1
Lessons from Short-term Supply Disruptions: Providing Confidence and Context
to FEMA’s Methodology
Prepared for Industrial Economics Incorporated under EPA Contract
Number EP-W-10-002, Subcontract Number 8070-Aubuchon.
Craig P. Aubuchon, Analysis Group
Kevin M. Morley, PhD, American Water Works Association
The research described in this paper has been funded wholly or in part by the U.S.
Environmental Protection Agency through EPA Contract Number EP-W-10-002. The
paper has not been subjected to peer review. The views expressed in the paper are those of
the author(s) and not necessarily the EPA. No official endorsement by EPA has been
granted, nor should any such endorsement be inferred.
The views expressed in this article are those of the author(s) and do not necessarily represent the
views of the Analysis Group or the American Water Works Association.
2
Abstract
This analysis presents estimates for the economic value of water during short-term supply
disruptions and uses the valuation framework established by FEMA. The current research
conducts a partial sensitivity analysis on demand elasticity, industry resilience, and consumption
to provide several per capita, per day (pcpd) dollar values for the total economic impact due to
the loss of potable water service by industry at the state level. The current FEMA standard value
of water, $93 pcpd in year 2008 dollars, is used in benefit cost analyses to evaluate the efficiency
of hazard mitigation projects aimed at protecting public drinking water supplies. This is a useful
starting point for assessing the broader value of water in multiple sectors of the economy, and
state level estimates correctly acknowledge regional differences in water supply, consumer
preferences, and industrial composition. A primary motivation of this work is to acknowledge
some of the uncertainties and data parameters in the FEMA model and offer guidance on future
research. This study advocates for using population weighted, state level data and finds an
estimated range of economic losses per capita per day between $64 and $437.
3
Introduction
The words of Benjamin Franklin have been widely repeated: “When the well runs dry, we
learn the worth of water.” Water, and more specifically high quality potable water, lies at the
center of many daily activities. Humans need a basic quantity of water for consumption, cooking
and sanitation to survive, but water is also a valuable resource in industrial processes because of
its ability to transfer heat, remove contaminants, and as an input to production. Historically,
potable water service has represented a minor portion of consumers’ or businesses’ total budget,
but as Raucher (2005) points out, cost and value are not equivalent. Franklin seems to have
understood this distinction, and one current valuation method is to estimate the costs imposed on
businesses and consumers during times of water supply disruption.
This research conducts a sensitivity analysis of the Federal Emergency Management
Agency’s (FEMA) standard value of water. FEMA estimates a standard value of water based on
estimated losses to businesses and residents during supply disruptions due to natural disasters
and uses this value when assessing the efficiency of project applications to its Hazard Mitigation
Grant program.1 This value of water is based on key assumptions regarding industry resilience
to disruptions and consumer preferences for water demand. Equally important, FEMA assumes
a complete disruption of water, which provides a useful upper bound for the value of water in the
short term.
1
Hazard mitigation is defined as “any sustained action taken to reduce or eliminate long-term risk to people and
property from natural hazards and their effects.” The Hazard Mitigation Assistance program includes five
programs: Hazard Mitigation Grant Program, Flood Mitigation Assistance Grant Program, Pre-Disaster Mitigation
Program, Repetitive Flood Claims Program and Severe Repetitive Loss Program. The Hazard Mitigation Grant
Program (HMGP) provides assistance ex post and accounts for the majority of historic hazard mitigation assistance.
In FY09, HMGP spent $359m (out of a total $574m); in FY08, HMGP accounted for $1.2b out of $1.48b. For more
detail, see http://www.fema.gov/government/grant/bca.shtm#1.
4
FEMA’s value is presented as a per capita, per day (pcpd) value and is thus easily
understood and communicated among utility managers, board members, and customers. This
value is used in the grant applications for several FEMA hazard mitigation grant programs, and
within a benefit cost analysis (BCA), it represents an estimate for the benefit of a proposed
mitigation project. In this sense, the benefit is an ex ante avoidance of future losses. The BCA
framework ensures that FEMA money is spent in a transparent and efficient manner; the 2011
‘budget crisis’ experience that centered on disaster relief for Hurricane Irene and Tropical Storm
Lee victims highlights the importance of effectively valuing mitigation benefits to minimize
future disaster relief payments.2 A comprehensive review of 5,500 FEMA Hazard Mitigation
grants between 1993 and 2003 found an overall benefit cost ratio of 4:1 (Rose et al., 2007). This
suggests that mitigation strategies can be highly valuable and socially beneficial, and
demonstrates how other agencies, EPA included, may be able to use estimates of the value of
water when developing and implementing future regulations and budget outlays.
