapt to climate change-induced water shortages
(Ivey et al. 2004; Pirie et al. 2004). We use system size as a proxy measure for this
capacity. Larger water systems tend to have more technical expertise and human
resources that are needed to assess supply vulnerability and to enforce usage restrictions.
Recent work on local management of terrorism risk found a significant relationship
between level of preparedness and city size (Gerber et al. 2005). Although smaller water
systems may be more vulnerable to supply shortages, all else equal we expect them to be
slower to enact a major policy initiative such as usage restrictions. H2: Large systems, as
measured by the population served, will adopt usage restrictions earlier than systems
delivering water to fewer customers.
We also expect that basic features of a water system that define its capacity and
resiliency will allow the system to endure water shortages for longer before enacting
usage restrictions. H3: Water systems that have less storage and fewer interconnections
will adopt usage restrictions earlier than systems with more storage and interconnections.
Another element of capacity is a water system’s governing structure. Drinking
water may be distributed by a city department in a municipality, with ultimate decision
making authority lying with the mayor and city council, or by a municipal utility district
or other form of limited-purpose special district. More rarely, a private nonprofit or forprofit water supply company may manage a community’s drinking water. All else held
constant, we expect water systems operated by cities to hesitate least in enacting
restrictions. Requiring or even requesting changes in behavior is a departure from the
usual types of policies that drinking water systems enact. City councils will have more
experience and more legitimacy than other types of water governing boards in issuing
10
mandates. As more visible members of their communities, they also likely have more
persuasive power in requesting voluntary usage restrictions. Special districts and private
water companies are not perceived to have the same level of authority as a city and do not
have access to the same enforcement tools, so should be more hesitant to enact
restrictions that they may be unable to implement. H4: Systems operated as city
departments will adopt usage restrictions earlier than special districts or private water
systems.
Our final measure of capacity is the relationship of water systems with the endusers of their water. Wholesale suppliers are required by law to prepare and implement
drought contingency plans, but because these agencies do not have direct relationships
with the households and businesses that consume the water, they are less well positioned
to design appropriate restrictions and enforce them effectively. Implementation of a
drought plan requires coordination with retail water systems, creating significant
transaction costs and raising questions about equal treatment among the water agencies
served by a wholesale provider. Thus, although wholesalers are likely to encourage water
usage reductions during drought emergencies, we hypothesize that they will hesitate to
enact mandatory use restrictions. H5: Water systems that serve a larger percentage of
retail customers will adopt usage restrictions earlier than predominantly wholesale
systems.
In addition to assessing the impacts of problem status and system capacity, we test
how the customer base influences a water system’s approach to enacting use restrictions.
Constituent interests have an important influence on local land use regulation outcomes
(Gerber and Phillips 2004; Lubell et al. 2005; Ramirez de la Cruz 2009; Lubell et al.
11
2009), and evidence suggests that local government efforts to reduce earthquake risk may
be related more to local political demands than to objective earthquake risk (May and
Birkland 1994). Whether they are elected or appointed to their positions, water
officials—like politicians making land use decisions—have an incentive to make
decisions that allow them to keep their jobs (Mullin 2009). As evidenced in the guidance
quoted above, even state regulators advise that local officials be sensitive to the potential
for use restrictions to incite political outcry. We expect this type of outcry will be most
likely in communities with high per capita water usage, where the built infrastructure of
large lawns and swimming pools or the water-intensity of commercial activity heighten
the real or perceived costs of water restrictions. Because water usage is strongly
correlated with income, we also include a measure of poverty. This is an especially hard
test of political interests, because high levels of water usage prior to drought indicates
more flexibility in water demand, allowing usage restrictions to produce bigger
conservation gains (Kenney 2014). H6: Systems with low rates of consumption per
connection and higher poverty rates will adopt usage restrictions earlier than systems
with high consumption rates and lower poverty. We also hypothesize that customer bases
that are more Republican will be more sensitive to the potential impact of restrictions on
personal liberty and business activity, so will resist at least mandatory restrictions most
actively. H7: Systems that serve customer bases with higher proportions of Democratic
voters will adopt usage restrictions earlier than systems with more fewer Democratic
voters. Following work showing that land use regulation is associated with the
socioeconomic composition of communities, we include variables measuring racial and
12
educational composition as controls, although we do not have strong predictions about
the direction of their effects. We also include the percentage of houses built after 1980
Data and Model
Part of a public water system’s drought contingency obligations is to report to the
TCEQ within five days of the implementation of any mandatory provisions of a drought
contingency plan. We use these reports to measure the implementation and timing of
usage restrictions. The TCEQ’s reporting form asks water systems to report the level of
usage restriction by the following code: mandatory outdoor watering schedule; no outside
watering, limited hand-held hose use only; and no outside water use. Because these
categories do not match the type or threshold of restrictions designated in many local
contingency plans, to avoid measurement error we bundle the categories into a single
dichotomous variable indicating whether a mandatory restriction is in place. Many local
water systems also report the enactment of voluntary usage restrictions, and in a separate
model we include voluntary as well as mandatory limitations. We obtained data on all
reported usage restrictions between January 1, 2010 and November 22, 2013 from a
Freedom of Information Act (FOIA) request to the TCEQ. In early 2010 when our data
begin, only a small portion of east Texas was experiencing drought conditions, unrelated
to the statewide event starting in summer of that year.
