Benefit-Cost Analysis
Sustainment and
Enhancements
Draft Standard Economic Value Methodology Report
Version 11.0
September 2022
Contract #: HSFE60-16-D-0200
Task Order #: 70FA6021F00000002
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Acknowledgements
Development of this document was directed by:
Tara Seibold, FEMA Project Monitor
Rebecca Carroll, FEMA Technical Monitor
Jody Springer, FEMA Data Analytics Section Chief
Tanya Canady, FEMA Contract Officer’s Representative
Development of this document was aided by:
Ideation, Inc.
12120 Sunset Hills Road, Suite 510
Reston, VA 20190
Contract: HSFE60-16-D-0200
Task Order: 70FA6021F00000002
Ideation Personnel:
David Coker, Ideation, Inc.
Steve McMaster, Ideation, Inc.
Gopal Raja, Ideation, Inc.
Deborah Zurielle, Ideation, Inc.
Purpose: The information and analysis contained in this report is intended for use when conducting
an economic analysis for FEMA’s grant programs. Any application outside of this intended purpose
is not endorsed by FEMA.
September 2022 i
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Revision History
Version Date Revisions/Updates
4.0 May 2011 Consolidated individual papers prepared in 2008 for FEMA’s
Benefit Cost Analysis Re-engineering effort into one report
(economic values were not updated)
5.0 August 2011 Updated value of lost time to reflect changes to average hourly
wage for 2011
Updated values for the electric, wastewater, and water service to
account for Gross Domestic Product (GDP) temporal changes for
2011
6.0 December 2011 Updated value of statistical life and the related injury values to
inflate the results to current (2011) dollars
7.0 August 2013 Updated value of lost time to reflect changes to average hourly
wage for 2013
Updated values for the electric, wastewater, and water service to
account for GDP temporal changes for 2013
Changed the residential displacement costs methodology from a
square footage basis to General Services Administration (GSA)
lodging and meals per diem rates
Added National Flood Insurance Program (NFIP) and Increased
Cost of Compliance (ICC) claim payments avoided as a new benefit
8.0 July 2016 Updated value of lost time to reflect changes to average hourly
wage for 2016
Updated values for the electric, wastewater, and water service to
account for GDP temporal changes for 2016
Updated value of statistical life and the related injury values to
inflate the results to current (2016) dollars
Updated fire and response statistics used for the loss of fire station
calculation to use the most recent available data (as of 2014)
Updated crime statistics used for the loss of police station
calculation to use the most recent available data (as of 2014)
Updated the non-residential displacement values for rental and
disruption costs to reflect updated HAZUS data
9.0 June 2020 Updated value of lost time to reflect changes to an average hourly
wage for 2019
Updated values for the electric, wastewater, and water service to
account for GDP temporal changes for 2019
Updated value of statistical life and the related injury values to
inflate the results to current (2019) dollars
September 2022 ii
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Version Date Revisions/Updates
9.1 August 2021 Minor changes to correct errors in examples for loss of emergency
medical services and loss of hospital services
10.0 September 2021 Updated the value of statistical life and updated injury weightings
by using different methodologies
11.0 September 2022 Updated Value of Lost Time and Traffic Delays for Roads and
Bridges to reflect changes to average hourly wage as of December
2021
Updated values for the electric, wastewater, and water service to
account for GDP temporal changes as of December 2021
Added Communications and Information Technology to the list of
utilities that have standard economic values
Updated values for Cost of Food at Home and NFIP Federal Policy
Fee
September 2022 iii
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Table of Contents
1. Introduction ...................................................................................................................... 1
2. Economic Values .............................................................................................................. 2
2.1. Value of Lost Time ................................................................................................................... 2
2.2. Traffic Delays for Roads and Bridges ....................................................................................... 3
2.3. Displacement Time and Cost ................................................................................................... 4
2.3.1. Residential................................................................................................................. 4
2.3.2. Non-Residential......................................................................................................... 5
2.4. Life Safety ..............................................................................................................................17
2.4.1. Tornado ................................................................................................................... 19
2.4.2. Earthquake ..............................................................................................................20
2.4.3. Wildfire.................................................................................................................... 20
2.5. Loss of Fire Station Services...................................................................................................21
2.6. Loss of Emergency Medical Services .....................................................................................25
2.7. Loss of Hospital Services........................................................................................................ 30
2.8. Loss of Police Services ...........................................................................................................37
2.9. Loss of Electric Services .........................................................................................................46
2.9.1. Impacts to Economic Activity.................................................................................. 46
2.9.2. Impacts to Residential Customers........................................................................... 48
2.9.3. Summary .................................................................................................................48
2.10. Loss of Wastewater Services ................................................................................................. 50
2.10.1. Impacts to Economic Activity.................................................................................. 50
2.10.2. Impacts to Residential Customers........................................................................... 50
2.10.3. Summary .................................................................................................................52
2.11. Loss of Water Services........................................................................................................... 52
2.11.1. Impacts to Economic Activity.................................................................................. 52
2.11.2. Impacts to Residential Customers........................................................................... 54
2.11.3. Summary .................................................................................................................55
2.12. Loss of Communications/Information Technology Services .................................................56
2.12.1. Impacts to Economic Activity.................................................................................. 56
2.12.2. Impacts to Residential Customers........................................................................... 57
2.12.3. Summary .................................................................................................................59
2.13. Reduced Flood Insurance Administrative Costs and Fees ..................................................... 59
2.13.1. General NFIP Policy Administration ........................................................................ 60
2.13.2. NFIP Claim Fees .......................................................................................................60
2.13.3. Increased Cost of Compliance Claim Administration..............................................61
3. References .....................................................................................................................62
4. Acronyms........................................................................................................................69
September 2022 iv
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
List of Tables
Table 1: Updated Standard Values.............................................................................................................1
Table 2: Value of Lost Time Changes .........................................................................................................2
Table 3: Rental Costs and Disruption Costs by Occupancy Class
1
...........................................................6
Table 4: Recovery Time by Occupancy Type and Flood Depth
1
................................................................8
Table 5: AIS Injury Level Categories
1
....................................................................................................... 18
Table 6: AIS Injury Severity Levels, Fraction of VSL, and Economic Values (2020 Dollars)................. 19
Table 7: Injury Classes Used in the Tornado Module ............................................................................. 19
Table 8: Cost of Injury and Unsurvivable Values Used in the Tornado Module .................................... 19
Table 9: Injury Classes Used in the Earthquake Modules...................................................................... 20
Table 10: Cost of Injury and Unsurvivable Values Used in the Earthquake Module ............................ 20
Table 11: Percentage Change in Number of Deaths Due to a Mile Increase in Distance to the
Hospital
1
.................................................................................................................................................... 34
Table 12: Impact of Number of Police Officers on Crime Rate
1
............................................................. 38
Table 13: Total Cost of Crime in 2019 Dollars
1
...................................................................................... 39
Table 14: Example of Crime Statistics for the State of Alabama
1
......................................................... 41
Table 15: Example of Crime Statistics for the State of Missouri, Part 1
1
.............................................. 43
Table 16: Example of Crime Statistics for State of Missouri, Part 2
1
.................................................... 44
Table 17: Loss of Electric Service Impact to Economic Activity ............................................................. 47
Table 18: Economic Impacts of Loss of Electric Power Per Capita Per Day (in 2021 dollars)............. 49
Table 19: Evolution of Electric Service Value Used in the BCA Toolkit.................................................. 49
Table 20: Loss of Wastewater Service Impact to Economic Activity...................................................... 51
Table 21: Economic Impact of Loss of Wastewater Service per Capita per Day (in 2021 dollars)..... 52
Table 22: Loss of Water Service Impact to Economic Activity ............................................................... 53
Table 23: Economic Impact of Loss of Water Service per Capita per Day (in 2021 dollars)............... 55
Table 24: Loss of Communications/Information Technology Service Impact to Economic Activity.... 56
Table 25: Willingness to Pay for Monthly Internet Service..................................................................... 58
Table 26: Economic Impact of Loss of Communications/Information Technology Services per Capita
per Day (in 2021 dollars).......................................................................................................................... 59
Table 27: Relationship Between NFIP Claim Fee and Damage Cost
1
................................................... 60
Table 28: Relationship Between ICC Claim Fee and Damage Cost
1
..................................................... 61
September 2022 v
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
1. Introduction
This document describes the methodologies for developing the standard economic values used in
the Federal Emergency Management Agency (FEMA) Benefit-Cost Analysis (BCA) Toolkit Version 6.0
and later. This document is intended to describe how the standard default values were developed
and is not intended as guidance for using the values in the BCA Toolkit. Guidance on how to use the
values in the software can be found in the BCA Toolkit Help Content, BCA training course, or by
contacting or emailing the FEMA BC Helpline
at [email protected]. This report consolidates
the individual papers originally prepared in 2008 to document the economic values and updates
values that are used in the BCA Tool Version 6.0. To stay current with economic conditions, values in
this report are updated on a periodic basis.
Table 1 shows the standard economic values that have been updated in this September, 2022
version of the methodology report. More detailed information about the updated values is provided
in the relevant portions of Section 2 of this report.
Table 1: Updated Standard Values
Standard Economic Value Former Value Updated Value
Value of Lost Time $34.72 $38.07
Traffic Delays for Roads and Bridges $32.18 $35.60
Loss of Electric Services $174 $182
Loss of Wastewater Services $58 $60
Loss of Potable Water Services $114 $116
Loss of Communications/IT Services — $130
Cost of Food at Home $7/person/day $9/person/day
NFIP Federal Policy Fee $50/year $47/year
September 2022 1
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
2. Economic Values
2.1. Value of Lost Time
Assessing the value of lost time is straightforward and consistent with economic theory applied in a
variety of fields, including recreational and transportation economics. Lost time can be incurred by
individuals who must take pre-disaster preventative measures, evacuate their homes or business,
clean up or repair damage, manage insurance claims, experience increased travel time due to bridge
or road closures, and deal with other disaster-related matters. The economic concept is that
personal time has value, regardless of formal employment compensation. Therefore, it can be
argued that one hour of work is equal to one hour of leisure time because the “opportunity cost”
1
of
a leisure hour is equal to the wage earned for an hour of work time.
Table 2 shows how the Value of Lost Time economic value has changed over time.
Table 2: Value of Lost Time Changes
Year Updated Value
a
Source
b
2001 $21.16 US Department of Labor, Bureau of Labor Statistics, 2000
2007 $27.31 US Department of Labor, Bureau of Labor Statistics, 2006
2009 $28.11 US Department of Labor, Bureau of Labor Statistics, 2008
2011 $30.07 US Department of Labor, Bureau of Labor Statistics, 2011 (March)
2016 $33.94 US Department of Labor, Bureau of Labor Statistics, 2016 (March)
2020 $34.72 US Department of Labor, Bureau of Labor Statistics, 2019
(December)
2022 $38.07 US Department of Labor, Bureau of Labor Statistics, 2021
(December)
a
This value is the “Total employer compensation costs for private industry.”
b
The month and year indicates the specific quarterly data release from the Bureau of Labor Statistics at
http://www.bls.gov/news.release/pdf/ecec.pdf. This date may be different than the “Year Updated” value because of the
time lag that is inherent in government-provided economic statistics.
1
An opportunity cost is the cost of an alternative that must be foregone in order to pursue a certain action. In other words,
it is the benefits received by taking an alternative action.
September 2022 2
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
The 2022 hourly rate of $38.07 (BLS, 2022a) should be used to measure the value of hours spent
by individuals on disaster-related activities (i.e., pre-disaster preventative measures, evacuation,
clean up or repair of damage, managing insurance claims) that are not accounted for in a separate
part of the BCA modules.
2.2. Traffic Delays for Roads and Bridges
This section presents the methodology used in the BCA Toolkit to estimate the value of delays due to
road and bridge closures. The methodology builds on estimates for the value of lost time (described
above) and is consistent with the methodology applied by the U.S. Department of Transportation
(DOT) in calculating the benefits of reducing travel time.
The DOT distinguishes between business or commercial travel time and personal and recreational
time. While commercial travel time is reimbursed at 100 percent of the wage rate, the DOT values
personal travel time (including commute time) at 50 percent of the wage rate (FHWA, 2007). Travel
time in recreational economics is generally valued at one-third of the wage rate, though some
studies use 50 percent of the wage rate, similar to DOT (Champ et al., 2003). The full wage rate is
not typically used to measure personal travel or recreation travel because it is assumed that
individuals benefit from the travel (e.g., a scenic drive), or they are willing to accept the travel time in
order to gain something (e.g., a higher paying job).
FEMA determined that requiring BCA Toolkit users to distinguish business/commercial travel delay
time from personal/recreational travel delay time would place an unnecessary burden on the user.
Additionally, because the value of travel delay is based on a per-person wage rate basis and not a
per-vehicle basis, users would have to identify the number of people in each affected vehicle. To
simplify this benefit calculation, the BCA Toolkit uses an average vehicle occupancy to capture time
costs caused by delays due to road and bridge closures. This average should be applied to all
vehicles, regardless of the vehicle type, purpose of the trip, and number of persons in each vehicle.
According to the Bureau of Transportation Statistics
2
(DOT, 2020), 88 percent of all miles traveled
on the Nation’s roadways were from personal passenger vehicles, with the remaining 12 percent
being commercial vehicles. The 2017 National Household Travel Survey (FHWA, 2017) determined
that the average number of persons per vehicle was 1.67. This average vehicle occupancy value was
unchanged from the previous National Household Travel Survey from 2009 (FHWA, 2009).
Employing the national average hourly wage of $38.07 (BLS, 2022a), average number of persons
per vehicle of 1.67 and DOT’s methodology for per-hour value of time, the equation below was used
to determine the hourly value of time per vehicle:
2
Passenger vehicles defined as the “Light duty” vehicle rows, divided into the “Highway, Total” value for 2020.
September 2022 3
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
%
(
0.5)) + (%
)
(1)
=
0.88
(
$38.07 0.5
)
+
(
0.12 $38.07
)
1.67 = $35.60
Therefore, a value of $35.60 is applied per vehicle per hour to account for the lost time cost of road
and bridge closures or delays as a result of a disaster.
2.3. Displacement Time and Cost
2.3.1. Residential
LODGING
Prior to BCA Toolkit Version 5, the methodology for residential displacement cost was a standard
value of $1.44 per square foot per month (sf/mo). The methodology was changed for BCA Toolkit
Version 5 because the displacement cost value was difficult for subapplicants to understand,
especially if they were seeking to determine if the $1.44/sf/mo value was reasonable for their
community. Additionally, there was uncertainty whether the $1.44/sf/mo value was a true reflection
of reality given the increasing displacement costs, especially for very long-term displacements
involving FEMA-supplied trailers.
The new methodology for residential displacement costs involves the lodging per diem rates
published by the U.S. General Service Administration (GSA). The lodging per diem rates are a more
reasonable reflection of the variable lodging costs than a national average based on a residence’s
square footage. The GSA publishes and updates lodging per diem rates for locations in the
continental United States (CONUS). These rates are available by entering the city/state or zip code
here: https://www.gsa.gov/travel/plan-book/per-diem-rates
(GSA, 2022). For areas that are not
studied in detail, the GSA applies a Standard Rate, which is updated annually. For FY2023, the
Standard Rate is $98 per room per day. Locations outside of the CONUS (OCONUS) including Alaska,
Hawaii, and US territories and possessions have values determined by the US Department of
Defense here:
http://www.defensetravel.dod.mil/site/perdiemCalc.cfm (DOD, 2022).
For locations that have lodging per diem with a seasonal variation, the lodging per diem is auto-
calculated as the average value for all 12 months. It is assumed that an entire family will fit into one
hotel room. In the case of large families being displaced, the subapplicant should provide
documentation for a reasonable number of hotel rooms and multiply the daily lodging per diem value
by the number of occupied rooms.
Lodging taxes may also be included in the lodging rate since it is a cost paid by displaced residents.
Depending on the location, lodging taxes may be collected by the State, multi-county, county, city, or
sub-city levels of government, and some locations may also charge a sales tax.
September 2022 4
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
MEALS AND INCIDENTAL EXPENDITURES
When displaced, food is more expensive than when eating at home. Prior to BCA Toolkit Version 5,
this increased cost for basic provisions was not considered in the displacement value. The GSA per
diem rate for meals and incidental expenditures (M&IE) will be used for each person displaced in the
residence. Like the lodging per diem, the GSA determines a Standard Rate for the M&IE per diem,
which is subject to change annually. For FY2023, the standard rate is $59 per person per day. For
the meals portion, the per diem rate is a combination of expected maximum values for three
separate meals per day. Incidental expenditures account for smaller out-of-pocket expenses such as
tips and transportation to get food, but also for increased general expenses of living out of a hotel
room such as a local calling charge, laundry, and related items.