This paper presents a partial sensitivity analysis of the FEMA standard value for the loss
of potable water service by varying key parameters for industry resiliency, consumer demand
elasticity, and consumer per capita consumption. The rest of the paper proceeds as follows:
section 2 provides background on the FEMA mitigation grants program; section 3 details the
methodology for estimating both business and residential impacts; section 4 presents key results
of the sensitivity analysis, including recommendations for appropriate values; section 5 provides
suggestions for future research; and section 6 offers summary conclusions.
2
A number of popular media articles detailed the budget resolution. For example, see Hook and Johnson (2011):
http://online.wsj.com/article/SB10001424052970204831304576594872744108788.html.
5
Background
In order to secure a FEMA mitigation grant, an interested community must file an application
for funds that, according to the FEMA’s Hazard Mitigation Assistance Unified Guidance (2010),
should use FEMA-approved methodologies and software to demonstrate the cost-effectiveness of
proposed projects. The Hazard Mitigation Assistance Unified Guidance document stipulates that
“…documentation that shows how values for each data input were derived must be provided so
that the credibility and validity of the approach can be evaluated. If FEMA standard values are
used, no documentation is required” (FEMA, 2010, p. 34). A full listing of standard values,
including loss of service values for utilities, is available in Appendix C of the BCA Reference
Guide (FEMA, 2009).
While the value provided by FEMA simplifies communication, it may also provide a false
sense of certainty for mitigation benefits, where benefits are defined as avoided economic losses.
FEMA currently estimates that the value of the loss of potable water service is equal to $93 per
capita per day (in 2008 dollars). The FEMA loss of service impact includes both economic
losses to 1) businesses and the regional economy3 and 2) the estimated value of water services to
residential consumers. The two types of losses are estimated separately based on different
economic models. The model for business impacts only considers the direct loss from reductions
in gross domestic product, which is largely a function of industry resilience to lifeline
disruptions. FEMA presents these losses at the national level, but they can also be represented at
the state level using state level estimates of gross domestic product from the Bureau of Economic
Analysis (BEA). In the second model, residential losses are estimated with the economic loss
function presented by Brozovich et al. (2007), which uses a constant elasticity demand curve.4
3
Throughout the text, we use the term ‘business’ and ‘industry’ loss interchangeably.
Brozovich et al. (2007) also accounts for the extent and severity of a disruption. In contrast, FEMA assumes a full
disruption. The estimated economic loss, calculated as the area under a consumer’s demand curve, depends
4
6
This approach is easily modified to address regional impacts by considering varying estimates of
regional consumption. This regional modification is the focus of this analysis.
In contrast, other studies have used either an input-output framework or a computable general
equilibrium model to estimate losses. The former estimates long term disruptions and as such,
provides a strict upper limit to losses, while the latter approach requires localized estimates of the
percent change in industrial output due to disruption, which may be difficult for individual water
utilities to obtain (Rose and Liao, 2005).
Data and Methodology
Business Losses
To calculate business losses, FEMA multiplies industry level Gross Domestic Product
(GDP), a measure of broad economic activity, by resilience factors for each sector, where ri
represents industry i’s percent capacity to operate without water, due to contingency plans,
substitute inputs for water, or short term reserve supplies.5 For example, a resilience factor of
0.8 means that during a complete, short-term disruption (less than 1 week) of water service,
industry i can operate at 80 percent capacity. The resilience factors presented in table 1 estimate
the impact solely attributable to water disruption, although new research considers the interaction
effect of multiple lifeline disruptions (Kajitani and Tatano, 2009). The economic loss across all
industries is calculated using equation 1, which expresses the loss on a dollar per capita per day
basis:
significantly on the amount of water provided. Because of the constant elasticity assumption, any supply of water
above a total disruption results in a sharp decrease in the total economic loss. This was modeled with a Monte Carlo
simulation, with extent and severity drawn from a uniform distribution (0,1) and elasticity from a normal
distribution with mean (-0.41) and standard deviation (0.11). Results are available from the author upon request.
5
Rose (2009) provides a comprehensive definition and review of economic resilience and points out that resilience
occurs at the individual, economic, and macroeconomic scale. Resilience is also comprised of direct impacts and
indirect impacts, which can happen in either the positive or negative direction. This analysis works entirely within
the current FEMA hazard mitigation methodology, which only considers direct impacts.
7
∑
(Equation 1)
Here, resiliency factors assume a complete loss of water service and
(that is,
resiliency factors are constant across time). From equation 1, it is clear that industry level
resiliency factors are the primary model assumption. One of the first attempts to quantify
resiliency came from the Applied Technology Council-25 (ATC, 1991). This study represented
a major step forward in quantifying the impact of the loss of service of various lifelines, but the
authors go to great lengths to moderate expectations and advocate for additional research. The
resiliency factors presented in the 1991 study were adapted from an earlier ATC-13 (1985) report
that evaluated resiliency factors within California, and the ATC-25 (1991) report states that the
factors ‘represent a first approximation’ (Executive Summary, xiii), based on the author’s
assumptions, which could be improved by further research. However, the current FEMA
methodology uses these ATC-25 values.