Data on system characteristics also come from the TCEQ on FOIA request. We
requested data on all public water systems serving more than 3,300 connections, which
are the only systems required to submit drought plans to the state. In addition to variables
outlined in the hypotheses above, we included control variables for type of source water
13
(surface or groundwater), the percentage of houses built after 1980, and a water system’s
declaration—submitted to the TCEQ at the time of reporting usage restrictions—about its
“level of concern,” or the number of days’ water supply the system has remaining. The
estimate only sometimes coincides with triggering criteria in a water system’s drought
contingency plan. The variable is a five-point scale with values at emergency (could be
out of water in 45 days or less), priority (90 days or less), concern (180 days or less),
watch (greater than 180 days remaining), and resolved (all drought-related issues have
been resolved). We report models both with and without level of concern included.
Where concern level is included in the models, we are analyzing how system and demand
characteristics as well as drought severity influence variation in usage restrictions,
holding constant the estimated longevity of a system’s current water supply.
To measure drought severity, we used weekly U.S. Drought Monitor maps
produced by the National Drought Mitigation Center at the University of NebraskaLincoln in cooperation with the U.S. Department of Agriculture and the National Oceanic
and Atmospheric Administration. The U.S. Drought Monitor uses a five-category scale to
characterize drought intensity, ranging from abnormally dry conditions to exceptional
drought. The variable thus is a six-point measure including areas unaffected by drought.
We assigned scores to water systems by using GIS to match boundaries of drought
classified areas for each week of our study period to the boundaries of water system
service areas, as indicated on spatial maps provided by the TCEQ. Water systems
received the drought score covering the plurality of the spatial area of their jurisdiction,
so long as the majority of the spatial area fell into some category of drought. If the
14
majority of area fell outside the bounds of area classified as being in drought, the water
system received a 0 score for drought severity in that week.
To calculate variables measuring characteristics of the population served by the
water system, we aggregated data collected at the Census block group levels up to
boundaries of water system service areas, assigning blocks according to where the
plurality of their spatial area lies. The included variables—the percentage of a water
system’s customer base in poverty, percentage black, percentage Hispanic, percentage
with college degrees, and the percentage of houses built after 1980—are characteristics
that might affect water demand as well as preferences about government regulation. We
used the same process to aggregate precinct-level vote returns up to water service areas.
Our measure of Democratic composition is the average share of the vote for the
Democratic party candidate across all contested gubernatorial and presidential elections
between 2002 and 2010 (Ansolabehere, Palmer, and Lee 2014). Summary statistics for all
our independent variables appear in Table 1.
Because our hypotheses address relative timing of the implementation of usage
restrictions, not simply to whether or not they get implemented, we employ event history,
or survival, analysis. This technique allows us to model the hazard rate of
implementation, or the likelihood that a water system will implement usage restrictions at
a particular point in time, given that the restrictions are not in place already. We employ
the Cox proportional hazards model, which makes no assumptions about the form of the
baseline hazard rate, but assumes that covariates have a constant effect on the hazard rate
over time. We will subject the assumption to testing in future versions of the paper.
Figure 1 shows smoothed hazard functions for the adoption of restrictions: the left side
15
shows adoption of either voluntary or mandatory restrictions, and the right side
mandatory restrictions only.
We received data from the TCEQ about 307 public water systems, and after
merging with geographic shapefiles gathered from the state and the U.S. Census Bureau,
we retained 280 systems for analysis, 62 of which never adopted either voluntary or
mandatory use restrictions during our time of analysis. Numerous water systems
experienced multiple spells of restrictions, adopting and removing restrictions only to
readopt them on a later date, producing 456 implementations of voluntary or mandatory
usage restrictions and 271 implementations of mandatory restrictions only. Water
systems are observed every time the value of a time-varying variable (i.e., voluntary or
mandatory restrictions, level of concern, or drought severity) changes.