To be a true reflection of the increased cost of food, the M&IE per diem rate should be reduced by an
average cost for eating meals at home. The U.S. Department of Agriculture (USDA) publishes a
monthly Cost of Food at Home value for children, men, and women of different ages and different
levels of food plans. The USDA published a report for a thrifty meal plan (USDA, 2022ª) and a
combined report for low-cost, moderate-cost, and liberal meal plans (USDA, 2022b). Because there
is a wide variation in average at-home food expenses among these categories, the methodology calls
for taking an average value for the 60 values for the most recently published month at the time of
the update research.
The first value for the Cost of Food at Home was $7.10 per person per day came from the average of
the 60 values for the month of June 2013 and a value of $7.00 per person per day was used in the
Tool. As of April 2016, the Cost of Food at Home was $7.38. As of February 2020, the Cost of Food
at Home was $7.47 and as of March 2022, the value was $8.83. It is recommended that the cost of
food at home be increased to $9.00 per person per day (i.e., $8.83 rounded to the nearest dollar).
2.3.2. Non-Residential
Displacement time is a category of damages that accounts for the duration for which people are
forced to evacuate their business or other structure type. The source of the baseline estimates used
in the BCA Toolkit for displacement time and cost is the Hazards U.S. (HAZUS) software (FEMA, n.d.),
a risk assessment software for analyzing potential losses from disasters.
The displacement cost consists of a one-time disruption cost along with a recurring monthly rental
cost for the duration of the displacement. The rental and disruption costs are calculated based on a
building per square-foot/content inventory dataset compiled by nationally recognized cost-estimating
software and Applied Technology Council (ATC) Reports 12 and 25. Table 3 shows both the standard
one-time and the monthly per-square-foot values for each of the commercial and public structure
classifications adopted by FEMA and used in the BCA Toolkit (FEMA, undated).
For example, the recovery time from when a structure is damaged by flooding until it can be
reoccupied is a function of the physical restoration time, contractor availability, hazardous materials
(hazmat) removal processes, inspections, and permits and approvals. HAZUS provides estimates of
the flood-specific restoration times for structures of different occupancy classes based on depth of
September 2022 5
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
flooding. In the HAZUS model, flood depths shown in Table 4 are generally evaluated in increments
of 4 feet to coincide with likely physical repair strategies.
The total displacement cost is estimated by adding the disruption cost and the rental costs. This can
be expressed in the equation below:
=
(
2
)
+
(
2
ℎ
)
(2)
The default displacement time in the BCA Toolkit is based on the combination of physical restoration
time and recovery time estimates for structures affected by flooding.
Table 3: Rental Costs and Disruption Costs by Occupancy Class
1
No. Label Occupancy Class Rental Cost
($/sq.ft./mo.)
Disruption
Costs ($/sq.ft.)
1 RES1 Single Family Dwelling (Residential) 0.68 0.82
2 RES2 Mobile Home (Residential) 0.48 0.82
3-8 RES3a-f Multi Family Dwelling (Residential) 0.61 0.82
9 RES4 Temporary Lodging (Residential) 2.04 0.82
10 RES5 Institutional Dormitory (Residential) 0.41 0.82
11 RES1 Nursing Home (Residential) 0.75 0.82
12 COM1 Retail Trade (Commercial) 1.16 1.09
13 COM2 Wholesale Trade (Commercial) 0.48 0.95
14 COM3 Personal and Repair Services (Commercial) 1.36 0.95
15 COM4 Professional/Technical/Business
(Commercial)
1.36 0.95
16 COM5 Banks (Commercial) 1.70 0.95
17 COM6 Hospital (Commercial) 1.36 1.36
18 COM7 Medical Office/Clinic (Commercial) 1.36 1.36
19 COM8 Entertainment and Recreation (Commercial) 1.70 N/A
20 COM9 Theaters (Commercial) 1.70 N/A
21 COM10 Parking (Commercial) 0.34 N/A
22 IND1 Heavy (Industrial) 0.20 N/A
September 2022 6
23
24
25
26
27
28
29
30
31
32
33
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
No. Label Occupancy Class Rental Cost
($/sq.ft./mo.)
Disruption
Costs ($/sq.ft.)
IND2 Light (Industrial) 0.27 0.95
IND3 Food/Drugs/Chemicals (Industrial) 0.27 0.95
IND4 Metals/Mineral Processing (Industrial) 0.20 0.95
IND5 High Technology (Industrial) 0.34 0.95
IND6 Construction (Industrial) 0.14 0.95
AGR1 Agriculture (Agricultural building) 0.68 0.68
REL1 Church/Membership Organization (Religious) 1.02 0.95
GOV1 General Services (Government) 1.36 0.95
GOV2 Emergency Response (Government) 1.36 0.95
EDU1 Schools/Libraries (Education) 1.02 0.95
EDU2 College/Universities (Education) 1.36 0.95
1
Source: Flood Model Hazus®-MH Technical Manual: https://www.fema.gov/media-library-data/20130726-1820-25045-
8292/hzmh2_1_fl_tm.pdf (Table 14.10)
September 2022 7
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Table 4: Recovery Time by Occupancy Type and Flood Depth
1
Occupancy Flood
Depth
(feet)
Location Physical
Restoration
Time
(months)
Dry-out
and
Cleanup
Add-on
(months)
Inspection,
Permits,
Approvals
Add-on
(months)
Contractor
Availability
Add-on
(months)
Hazmat
Delay
Add-on
(months)
Recovery
Time Min
(months)
Recovery
Time Max
(months)
Retail Trade 0’–4’ n/a
2
7 to 13 1 2 3 0 13 19
Retail Trade 4’–8’ n/a 10 to 15 1 2 3 0 16 21
Retail Trade 8’–12’ n/a 25 1 2 3 0 31 31
Retail Trade 12’ +
Outside 100-
year Floodplain
12 1 2 3 0 18 18
Retail Trade 12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
Wholesale Trade 0’–4’ n/a 7 to 13 1 2 3 0 13 19
Wholesale Trade 4’–8’ n/a 10 to 15 1 2 3 0 16 21
Wholesale Trade 8’–12’ n/a 25 1 2 3 0 31 31
Wholesale Trade 12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Wholesale Trade 12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
Personal and
Repair Services
0’–4’ n/a 3 to 6 1 2 3 0 9 12
Personal and
Repair Services
4’–8’ n/a 6 to 9 1 2 3
0 12 15
September 2022 8
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Occupancy Flood
Depth
(feet)
Location Physical
Restoration
Time
(months)
Dry-out
and
Cleanup
Add-on
(months)
Inspection,
Permits,
Approvals
Add-on
(months)
Contractor
Availability
Add-on
(months)
Hazmat
Delay
Add-on
(months)
Recovery
Time Min
(months)
Recovery
Time Max
(months)
Personal and
Repair Services
8’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Personal and
Repair Services
8’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
Professional/
Technical/
Business
Services
0’–4’ n/a 6 to 10 1 2 3 0 12 16
Professional/
Technical/
Business
Services
4’–8’ n/a 10 to 15 1 2 3 0 16 21
Professional/
Technical/
Business
Services
8’–12’ n/a 19 1 2 3 0 25 25
Professional/
Technical/
Business
Services
12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Professional/
Technical/
Business
Services
12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
September 2022 9
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Occupancy Flood
Depth
(feet)
Location Physical
Restoration
Time
(months)
Dry-out
and
Cleanup
Add-on
(months)
Inspection,
Permits,
Approvals
Add-on
(months)
Contractor
Availability
Add-on
(months)
Hazmat
Delay
Add-on
(months)
Recovery
Time Min
(months)
Recovery
Time Max
(months)
Banks/Financial
Institutions
0’–4’ n/a 6 to 10 1 2 3 0 12 16
Banks/Financial
Institutions
4’–8’ n/a 10 to 15 1 2 3 0 16 21
Banks/Financial
Institutions
8’–12’ n/a 19 1 2 3 0 25 25
Banks/Financial
Institutions
12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Banks/Financial
Institutions
12’ +
Inside 100-year
Floodplain
18 1 2 3 0 24 24
Hospital
(With Basement)
(-8)’–
(-4)’
n/a 6 1 2 3 0 12 12
Hospital
(With Basement)
(-4)’– 0’ n/a 12 1 2 3 0 18 18
Hospital
(With Basement)
0’–4’ n/a 18 1 2 3 0 24 24
Hospital
(With Basement)
4’–8’ n/a 24 1 2 3 0 30 30
Medical
Office/Clinic
0’–4’ n/a 6 to 10 1 2 3 0 12 16
Medical
Office/Clinic
4’–8’ n/a 10 to 15 1 2 3 0 16 21
September 2022 10
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Occupancy Flood
Depth
(feet)
Location Physical
Restoration
Time
(months)
Dry-out
and
Cleanup
Add-on
(months)
Inspection,
Permits,
Approvals
Add-on
(months)
Contractor
Availability
Add-on
(months)
Hazmat
Delay
Add-on
(months)
Recovery
Time Min
(months)
Recovery
Time Max
(months)
Medical
Office/Clinic
8’–12’ n/a 19 1 2 3 0 25 25
Medical
Office/Clinic
12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Medical
Office/Clinic
12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
Entertainment
and Recreation
0’–4’ n/a 7 to 13 1 2 3 0 13 19
Entertainment
and Recreation
4’–8’ n/a 10 to 15 1 2 3 0 16 21
Entertainment
and Recreation
8’–12’ n/a 25 1 2 3 0 31 31
Entertainment
and Recreation
12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Entertainment
and Recreation
12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
Theaters 0’–4’ n/a 7 to 13 1 2 3 0 13 19
Theaters 4’–8’ n/a 10 to 15 1 2 3 0 16 21
Theaters 8’–12’ n/a 25 1 2 3 0 31 31
Theaters 12’ +
Outside 100-
year Floodplain
12 1 2 3
0 18 18
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Occupancy Flood
Depth
(feet)
Location Physical
Restoration
Time
(months)
Dry-out
and
Cleanup
Add-on
(months)
Inspection,
Permits,
Approvals
Add-on
(months)
Contractor
Availability
Add-on
(months)
Hazmat
Delay
Add-on
(months)
Recovery
Time Min
(months)
Recovery
Time Max
(months)
Theaters 12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
Parking > 0’ n/a 0 1 0 0 0 1 1
Heavy Industrial > 0’ n/a 1 to 3 1 2 0 1 5 7
Light Industrial > 0’ n/a 1 to 2 1 2 0 0 4 5
Food/Drugs/
Chemicals
0’–4’ n/a 6 to 10 1 2 3 1 13 17
Food/Drugs/
Chemicals
4’–8’ n/a 10 to 15 1 2 3 1 17 22
Food/Drugs/
Chemicals
8’–12’ n/a 19 1 2 3 1 26 26
Food/Drugs/
Chemicals
12’ + Outside 100-
year Floodplain
12 1 2 3 1 19 19
Food/Drugs/
Chemicals
12’ + Inside 100-year
Floodplain
18 1 2 3 1 25 25
Metals/Minerals
Processing
0’–4’ n/a 6 to 10 1 2 3 2 14 18
Metals/Minerals
Processing
4’–8’ n/a 10 to 15 1 2 3 2 18 23
Metals/Minerals
Processing
8’–12’ n/a 19 1 2 3 2 27 27
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Occupancy Flood
Depth
(feet)
Location Physical
Restoration
Time
(months)
Dry-out
and
Cleanup
Add-on
(months)
Inspection,
Permits,
Approvals
Add-on
(months)
Contractor
Availability
Add-on
(months)
Hazmat
Delay
Add-on
(months)
Recovery
Time Min
(months)
Recovery
Time Max
(months)
Metals/Minerals
Processing
12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Metals/Minerals
Processing
12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
High Technology 0’–4’ n/a 7 to 13 1 2 3 2 15 21
High Technology 4’–8’ n/a 13 to 19 1 2 3 2 21 27
High Technology 8’–12’ n/a 25 1 2 3 2 33 33
High Technology 12’ + Outside 100-
year Floodplain
12 1 2 3 2 20 20
High Technology 12’ + Inside 100-year
Floodplain
18 1 2 3 2 26 26
Construction > 0’ n/a 1 to 2 1 2 0 0 4 5
Agriculture > 0’ n/a 1 to 2 1 2 0 2 6 7
Churches/
Membership
Organizations
0’–4’ n/a 7 to 13 1 2 3 0 13 19
Churches/
Membership
Organizations
4’–8’ n/a 10 to 15 1 2 3 0 16 21
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Occupancy Flood
Depth
(feet)
Location Physical
Restoration
Time
(months)
Dry-out
and
Cleanup
Add-on
(months)
Inspection,
Permits,
Approvals
Add-on
(months)
Contractor
Availability
Add-on
(months)
Hazmat
Delay
Add-on
(months)
Recovery
Time Min
(months)
Recovery
Time Max
(months)
Churches/
Membership
Organizations
8’–12’ n/a 25 1 2 3 0 31 31
Churches/
Membership
Organizations
12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Churches/
Membership
Organizations
12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
General Services 0’–4’ n/a 6 to 10 1 2 3 0 12 16
General Services 4’–8’ n/a 10 to 15 1 2 3 0 16 21
General Services 8’–12’ n/a 19 1 2 3 0 25 25
General Services 12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
General Services 12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
Emergency
Response
0’–4’ n/a 6 to 10 1 2 3 0 12 16
Emergency
Response
4’–8’ n/a 10 to 15 1 2 3 0 16 21
Emergency
Response
8’–12’ n/a 19 1 2 3 0 25 25
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Occupancy Flood
Depth
(feet)
Location Physical
Restoration
Time
(months)
Dry-out
and
Cleanup
Add-on
(months)
Inspection,
Permits,
Approvals
Add-on
(months)
Contractor
Availability
Add-on
(months)
Hazmat
Delay
Add-on
(months)
Recovery
Time Min
(months)
Recovery
Time Max
(months)
Emergency
Response
12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Emergency
Response
12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
Schools/Libraries 0’–4’ n/a 6 to 10 1 2 3 0 12 16
Schools/Libraries 4’–8’ n/a 10 to 15 1 2 3 0 16 21
Schools/Libraries 8’–12’ n/a 19 1 2 3 0 25 25
Schools/Libraries 12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Schools/Libraries 12’ + Inside 100-year
Floodplain
18 1 2 3 0 24 24
Colleges/
Universities
0’–4’ n/a 6 to 10 1 2 3 0 12 16
Colleges/
Universities
4’–8’ n/a 10 to 15 1 2 3 0 16 21
Colleges/
Universities
8’–12’ n/a 19 1 2 3 0 25 25
Colleges/
Universities
12’ + Outside 100-
year Floodplain
12 1 2 3 0 18 18
Colleges/
Universities
12’ +
Inside 100-year
Floodplain
18 1 2 3
0 24 24
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1
Source: Flood Model Hazus®-MH Technical Manual: https://www.fema.gov/sites/default/files/2020-09/fema_hazus_flood-model_technical-manual_2.1.pdf (Table
14.12)
2
Location values with “n/a” denote the recovery time is dictated by flood depth and not whether the building is inside or outside of a 100-year floodplain.
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2.4. Life Safety
Life safety is the value of lives saved and injuries prevented resulting from mitigation measures. A
review of existing literature has found different values used by different government agencies and
even multiple values used within one agency. The current Value of Statistical Life (VSL)
3
is from a
DHS memo (Houser, 2021), which recommends using a VSL of $11.6 million with a base year of
2020. Future updates of the VSL should inflate the value of $11.6 million in 2020 dollars to a
current-year dollar value and then round that value to the nearest one hundred thousand dollars.
The official guideline for determining and using a reasonable VSL is found in Office of Management
and Budget (OMB) Circular A-4. Last updated in 2003, Circular A-4 (The White House, 2003)
documented the results of a literature search and recommended VSL values between $1 million and
$10 million. The OMB further clarified that most federal agencies are using VSLs between $5 million
and $9 million and that values outside of this range would be difficult to justify (OMB, 2010). The
2021 DHS memo (Houser, 2021) is an improvement on Circular A-4 because it presents the results
of a more recent analysis that includes real income gains in addition to inflation over time.