In contrast, Chang et al. (2002) develop a set of resiliency factors for 16 industries based
on pre-disaster empirical surveys of 737 businesses in the Memphis area, and 1,110 businesses in
the Los Angeles region impacted by the Northridge earthquake. 6 This approach attempts to
account, albeit with few samples, for regional differences in emergency preparedness. The
authors found that resiliency was generally higher in California businesses, likely due to an
increased awareness about natural disaster related damages. This suggests that economic losses
using the ATC-25 values may be biased downwards. Using empirical data, the authors also
calculated resiliency factors across time, at outages less than 1 week, 1 to 2 weeks, and greater
6
Chang et al. (2002) use the raw data from the Disaster Research Center at the University of Delaware on the
Northridge 1994 earthquake in Los Angeles and 1993 floods in Iowa (Tierney, 1997; Tierney and Dahlhamer,
1998a, 1998b). They model the probability of business closure as a function of severity and time duration with a
Monte Carlo analysis framework.
8
than 2 weeks; by doing so, they allow for the possibility that a business or industry may exhaust
its back up plans/reservoirs and face a higher probability of closure under longer durations of
service losses.
In addition, the FEMA methodology does not consider regional variation in GDP due to
the relative size of different industries by state. This analysis presents estimates for losses based
on 1) U.S. totals for GDP and population (the FEMA model), 2) state totals for GDP and
population, and 3) population weighted values for state totals of GDP and population. The latter
two approaches are derived, respectively, for each industry i and state j. Summing across all
states yields an estimate of per capita per day business economic losses for the nation as a whole:
∑
∑
(Equation 2)
∑
∑
(Equation 3)
Equation 3 represents an improvement over current estimation methods that only
consider national inputs to economic losses because it correctly accounts for regional variations
in business size and water use intensity. It preserves the simplicity of a national estimate, which
is important for communication, but it also more accurately assigns values based on population
distributions and more appropriately captures the costs borne by the majority of the population.
To illustrate the range of possible values, and to demonstrate the influence of population
weighting, the economic loss to businesses is calculated using equations 1-3 for both the ATC-25
(1991) and Chang et al. (2002) resiliency factors. Table 1 presents the mapping of resiliency
factors for each industry. The main difference is not an aggregate change in resiliency, but
rather, a difference in distribution among sectors. For example, health care is judged to be less
9
resilient under Chang et al. (2002), while manufacturing, particularly among durable goods such
as computers/electronics, is believed to be more resilient. The difference in resilience factors
between the two studies may be due to intra-industry changes in resilience over time or to
differences in methodology. The ATC-25 (1991) report relies, in part, on expert judgment and
analyst discretion, while the Chang et al. (2002) study represents a more empirical estimate of
resilience estimated from a more diverse set of businesses. The point of this study is not to
advocate for one set of resiliency measures over another but rather to demonstrate the sensitivity
of the estimates to these variables. Clearly, however, a more accurate study of resiliency factors
in the face of total and partial disruption at the state level is an important next step in future
research and improving the current understanding of loss of service impacts.
Residential Losses
In contrast to business impacts, residential losses require more data and methodological
assumptions because customers purchase water for a wider range of discretionary uses. The
value of water is separate from the price (or cost) of water (Raucher, 2005); safe, clean water for
drinking, cooking and sanitation is necessary for life and therefore, highly valuable, while water
for other residential uses is valuable, but not always necessary, in a disaster situation. To
estimate the value of water to residential consumers, FEMA considers the total economic loss as
the area under the demand curve. A key distinction in this type of loss function is the
consumer’s price elasticity, where elasticity is defined as the change in demand due to a change
in price.7 Basic water requirements (BWR) are highly inelastic. In contrast, more discretionary
uses generally exhibit higher elasticity values because consumers are willing to change their
7
Formally, price elasticity is:
. Values between 0 and -1 are termed ‘inelastic’ since a one percent price
increase results in less than one percent reduction in demand; a value of -1 is perfectly elastic and magnitudes
greater than -1 are considered elastic.
10
behavior in the face of higher prices.8 Figure 1 illustrates the associated economic loss due to
water disruption (adapted from Jenkins et al., 2003). Elasticity measures the slope of the demand
line; a more inelastic value indicates a steeper (more vertical) demand curve and therefore, a
higher estimate of loss. Brozovic et al. (2007) assume a constant elasticity of demand function
and estimate the total economic loss from a complete disruption as:
[
]
(Equation 4)
Where:
Residential Economic Loss is in $ per capita per day,
is the price elasticity of demand,
P is the average water price under normal operating conditions in $ per gallon,
Q is the average per capita consumption per day in gallons,
BWR is the basic water requirement for human life.