Results
Table 2 shows results from our analyses as hazard ratios, which can be interpreted
relative to the baseline hazard rate of restrictions implementation when covariates in the
model are scored zero. The first two columns show models predicting adoption of
voluntary or mandatory restrictions, and the second two columns show models for
mandatory restrictions only. For each type, we separately estimate models omitting and
including a variable measuring a water system’s self-reported level of concern with
respect to its remaining water supply. Unsurprisingly, level of concern has a large and
highly significant hazard ratio, indicating that water systems facing more severe supply
constraints are quicker to implement use restrictions. A one-unit increase in the five-point
scale indicating level of concern is associated with 3.4 times as much risk that the system
16
will adopt some type of usage restrictions, and 3.7 times the risk of mandatory
restrictions.
Among our hypotheses, the most noticeable result is the consistently significant
positive coefficient on drought severity. A one-unit increase in the Drought Monitoring
index is associated with a 7% increase in adoption of voluntary restrictions during the
time period, and a 9% increase for mandatory restrictions, hazard rates that shrink little
when controlling for a water system’s level of concern about its remaining supply.
Regardless of whether a system is experiencing diminished supply, therefore, systems
where drought levels are more severe adopt usage restrictions more quickly.1 Figure 2
shows the difference in hazard functions between water systems not experiencing any
drought and those in most extreme drought, based on the models in Table 2 that control
for level of concern about remaining water supply. The data suggest that objective
problem conditions are strongly associated with the timing and severity of usage
restrictions.
Findings for our hypotheses about water systems’ institutional and technical
capacity are mixed. System size, as expected, is associated with faster adoption of
restrictions, likely because large systems have the ability to forecast future shortages and
enforce enacted restrictions. We find some support for our hypothesis that city water
systems adopt restrictions faster than other types of systems. City water systems adopted
mandatory restrictions at over 1.4 times the rate of special districts and private systems,
after controlling for a system’s level of concern about its remaining supply. We take this
1
The relationship between drought and water supplies is highly variable across water systems. In a model
not presented here, we find that a unit increase in drought severity more than doubles risk of a system
having concern about its water supply (i.e., estimating that it has fewer than 180 days of supply remaining),
but that result is only weakly significant (p<0.061).
17
is as suggestive evidence that cities may be more comfortable with regulatory authority
and therefore able to enact policies that limit resource use even in the absence of
immediate resource shortages. We do not find any differences between cities and other
governance types when examining adoption of any type of restrictions. In light of
Mullin’s (2009) finding that the effect of governing system may be conditional on
problem severity, in models not shown here we also included an interaction between city
governance and drought severity, but found null results. We find little evidence that a
system’s supply and resiliency, as measured by storage and interconnections, slows the
adoption of usage restrictions. Similarly, serving a larger percentage of wholesale rather
than retail customers appears to have little relationship with a water system’s policy
response to drought. Overall, these findings indicate that it is the capacity to make and
enforce decisions that matters more than the physical capacity of the water system.
Politics appears to have surprisingly little role in influencing the type of
emergency decisions we are studying. Partisan composition of a water system has no
significant effect on timing of voluntary or mandatory restrictions. With respect to water
usage, results indicate that high consumption actually speeds the enactment of voluntary
use restrictions, even after controlling for concern levels, but that result does not persist
for mandatory limitations only. The results may reflect the difference in potential gains
from restrictions, which tend to be higher in high-consumption water systems.
We find several significant results among our variables measuring the
demographic composition of a water system’s customer base, but the patterns are difficult
to decipher. The most consistent result is that water systems serving newer communities,
with a larger percentage of houses built after 1980, adopt both voluntary and mandatory
18
restrictions more rapidly. Several explanations could account for this finding: new
housing may be associated with a larger percentage of discretionary water usage that is
easier to scale back during drought emergencies; occupants of new housing may be more
supportive of usage restrictions; or new housing communities may face supply
constraints that are unmeasured by variables in the models. Unfortunately, our data do
not allow us to evaluate these alternative explanations. With respect to population, a
larger percentage of white residents and a smaller percentage of college graduates both
are associated with a higher rate of adoption.
Discussion
Research on the diffusion of public policies across states or localities rarely
addresses contexts in which exogenous conditions lend urgency to finding a policy
solution. In the case of drought response, decision makers must balance competing
pressures to respond quickly with usage restrictions that might protect water supplies
against worsening conditions and waiting longer in order to protect users against
potentially needless restrictions. In this paper, we examine how problem conditions,
institutional capacity, and political interests influence a water system’s decisions to
implement usage restrictions. Our findings suggest that system decision-making in
drought response in better predicted by problem conditions and institutional capacity than
by political concerns. The severity of drought conditions is the most consistent
determinant of adoption of usage restrictions. Water systems were far quicker to adopt
restrictions when they anticipated shortages in the water supplies, but even holding
constant a system’s level of concern about its remaining water, more severe drought in a
19
community prompted water officials to adopt both voluntary and mandatory restrictions
more rapidly.