Historically, the VSL values and methodologies used in the calculations within FEMA’s BCA Tool have
changed as follows:
From 2008 to 2012, Versions 4.5.5, and 4.8 of the BCA Toolkit used a VSL of $5.8 million
provided by the Federal Aviation Administration (FAA).
In 2012, the methodology was changed for Version 5 in order to create a standard methodology
rather than using comparative literature review and to incorporate research completed on behalf
of the Department of Homeland Security (Robinson, 2008). The Robinson report (2008) depends
on the research of W. Kip Viscusi, which established a value of $4.7 million with 1997 as the
base year. Periodic updates after 2012 for BCA Toolkit Versions 5.0 and 5.2 used this
methodology and increased the VSL to $6.1 million and $6.6 million, respectively. When updated
in 2016 for BCA Toolkit Version 5.3, the VSL was inflated to the full-year 2015 value of $6.9
million. When updated in 2020 for BCA Toolkit Version 6.0, the VSL was inflated using the CPI
Inflation Calculator (BLS, 2022b) to December 2019, which resulted in a value of $7.5 million.
The new methodology using the Houser memo (2021) was implemented in September 2021 in
an update to BCA Toolkit Version 6.0.
Nonfatal injuries are far more common than fatalities. In principle, the resulting losses in quality of
life, including both pain and suffering and reduced income, should be calculated for various injury
levels that could be avoided because of a hazard mitigation project. Because detailed willingness-to-
3
VSL is defined as the value of improvements in safety that result in a reduction by one in the expected number of fatalities
(U.S. DOT).
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pay estimates covering the entire range of potential disabilities are unobtainable, a standardized
method is used to interpolate values of expected outcomes, scaled in proportion to the VSL.
Relative value coefficients for preventing injuries of varying severity and duration are based on the
Abbreviated Injury Scale (AIS), which categorizes injuries into levels, ranging from AIS 1 (Minor) to AIS
5 (Critical), with AIS 6 being Unsurvivable. (For more information about the research conducted to
determine these values, see reports by Miller, Brinkman, and Luchter [1989] or by Rice, et al
[1989].) This valuation technique relied on a panel of experienced physicians to relate injuries in
each AIS level to the loss of quality and quantity of life. A narrative description for the AIS classes is
provided in Table 5.
Table 5: AIS Injury Level Categories
1
AIS Code Injury Severity Level Selected Injuries
1 Minor Superficial abrasion or laceration of skin; digit sprain; first-
degree burn; head trauma with headache or dizziness (no
other neurological signs).
2 Moderate Major abrasion or laceration of skin; cerebral concussion
(unconscious less than 15 minutes); finger or toe
crush/amputation; closed pelvic fracture with or without
dislocation.
3 Serious Major nerve laceration; multiple rib fracture (but without flail
chest); abdominal organ contusion; hand, foot, or arm
crush/amputation.
4 Severe Spleen rupture; leg crush; chest-wall perforation; cerebral
concussion with other neurological signs (unconscious less
than 24 hours).
5 Critical Spinal cord injury (with cord transection); extensive second-
or third- degree burns; cerebral concussion with severe
neurological signs (unconscious more than 24 hours).
6 Unsurvivable Injuries, which although not fatal within the first 30 days after
an accident, ultimately result in death.
1
Source: FAA, 2021
Federal agencies such as the FAA, DOT, and National Highway Traffic Safety Administration (NHTSA)
calculate an economic value for avoiding different AIS scale injuries by using the relative value
coefficients as a fraction of the VSL. By following this method, FEMA is able to establish an economic
value for the various injury levels that could be avoided—and therefore counted as benefits—from a
hazard mitigation project. These economic values are shown in Table 6. Values are rounded to the
nearest thousand dollars. The Economic Value is calculated as the VSL, multiplied by the Fraction of
VSL. The Fraction of VSL values used from 2008 to 2021 were taken from the FAA (FAA, 2007). In
September 2021, they were updated to match the FAA (2021) and DOT (2021) fractional values. The
BCA Toolkit uses the following values for the different hazard types.
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Table 6: AIS Injury Severity Levels, Fraction of VSL, and Economic Values (2020 Dollars)
AIS Code Description of Injury Fraction of VSL
1
Economic Value
AIS 1
Minor
0.003
$ 35,000
AIS 2
Moderate
0.047
$ 545,000
AIS 3
Serious
0.105
$ 1,218,000
AIS 4
Severe
0.266
$ 3,086,000
AIS 5
Critical
0.593
$ 6,879,000
AIS 6
Unsurvivable
1.000
$ 11,600,000
1
Sources: FAA, 2021 and DOT, 2021
2.4.1. Tornado
The Tornado Module uses a modified version of Table 6. Based on post-disaster research conducted
by the Tornado Expert Panel, which is made up of experts on tornadoes and injuries and fatalities
from hazards, the panel members determined that the six AIS categories needed to be reduced to
four, as shown in Table 7. Prior to 2016, the methodology used an average of AIS Codes 5 and 6 for
the “Unsurvivable” value. To maintain consistency with the other modules, in 2016 the methodology
was changed so that the Unsurvivable value was just AIS Code 6.
Table 7: Injury Classes Used in the Tornado Module
Injury Classes AIS Code
1
Unsurvivable
6
Hospitalized
3,4,5
Treat and release
1,2
Self-treat
1
1
Source: FEMA, 2008a
The associated costs for each AIS Code from Table 6 were used to develop the cost for injuries and
fatalities to match the injury classes used in the Tornado Module shown in Table 6. Table 8 lists each
of the injury classes based on the economic values provided in Table 6 with values rounded to the
nearest thousand dollars. Multiple AIS codes represent the average values of the codes listed.
Table 8: Cost of Injury and Unsurvivable Values Used in the Tornado Module
Injury Severity Levels AIS Code Economic Value
Unsurvivable
6
$ 11,600,000
Hospitalized
3,4,5
$ 3,728,000
Treat and release
1,2
$ 290,000
Self-treat
1
$ 35,000
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2.4.2. Earthquake
The Earthquake Structural and Nonstructural modules also use a modified version of the AIS Injury
Severity Levels. Each module uses injury rates corresponding to the severity of physical damage
computed in each module. During development of the FEMA BCA Toolkit (Version 4), it was decided
that the injury classifications used in the previous version of the FEMA BCA Toolkit (Version 3) would
remain the same. These injury classes are shown in Table 9.
Table 9: Injury Classes Used in the Earthquake Modules
Injury Classes AIS Code
1
Unsurvivable
6
Major
2,3,4,5
Minor
1
1
Source: FEMA, 2008b and FEMA, 2008c
The associated cost for each AIS Code from Table 6 was used to develop the cost for injuries and
fatalities to match the injury classes used in the Earthquake modules. Table 10 lists each of the
injury classes and the rounded values based on Table 6. The “Major” Injury Severity Level value is an
average of the economic values of the four listed AIS Code values.
Table 10: Cost of Injury and Unsurvivable Values Used in the Earthquake Module
Injury Severity Levels AIS Code Economic Value
Unsurvivable
6
$ 11,600,000
Major
2,3,4,5
$ 2,932,000
Minor
1
$ 35,000
2.4.3. Wildfire
The Wildfire module uses the values of Unsurvivable ($11,600,000), “Major injuries,” and “Minor
injuries.” As shown in Equation 3 below, the methodology for major injuries is to average the values
for AIS Codes 2 through 5, which equals $2,932,000. The minor injury (AIS Code 1) is $35,000.
(545,000+1,218,000+3,086,000+6,879,000)
Major Injuries =
4
= $2,932,000 (3)
(
35,000+2,932,000
)
Statistical Value of All Injuries =
2
= $1,501,000 (4)
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2.5. Loss of Fire Station Services
Fire stations may provide a wide range of services, such as firefighting, search and rescue, public
shelter, and emergency medical services (EMS). The methodology presented estimates the social
cost for a loss of a fire station’s services, also referred to as a “loss of function.” Specifically, the
methodology estimates how the temporary loss of function of a fire station will affect fire losses
(human injuries and mortality, direct financial loss to property, and indirect losses). When a fire
station offers public shelter during emergencies, a separate category should be added to account for
any benefits. The impact of a loss of EMS is discussed in a separate section of this document.
This methodology assumes that if a fire station (for example, Fire Station A) is temporarily shut down,
then the closest fire station (Fire Station B) will serve the population usually served by Fire Station A.
A universal measure used across public safety functions is response time. Intuitively, the relationship
between response time and a fire department’s success is clear: the sooner a fire company arrives
at a fire scene, the better the chance of a successful outcome. Different studies have found a
significant relationship between the response time and the resulting fire losses (Tomes, 2007; Ignall
et al., 1978; Hogg, 1973).
Response time has a positive relationship with distance: the shorter the distance between the fire
station and the fire scene, the shorter the response time. When Fire Station A is out of service,
forcing Fire Station B to serve a larger geographical area, the average response time will increase.
With the increase in the response time, fire losses will increase as well.
The steps to estimate the loss-of-function impact of firefighting services are:
1. Determine the fire station that would temporarily replace the fire station that is out of service
2. Establish the distance between the two fire stations
3. Estimate the population served by the non-operating fire station (Fire Station A)
4. Determine the dollar loss expected due to the shutdown
To determine the expected dollar loss (Step 4 above), a series of calculations need to be performed.
a. Estimate the number of fire incidents (I) in the area served by the non-operating fire station
(Fire Station A). The population served as determined in Step 3 above is used to obtain this
number. Because obtaining specific data for a fire station may be difficult, a national average
is used. According to the National Fire Protection Association (NFPA), the total number of
fires in the United States in 2014 was 1,298,000 (Ahrens, 2016). The 2015 U.S. population
estimate given by the U.S. Census Bureau is 322,755,353 (U.S. Census Bureau, 2020).
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Therefore, the number of incidents per capita is equal to 0.0040 per year, or 4.0 incidents
per 1,000 people.
4
b. Estimate the average response time in the area before and after the fire station shutdown.
For the situation before the fire station shutdown, it is assumed that the response time is
equal to the national average. According to the U.S. Fire Administration (2006), the median
response time for structure fires is 5 minutes.
5
The extra response time will be approximated
using the distance between the two fire stations established in Step 2 above. The following
formula developed by the New York City Rand Institute in the 1970s (Chaiken et al., 1975) is
used to determine the relationship between expected response time (RT) in minutes and the
distance (D) in miles:
= 0.65 + 1.70 (5)
Hence, the response time (in minutes) after the fire station shutdown (RT
After
) will be estimated to be:
= 5 +
(
0.65 + 1.70
)
(6)
c. Determine the probability of a no-loss incident before and after the fire station shutdown.
This is the probability of an event having zero losses as a function of the response time. The
estimate was obtained from Air Force Protection Cost Risk Analysis (Air Force Civil Engineer
Support Agency, 1994). The study used data from the National Fire Incident Reporting
System for 760,000 nationwide records from 1989 to investigate the effect of response time
on dollar losses and the amount of damages.
6
The probability of a zero dollar loss (P0) is
given by the following formula:
0
= 0.456 − 0.00264 (7)
d. Determine the average property dollar loss per incident before and after the fire station
shutdown. This is a function of the response time. The relationship was also obtained from
the Air Force Protection Cost Risk Analysis study (1994).
7
The dollar loss (DL), in 1993
dollars, is given by:
= 3,845 + 431 (8)
4
No studies were found regarding how a natural disaster will affect the number of fire incidents.
5
Because this value has a considerable impact on the benefit estimate, when available, reliable local data may be used
instead; proper documentation to justify their use should be provided.
6
Only data for fixed property were analyzed to obtain these estimates. According to NFPA data for 2006, even though
structure fires only account for 32 percent of total fires, they represent 85 percent of property damage, 88 percent of
civilian injuries, and 83 percent of civilian deaths.
7
This relationship was calculated using data for residential structures. NFPA data show that residential structure fires
represent 78 percent of all structure fires.
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e. Calculate the increase in the property dollar loss due to the fire station shutdown. This is
done using the following formula:
$ =
1
1
× (9)
Where:
P
0After
and P
0Before
are the probabilities of a no-loss incident after and before the fire station shutdown,
respectively
DL
After
and DL
Before
are the average dollar loss per incident after and before the fire station shutdown,
respectively
I is the number of fire incidents in the area served by the fire station. Because the number of
incidents is in per-year terms, the increase in the dollar loss is also in per-year terms
a. Add indirect losses. NFPA adds 10 percent for indirect loss as a fraction of direct loss in
residential fires (Hall, 2014). Indirect losses refer to the costs of temporary housing, missed
work, and lost business:
$ = $ × 1.10 (10)
Estimate the losses related to mortality and human injuries. According to NFPA estimates,
direct and indirect property losses due to fire totaled $14.9 billion (in 2011 dollars), while
the total dollar losses for deaths and injuries were estimated to be $31.7 billion (Hall,
2014).
8
That gives a ratio of 2.13 in losses for deaths and injuries per dollar of property loss.
The losses for mortality and human injuries can be obtained by multiplying the total property
loss calculated in Step e by 2.13:
$ = $ × 2.13 (11)
b. Update the values to current-year dollars. Because the relationships used to estimate the
dollar losses are in 2011 dollars, it is necessary to adjust this value for inflation variation
between 2011 to the current year.
c. Obtain the total dollar loss due to the fire station shutdown. This is done by adding the
estimates obtained in Steps f (total property loss) and g (mortality and human injuries
losses):
$ = $ + $ (12)
8
This estimate was obtained using the values of $5 million per death and $166,000 per injury as 1993 values, then
inflating to current dollar values. The report offers a value of $31.7 billion in 2010, which is $32.7 billion in 2011 dollars.
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Application of the Methodology: An Example
Consider a situation where Fire Station A is shut down due to a flood event. The information needed
to estimate the social cost of the shutdown is the following:
1. Fire Station B will cover the geographical area usually covered by Fire Station A.
2. The population served by Fire Station A is 30,000 people.
3. The distance between the two fire stations is 5 miles.
These are the steps to determine the increase in the dollar losses due to the shutdown:
a. The number of fire incidents (I) in the affected area will be equal to:
30,000 x 0.004 = 120 incidents per year.
b. Response time will be equal to:
before the shutdown (RT
Before
): 5 minutes
after the shutdown (RT
After
): [5 + (0.65 + 1.70 x 5 miles)] = 14.15 minutes
c. The probability of a no-loss incident (P
0
) will be equal to:
before the shutdown (P
0Before
): (0.456 – 0.00264 x 5) = 0.4428
after the shutdown (P
0After
): (0.456 – 0.00264 x 14.15) = 0.4186
d. The dollar loss per incident will be equal to:
before the shutdown (DL
Before
): (3,845 + 431 x 5) = $6,000
after the shutdown (DL
After
): (3,845 + 431 x 14.15) = $9,944
e. The increase in the dollar loss due to the fire station shutdown will be equal to:
[(1 – 0.4186) x 9,944 – (1 – 0.4428) x 6,000] x 120 = $292,589 per year, or $802 per day
of lost service (in 2011 dollars).
f. After adding the indirect losses, the daily dollar loss will be equal to:
$802 x 1.10 = $882 per day in 2011 dollars.
g. Updating this value to 2020 dollars gives:
$882 x 1.1542 = $1,018 per day of lost service.
h. The losses for deaths and human injuries will be equal to:
$1,018 x 2.13 = $2,168 per day of lost service.
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i. Total losses will be equal to:
$1,018 + $2,168 = $3,186 per day of lost service.
2.6. Loss of Emergency Medical Services
In a life-threatening situation, timely emergency care is a key factor that affects the chances of
survival. If the shutdown of an EMS provider such as a fire station causes a considerable increase in
the EMS response time, there may be a cost in lives. The methodology presented estimates the
social cost for a loss of an EMS provider, which is the potential cost in lives resulting from the
increased response time. To measure changes in EMS response times, the methodology assumes
that if an EMS provider (for example, Fire Station A) is temporarily shut down, then the closest EMS
provider (Fire Station B) will temporarily serve the population served by Fire Station A.
Different medical studies have analyzed the link between mortality and EMS response times (for
example, see Blackwell and Kaufman, 2002). However, all the studies that estimated a “survival
function” focus on cardiac arrests.