The economic loss is calculated as the difference between the BWR and normal demand,
under the assumption that relief agencies and/or government agencies will provide the BWR for
every citizen. Brozovich et al. (2007) also consider the severity and extent of the disruption, and
allow for additional water above the BWR to enter the demand function. This lowers the total
economic loss, since consumers only lose the benefit derived from a smaller quantity of water.
However, the FEMA methodology assumes a total disruption of water service. The BWR is
assumed to be 6.6 gallons per person per day, as defined by the United Nations, in order to meet
basic human needs. In this manner, the FEMA approach considers economic losses from a
8
The concept of consumer’s demand elasticity is central to designing and implementing water conservation rates.
Utilities that face high levels of population growth, large peaking factors (maximum production to average
production) or water scarcity may implement conservation rate structures that charge higher prices at higher levels
of water consumption. A number of Journal AWWA articles explore conservation rate structures in detail, including
Chesnutt and Beecher (1998), Mayer et al. (2008) and Aubuchon and Roberson (2012).
11
standpoint similar to the Triple Bottom Line (TBL) accounting framework, in which social needs
and concerns are included as part of a broader economic calculation. FEMA assumes that BWR
water will be provided at an average cost of $1.85 per gallon, which represents the mid-point of
EPA estimates for the cost of bottled water. Another important distinction, noted by Brozovic et
al. (2007), is that BWR provision entails a cost greater than the typical purchase price due to
transportation and logistics of distribution. Without considering these distribution costs, the
total residential economic loss should be viewed strictly as a lower limit. Total residential
economic loss is calculated as:
(
)
In applying equation 4, FEMA uses an average water price of $2.85 per 1,000 gallons (in 2005
$), based on an American Water Works Association (AWWA) 2005 survey, and average daily
per capita consumption of 172 gallons provided by Mayer et al. (1999). FEMA employs a price
elasticity of -0.41, which is the mean value of 314 elasticity measures from the meta-analysis
conducted by Dalhuisen et al. (2003).
An important consideration is that per capita consumption varies significantly by region,
due to differences in local climate conditions, price structures and socio-economic characteristics
(Dalhuisen et al., 2003; Rockaway et al., 2011). For comparison, this study presents a range of
loss values using state level per capita per day consumption, as estimated by the United States
Geological Survey (2009) in the recurring publication Estimated Use of Water in the United
States in 2005. Besides capturing local variation, the USGS data also account for the reduction
in per capita water use that has occurred since 1999, a change due in part to more stringent
12
building codes, technological advances including greater adoption of more water efficient
appliances, and conservation rate structures (Rockaway et al., 2011). The average consumption
across all states is 98 gallons per person per day, while the population weighted average is 97
gallons per person per day. Both estimates are significantly lower than the FEMA estimate of
172 gallons per person per day.
Similar to economic impacts, state level and population weighted estimates for loss of
service impacts to residents can be calculated as:
∑
[
(
)
]
(Equation 6)
∑
[
])
(Equation 7)
Here, Qj is the per capita per day water consumption by state taken from the USGS
(2009). Similar to regional economic impacts, equation 7 represents an improvement over
current estimation methods that only consider a national input for per capita per day consumption
because it correctly accounts for regional variations due to local cultures, prices and climates. A
more current estimate of consumption also correctly includes the nationally observed decline in
per capita consumption.
Measures of elasticity also vary with time. Dalhuisen et al. (2003) found that estimates
for long term elasticity were generally 0.15 greater in magnitude than short run estimates. If
consumers know there will be a shortage of water in the future, then they may respond to future
expectations of price increases by installing low flow appliances, introducing drought tolerant
landscaping, or removing an outdoor pool. In the short term, however, consumers do not have
this luxury and often will pay more to maintain the status quo. Indeed, a dichotomous choice
13
referendum willingness to pay survey found that customers were more willing to pay to avoid
low probability, high disruption events than more frequent, smaller disruptions (Barakat and
Chamberlin, 1994). To account for the differences in short term elasticity, this analysis estimates
residential losses at the median value (-0.35) and short run value (-0.26) from Dalhuisen et al.
(2003). A more inelastic estimate may also represent some of the indirect costs that consumers
are willing to accept in the face of water shortages or disruptions. These costs could include the
value of time lost due to boil orders or time spent waiting in line to purchase or obtain BWR
provided by a retail outlet or government relief agency. Previous iterations of the FEMA
methodology have included a ‘disruption impact’ equal to two to three hours of disruption time
multiplied by the national employer compensation rate of $28.90 (in $2008). The current
methodology does not take into account the time value of disruptions, but more accurate values
of short run elasticity measures correctly compensate for consumers’ willingness to avoid short
term supply disruptions.