Institutional capacity, by several measures, predicts water system decisionmaking. City systems adopted mandatory restrictions faster than other types of systems,
likely reflecting higher levels of authority and access to enforcement tools that are unique
to general-purpose local governments and central to seeking a behavioral change from
system users. Large water systems that have more financial, technical, and managerial
capacity to evaluate drought threats responded more quickly with both types of policies,
offering further evidence that water systems appear to respond more to objective
conditions than to political constraints in managing drought emergencies.
The specificity of the data leveraged in this study allows us to evaluate not only
whether water systems attempt to enforce conservation, but when they choose to act. In
future work, we plan to exploit this feature of the data in order to address spatial diffusion
of restriction adoptions across communities. Using data about a community’s supply
source will allow us to analyze how competition over a shared resource influences policy
diffusion to neighboring jurisdictions and test differing theories of policy diffusion
against one another.
20
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24
Figure 1. Hazard Rates of Water Use Restriction Adoptions
Curves show smoothed hazard functions based on the models that include level of concern (columns 2 and
4) in Table 2 and estimated with values of covariates at their means.
25
Figure 2. Drought Severity and Water Use Restriction Adoptions
Curves show smoothed hazard functions based on the models that include level of concern (columns 2 and
4) in Table 2 and estimated with values of covariates at their means.
26
Table 1. Summary Statistics
Voluntary or mandatory restrictions
Mandatory restrictions
Drought score
Groundwater
Population served (logged)
Average daily consumption per 1k
people
Total storage per 1k people
% customers served wholesale
Number of interconnections
% poverty
% houses built after 1980
% black
% Hispanic
% with 4-year college degree
City water system
Level of concern
Mean
0.430
0.267
2.166
0.125
9.912
0.137
Std. Dev.
0.495
0.442
1.431
0.322
1.144
0.060
Min.
0
0
0
0
-1.093
0
Max.
1
1
5
1
15.357
0.481
0.215
0.160
3.241
15.250
57.631
9.583
29.261
25.768
0.688
0.476
0.121
0.295
5.121
9.571
23.990
10.963
23.228
15.628
0.463
0.536
0
0
0
1.504
6.186
0
0.733
4.983
0
0
0.773
1
53
49.096
99.027
68.053
98.927
81.934
1
4
27
Table 2. Predicting Water Use Restrictions
Voluntary/Mandatory
Mandatory Only
1.072**
(3.32)
1.051**
(2.76)
1.091**
(2.72)
1.078**
(2.53)
Population served (logged)
1.023
(0.87)
1.108**
(3.71)
1.113**
(2.02)
1.179**
(3.44)
Total storage per 1k people
0.676
(-1.10)
0.626*
(-1.71)
1.633
(0.84)
1.780
(1.04)
Number of interconnections
1.000
(0.05)
1.001
(0.20)
1.000
(-0.02)
1.001
(0.10)
City water system
1.030
(0.26)
1.100
(1.09)
1.309*
(1.67)
1.419**
(2.55)
% Customers served wholesale
1.103
(0.57)
0.867
(-0.93)
0.811
(-0.64)
0.663
(-1.29)
11.52**
(3.15)
4.036**
(2.11)
3.373
(1.08)
0.881
(-0.13)
% Democrat
1.006
(0.94)
0.998
(-0.31)
1.014
(1.42)
1.004
(0.47)
Groundwater
1.066
(0.50)
0.942
(-0.43)
1.074
(0.32)
0.901
(-0.50)
% Poverty
0.991
(-1.17)
1.001
(0.18)
0.970**
(-2.11)
0.980
(-1.56)
% Houses built after 1980
1.004*
(1.67)
1.006**
(3.03)
1.008**
(1.99)
1.010**
(2.52)
% Black
0.998
(-0.32)
1.000
(0.04)
0.977**
(-2.22)
0.982**
(-2.10)
% Hispanic
0.992*
(-1.90)
0.990**
(-2.93)
0.986**
(-2.10)
0.985**
(-2.63)
% 4-year college degree
0.998
(-0.65)
0.993**
(-2.29)
0.986*
(-1.88)
0.981**
(-2.51)
Drought score
Average daily consumption per 1k people
3.412**
(9.93)
Level of concern
N
N water systems
3.710**
(10.12)
10817
10817
10817
10817
280
280
280
280
Cells show hazard ratios from Cox proportional hazard models with robust standard errors in parentheses:
* p<0.10, ** p<0.05.
28