9
As suggested by Erkut et al. (2007), the reason for choosing
cardiac arrests in this type of study is that cardiac arrest calls are of the highest priority, and,
according to the researchers, those victims are the most “savable”; the response to cardiac arrest
calls is the most accurate measure of emergency medical performance. Current EMS response time
standards are based on cardiac arrest survival studies, and these calls account for a considerable
portion of high-priority EMS calls.
This methodology uses the results obtained by Valenzuela et al. (1997). This particular study was
selected because it is based on data from the United States and used a larger database compared
to other studies.
10
The study used data from the EMS systems of Tucson, AZ (population, 415,000;
area, 406 km
2
), and King County, WA (population, 1,038,000; area, 1,399 km
2
). The Tucson data
were collected from 1988 through 1993, and the King County data were collected from 1976
through 1991. The authors estimated a survival function that included the time interval from
collapse to cardiopulmonary resuscitation (CPR), and the time interval from collapse to defibrillation.
The estimated survival function is the following:
=
1 +
..
.
(13)
Where:
9
A “survival function” measures the probability of survival for a patient as a function of the response time of an EMS
vehicle to the patient.
10
Some of the mentioned studies used data from Canada, the Netherlands, and the United Kingdom.
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Survival probability = survival probability after out-of-hospital cardiac arrest due to ventricular
fibrillation
I
CPR
= time interval from collapse to CPR
I
Defib
= time interval from collapse to defibrillation
The steps to estimate the impact of losing an EMS provider are the following:
1. Determine the EMS provider that will temporarily replace the EMS provider that is out of service
2. Establish the distance between the two
3. Estimate the population served by the non-operating EMS provider
4. Determine the dollar loss expected due to the shutdown
To determine the expected dollar loss (Step 4 above), a series of calculations need to be performed.
a. Estimate the number of cardiac arrests treated by EMS in the affected area. These numbers
were obtained using the population served as determined in Step 3.
11
Because obtaining
specific data for an area may be difficult, a national average was used instead. The American
Heart Association estimates that in the United States, EMS treats 36 to 81 out-of-hospital
cardiac arrests per 100,000 people (American Heart Association, 2013).
12
The middle point
of that estimate is 63.8 per 100,000 people. Therefore, the number of cardiac arrests
treated in the affected area (e.g., the area served by EMS Provider A) can be approximated
as:
=
×63.8
(14)
100,000
b. Estimate the average EMS response time in the area before and after the shutdown. In the
United States, response times are typically different for urban and rural areas. For the
situation before the shutdown, it is assumed that the response time is equal to the national
average. According to the National EMS Information System (n.a., 2016), the 50
th
Fractile
11
No studies were found regarding how a natural disaster will increase the mortality rate from cardiac arrests. Even if that
data were available, it would need to be established how an increased distance to a hospital would affect the increase in
the mortality rate.
12
No national data could be obtained about EMS calls. In 2001, the National Association of State EMS Directors, in
conjunction with the National Highway Traffic Safety Administration (NHTSA) and the Trauma/EMS Systems program of the
Health Resources and Services Administration’s (HRSA) Maternal Child Health Bureau created a national EMS database
known as NEMSIS (National EMS Information System). It is expected that in future years national data related to EMS
would be available through this system.
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Elapsed Time By Urbanicity of EMS Service Area for cardiac arrest calls is 6 minutes for
urban, 7 minutes for suburban, 8 minutes for rural, and 9 minutes for wilderness.
13
c. The extra response time will be approximated using the distance between the EMS providers
established in Step 2 above. The following formula, developed by the New York City Rand
Institute in the 1970s (Chaiken et al., 1975), is used to determine the relationship between
expected response time (RT) in minutes and the distance (D) in miles:
= 0.65 + 1.70 (15)
Hence, the response time after the EMS provider shutdown (RT
After
) will be estimated to be (in
minutes):
= 6 +
(
0.65 + 1.70
)
for urban (16)
= 7 +
(
0.65 + 1.70
)
for suburban (17)
= 8 +
(
0.65 + 1.70
)
for rural (18)
= 9 +
(
0.65 + 1.70
)
for wilderness (19)
d. Determine the probability of survival before and after the shutdown. This is done using the
survival function given in equation (6). It is assumed that a call is placed to EMS as soon as
the patient experiences cardiac arrest, and that all EMS units are equipped with defibrillators
and staff who are trained to use them. Following Valenzuela et al. (1997), it is also assumed
that the time interval to EMS-initiated CPR (I
CPR
) is equal to the EMS response interval plus 1
minute, and the time interval to defibrillation (I
Defib
) is equal to the EMS response time plus 2
minutes. The survival probabilities before and after the shutdown are given by the following
formulas:
o Before shutdown:
=
1 +
..×
(
)
.×
(
)
for urban (20)
13
The definition of each category is based on an “Urban Influence” coding system used by the United States Department of
Agriculture (USDA) and the Office of Management and Budget (OMB). These codes take into account county population
size, degree of urbanization, and adjacency to a metropolitan area or areas. The categories are defined as follows:
• Urban: counties with large (more than 1 million residents) or small (less than 1 million residents) metropolitan areas.
• Suburban: micropolitan (with an urban core of at least 10,000 residents) counties adjacent to a large or small
metropolitan area.
• Rural: non-urban core counties adjacent to a large or small metropolitan area (with or without town).
• Wilderness: non-core counties that are adjacent to micropolitan counties (with or without town).
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=
1 +
..×
(
)
.×
(
)
for suburban (21)
=
1 +
..×
(
)
.×
(
)
for rural (22)
=
1 +
..×
(
)
.×
(
)
for wilderness (23)
o After shutdown:
= 1 +
..×
.×
for urban,
suburban, rural, and wilderness (24)
e. Calculate the increase in the number of deaths from cardiac arrests due to the increased
EMS response time. The survival probabilities obtained in Step d, and the number of cardiac
arrests estimated in Step a, will be used to approximate the potential increase in the number
of deaths:
=
×
1
(25)
=
×
1
(26)
=
(27)
f. Assign a dollar value to the potential cost in lives due to the increased EMS response time.
This methodology uses the Value of Statistical Life of $11,600,000 from the Life Safety
section above. Hence, the potential cost in lives can be estimated using the following
formula:
=
(
)
× $11,600,000 (28)
Application of the Methodology: An Example
Consider a situation where EMS Provider A in a suburban area is shut down due to a flood event. The
information needed to estimate the social cost of the shutdown related to EMS is the following:
1. EMS Provider B will cover the geographical area usually covered by EMS Provider A.
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2. The population served by EMS Provider A is 30,000 people.
3. The distance between the two providers is 5 miles.
These are the steps to estimate the potential dollar losses due to the EMS loss of function:
a. The number of cardiac arrests treated by EMS in the affected area is equal to:
,×.
= = 19.1 (29)
,
b. The average EMS response time in the area before and after the EMS provider shutdown are
equal to:
= 7 (30)
= 7 +
(
0.65 + 1.70 × 5
)
= 16.2 (31)
c. The probabilities of survival before and after the shutdown are equal to:
=
1 +
..×
(
)
.×
(
)
= 0.1372
=
1 +
..×
(
.
)
.×
(
.
)
= 0.0164 (32)
d. The increase in the number of deaths from cardiac arrests due to the increased EMS
response time is equal to:
= 19.1 ×
(
1 0.1372
)
= 16.4795
= 19.1 ×
(
1 0.0164
)
= 18.7868
= 18.7876
16.4795 = 2.3073
(33)
e. The dollar value of the potential cost in lives due to the increased EMS response time is
equal to:
.
= ×
$11,600,000 = $73,327 (34)
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2.7. Loss of Hospital Services
The methodology presented estimates how the temporary loss of function of a hospital affects the
users of the Emergency Department (ED). This methodology assumes that if a hospital (for example,
Hospital A) is temporarily shut down, then its users will choose the second nearest hospital (Hospital
B) in case of an emergency. It also assumes that only patients using the ED, whether they are
admitted to the hospital or not, will be affected by the temporary hospital shutdown. This is because
most non-emergency patients will likely reschedule their hospital admission if the hospital is
temporarily closed. For this reason, the impacts estimated in this paper should be allowed only for
mitigation activities that sustain emergency room services, rather than the whole hospital building.
It should be noted that this methodology does not cover the emergency response actions taken by
the hospital (e.g., evacuation procedures) to reduce the potential loss of property or life of patients
(e.g., intensive care unit [ICU] patients who require specialized care). The actions taken by the
hospital and associated impacts should be addressed separately when estimating the total impacts
of an event.
The cost to users in this methodology can be disaggregated into three parts:
A. The cost of the extra distance to get to the hospital: If Hospital A is temporarily shut down,
the population served by this hospital will have to use Hospital B instead. This implies driving
a longer distance, and consequently incurs a higher cost in terms of time, fuel, and other
costs of the trip.
B. The cost of additional waiting time at the hospital: The increased patient load at Hospital B
will cause delays in treatment. This extra time affects users of both Hospital A and Hospital
B.
C. The potential cost in lives of the extra time to get to the hospital: In a life-threatening
situation, timely emergency care is a key factor that affects the chances of survival. If the
increase in distance to the nearest hospital is long enough, the cost in lives may need to be
considered in the analysis.
The steps to estimate the impacts of losing hospital services are the following:
1. Determine which alternate hospital (Hospital B) will temporarily replace the hospital that is out of
service (Hospital A)
2. Establish the distance between the hospitals
3. Estimate the population served by each hospital
4. Determine the dollar loss due to the shutdown in terms of:
a. The cost of traveling the extra distance to Hospital B
b. The cost of extra waiting time at Hospital B
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c. The potential cost in lives due to the increased distance to Hospital B for Hospital A’s
patients
To estimate the dollar loss (Step 4 above), a series of calculations need to be performed:
a. Cost of traveling the extra distance to the hospital:
i. Estimate the extra travel time due to the hospital shutdown: It is assumed that, on
average, the additional travel distance for the non-operating hospital (Hospital A) patients
will be equal to the distance between the non-operating hospital and the second nearest
hospital (Hospital B). Hence the extra travel distance will be approximated through the
distance between both hospitals established in Step 2. It is assumed that the trip to the
hospital implies a round trip (a trip to the hospital and a trip from the hospital), so the
travel time is multiplied by 2 (based on Capps et al., 2006). The extra travel time can be
approximated using the formula developed by the New York City Rand Institute in the
1970s (Chaiken et al., 1975) to estimate the relationship between time (T) in minutes and
distance (D) in miles:
(
)
= 0.65 + 1.70
(
)
(35)
Then the formula to estimate the extra distance will be:
..×
(
)
.
(
)
= × 2 (36)
ii. Estimate the number of daily ED visits to the non-operating hospital: The population served
determined in Step 3 will be used to obtain this number. Since obtaining specific data for a
hospital may be difficult, a national average will be used instead. According to the National
Center for Health Statistics of the U.S. Department of Health and Human Services (CDC,
2011), the number of visits to Eds in 2011 was 136.3 million, or 44.5 visits per 100
persons. Additionally, during an emergency (such as a hurricane or tornado) the number of
ED visits may increase. There are different studies analyzing the effect of natural disasters
on the use of Eds. The results vary depending on the event magnitude. For this analysis,
the results obtained by Smith and Graffeo (2005) on the impacts of Hurricane Isabel (a
Category 2 hurricane that hit the mid-Atlantic region in 2003) were used. The purpose of
this study was to investigate the impact of the hurricane on the number and type of ED
patient visits. The results showed that during the subsequent 4 days post-landfall, there
was an increase in average daily aggregate ED visits of 25 percent. This number will be
used to increase the number of visits per day for both hospitals.
Therefore, the number visits to the non-operating hospital can be approximated as:
.×
.
=
× 1.25 (37)
Determine the cost of the extra distance to get to the hospital: It is assumed that the trip to the
hospital will involve two people per patient (patient and companion). Additionally, the cost of time is
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estimated using the average employer cost for employee compensation per hour from the U.S.
Department of Labor. The employer cost in December 2021 was $38.07 per hour. Finally, the cost of
the extra mileage is estimated using the Federal government mileage reimbursement rate for
January 1 to June 30, 2022, which is $0.585 per mile for passenger vehicles.
14
The cost of traveling
the extra distance to the hospital is given by the following formula:
= × $38.07 ×
(
× 2
)
+$0.585 ×
(
ℎ × 2
)
× (38)
b. Cost of extra waiting time at the hospital:
i. Estimate the number of ED visits per year for both hospitals. These numbers can be
estimated using the population served as determined in Step 3, the average number of ED
visits per year (44.5 per 100 people in 2011, as discussed in Step a.ii.), and the increase
in the number of visits during the disaster:
=
× 0.445 × 1.25
(39)
=
× 0.445 × 1.25
(40)
ii. Estimate the waiting time increase at the replacing hospital for both groups of patients:
This can be obtained using a relationship between the number of ED users and waiting
time. Such a relationship was estimated using data from the survey Emergency
Department Pulse Report (Press Ganey Associates, 2007). This survey analyzes the
experiences of more than 1.5 million patients treated at more than 1,500 hospitals in the
United States. The survey shows that the average waiting time at the ED increases as the
number of patients increases. Using that information, a regression analysis was conducted
to obtain the relationship between waiting time and the number of patients, measured as
the number of annual visits to EDs
15
:
(
ℎ
)
= 2.49 + 0.000042 ×
(41)
The extra waiting time for both groups of patients (Hospital A users that will have to use Hospital B
due to the shutdown, and Hospital B users) can be estimated using the following formulas:
14
The extra mileage cost is included because only 4.2 percent of the patients visiting EDs use emergency medical
transport (Institute of Medicine of the National Academies, 2006).
15
The regression R
2
is equal to 0.9910.
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= 2.49 + 0.000042 ×
(42)
= 2.49 + 0.000042 ×
(43)
= 2.49 + 0.000042
×
+
(44)
The waiting time increases per patient are then calculated:
=
(45)
=
(46)
iii. Calculate the cost of the extra waiting time: As in Step a.iii, it is assumed that the trip to
the hospital involves two people per patient, and that the cost of time is estimated using
the average employer cost for employee compensation per hour from the U.S. Department
of Labor ($38.07 per hour in December 2021). The cost per day of the extra waiting time at
the hospital would be:
=
×
× 2 × $38.07
365
+
×
× 2 × $38.07 (47)
c. Potential cost in lives due to the increased distance to hospital:
After conducting an extensive literature search, only one study could be found that analyzed the link
between mortality and distance to a hospital (Buchmueller et al., 2005). The study uses data from
the Los Angeles County Health Surveys for 8,000 cases between 1997 and 2003 to test the effect of
distance on mortality from emergency (i.e., acute myocardial infarction [AMI] and unintentional
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injuries)
16,17
and non-emergency conditions (i.e., cancer or chronic heart disease). The results show
that increased distance to the nearest hospital is associated with an increase in deaths from AMI
and unintentional injuries, but not from the other causes for which timely emergency care is less
important. The results are the presented in Table 11:
Table 11: Percentage Change in Number of Deaths Due to a Mile Increase in Distance to the
Hospital
1
AMI Unintentional Injuries
Increase in the number of deaths due
to a 1-mile increase in distance
6.04 percent 6.14 percent
1
Source: Buchmueller et al. (2005).
The steps to determine the potential cost in lives are the following:
i. Estimate the number of deaths from AMI and unintentional injuries in the affected area:
These numbers were obtained using the population served as determined in Step 3.
18
Because obtaining specific data for an area may be difficult, a national average was used.
The National Center for Health Statistics of the U.S. Department of Health and Human
Services publishes the National Vital Statistics Report, which contains data on death rates
and causes of death. The last report available contains data for 2013 (CDC, 2016). The
death rate in 2013 was 821.5 per 100,000 population, while the death rates for AMI and
unintentional injuries were 50.9
19
and 41.3 per 100,000 population, respectively.
Therefore, the number of deaths in the affected area (i.e., the area served by Hospital A)
can be approximated as:
×50.9
ℎ = (48)
100,000
16
AMI are covered by the International Classification of Diseases, Tenth Revision (ICD-10) codes I21-I22, and unintentional
injuries are covered by codes V01-X59 and Y85-Y86.
17
Unintentional injuries are: 1) transport accidents and their consequences, and 2) other external causes of accidental
injury and their consequences.
18
No studies were found regarding how a natural disaster will increase the mortality rate from AMI and unintentional
injuries. Even if that data were available, it would need to be established how an increased distance to a hospital would
affect the increase in the mortality rate.