Results
Table 2 highlights the implications of employing equation 1 (the standard FEMA
methodology) versus equation 2 or 3 in calculating losses to business economic activity, as well
as the sensitivity of the results to the use of different resiliency factors. In all situations, the
Chang et al. (2002) resiliency factors present a higher loss estimate. This finding is primarily
due to the fact that the Chang (2002) resilience factor estimates are lower for higher value
professional services (such as health care; retail/wholesale trade; and finance, insurance and real
estate) than the same resilience factor estimates from the ATC-25 (1991) study. This suggests
that the ATC-25 figures may have overestimated the resilience of certain professional services
and therefore underestimated the economic value of water in those settings. Figure 2 illustrates
14
the per capita per day estimates of business loss impacts by state. On a per capita basis,
Delaware and Connecticut have the two highest values of economic business loss. This result is
driven in part by the size of each state’s economy, particularly in the FIRE (Finance, Insurance
and Real Estate) industries, relative to its population. Delaware and Connecticut rank 39th and
23rd in State level GDP, respectively, but 45th and 29th in total population. By industry, Delaware
and Connecticut receive 45 and 31 percent of their total state GDP from FIRE, the first and
fourth highest among all states. However, because Delaware and Connecticut represent just 1.1%
and 0.2% of the total U.S. population, respectively, each state adds relatively little to the total
U.S. economic value on a population weighted basis. For these reasons, the population weighted
values are slightly lower than the U.S. totals; a more appropriate method for calculating the loss
of service should use population weighted values from equation 3. Table 2 also demonstrates the
importance of resilience factor data assumptions in the business economic loss estimates and
provides an estimated range of business losses between $39 and $66. Because these values
represent a probable range of values, the mean value is likely the most appropriate number. The
mean of the population weighted values leads to an estimated economic loss due to water supply
disruption of $53 per person per day.
Similarly, table 3 presents nine different estimates for residential losses using equations
4-7, evaluated at the combinations of one of three price elasticities (-0.41, -0.35, -0.26) and one
of three per capita per day water consumption rates (FEMA’s current assumption of 172 gallons
per capita per day, state level consumption rates from the USGS, and population weighted state
consumption rates). Compared against the model assumptions used by FEMA (172 gallons pcpd
and elasticity of -0.41), state level estimates with lower elasticities could be interpreted as
models in which consumers use less water but value it more highly. Table 3 also includes the
15
cost to provide BWR. It has been noted that BWR values represent a strict lower bound
estimate, since these values do not account for transportation or distribution costs of the
emergency supply. As expected, the value of water in table 3 varies greatly with different
assumptions of consumer price elasticity. Values for the economic loss to residential consumers
range from a low of $22 (using USGS state level consumption data and an elasticity of -0.41) to
a high of $2,046 (using 172 gallons per person per day and an elasticity of -0.26). Using the
FEMA values of 172 gal pcpd and an elasticity of -0.41 provides an estimate of $53, which is the
median value of the nine scenarios calculated in table 3. However, the mean value for the nine
scenarios is almost six times as large, calculated as $330. Again, the preferred value is the
average of the population weighted values, equal to $146.
FEMA’s estimates likely understate the true value of residential losses because the
majority of water disruptions are primarily short term events. It is more appropriate to use a
lower elasticity value that better captures the short term tradeoffs between price and demand.
The USGS data also accounts for recent declines in per capita per day water consumption and
regional variation in consumption. The average of the population weighted consumption
measures suggests that the value associated with residential losses may be closer to $145 per
capita per day. Figure 3 shows individual states measured against the current FEMA estimate
based on a national consumption average of 172 gallons per person per day. Only Utah, Idaho
and Nevada consume a greater amount of water per capita per day, while the majority of states
use far less.
Finally, table 4 presents the total economic impacts, calculated as the sum of business and
residential impacts and illustrates the wide range of dollar value estimates that are possible using
reasonable data assumptions and operating strictly within current FEMA methodology. This
16
research demonstrates that value estimates are strongly dependent on the choice of business
resiliency and consumer price elasticity, and it also highlights that it is possible to calculate state
level values that take into account regional differences in industrial output and residential
consumption.
Population weighted, state level estimates offer the best approximation for the
value of water by considering differences in local water resource availability and use. The
average of the six population weighted values, calculated across all estimates of consumer price
elasticity and both measures of resiliency, presents a range of the total water values between $64
and $437 per capita per day. The current FEMA estimate of $93 per capita per day, which is
based on national aggregate data, would be the median value if it were included in this state level
sample; however, it is far below the average of $198 for the six population-weighted values.
Future Research
As this analysis has acknowledged, estimating the value of water with any level of
precision is difficult. This does not detract from FEMA’s current methodology or the state of
knowledge regarding important data parameters. Rather, it highlights the need for a continued
and ongoing discussion about how water is used and valued in regional economies. An
important next step is to consider the non-linear aspect of water disruption. Future work should
address the probability and relative impact from a boil order, do not drink and/or do not use
order, and various levels of disruption. Water service may be particularly susceptible to the
‘social amplification’ of risk (Rose et al., 2009), and the threat of real or imagined contamination
may lead neighboring utilities to shut down for testing, cause individuals to avoid societal
contact out of fear, or shift to more expensive bottled water for non-BWR needs. Consumer
expectations may change over time and consumption patterns adjust accordingly. In the short
term, consumers are likely more affected by boil orders than commercial and industrial
17
consumers, to the extent that boil orders are a health/quality disruption and not a supply
disruption. Another important next step will better understand the relative impacts to consumers
during these supply disruptions. It is equally important to assess the changes in resilience and
elasticity over time and among industries. It is difficult to imagine that an industry can operate
with the same resiliency in week 3 of a water supply disruption as in week 1. At some point, the
challenges imposed by disruption will overwhelm even the best preparedness measures as
emergency supplies are exhausted. Likewise, consumer behavior is not well understood during
extended disruptions. At some point, lack of water may overwhelm a willingness to pay for
‘normal’ conditions. Rose (2009) also advocates for developing a better understanding of the
differences in potential resilience and actual resilience, since individuals and economies may
face decision uncertainty during stressful disaster situations.
Conclusions
This analysis presents the FEMA methodology and data assumptions for calculating the
economic value for the loss of potable water service in the event of complete disruption and
advocates for a population weighted average using state level data, in order to more accurately
account for state level variations in consumption and GDP. The FEMA value of $93 per person
per day is consistent with the median value of the calculated sample, but compared to the
average value of the six population weighted models (equations 3 and 7), the FEMA value
greatly underestimates the total economic loss of potable water services due to a total disruption
in supply. The average value among the six preferred model estimates is equal to $198 per
person per day. This value more correctly accounts for regional differences in economic water
use, residential consumption patterns, and the larger willingness to pay of consumers to avoid
short term disruptions. At the same time, it provides important context and confidence to the
18
FEMA point estimate. This allows utilities to better understand how to estimate supply
disruption valuations in their own service areas and choose the appropriate risk level for their
benefit-cost decisions with regards to security preparedness infrastructure improvements.
19
Table 1: Resiliency Factors by Industry
ATC-25
Industry
(1991)
Utilities
0.6
Construction
0.5
Durable goods
0.4
Wood product manufacturing
0.5
Nonmetallic mineral product manufacturing
0.5
Primary metal manufacturing
0.1
Fabricated metal product manufacturing
0.2
Machinery manufacturing
0.4
Computer and electronic product manufacturing
0.1
Electrical equipment and appliance manufacturing
0.1
Motor vehicle, body, trailer, and parts manufacturing
0.4
Other transportation equipment manufacturing
0.4
Furniture and related product manufacturing
0.5
Miscellaneous manufacturing
0.4
Nondurable goods
0.2
Food product manufacturing
0.3
Textile and textile product mills
0.3
Apparel manufacturing
0.3
Paper manufacturing
0.4
Printing and related support activities
0.7
Petroleum and coal products manufacturing
0.5
Chemical manufacturing
0.2
Plastics and rubber products manufacturing
0.5
Wholesale trade
0.8
Retail trade
0.8
Transportation and warehousing, excluding Postal Service
0.8
Information
0.8
Finance and insurance
0.8
Real estate and rental and leasing
0.8
Professional and technical services
0.8
Management of companies and enterprises
0.8
Administrative and waste services
0.8
Educational services
0.6
Health care and social assistance
0.6
Arts, entertainment, and recreation
0.2
Accommodation and food services
0.2
Other
0.8
Government
0.75
MEAN
0.50
Chang et al.
(2002)
0.65
0.68
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.42
0.51
0.46
0.65
0.65
0.44
0.44
0.45
0.45
0.45
0.45
0.27
0.45
0.45
0.45
0.45
0.45
Difference
0.05
0.18
0.02
-0.08
-0.08
0.32
0.22
0.02
0.32
0.32
0.02
0.02
-0.08
0.02
0.22
0.12
0.12
0.12
0.02
-0.28
-0.08
0.22
-0.08
-0.29
-0.34
-0.15
-0.15
-0.36
-0.36
-0.35
-0.35
-0.35
-0.15
-0.33
0.25
0.25
-0.35
-0.3
-0.04
20
Table 2: Impact to Business Economic Activity, Per Capita Per Day $2008
U.S. Total
ATC-25 (1991)
Resiliency Factors
Chang et al (2002)
Resiliency Factors
Current Value
State Level
GDP
$40
$39
$40
$66
$65
$66
Recommended Value = $53
Table 3: Impact to Residential Customers, Per
Capita Per Day $2008
Includes the FEMA cost for Basic Water Requirements
(6.6 gal @ $1.85/gal)
Elasticity of Demand:
Per Capita Per Day
Consumption (gal):
172
State Level USGS
estimate
State Level,
Population Weight
Current Value
State Level GDP,
Population Weight
-0.41
$53
-0.35
$136
-0.26
$2,046
$22
$37
$246
$24
$43
$371
Recommended Value = $146
21
Table 4: Total Economic Impact expressed in Per Capita Per Day $2008
U.S. and State Level (GDP and Consumption) Totals
Resiliency Factors
U.S. Total, ATC-25
U.S. Total, Chang et al
(2002)
State Level Data,
ATC-25
State Level Data,
Chang et al. (2002)
State Level, Population
Weighted, ATC-25
State Level, Population
Weighted, Chang et al.