19
The data for AMI could not be updated from 2005 to 2013 because for the 2013 data grouped AMI together with all
heart diseases. Please see: http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_02.pdf. Acute Myocardial Infarction is
coded as “I21−I22” (Table A, page 4), while “Diseases of the heart” includes codes I00−I09, I11, I13, and I20−I51 (Table
1, page 18). No documentation could be found that breaks out AMI from other heart diseases.
September 2022 34
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=
×.
(49)
,
ii. Calculate the increase in the number of deaths from AMI and unintentional injuries due to
the increased distance to the hospital: The percentages provided in Table 11, the
estimates obtained in the previous step, and the distance between Hospital A and Hospital
B will be used to approximate the potential increase in the number of deaths:
=
× 0.0604 ×
(50)
=
× 0.0614
×
(51)
iii. Assign a dollar value to the potential cost in lives due to the increased distance to the
hospital: This methodology uses the VSL developed in the Life Safety section above. The
September 2021 estimate for the VSL is $11,600,000. Hence, the potential cost in lives
can be estimated using the following formula:
=
(
)
× $11,600,000
365
(
)
+
× $11,600,000 (52)
The total dollar loss due to the hospital shutdown then is obtained as the sum of items I, II, and III:
= +
+ (53)
Application of the Methodology: An Example
Consider a situation where Hospital A is shut down due to a flood event. The information needed to
estimate the social cost of the shutdown is the following:
1. Hospital B will serve the geographic area usually served by Hospital A.
2. The distance between the hospitals is 10 miles.
3. The population served by Hospital A is 10,000 people, and the population served by Hospital B is
30,000 people.
4. These are the steps to determine the dollar losses due to the shutdown:
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a. Cost of travelling the extra distance to the hospital
i. The extra travel time due to the hospital shutdown is equal to:
..×
=
× 2 = 0.6 (54)
ii. The number of daily ED visits to Hospital A is equal to:
.×,
=
× 1.25 = 15.24
(55)
iii. The costs of traveling the extra distance to the hospital is equal to:
= 0.6 × $38.07 ×
(
15.24 × 2
)
+$0.585 ×
(
10 × 2
)
× 15.24 = $875
(56)
b. Cost of extra waiting time at the hospital
i. The number of ED visits per year for both hospitals are equal to:
= 10,000 × 0.445 × 1.25 = 5,563
= 30,000 × 0.445 × 1.25 = 16,688
(57)
ii. The waiting time increase at the replacing hospital for both groups of patients is calculated
following these steps:
= 2.49 + 0.000042 × 5,563 = 2.7
= 2.49 + 0.000042 × 16,688 = 3.2
= 2.49 + 0.000042 ×
(
5,563 + 16,688
)
= 3.4
= 3.4 2.7 = 0.7
= 3.4 3.2 = 0.2
(58)
iii. The cost of the extra waiting time is equal to:
5,563 16,688
= 0.7 × × 2 × $38.07 + 0.2 × × 2 × $38.07
365 365
= $1,509
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(59)
c. Potential cost in lives due to the increased distance to hospital
i. The number of deaths from AMI and unintentional injuries in the affected area is equal to:
10,000×50.9
ℎ = = 5.09
100,000
(60)
10,000×41.3
ℎ = = 4.13
100,000
(61)
ii. The increase in the number of deaths from AMI and unintentional injuries due to the
increased distance to the hospital is equal to:
ℎ ℎ = 5.09 × 0.0604 × 10
= 3.074
ℎ ℎ
= 4.13 × 0.0614 × 10 = 2.536 (62)
iii. The dollar value of the potential cost in lives due to the increased distance to the hospital
is equal to:
ℎ ℎ =
3.074
× $11,600,000 +
2.536
× $11,600,000 = $178,290
365 365
(63)
The total dollar loss due to the hospital shutdown is equal to:
= $875 + $1,509 + $178,290 = $180,674 (64)
2.8. Loss of Police Services
The methodology presented estimates the cost to society of a temporary loss-of-function of a police
station. The estimation of this cost has two main components. The first is to measure how a reduced
police presence would affect the population of that area. The second is to assign a dollar value to
those effects.
It should be noted that this method only accounts for the effects of a reduced police presence
resulting from the loss of a police station. In many situations, activities typically conducted at a police
station can be assigned to another police station with no apparent loss of service to the community.
However, during a catastrophic event, such as a flood in the community, there may be an increased
cost for emergency response activities, including an increase in overtime for police officers. This
method does not account for emergency response activities; these costs should be considered
separately with proper documentation.
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It is widely accepted that impaired police activity could potentially result in an increase in crime. The
first component mentioned above can be approximated by the relationship between the number of
police officers per capita and the crime rate. Many studies tried to estimate the impact of police
force size on crime (Goodman and Mann, 2005; New York City Area Consortium for Earthquake Loss
Mitigation, 2003; Levitt, 2002; Levitt, 1998).
This methodology uses the results obtained by Evans
and Owens (2007). The Evans and Owens study used panel data for 2,074 cities and towns for the
period 1990–2001. They found a statistically significant relationship between the number of police
officers and both property crime (such as burglaries, auto thefts, and larceny) and violent crime
(such as murders, rapes, robberies, and aggravated assaults). Table 12 shows the estimated
elasticities; that is, the percentage change in different types of crime generated by a percentage
change in police force. For example, a value of -2 means that a 1 percent reduction in the number of
police officers will cause an increase of 2 percent in that type of crime.
Table 12: Impact of Number of Police Officers on Crime Rate
1
Type of Crime Percent Change in Crime Rate
Generated by a 1-percent Change
in Police Force
Property Crimes
Burglary -0.59
Motor Vehicle Theft -0.85
Larceny -0.08
Violent Crimes
Robbery -1.34
Murder -0.84
Rape -0.42
Assault -0.96
1
Source: Evans and Owens, 2007
The second component is the cost of crime to society. This methodology uses the costs of crime
estimated by McCollister (2010), which provides economic values for the cost of crime in 2008
dollars. The approach used for estimating the cost of crime to society includes tangible costs and
intangible costs. Tangible costs may include direct victim costs, mental health costs, and criminal
justice system costs. Intangible costs include estimates of pain and suffering. Table 13 shows the
costs of crime that were used. The economic values shown in Table 13 were inflated from
McCollister’s May 2008 dollars to December 2019 dollars using the Bureau of Labor Statistics CPI
calculator.
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Table 13: Total Cost of Crime in 2019 Dollars
1
Type of Crime Total Cost
Property Crimes
Burglary $7,665
Motor Vehicle Theft $12,778
Larceny $4,190
Violent Crimes
Robbery $50,189
Murder $10,655,737
Rape $285,614
Assault $126,950
1
Source: McCollister, 2010
The steps to estimate the loss-of-function impact of police services are the following:
1. Determine the number of police officers working at the police station before shutdown
2. Estimate the population regularly served by the police station
3. Establish the number of police officers that would serve the affected area after the police station
shutdown
4. Determine the expected dollar loss due to the shutdown
To determine the expected dollar loss (Step 4 above), a series of calculations need to be performed:
a. Determine the number police officers per capita in the area served by the police station
before the station shutdown (Ppc
Before
): The number of police officers and the population
served, determined in Steps 1 and 2, respectively, will be used to obtain these numbers:
= (65)
b. Obtain the number of police officers per capita after the police station shutdown (Ppc
After
): To
calculate this value, the number of police officers determined in Step 3 (Police officers
After
)
will be used:
= (66)
c. Calculate the percent change in the number of police officers per capita: This is done using
the values obtained in Steps a and b:
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Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
−
% = × 100 (67)
d. Calculate the percent change in the number crimes per capita (Cpc): For each crime, this is
done using the crime elasticities (i.e., the percent change in crime generated by a 1 percent
change in the police force) provided in Table 12 and the percent change in the number of
police officers obtained in Step c:
% = % × (68)
e. Estimate the number of crimes in the area: This can be calculated using data from the
Uniform Crime Reporting (UCR) Program, provided yearly by the U.S. Department of Justice,
Federal Bureau of Investigation (FBI). Because crime rates vary considerably across and
within States, it is suggested to use data from Table 5 of the UCR Program (FBI, 2014), which
provides crime data by State disaggregated between metropolitan and non-metropolitan
areas. For every State, the data are presented as shown in Table 14.
20
The following are the steps to determine the number of crimes in the area:
i. Determine if the affected area is in a metropolitan statistical area (MSA), a city outside a
metropolitan area, or a nonmetropolitan county.
21
ii. For each For each of the crimes, obtain the crime rates per 100,000 inhabitants per year using
the “estimated total” number of crimes in Table 5 of the UCR Program:
(
100,000 ℎ.
)
= × 100,000 (69)
Using the example in Table 14, if the area is in an MSA, then the robbery rate would be equal to:
4,228
(
100,000 ℎ.
)
= × 100,000 = 114.5 (70)
3,692,100
20
Only violent crime data are shown in this example.
21
An MSA contains a principal city or urbanized area with a population of at least 50,000 inhabitants. MSAs include the
principal city, the county in which the city is located, and other adjacent counties that have, as defined by the OMB, a high
degree of economic and social integration with the principal city and county as measured through commuting. In the UCR
Program, counties within an MSA are considered metropolitan. Nonmetropolitan (rural) counties are those outside MSAs
that are composed of mostly unincorporated areas.
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Table 14: Example of Crime Statistics for the State of Alabama
1
Measure Population or
Percentage of
Population
No. Violent
Crimes
No. Murders and
Nonnegligent
Manslaughters
No.
Forcible
rapes
No.
Robberies
Metropolitan Statistical
Area
Population of area 3,692,100 n/a n/a n/a n/a
Percentage of area
actually reporting
95.5% 16,204 232 1,033 4,131
Estimated total 100.0% 16,702 236 1,076 4,228
Cities Outside
Metropolitan Areas
Population of area 529,129 n/a n/a n/a n/a
Area actually reporting 93.3% 2,605 17 214 363
Estimated total 100.0% 2,769 18 226 386
Nonmetropolitan
Counties
Population of area 628,148 n/a n/a n/a n/a
Area actually reporting 99.4% 1,247 22 133 86
Estimated total 100.0% 1,256 22 134 87
Alabama State Total
4,849,377
20,727
276
1,436
4,701
Rate per 100,000
inhabitants
n/a 427.4 5.7 29.6 96.9
1
Source: FBI, 2014
iii. For each of the crimes, calculate the number of crimes per year that occur in the affected area:
×
= (71)
100,000
a. Calculate the change in the number of crimes: For each crime, this is obtained by multiplying
the number of crimes estimated in Step v and the percent change in crime estimated in Step
iv:
= × % (72)
September 2022 41
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
b. For each crime, assign a dollar value to the change in the number of crimes: This is done by
multiplying the change in the number of crimes obtained in Step vi and the cost of crime
provided in Table 13:
=
×
(73)
c. Obtain the total dollar loss due to the police station shutdown: The total cost per year is
obtained by adding the costs of each of the crimes:
=
(74)
The total cost per day is equal to:
=
(75)
Application of the Methodology: An Example
Consider a situation where Police Station A, located in an MSA in Missouri, is shut down due to a
flood event. The information needed to estimate the social cost of the shutdown is the following:
1. The number of police officers working at the police station before the shutdown was 100.
2. The population regularly served by the police station is 50,000.
3. The number of police officers that would serve the affected area after the police station
shutdown is 80.
These are the steps to determine the increase in the dollar losses due to the shutdown:
a. The number of police officers per capita before the station shutdown (Ppc
Before
) is equal to:
=
= 0.002 (76)
,
b. The number of police officers per capita after the police station shutdown (Ppc
After
) is equal
to:
=
= 0.0016 (77)
,
c. The percent change in the number of police officers per capita is equal to:
..
% = × 100 = 20% (78)
.
d. Using the elasticities provided in Table 12, the percent changes in the number of crimes per
capita (Cpc) are the following:
%
= % ×
(
0.59
)
(79)
%
= % ×
(
0.85
)
(80)
%
= % ×
(
0.08
)
(81)
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Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
%
= % ×
(
−1.34
)
(82)
%
= % ×
(
−0.84
)
(83)
%
= % ×
(
−0.42
)
(84)
%
= % ×
(
−0.96
)
(85)
e. The number of crimes in the area is estimated using the crime data published by the UCR
Program from the FBI. The latest data available is for 2014. Table 5 (FBI, 2014) includes the
following data for the State of Missouri, as shown in Tables 15 and 16:
Table 15: Example of Crime Statistics for the State of Missouri, Part 1
1
Measure Population or
Percentage
of Population
No.
Violent
Crimes
No. Murders and
Nonnegligent
Manslaughters
No.
Forcible
Rapes
No.
Robberies
Metropolitan Statistical
Area
Population of area 4,507,971 n/a n/a n/a n/a
Area actually reporting 99.9% 22,257 348 1,441 5,295
Estimated total 100.0% 22,258 348 1,441 5,295
Cities Outside
Metropolitan Areas
Population of area 665,780 n/a n/a n/a n/a
Area actually reporting 99.6% 2,649 22 160 255
Estimated total 100.0% 2,658 22 160 256
Nonmetropolitan
Counties
Population of area 889,838 n/a n/a n/a n/a
Area actually reporting 100.0% 1,940 33 105 41
Missouri State Total
6,063,589
26,856
403
1,706
5,592
Rate per 100,000
inhabitants
n/a 442.9 6.6 28.1 92.2
1
Source: FBI, 2014
September 2022 43
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Table 16: Example of Crime Statistics for State of Missouri, Part 2
1
Area No.
Aggravated
Assaults
No.
Property
Crimes
No.
Burglaries
No. Larceny
Thefts
No. Motor
Vehicle Thefts
Metropolitan Statistical
Area
Area actually reporting 14,612 140,143 27,421 98,371 14,351
Estimated total 14,613 140,163 27,424 98,387 14,352
Cities Outside
Metropolitan Areas
Area actually reporting 2,155 24,487 4,085 19,441 961
Estimated total 2,163 24,581 4,101 19,515 965
Nonmetropolitan
Counties
Area actually reporting 1,706 11,493 3,733 6,720 1,040
Missouri State Total
18,482
176,237
35,258
124,622
416,357
Rate per 100,000
inhabitants
308.4 2,906.5 581.5 2,055.3 269.8
1
Source: FBI, 2014
i. The affected area is in an MSA.
ii. The crime rates (per 100,000 inhabitants) are estimated to be:
27,424
= × 100,000 = 608.3
4,507,971
14,352
ℎ
= × 100,000 = 318.4
4,507,971
98,387
= × 100,000 = 2,182.5
4,507,971
5,295
= × 100,000 = 117.5
4,507,971
348
= × 100,000 = 7.7
4,507,971
1,441
= × 100,000 = 32.0
4,507,971
14,613
= × 100,000 = 324.2
4,507,971
iii. The number of crimes per year in the affected area is calculated as:
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608.3 × 50,000
= = 304.2
100,000
318.4 × 50,000
ℎ
= = 159.2
100,000
2,182.5 × 50,000
= = 1,091.3
100,000
117.5 × 50,000
= = 58.8
100,000
7.7 × 50,000
= = 3.9
100,000
32.0 × 50,000
= = 16.0
100,000
324.2 × 50,000
= = 162.1
100,000
f. The change in the number of crimes is equal to:
= 304.2 × −20% ×
(
−0.59
)
= 35.9
ℎ
= 159.2 × −20% ×
(
−0.85
)
= 27.1
= 1091.3 × −20% ×
(
−0.08
)
= 17.5
= 58.8 × −20% ×
(
−1.34
)
= 15.8
= 3.9 × −20% ×
(
−0.84
)
= 0.7
= 16.0 × −20% ×
(
−0.42
)
= 1.3
= 162.1 × 20% ×
(
−0.96
)
= 31.1
g. Using the estimates provided in Table 13, the dollar values for the change in the number of
crimes are the following:
= 35.9 × $7,665 = $275,174
ℎ
= 27.1 × $12,778 = $346,284
= 17.5 × $4,190 = $73,325
= 15.8 × $50,189 = $792,986
= 0.7 × $10,655,737 = $7,459,016
= 1.3 × $285,614 = $371,298
= 31.1 × $126,950 = $3,948,145
h. The total dollar loss due to the police station shutdown would be equal to:
2019
= $13,266,228 ; or
$13,266,228
2019
=
365
= $36,346
September 2022 45
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
2.9. Loss of Electric Services
The methodology currently used by FEMA for calculating the direct economic impacts of losing
electricity services follows five steps to perform benefit-cost analysis of hazard mitigation projects for
electric power systems:
4. Estimate the physical damages to the electric power system in dollars
5. Estimate the functional downtime (system days of lost service)
6. Obtain the number of people served by the electric power utility
7. Calculate the economic impacts of lost electric power service, using the per capita economic
impacts and the affected population
In the 2009 BCA Tool update, an additional step of determining the revenue loss to the electric
power utility was discontinued because of the concern it was double-counting impacts. As a general
rule, double counting can be avoided by not attributing losses to more than one entity in the case of
private goods (Rose, 2004) (e.g., avoiding counting utility sales as a loss to both the utility company
and its customers).