(2002)
Current Value
Mean:
Median:
Std Dev:
Min:
Max:
Elasticity of Demand:
Per Capita Per Day
Consumption (gal):
-0.41
-0.35
-0.26
172
State Level
$93
$62
$176
$77
$2,086
$286
State Level, Population
Weight
$64
$83
$411
172
$119
$202
$2,113
State Level
$89
$103
$312
State Level, Population
Weight
$90
$109
$437
172
$91
$175
$2,085
State Level
$61
$76
$285
State Level, Population
Weight
$63
$81
$409
172
$118
$201
$2,112
State Level
$87
$102
$311
State Level, Population
Weight
$89
$108
$436
172
$92
$176
$2,086
State Level
$62
$77
$286
State Level, Population
Weight
$64
$82
$410
172
$119
$202
$2,112
State Level
$88
$103
$312
State Level, Population
Weight
$90
$109
$437
$124
$106
$49
$76
$202
$940
$423
$845
$285
$2,113
Recommended Value =$198
$86
$89
$20
$61
$119
22
23
24
25
References
Applied Technology Council (ATC). 1991. Seismic Vulnerability and Impact of Disruption of
Lifelines in the Counterminous United States. Federal Emergency Management Agency
Report 224.
Applied Technology Council (ATC). 1985. Earthquake Damage Evaluation Data for California.
United States. Federal Emergency Management Agency. 492 pages.
Aubuchon, C.P. and J.A. Roberson. 2012. Price perception and non-price controls under
conservation rate structures. Journal of American Water Works, 104(8), pp. 33-34.
Barakat and Chamberlin Inc. 1994. The Value of Water Supply Reliability: Results of a
Contingent Valuation Survey of Residential Customers. Prepared for the California Urban
Water Agencies.
Brozovic, N., D.L. Sunding, and D. Zilberman. 2007. Estimating Business and Residential
Water Supply Interruption Losses from Catastrophic Events, Water Resources Research, 43,
pp. 1-14.
Chang, S.E., W.D. Svekla, and M. Shinozuka. 2002. Linking infrastructure and urban economy:
simulation of water-disruption impacts in earthquakes. Environment and Planning B:
Planning and Design, 29, pp. 281-301.
Chesnutt, T.W. and J.A. Beecher. 1998. Conservation Rates in the Real World. Journal of
American Water Works Association. February, 90(2), pp. 60-70.
Dalhuisen, J.M., R. Florax, H. de Groot and P. Nijkamp. 2003. Price and Income Elasticities of
Residential Water Demand: A meta-analysis. Land Economics, 79(2), pp. 292-308.
Environmental Protection Agency. 2009. 2006 Community Water System Survey: Volume I
Overview. Accessed Online: http://water.epa.gov/aboutow/ogwdw/cwssvr.cfm
26
Federal Emergency Management Agency, 2010.
Hazard Mitigation Assistance Unified
Guidance.
Federal Emergency Management Agency, 2009. BCA Reference Guide: Final. Prepared by
URS group.
Hook, J. and Johnson, K., 2011. “Stopgap Fix Ends Budget Impasse: Senate Approves ShortTerm Funding Measure After Sticking Point is Resolved”. Wall Street Journal, September
27.
Jenkins, M.W., J.R. Lund, and R.E. Howitt. 2003. Using Economic Loss Functions to Value
Urban Water Scarcity in California. Journal American Water Works Association, 95(2), pp.
58-70.
Kajitani, Y., and H. Tatano. 2009. Estimation of Lifeline Resilience Factors based on Surveys of
Japanese Industries. Earthquake Spectra, 25(4), pp. 755-776.
Kenny, J.F., Barber, N.L., Hutson, S.S., Linsey, K.S., Lovelace, J.K., and Maupin, M.A., 2009,
Estimated use of water in the United States in 2005: U.S. Geological Survey Circular 1344,
52 p. Referenced as (USGS, 2009) in text.
Mayer, P.W., W.B. DeOreo, E.M. Opitz, J.C. Kiefer, W.Y. Davis, B. Dziegielewski, and J.O.