The sections below discuss the methodology used to estimate the economic impacts of the lost
electric power service (Step 4 above) to economic activity and residential customers.
2.9.1. Impacts to Economic Activity
In general, the original methodology outlined in FEMA’s original economic valuation document What
Is a Benefit? Is similar to the methodologies employed in other studies of the electricity industry
(Greenberg et al., 2007; Kunreuther et al., 2006; Greenberg, 2005; Chang et al., 1996).
The 2009 BCA Tool update changed the way the direct economic impact of loss of electric service
was calculated. The new process uses national Gross Domestic Product (GDP) dollar values in order
to estimate the economic impact to commercial and industrial customers. The dollar numbers were
combined with importance factors for each economic sector, which were determined by ATC
Publication 25, Appendix D (FEMA, 1991). The importance factors published by the ATC-25 are
widely used in this type of study, and the values in the document have not been updated since 1991.
Table 17 shows the estimation of the impact to economic activity per capita per day using GDP data
and the ATC-25 factors. The table contains GDP sector value added figures for December 2021 (BEA,
2022) as the most recent annual GDP data available.
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Table 17: Loss of Electric Service Impact to Economic Activity
Economic Sector
1
Electric
Power
Importance
Factor
2
GDP 2021
(in billions of
dollars)
1
GDP per
Capita per
Day
3
Economic
Impact per
Capita per Day
of Lost Service
in 2021
4
Dollars
Agriculture, Livestock
5
—
—
—
—
Mining
5
—
—
—
—
Utilities
0.80
$380.6.8
$3.137
$2.51
Construction
0.40
$958.80.8
$7.903
$3.16
Manufacturing – Nondurable
0.98
$1,177.63
$9.706
$9.51
Manufacturing – Durable Good
7
0.99
$1,385.7
$11.421
$11.31
Wholesale Trade
0.90
$1,383.09
$11.399
$10.26
Retail Trade
0.90
$1,385.58
$11.420
$10.28
Transportation, Warehousing
0.30
$642.6
$5.296
$1.59
Information
0.90
$1,300.77
$10.721
$9.65
Finance, Insurance, Real Estate,
Rental, and Leasing
0.90 $4,885.0 $40.263 $36.24
Professional & Business Services 0.90 $2,973.4 $24.507 $22.06
Education, Healthcare, Social
Assistance
0.80 $1,932.9 $15.931 $12.75
Arts, Entertainment, Recreation,
Accommodation, and Food Services
0.80 $839.6 $6.920 $5.54
Other Services, Except Government 0.90 $447.9 $3.692 $3.32
Government
0.60
$2,772.6
$22.852
$13.71
TOTAL
n/a
$22,465.9
$185.168
$151.87
1
Source: Bureau of Economic Analysis.
2
Source: FEMA Publication 224 (FEMA, 1991)
3
Population data from US Census Bureau (December 31, 2021): 332,402,978.
4
Rows in this column calculated as Electric Power Importance Factor * GDP per Capita per Day. Total value is summation
of each row and is subject to rounding.
5
— = Agriculture, livestock, and mining data excluded from the analysis because they are not relevant for municipal
systems.
6
Weighting value of 0.98 averaged the eight sub-sectors with the following values: food/beverage/tobacco products
(0.90), paper products (1.00), printing and related support (1.00), chemical products (0.90), textiles/textile product mills
(1.00), apparel/leather/allied products (1.00), petroleum/coal products (1.00), and plastic/rubber products (1.00).
7
Weighting value of 0.99 averaged the nine sub-sectors with the following values: wood & furniture (1.00), nonmetallic
mineral products (1.00), primary metal manufacturing (0.90), fabricated metal products (1.00), machinery (1.00),
computer/electronic (1.00), equipment/appliances/etc. (1.00), transportation equipment (1.00), and miscellaneous
equipment (1.00).
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2.9.2. Impacts to Residential Customers
This methodology for this variable was revised in the 2009 update by using a contingent valuation
method instead of using questionable electric service statistical data. The contingent valuation
method relies on consumers’ responses to a survey questionnaire to estimate the willingness-to-pay
(WTP) for a good or service. In this case, the analysis examines the WTP to avoid power outages. This
method has been employed in several studies to measure the impact of lifeline interruptions (Layton
et al., 2005; Devicienti et al, 2004). The data used in this paper was obtained from the study A
framework and review of customer outage costs: integration and analysis of electric utility outage
cost surveys prepared by Lawton, Sullivan, Van Liere, Katz, and Eto for the Department of Energy
(2003). The authors analyzed six large-scale studies conducted by five major electric utilities over 15
years to assess the value of electric service to their residential customers. There was a total of
11,368 respondents that determined the amount they would be willing to pay in order to avoid an
outage of a certain duration. The average WTP to avoid a 12-hour outage is $26.27 in 2002 dollars
(Table 5-2, p. 36). Projecting that amount for a 24-hour outage, and updating the value to December
2019 dollars, the cost per day becomes $75.05. Because the WTP is calculated at a household
level, this estimate needs to be adjusted so it is expressed in per capita terms. According to 2015
Families and Living Arrangements data from the U.S. Census, the average household occupancy is
2.54 people (U.S. Census Bureau, 2020). Therefore, the per capita WTP can be estimated at $29.78.
In the United States, the average person is heavily dependent upon electricity in his or her daily life,
and technological advances make this dependency even more critical. Yet little research—WTP or
otherwise—has been done to place an economic value on electric service. The two most relevant
research papers in the field are cited in the paragraph above and have publication dates of 2004
and 2005, now at least 15 years old. Additionally, there is a concern that the methodology outlined
in the previous paragraph assumes that the WTP for electric service is a linear function. A residential
customer might place an incrementally higher value on avoiding a 24-hour outage versus a doubling
factor for a 12-hour outage. There is some research that finds that this is not a linear function
(Carlsson and Martinsson, 2004); however, more research is needed to determine actual value
numbers that could be used in the BCA Tool.
2.9.3. Summary
Table 18 summarizes the proposed values to measure the economic impact of loss of electric power.
It is recommended that the total economic impact of $181.65 be rounded to the nearest dollar,
$182.
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Table 18: Economic Impacts of Loss of Electric Power Per Capita Per Day (in 2021 dollars)
Category
Economic Impact
Impact on Economic Activity
$151.87
Impact on Residential Customers
$29.78
Total Economic Impact (rounded to nearest dollar)
$182
Based on changes in methodologies used and changes with updated values, Table 19 summarizes
the historical changes in the value of electric service. The “Initial Value” was provided by the FEMA
publication What is a Benefit?
22
The “BCAR Updated Value” sought to update the Initial Value during
the Benefit-Cost Analysis Reengineering in 2007. The “2008 Updated Value” changed the
methodology for electric service loss of function, and the variables that changed are marked as
“Discontinued”. Starting with the 2008 Updated Value column, the values are provided for how the
same methodology resulted in different values of service. The values were updated in 2013, 2016,
and 2019.
Table 19: Evolution of Electric Service Value Used in the BCA Toolkit
Value Category Initial BCAR 2008 2013 2016 2019
Value Updated Updated Updated Updated Updated
Value Value Value Value Value
Average Price per
Kilowatt-Hour (national)
6.47 cents 8.72
cents
—
1
—
1
—
1
—
1
Direct Economic Impact
on Residents
$30 to 35 —
1
—
1
—
1
—
1
—
1
Disruption of Activity per
Day
3 to 4
hours
3 to 4
hours
—
1
—
1
—
1
—
1
Cost of Activity Disruption
per Day
$63 to
$85
$82 to
$109
—
1
—
1
—
1
—
1
Per Capita, Per Day
Direct
$93 to
$110
$82 to
$109
—
1
—
1
—
1
—
1
Best Estimate for
Residential Customers
$101 $95 —
1
—
1
—
1
—
1
22
This document was first made available to BCA analysts in 2001 and is no longer in use.
September 2022 49
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Value Category Initial
Value
BCAR
Updated
Value
2008
Updated
Value
2013
Updated
Value
2016
Updated
Value
2019
Updated
Value
Per Capita, Per Day
Direct Regional
Economic Impact (Impact
on Economic Activity)
$87 $113 $102 $106 $121 $144
Impact on Residential
Customers
none none $24 $25 $27 $30
Total Economic Impacts
$188
2
$208
2
$126
$131
$148
$174
1
This category was discontinued
2
These Total Economic Impacts = Best Estimate for Residential Customers + Per Capita, Per Day Direct Regional Economic
Impact (Impact on Economic Activity) + Impact on Residential Customers
2.10. Loss of Wastewater Services
The methodology presented estimates the value of loss of wastewater service. The loss of
wastewater service measures the impact to the economic activity of the country as a whole and for
residential customers. The methodology applies to the loss of service resulting from the closure of, or
damage to, a wastewater treatment facility. It may not be appropriate to use this methodology to
estimate the losses from events that affect a localized area (e.g., it is not appropriate to use the
Total Economic Impact standard value from Table 20 below for a break in the wastewater line
servicing a residential neighborhood). Localized loss of service situations should be evaluated
separately to account for the full impacts to both economic activity and residential customers.
2.10.1. Impacts to Economic Activity
The direct economic impact of loss of wastewater is estimated using GDP data and the importance
factors published in ATC-25 (FEMA, 1991). The importance factors published by ATC-25 are widely
used in this type of study. These studies typically use GDP data (or Gross State Product data, when
studies are focused on a smaller geographic area) to estimate the economic impact to commercial
and industrial customers.
Table 20 shows the estimation of the impact to economic activity per capita per day using GDP data
and the ATC-25 factors.
2.10.2. Impacts to Residential Customers
According to current FEMA guidelines, the loss of wastewater service for a short time (a few hours or
a few days) does not impose significant economic impacts on residential customers. FEMA assumes
that a temporary loss of wastewater service entails a total or partial loss of capacity to
treat
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Table 20: Loss of Wastewater Service Impact to Economic Activity
Economic Sector
1
Wastewater
Service
Importance
Factor
2
GDP 2021
(in billions
of dollars)
1
GDP per
Capita per
Day
3
Economic
Impact per
Capita per Day
of Lost Service
in 2021
4
D ll
Agriculture, Livestock
5
—
—
—
—
Mining
5
—
—
—
—
Utilities
0.24
$380.6
$3.137
$0.75
Construction
0.20
$958.8
$7.903
$1.58
Manufacturing – Nondurable 0.65 $1,177.6 $9.706 $6.31
Manufacturing – Durable Goods
7
0.75
$1,385.7
$11.421
$8.57
Wholesale Trade
0.10
$1,383.0
$11.399
$1.14
Retail Trade
0.20
$1,385.5
$11.420
$2.28
Transportation, Warehousing
0.10
$642.6
$5.296
$0.53
Information
0.20
$1,300.7
$10.721
$2.14
Finance, Insurance, Real Estate,
Rental, and Leasing
0.20 $4,885.0 $40.263 $8.05
Professional & Business Services
0.20
$2,973.4
$24.507
$4.90
Education, Healthcare, Social
Assistance
0.80 $1,932.9 $15.931 $12.75
Arts, Entertainment, Recreation,
Accommodation, and Food
Services
0.80 $839.6 $6.920 $5.54
Other Services, Except
Government
0.20 $447.9 $3.692 $0.74
Government
0.20
$2,772.6
$22.852
$4.57
TOTAL
n/a
$22,465.9
$185.168
$59.85
1
Source: Bureau of Economic Analysis.
2
Source: FEMA Publication 224 (FEMA, 1991)
3
Population data from U.S. Census Bureau (December 31, 2021): 332,402,978.
4
Rows in this column calculated as Electric Power Importance Factor * GDP per Capita per Day. Total value is summation
of each row.
5
— = Agriculture, livestock, and mining data excluded from the analysis because they are not relevant for municipal
systems.
6
Weighting value of 0.65 averaged the eight sub-sectors with the following values: food/beverage/tobacco products
(0.70), paper products (0.80), printing and related support (0.30), chemical products (0.80), textiles/textile product mills
(0.70), apparel/leather/allied products (0.50), petroleum/coal products (0.50), and plastic/rubber products (0.50).
7
Weighting value of 0.75 averaged the nine sub-sectors with the following values: wood & furniture (0.50), nonmetallic
mineral products (0.50), primary metal manufacturing (0.80), fabricated metal products (0.80), machinery (0.80),
computer/electronic (0.90), equipment/appliances/etc. (0.60), transportation equipment (0.80), and miscellaneous
equipment (0.60).
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wastewater without affecting the residential disposal of sewage or other wastewater. Residential
customers would likely be willing to pay some value to avoid the water pollution of passing untreated
sewage through the wastewater system directly into the receiving stream. However, no research
value could be found that placed an economic value on wastewater service to customers. Therefore,
even though no value was assigned for the loss of wastewater to residential customers, it is unlikely
that a real economic value of $0 would be placed on wastewater service. In the BCA Tool,
communities are encouraged to include the impacts on residential customers in situations where a
cost is incurred or where the impacts can be documented. For example, a city may need to provide
portable toilets to residents if a sewer line to a residential neighborhood is severed.
2.10.3. Summary
Table 21 summarizes the values to measure the economic impact of loss of wastewater services and
shows the recommended value of $60. Over time, this value has increased from $58 in 2019, $49
in 2016, $45 in 2013, and $41 in 2009.
Table 21: Economic Impact of Loss of Wastewater Service per Capita per Day (in 2021 dollars)
Category
Economic Impact
Impact on Economic Activity
$59.85
Impact on Residential Customers
$0
Total Economic Impact (rounded to nearest dollar)
$60
2.11. Loss of Water Services
The methodology presented estimates the value of loss of potable water service. The loss of water
service measures the impact to the economic activity of the country and for residential customers.
2.11.1. Impacts to Economic Activity
The direct economic impact of loss of water is estimated using GDP data and the importance factors
published in ATC-25 (FEMA, 1991). The importance factors are widely used in this type of study.
These studies typically use GDP data (or Gross State Product data, when studies are focused on a
smaller geographic area) to estimate the economic impact on commercial and industrial customers.
Table 22 shows the estimation of the impact on economic activity per capita per day using GDP data
and the ATC-25 factors.
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Table 22: Loss of Water Service Impact to Economic Activity
Economic Sector
1
Water
Service
Importance
Factor
2
GDP 2021
(in billions of
dollars)
1
GDP per
Capita per
Day
3
Economic Impact
per Capita per
Day of Lost
Service in 2021
Dollars
4
Agriculture, Livestock
5
—
—
—
—
Mining
5
—
—
—
—
Utilities
0.40
$380.6
$3.137
$1.25
Construction
0.50
$958.8
$7.903
$3.95
Manufacturing – Nondurable Goods
6
0.60
$1,177.6
$9.706
$5.82
Manufacturing – Durable Goods
7
0.70 $1,385.7 $11.421 $7.99
Wholesale Trade
0.20
$1,383.0
$11.399
$2.28
Retail Trade
0.20
$1,385.5
$11.420
$2.28
Transportation, Warehousing
0.20
$642.6
$5.296
$1.06
Information
0.20
$1,300.7
$10.721
$2.14
Finance, Insurance, Real Estate,
Rental, and Leasing
0.20 $4,885.0 $40.263 $8.05
Professional & Business Services
0.20
$2,973.4
$24.507
$4.90
Education, Healthcare, Social
Assistance
0.40 $1,932.9 $15.931 $6.37
Arts, Entertainment, Recreation
0.80
$839.6
$6.920
$5.54
Other Services, Except Government 0.20 $447.9 $3.692 $0.74
Government
0.25
$2,772.6
$22.852
$5.71
TOTAL
n/a
$22,465.9
$185.168
$58.11
1
Source: Bureau of Economic Analysis.
2
Source: FEMA Publication 224 (FEMA, 1991)
3
Population data from U.S. Census Bureau (December 31, 2021): 332,402,978.
4
Rows in this column calculated as Electric Power Importance Factor * GDP per Capita per Day. Total value is summation
of each row.
5
— = Agriculture, livestock, and mining data excluded from the analysis because they are not relevant for municipal
systems.
6
Weighting value of 0.60 averaged the eight sub-sectors with the following values: food/beverage/tobacco products
(0.70), paper products (0.60), printing and related support (0.30), chemical products (0.80), textiles/textile product mills
(0.70), apparel/leather/allied products (0.50), petroleum/coal products (0.50), and plastic/rubber products (0.50).
7
Weighting value of 0.70 averaged the nine sub-sectors with the following values: wood & furniture (0.50), nonmetallic
mineral products (0.50), primary metal manufacturing (0.90), fabricated metal products (0.80), machinery (0.60),
computer/electronic (0.90), equipment/appliances/etc. (0.60), transportation equipment (0.60), and miscellaneous
equipment (0.60).
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2.11.2. Impacts to Residential Customers
The methodology used to estimate the economic impact of water supply disruptions was to develop a
demand curve for potable water and measure the “welfare loss” associated with a loss of supply.
The method of this approach is to obtain the WTP to avoid water supply interruptions, which is
defined as the amount of money that residential customers would pay to avoid a loss of water
service of a given duration. The mechanism to estimate the consumer’s WTP is the integration of a
demand curve for water services. This method has been employed in several studies to measure the
impact of lifeline interruptions (Dalhuisen et al., 2003; Jenkins et al., 2003; Devicienti et al., 2004).
The specification of the demand curve, and hence the welfare loss, was developed in the study
Estimating business and residential water supply interruption losses from catastrophic events by
Brozovic et al. (2007).
The daily welfare loss for a consumer experiencing a loss of water service is given by:
=
1 (86)
Where:
W = economic impact per capita per day
P
baseline
= the average water price when there are no interruptions
Q
baseline
= the average amount of water consumed when there are no interruptions
BWR = Basic Water Requirement, which represents the minimum amount of water per capita per day
required for drinking and basic sanitation
η = the price elasticity of the water demand, defined as =
, which measures the change in
the quantity demanded of water in response to a change in the price of water
Based on results obtained in different empirical studies, the residential price elasticity of the
demand for water is assumed to be equal to -0.41. The average price for water was obtained from a
survey conducted by the American Water Works Association (2015) that gathered data from 231
water utility services nationwide. This reports states that the “average” customer pays an average of
$34.28 per 1,000 cubic feet (7,480.52 gallons). This figure converts to $4.58 per 1,000 gallons,
which is the unit of measurement required for the equation. The average quantity of water consumed
was estimated to be 160 gallons per person per day, 23 and was obtained from the Residential end
23
91 gallons per capita from outdoor uses and 58.6 gallons per capita from indoor uses
September 2022 54
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uses of water study conducted by the AWWA Water Research Foundation (2016).
24
Finally, the BWR
is assumed to be equal to 6.6 gallons per person per day, as defined by Gleick (1996) and the
United Nations (UNESCO, 2006) as the minimum needed for drinking and basic sanitation. Most
research on basic water requirement is grounded in Gleick’s work, which recommends a value
between 30 and 50 liters per day of basic water need, which equates to 7.9 to 13.2 gallons per day.
Gleick recommended 5 liters per day for drinking water and 20 liters per day for sanitation. The
combined value of 25 liters per day equals 6.6 gallons per day, which is the value used in the
equation. Inserting the values into the equation, the average individual welfare loss equals $49.54
per capita per day.
According to the International Bottled Water Association, the average price of domestic bottled water
was $1.20 per gallon in 2014 (International Bottled Water Assoc., 2014) and has remained steady
due to industry competition since 2011. At 6.6 gallons per capita per day, this equates to $7.92 of
bottled water required to meet basic water requirements in a post-disaster situation.
The average individual welfare loss equals $49.54 per capita per day. Adding the cost to meet basic
water needs of $7.92, the economic impact for residential consumers was estimated as $57.46 per
capita per day.
2.11.3. Summary
Table 23 summarizes the values to measure the economic impact of loss of water service. It shows
that the economic impact of water service is $115.57 per person per day and is recommended
rounded to the nearest dollar, $116 per person per day. This represents an increase from $114 in
2019, $105 in 2016, $103 in 2013, and $93 in 2009.
Table 23: Economic Impact of Loss of Water Service per Capita per Day (in 2021 dollars)
Category
Economic Impact
Impact on Economic Activity
$58.11
Impact on Residential Customers
$57.46
Total Economic Impact (rounded to nearest dollar)
$116
24
The study collected data from 23 U.S. cities and included records from a random sample of 1,000 residential customers
for each of the cities
.
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2.12. Loss of Communications/Information
Technology Services
The methodology presented estimates the value of loss of communications and information
technology (IT). The loss of communications/IT service measures the impact to the economic activity
of the country and for residential customers.
2.12.1. Impacts to Economic Activity
The Communications importance factors were determined by subject matter experts at the
Department of Homeland Security Cybersecurity and Infrastructure Security Agency. Table 24 shows
the estimated impacts on economic activity per capita per day using GDP data and the economic
sector importance factors.
Table 24: Loss of Communications/Information Technology Service Impact to Economic Activity
Economic Sector
1
Communica-
tions/IT
Importance
Factor
2
GDP 2021
(in billions of
dollars)
1
GDP per
Capita per
Day
3
Economic
Impact per
Capita per Day
of Lost Service
in 2021
4
Dollars
Agriculture, Livestock
5
—
—
—
—
Mining
5
—
—
—
—
Utilities
0.90
$380.6
$3.137
$2.82
Construction
0.10
$958.8
$7.903
$0.79
Manufacturing – Nondurable
0.26
6
$1,177.6
$9.706
$2.52
Manufacturing – Durable Good
0.32
7
$1,385.7
$11.421
$3.65
Wholesale Trade
0.80
$1,383.0
$11.399
$9.12
Retail Trade
0.80
$1,385.5
$11.420
$9.14
Transportation, Warehousing
0.70
$642.6
$5.296
$3.71
Information
1.00
$1,300.7
$10.721
$10.72
Finance, Insurance, Real Estate,
Rental, and Leasing
0.80 $4,885.0 $40.263 $32.21
Professional & Business Services 0.80 $2,973.4 $24.507 $19.61
Education, Healthcare, Social
Assistance
0.70 $1,932.9 $15.931 $11.15
Arts, Entertainment, Recreation,
Accommodation, and Food Services
0.40 $839.6 $6.920 $2.77
September 2022 56
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Economic Sector
1
Communica-
tions/IT
Importance
Factor
2
GDP 2021
(in billions of
dollars)
1
GDP per
Capita per
Day
3
Economic
Impact per
Capita per Day
of Lost Service
in 2021
4
Dollars
Other Services, Except Government 0.50 $447.9 $3.692 $1.85
Government
0.80
$2,772.6
$22.852
$18.28
TOTAL
n/a
$22,465.9
$185.168
$128.34
1
Source: Bureau of Economic Analysis.
2
Source: Determined by subject matter experts at the DHS Cybersecurity and Infrastructure Security Agency
3
Population data from U.S. Census Bureau (December 31, 2021): 332,402,978.
4
Rows in this column were calculated as Communications/IT Service Importance Factor * GDP per Capita per Day.
5
— = Agriculture, livestock, and mining data are excluded from the analysis because they are not relevant for municipal
systems.
6
Weighting value of 0.26 averaged the eight sub-sectors with the following values: food/beverage/tobacco products
(0..30), paper products (0.20), printing and related support (0.25), chemical products (0.30), textiles/textile product mills
(0.20), apparel/leather/allied products (0.10), petroleum/coal products (0.30), and plastic/rubber products (0.40).
7
Weighting value of 0.32 averaged the nine sub-sectors with the following values: wood & furniture (0.10), nonmetallic
mineral products (0.20), primary metal manufacturing (0.20), fabricated metal products (0.20), machinery (0.30),
computer/electronic (0.50), equipment/appliances/etc. (0.50), transportation equipment (0.50), and miscellaneous
equipment (0.40).
2.12.2. Impacts to Residential Customers
As noted, the estimation of loss of communication/IT service calculated in the Economic Valuation
Proposal for a Loss of Communications document does not specifically address the residential WTP
assessment. By conducting a review of previously conducted WTP studies about Internet service, the
following studies were identified with associated findings:
Savage (2005) used a 2002 nationwide survey of United States residences and reported that
“Consumers are willing to pay up to $16.54 (per month) for a more reliable service. Speed is the
next most important attribute with a discrete improvement in speed valued at $11.37. Always-on
is the third most important attribute with a WTP of $5.07.”
Rosston, et al. (2011), conducted surveys in 2009 and 2010 and found that speed and reliability
are important features of Internet service. Results suggest the representative household would
be willing to pay $59 per month for a basic service, $79 per month for a reliable service, $85 per
month for a premium service, and $98 per month for a premium plus service.
In a 2018 study, Liua et al. (2018) found that WTP is a function of both download speed
(megabits per second [Mbps]) and latency (milliseconds [ms]). Results are presented for nine
download speeds, ranging from $17.63 per month for 10 Mbps up to $88.86 per month for 500
Mbps without latency.
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In a 2020 study, Lai, et al, evaluated the disparity between rural and urban areas and found that
the mean WTP for residents is between $0.06/Mbps and $0.10/Mbps per month for broadband.
The WTP for Internet service from these studies, as shown in Table 25, were converted to 2020
dollars. All of these values are in terms of monthly fees. The WTP ranges from less than $10 per
month for minimal service to more than $100 per month for extremely fast Internet service. This
suggests a WTP for basic Internet service of less than $2.00 per day. However, these studies were
designed to assess WTP for various levels of service rather than to assess WTP to avoid loss of
service.
Recently, the FCC established the Emergency Broadband Benefit
(EBB) program
(https://www.fcc.gov/emergency-broadband-benefit-media-resources) to assist households
struggling to afford Internet service during the COVID-19 pandemic. This program offers $50 per
month to qualified households, which equates to $1.64 per day. This value can be assumed to
represent the value of basic Internet service.
Table 25: Willingness to Pay for Monthly Internet Service
Author Year Attribute WTP per
Month ($)
WTP per Month
(2020 $)
WTP per Day
(2020 $)
Savage 2002 Service 5.07 7.17 0.24
More Speed 11.37 16.07 0.53
More Reliability 16.54 23.38 0.77
Rosston 2010 Basic 59.00 68.81 2.26
Reliable 79.00 92.14 3.03
Premium 85.00 99.13 3.26
Premium Plus 98.00 114.29 3.76
Yu-Hsin 2018 10 Mbps 17.63 17.85 0.59
25 Mbps 53.39 54.07 1.78
50 Mbps 55.76 56.47 1.86
75 Mbps 65.04 65.87 2.17
100 Mbps 69.90 70.79 2.33
150 Mbps 76.55 77.53 2.55
300 Mbps 82.51 83.56 2.75
500 Mbps 88.86 89.99 2.96
1000 Mbps 88.15 89.27 2.94
Lai 2020 10 Mbps 8.00 8.00 0.26
25 Mbps 20.00 20.00 0.66
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Author Year Attribute WTP per
Month ($)
WTP per Month
(2020 $)
WTP per Day
(2020 $)
50 Mbps 40.00 40.00 1.32
75 Mbps 60.00 60.00 1.97
100 Mbps 80.00 80.00 2.63
150 Mbps 120.00 120.00 3.95
300 Mbps 240.00 240.00 7.89
500 Mbps 400.00 400.00 13.16
2.12.3. Summary
Table 26 summarizes the values to measure the economic impact of loss of communications and
internet technology service. It shows that the economic impact of communications/IT service is
$129.98 per person per day and is recommended rounded to the nearest dollar, $130 per person
per day.
Table 26: Economic Impact of Loss of Communications/Information Technology Services per
Capita per Day (in 2021 dollars)
Category
Economic Impact
Impact on Economic Activity
$128.34
Impact on Residential Customers
$1.64
Total Economic Impact (rounded)
$130
2.13. Reduced Flood Insurance Administrative
Costs and Fees
A transaction cost is the fee for making an economic exchange. For flood insurance, transaction
costs include all of the material and labor costs associated with the general administration of a
policy and transaction costs to administer an insurance claim or Increased Cost of Compliance (ICC)
claim. As a result of a flood mitigation project, there may be an associated reduction in the number
of claims submitted to the National Flood Insurance Program (NFIP) for private and public properties
with a flood insurance policy (FEMA, 2011). The NFIP experiences a reduction in the cost to
administer a NFIP flood insurance policy when an insured property is acquired and maintained as
open space in perpetuity. Additionally, there is a reduction in claim fees if the resultant flood
damages are reduced through mitigation activities such as elevation or flood reduction projects.
Such savings in transaction costs is a project benefit. This benefit was first calculated in 2013 and
incorporated into the BCA Toolkit with Version 5.0. To be eligible for this benefit, the sub-applicant
must provide documentation that the structure being mitigated has an NFIP policy.
September 2022 59
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2.13.1. General NFIP Policy Administration
According to the October 2021 NFIP Flood Insurance Manual (FEMA, 2021), each NFIP policy
contains a “Federal Policy Fee” that the policyholder must pay on each new or renewal policy to
defray certain administrative expenses incurred in carrying out the NFIP. According to Appendix I of
the manual, a value of $47 is used for the Federal Policy Fee. This fee will be eliminated in the event
a flood acquisition or if relocation eliminates the need for an insurance policy.
2.13.2. NFIP Claim Fees
All NFIP Insurance Claim Fees are based on Building Replacement Value multiplied by the percent of
damage determined from Depth-Damage Functions (DDF). This benefit will be automatically added
to the DDF calculation if the “NFIP policy” box is checked. Table 24 shows the relationship between
claim/damage cost and claim-processing fees for claims after August 24, 2017, which is the most
recently published data (FEMA, 2019). If the mitigated structure has a NFIP policy, the new
methodology will assign a NFIP claim value from Table 24 based on the total damage value
(structure and contents) for each flood depth.
Table 27: Relationship Between NFIP Claim Fee and Damage Cost
1
Claim/Damage Cost Range Fee
$0.01 – $1,000 $525
$1,000.01 – $5,000 $800
$5,000.01 – $10,000 $1,035
$10,000.01 – $15,000 $1,175
$15,000.01 – $25,000 $1,275
$25,000.01 – $35,000 $1,475
$35,000.01 – $50,000 $1,750
$50,000.01 – $125,000 3.4% but not less than $1,750
$125,000.01 – $300,000 2.6% but not less than $4,250
$300,000.01 – $1,000,000 2.4% but not less than $7,800
$1,000,000.01 and higher 2.2% but not less than $24,000
1
Source: FEMA, 2019
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2.13.3. Increased Cost of Compliance Claim Administration
For an insured structure that experiences substantial damage
25
from a flood event, an ICC claim can
be filed. Like the claim administration, there is a transaction cost avoided when an insured structure
is mitigated. This benefit will be automatically added to the DDF calculation if the “NFIP policy” box is
checked in the BCA Toolkit. Table 25 shows the relationship between claim/damage cost and claim-
processing fees for claims after September 1, 2004, which is the most recently published data
(FEMA, 2019).
Table 28: Relationship Between ICC Claim Fee and Damage Cost
1
Claim/Damage Cost Range Fee
$0.01 – $1,000 $300
$1,000.01 – $2,500 $425
$2,500.01 – $5,000 $500
$5,000.01 – $7,500 $575
$7,500.01 – $10,000 $650
$10,000.01 – $15,000 $750
$15,000.01 – $25,000 $850
$25,000.01 – $30,000 $1,000
1
Source: FEMA, 2019
According to NFIP data received from FEMA (personal communication, November 10, 2011), from
January 2008 to June 2011, FEMA has closed 3,250 ICC claims averaging $21,879. According to
Table 25, this average claim amount results in a fee of $850. This value should be included in the
BCA Tool for substantially damaged structures with a NFIP insurance policy.
If the mitigated structure has a NFIP policy, the methodology is to add $850 for each flood depth
that calculates a substantial damage scenario.
25
As defined by the NFIP, “substantial damage” refers to a loss of at least 50 percent of the structure’s market value.
Structures that are substantially damaged must come into compliance with the local floodplain management ordinance,
which typically means the structure must be elevated or demolished. ICC funds can be used for these activities.
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3. References
Ahrens, M. (2016). Trends and patterns of U.S. fire loss. Fire Analysis and Research Division,
National Fire Protection Association.