Nelson. 1999. Residential End Uses of Water. AwwaRF and AWWA, Denver.
Mayer, P., W. DeOreo, T. Chesnutt, and L. Summers. 2008. Water Budgets and Rate Structures:
Innovative Management Tools. Journal of American Water Works Association. 100(5), pp.
117-131.
National Drinking Water Advisory Council. 2010. Climate Ready Water Utilities Final Report.
White Paper, available at: http://water.epa.gov/drink/ndwac/climatechange/upload/CRWUNDWAC-Final-Report-12-09-10-2.pdf
27
Office of Management and Budget, 1992. Circular A-94: Guidelines and Discount Rates for
Benefit-Cost Analysis of Federal Programs.
Raucher, B. 2005. The Value of Water: What it Means, Why It’s Important and How Utility
Managers Can Use It. Journal American Water Works Association, 97(4), pp. 90-98.
Rockaway, T.D., P.C. Coomes, J. Rivard and B. Kornstein. 2011. Residential Water Use Trends
in North America. Journal of American Water Works Association. 103(2), pp. 76-89.
Rose, A. and S.Y. Liao. 2005. Modeling Regional Economic Resilience to Disasters: A
Computable General Equilibrium Analysis of Water Service Disruptions. Journal of
Regional Science, 45(1), pp. 75-112.
Rose, A. 2009. A Framework for Analyzing the Total Economic Impacts of Terrorist Attacks
and Natural Disasters. Journal of Homeland Security and Emergency Management, 6(1),
Article 9.
Rose, A., Porter, K., Dash, N., Bouabid, J., Huyck, C., Whitehead, J., Shaw, D., Eguchi, R.,
Taylor, C., McLane, T., Tobin, T., Ganderton, P., Godschalk, D., Kiremidjian, A.S., Tierney,
K. and West, C.T. 2007. Benefit-Cost Analysis of FEMA Hazard Mitigation Grants.
Natural Hazards Review, November, pp. 97- 111.
Tierney, K.J., 1997, Business Impacts of the Northridge Earthquake, Journal of Contingencies
and Crisis Management, 5, pp. 87-97.
Tierney, K.J. & Dahlhamer, J.M., 1998a, Business Disruption, preparedness, and recovery:
lessons from the Northridge Earthquake, in Proceedings of the NEHRP Conference and
Workshop on Research on the Northridge, California Earthquake of January 17, 1994
volume IV, California Universities for Research in Earthquake Engineering, Richmond,
California, pp. 87-97.
28
Tierney, K.J. & Dahlhamer, J.M., 1998b, Earthquake vulnerability and emergency preparedness
among businesses, Chapter 5 in Engineering and Socioeconomic Impacts of Earthquakes.
Eds. M. Shinozuka, A. Rose, R.T. Eguchi. Monograph No 2, Multidisciplinary Center for
Earthquake Engineering Research, Red Jacket Quadrangle, State University of New York at
Buffalo, Buffalo, NY 14261, pp. 53-74.
29
Author Affiliations and Qualifications
Craig P. Aubuchon is the primary author of both manuscripts prepared under Contract
Number EP-W-10-002, Subcontract Number 8070-Aubuchon. He is an Associate with the
Analysis Group in their Boston office. He holds a Master’s degree of Environmental Science
and a Master’s Degree of Public Administration with an emphasis on Water Resources from the
School of Public and Environmental Affairs (SPEA) at Indiana University, and a Bachelor’s
degree in Economics and Environmental Studies from Washington University in St. Louis. He
has published several technical and non-technical articles in peer-reviewed journals, including a
recent article on the effectiveness of water utility rate structures published in the Journal of the
American Water Works Association. He has extensive experience in applied econometrics for
policy analysis, first as a Senior Research Associate with the Federal Reserve Bank of St. Louis,
and second with a policy analysis concentration within his graduate studies. He actively
participates in water industry workshops and conferences, including recent presentations at the
Sustainable Water Management Conference (March 2012) in Portland, Oregon and the Water
Security and Preparedness Conference (September 2012) in St. Louis, MO.
Kevin M. Morley is the secondary author of the Providing Confidence and Context
paper and serves as the Security and Preparedness Program Manager for the American Water
Works Association (AWWA). He has over 10 years’ experience in the water industry as a
consultant and industry representative. He has a PhD from George Mason University in
Environmental Science and Public Policy. His research emphasizes the resilience of the water
sector, and he has been instrumental in coordinating industry and regulatory efforts in the
development of the Water/Wastewater Agency Response Network (WARN).
This research was supported in part by the American Water Works Association
(AWWA). AWWA is the authoritative resource on safe water. With over 50,000 members,
AWWA represents the largest collection of industry professionals and stakeholders. This
combined expertise improves water related workshops through thoughtful collaboration,
professional networking, and institutional knowledge related to water development, deployment,
and use.