Air Force Civil Engineering Support Agency, Office of the Air Force Fire Marshal. (1994). Air Force fire
protection cost risk analysis: Final report.
American Heart Association. (2013). Heart disease and stroke statistics—2013 update.
http://circ.ahajournals.org/content/early/2012/12/12/CIR.0b013e31828124ad.full.pdf
American Water Works Association (AWWA) and Raftelis Financial Consultants, Inc. (2015). 2014
Water and Wastewater Rate Survey.
AWWA Water Research Foundation. (2016). Residential end uses of water, Version 2.
Blackwell, T., & Kaufman, J. (2002). Response time effectiveness: Comparison of response time and
survival in an urban emergency medical services system. Academic Emergency Medicine,
9(4), 288–295.
Brozovic, N., Sunding, D., & Zilberman, D. (2007). Estimating business and residential water supply
interruption losses from catastrophic events. Water Resources Research, W08423.
Buchmueller, T., Jacobson, M., & Wold, C. (2005). How far to the hospital? The effect of hospital
closures on access to care, NBER Working Papers 10700. National Bureau of Economic
Research, Inc.
Bureau of Economic Analysis (BEA). (2022, March 30). Gross Domestic Product (Third Estimate),
Corporate Profits, and GDP by Industry, Fourth Quarter and Year 2021.
https://www.bea.gov/sites/default/files/2022-03/gdp4q21_3rd.pdf
,
Bureau of Labor Statistics (BLS), U.S. Department of Labor. (2022a). News release: employer costs
for employee compensation – December 2021.
http://www.bls.gov/news.release/pdf/ecec.pdf
BLS, U.S. Department of Labor. (2022b). CPI Inflation Calculator. U.S. Bureau of Labor Statistics.
http://www.bls.gov/data/inflation_calculator.htm
Capps, C., Dranove, D, & Lindrooth, R. (2006). Hospital closure and economic efficiency. Center for
Health Industry Market Economics, Kellogg School of Management, Northwestern University.
Chaiken, J., Ignall, E., & Walker, W. (1975). Deployment methodology for fire departments. The New
York City Rand Institute. R-1853-HUD.
September 2022 62
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Champ, P.A., Boyle, K.J., & Brown, T.C. (Eds). (2003). A primer on nonmarket valuation. Kluwer
Academic Publishers.
Carlsson, F., & Martinsson, P. (2004). Willingness to pay among Swedish households to avoid power
outages
- A random parameter Tobit model approach. Working Papers in Economics, No.
154, Gothenburg University.
Chang, S., Seligson, H., & Eguchi, R. (1996). Estimation of the economic impact of multiple lifeline
disruption: Memphis Light, Gas, and Water Division case study,” Multidisciplinary Center for
Earthquake Engineering Research (MCEER) Bulletin, Volume 10, Number 2.
Dalhuisen, J., Florax, R., de Groot, H., & Nijkamp, P. (2003). Price and income elasticities of
residential water demand: a meta analysis. Land Economics, 79:292–308.
Department of Defense (DOD). (2022). Per diem rates query. Defense Travel Management Office.
https://www.defensetravel.dod.mil/site/perdiemCalc.cfm
Devicienti, F., Klytchnikova, I., & Paternostro, S. (2004). Willingness to pay for water and energy: An
introductory guide to contingent valuation and coping cost techniques, Energy Working
Notes, No. 3. World Bank.
Department of Transportation (DOT). (2021). Treatment of the Value of Preventing Fatalities and
Injuries in Preparing Economic Analyses. (March)
https://www.transportation.gov/sites/dot.gov/files/2021-
03/DOT%20VSL%20Guidance%20-%202021%20Update.pdf
Erkut, E., Ingolfsson, A., & Erdogan, G. (2007). Ambulance location for maximum survival. Naval
Research Logistics, 55(1), 42–58.
Evans, W., & Owens, E. (2007). COPS and crime, Journal of Public Economics, 91(1–2), 181–201.
Federal Aviation Administration (FAA). (2008). Revised department guidance: Treatment of the value
of preventing fatalities and injuries in preparing economic analyses.
http://www.faa.gov/regulations_policies/policy_guidance/benefit_cost/
media/Revised%20Value%20Of%20Life%20Guidance%20Feburary%202008.pdf
FAA. (2007). Economic values for FAA investment and regulatory decisions, a guide.
http://www.faa.gov/regulations_policies/policy_guidance/benefit_cost/media/ECONOMICVA
LUESFORFAAINVESTMENTANDREGULATORYDECISIONS10032007.pdf
FAA. (2021). Benefit-Cost Analysis, Treatment of the value of life and injury in economic analysis.
https://www.faa.gov/regulations_policies/policy_guidance/benefit_cost/ (Section 2,
accessed September 21, 2021)
September 2022 63
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Federal Bureau of Investigation (FBI). (2016, June). 2014 crime in the United States. FBI Uniform
Crime Reporting (UCR) Program. https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-
u.s/2014/crime-in-the-u.s.-2014/tables/table-5
Federal Emergency Management Agency (FEMA). (2021). NFIP Flood Insurance Manual.
https://www.fema.gov/sites/default/files/documents/fema_nfip-flood-insurance-manual-
appendices_oct2021.pdf.
FEMA. (2019). National Flood Insurance Program claims manual. https://www.fema.gov/media-
library/assets/documents/180115
FEMA. (2011). Loss avoidance study: Riverine flood methodology report, Version 2.
FEMA. (2008a). Tornado safe room module methodology report. Version 4.0,
FEMA. (2008b). Earthquake non-structural full data module methodology report. Version 4.0,
FEMA. (2008c). Earthquake structural full data module. Version 4.0,
FEMA. (1991). Seismic Vulnerability and Impact of Disruption of Lifelines in the Conterminous United
States. Publication 224. https://permanent.fdlp.gov/lps117748/fema_224%5b1%5d.pdf
FEMA. (n.d.). Flood model HAZUS-MH technical manual. https://www.fema.gov/media-library-
data/20130726-1820-25045-8292/hzmh2_1_fl_tm.pdf
Federal Highway Administration (FHWA). (2017). Travel profile: United States: 2017 national
household travel survey. U.S. Department of Transportation.
https://nhts.ornl.gov/assets/2017_USTravelProfile.pdf
FHWA. (2009). Summary of travel trends: 2009 national household travel survey. U.S. Department of
Transportation. https://nhts.ornl.gov/2009/pub/stt.pdf
FHWA. (2007). Economic analysis primer: Benefit cost analysis. U.S. Department of Transportation.
http://www.fhwa.dot.gov/infrastructure/asstmgmt/
primer05.cfm
General Services Administration (GSA). (2022). Per diem rates. U.S. General Services Administration.
https://www.gsa.gov/travel/plan-book/per-diem-rates
Gleick, P.H. (1996). Basic water requirements for human activities: Meeting basic needs. Water
International, 21(1996), 83–92.
Goodman, D., & Mann, B. (2005). An empirical investigation of more police time: Crime and midsize
cities, 1990 v. 2000. Working Paper Series. Social Science Research Network.
September 2022 64
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Greenberg, M. (2005). Impact to New Jersey’s economy of the loss of electric power in New Jersey’s
urban industrial corridor. Center for Risk and Economic Analysis of Terrorism Events.
Greenberg, M, Mantell, N., Lahr, M., Felder, F., & Zimmerman, R. (2007). Short and intermediate
economic impacts of a terrorist-initiated loss of electric power: Case study of New Jersey.
Energy Policy. 35(722–733).
Hall, J., Jr. (2014). The total cost of fire in the United States. Fire Analysis and Research Division,
National Fire Protection Association.
Hogg, J. (1973). Losses in relation to the fire brigade’s attendance time. Fire Research Report No.
5173. Scientific Advisory Branch.
Houser, D. and Sunstein, Cass R. (2021). Best Practices for the Treatment of a Statistical Life in U.S.
Department of Homeland Security Regulatory Analyses. April 5, 2021.
https://www.dco.uscg.mil/Portals/9/VSL%20Final%20for%20DIST%205April21.pdf
Ignall, E., Rider, K., and Urbach, R. (1978). Fire severity and response distance: Initial findings. The
New York City Rand Institute.
Institute of Medicine of the National Academies. (2006). Hospital-based emergency care at the
breaking point. The National Academies Press.
International Bottled Water Association. (2016, July 14). How much does bottled water cost?
International Bottled Water Association.
http://www.bottledwater.org/economics/real-cost-
of-bottled-water
Jenkins, M., Lund, J. & Howitt, R. (2003). Using economic loss functions to value urban water scarcity
in California. Journal of the American Water Works Association, 95, 58–70.
Kunreuther, H., Cyr, C., Grossi, P., & Tao, W. (2006). Using cost-benefit analysis to evaluate
mitigation for lifeline systems. National Science Foundation.
Lai, J., Widmar, N.O., & Bir, C. (2020). Eliciting Consumer Willingness to Pay for Home Internet
Service: Closing the Digital Divide in the State of Indiana. Agricultural and Applied Economics
Association.
Lawton, L., Sullivan, M., Van Liere, K., Katz, A. & Eto, J. (2003). A framework and review of customer
outage costs: Integration and analysis of electric utility outage cost surveys. Office of Electric
Transmission and Distribution, U.S. Department of Energy.
https://emp.lbl.gov/sites/all/files/lbnl-54365.pdf
Layton, D., & Moeltner, K. (2005). The cost of power outages to heterogeneous households – An
application of the mixed gamma-lognormal distribution. In A. Alberini & R. Scarpa (Eds.),
September 2022 65
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Applications of Simulation Methods in Environmental and Resource Economics. Kluwer
Academic Press.
Levitt, S. (2002). Using electoral cycles in police hiring to estimate the effects of police on crime: A
reply. The American Economic Review, 92(4).
Levitt, S. (1998). The relationship between crime reporting and police: Implications of the use of
uniform crime reports. Journal of Quantitative Criminology, 14(1).
Liua, Y., Princea, J., & Wallsten, S. (2018). Distinguishing Bandwidth and Latency in Households’
Willingness-to-Pay for Broadband Internet Speed. Information Economics and Policy,
45(2018), 1-15.
McCollister, K., French, M., & Fang, H. (2010). The cost of crime to society: New crime-specific
estimates for policy and program evaluation. Drug Alcohol Dependence, 108(1-2), 98–109.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2835847
Miller, T.R., Luchter, S., & Brinkman, C.P. (1989). Crash costs and safety investment. Accident
Analysis & Prevention, 21(4), 303–15. http://www.ncbi.nlm.nih.gov/pubmed/2504178
Centers for Disease Control and Prevention (CDC). (2016). National vital statistics report, Volume 65,
Number 2. U.S. Department of Health and Human Services, National Center for Health
Statistics. http://www.cdc.gov/nchs/data/nvsr/nvsr65/nvsr65_02.pdf
CDC. (2011). Emergency department visits, National Center for Health Statistics.
http://www.cdc.gov/nchs/fastats/emergency-department.htm
n.a. (2016, June). National EMS Information System (NEMSIS). http://www.nemsis.org
New York City Area Consortium for Earthquake Loss Mitigation. (2003). Earthquake risks and
mitigation in the New York, New Jersey, and Connecticut region.
Office of Management and Budget (OMB). (2010). 2010 report to congress on the benefits and costs
of federal regulations and unfunded mandates on state, local, and tribal entities.
http://www.whitehouse.gov/sites/default/files/omb/legislative/reports/2010_Benefit_Cost
_Report.pdf
Press Ganey Associates, Inc. (2007). 2007 emergency department pulse report: Patient
perspectives on American health care.
Rice, D.P., MacKenzie, E.J., Jones, A.S., Kaufman, S.R., deLissovoy, G.V., Max, W., McLoughlin, E.,
Miller, T.R., Robertson, L.S., Salkever, D.S., & Smith, G.S. (1989). Cost of injury in the United
States: A report to Congress. University of California &Johns Hopkins University.
September 2022 66
Standard Economic Value Methodology Report Contract: HSFE60-16-D-0200; Task Order: 70FA6021F00000002
Robinson, L. A. (2008). Valuing mortality risk reductions in homeland security regulatory analyses.
U.S. Customs and Border Protection, Department of Homeland Security.
http://www.regulatory-analysis.com/robinson-dhs-mortality-risk-2008.pdf
Rose, A. (2004). Economic principles, issues, and research priorities in hazard loss estimation. In Y.
Okuyama and S. Chang (Eds.), Modeling the spatial economic impacts of natural hazards,
(pp. 13–36). Heidelberg: Springer.
Rosston, G., Savage, S., & Waldman, D. (2011, February). Economic and Business Dimensions
Household Demand for Broadband Internet Service: How Much are Consumers Willing to Pay
for Broadband Service?, Communications of the ACM, 54(2).
Savage, S.J., & Waldman, D. (2005). Broadband Internet Access, Awareness, and Use: Analysis of
United States Household Data. Telecommunications Policy, 29(615–633).
Smith, C.M., and Graffeo, C.S. (2005). Regional impact of Hurricane Isabel on emergency
departments in coastal southeastern Virginia. Academic Emergency Medicine, 12(12),
1201–1205.
Tomes, W. (2007). The correlation of fire department performance to improved public protection
classification ratings. Journal of Public Policy and Practice, 6(2).
The United Nations Educational Scientific and Cultural Organization (UNESCO). (2006). Water, a
shared responsibility: The United Nations World Water Development Report 2.
http://www.unesco.org/new/en/natural-sciences/environment/water/wwap/wwdr/wwdr2-
2006
The White House. (2003). Regulatory matters. Office of Management and Budget.
https://www.whitehouse.gov/omb/information-regulatory-affairs/regulatory-matters
U.S. Census Bureau. (2020, April). U.S. and world population clock. http://www.census.gov/popclock
U.S. Census Bureau. (2020). America’s Families and Living Arrangements: 2020 (Table AVG1).
https://www.census.gov/data/tables/2020/demo/families/cps-2020.html
U.S. Department of Agriculture (2022a). Official USDA Thrifty Food Plan: U.S. Average, March 2022.
Center for Nutrition Policy and Promotion. https://fns-
prod.azureedge.us/sites/default/files/media/file/CostofFoodMar2022Thrifty.pdf
U.S. Department of Agriculture (2022b). Official USDA Food Plans: Cost of Food at Home at Three
Levels, U.S. Average, March 2022. Center for Nutrition Policy and Promotion. https://fns-
prod.azureedge.us/sites/default/files/media/file/CostofFoodMar2022LowModLib.pdf
September 2022 67
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U.S. Department of Transportation (DOT). (2018). National transportation statistics. Bureau of
Transportation Statistics. https://www.bts.gov/topics/national-transportation-statistics,
Section 1.D – Travel and Goods Movement, Table 1-35.
U.S. Fire Administration/National Fire Data Center. (2006). Structure Fires Response Time. Topical
Fire Research Series, 5(7). Department of Homeland Security.
Valenzuela, T., Roe, D., Cretin, S., Spaite, D., & Larsen, M. (1997). Estimating the effectiveness of
cardiac arrest intervention. A logistic regression survival model.” Circulation, 96(10), 3308-
3313.
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4. Acronyms
AIS Abbreviated Injury Scale
AMI Acute Myocardial Infarction
ATC Applied Technology Council
BCA Benefit-Cost Analysis
BEA Bureau of Economic Analysis
BLS Bureau of Labor Statistics
BRV Building Replacement Value
CONUS Continental United States
CPI Consumer Price Index
CPR Cardiopulmonary Resuscitation
DDF Depth-Damage Functions
DOT Department of Transportation
ED Emergency Department
EMS Emergency Medical Service
FEMA Federal Emergency Management Agency
FAA Federal Aviation Administration
FBI Federal Bureau of Investigation
FHWA Federal Highway Administration
GDP Gross Domestic Product
GSA General Services Administration
HAZUS Hazards U.S.
Increased Cost of Compliance
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ICC
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ICU Intensive Care Unit
M&IE Meals and Incidental Expenses
MSA Metropolitan Statistical Area
NEMSIS National Emergency Medical Service Information System
NFIP National Flood Insurance Program
NFPA National Fire Protection Association
NHTSA National Highway Traffic Safety Administration
OCONUS Outside of the Continental United States
OMB Office of Management and Budget
UCR Uniform Crime Reporting
USDA United States Department of Agriculture
USDOT United States Department of Transportation
VSL Value of Statistical Life
WTP Willingness to Pay
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