U.S. ENVIRONMENTAL PROTECTION AGENCY (EPA) & MAJOR PARTNERS'
LESSONS LEARNED FROM IMPLEMENTING EPA's PORTION OF THE
AMERICAN RECOVERY AND REINVESTMENT ACT:
FACTORS AFFECTING IMPLEMENTATION AND PROGRAM SUCCESS
ECONOMIC IMPACTS OF LEVERAGED PROJECTS ON LOCALITIES
SEPTEMBER 2013
EPA- 100-K-13-010
PREPARED FOR
U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF THE CHIEF FINANCIAL OFFICER
WASHINGTON, DC
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ACKNOWLEDGEMENTS
This study could not have been possible without the help and cooperation of the many
U.S. Environmental Protection Agency (EPA) employees at Headquarters and Regional
offices who agreed to be interviewed, state staff and funding recipients who
participated in lively focus group sessions, and the many other EPA and state staff who
graciously provided answers to follow-up questions after the interviews and focus
groups were completed. The Science Applications International Corporation (SAIC) Team
appreciates the time given to share experiences beyond all the other audits and
questions. The recollections of those 'working in the trenches' during the intense period
of American Recovery and Reinvestment Act (ARRA) implementation were invaluable in
this study.
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TABLE OF CONTENTS
EXECUTIVE SUMMARY 1
PURPOSE 1
METHODOLOGY 1
FINDINGS 2
SECTION 1. INTRODUCTION 3
1.1 BACKGROUND AND OBJECTIVES OF THIS STUDY 3
1.2 STUDY QUESTIONS 4
SECTION 2. METHODOLOGY AND DATA SOURCES 7
2.1 STUDY DESIGN 7
2.2 CASE STUDY SELECTION 8
2.3 CASE STUDY DATA COLLECTION 10
2.4 QUALITATIVE ANALYSIS-ENVIRONMENTAL AND HEALTH RISK REDUCTION BENEFITS 11
2.5 QUANTITATIVE ANALYSIS-REGIONAL ECONOMIC IMPACT MODELING 12
2.6 STUDY LIMITATIONS 13
SECTIONS. FINDINGS 15
3.1 RESULTS OF QUALITATIVE ECONOMIC AND ENVIRONMENTAL IMPACT REVIEW 16
3.2 RESULTS OF QUANTITATIVE REGIONAL ECONOMIC IMPACT MODELING 18
3.2.1 Variations in Local Expenditures 19
3.2.2 Variations in RIMS II Modeling Results 21
3.2.3 Variations in Funding Leverage 27
REFERENCES 29
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TABLES
TABLE 1. STUDY QUESTIONS AND CORRESPONDING RESEARCH QUESTIONS 5
TABLE 2. CHARACTERISTICS USED TO CATEGORIZE ARRA PROJECTS FOR CASE STUDY SELECTION 9
TABLE 3. CASE STUDY PROJECT LOCATION AND DESCRIPTION 9
TABLE 4. CASE STUDY PROJECTS BY PROGRAM AND SELECTION CATEGORY 10
TABLES. DATA REQUIREMENTS AND SOURCES 11
TABLE 6. EXAMPLE OF MULTIPLIER ANALYSIS INPUTS AND OUTPUTS (SANTA CRUZ COUNTY) 13
TABLE?. STUDY QUESTIONS WITH BIG PICTURE FINDINGS 15
TABLES. MEDIUM-AND LONG-TERM BENEFITS OF PROJECT 17
TABLE 9. CASE STUDY FINANCIAL DATA($ IN MILLIONS) 18
TABLE 10. CASE STUDY PROJECT TYPE AND LOCAL EXPENDITURES ($ IN MILLIONS) 19
TABLE 11. VARIATIONS IN INDUSTRY-LEVEL RIMS II MULTIPLIERS 22
TABLE 12. DIRECT EXPENDITURES AND SUBSEQUENT INDIRECT AND INDUCED EXPENDITURES ($ IN MILLIONS)..23
TABLE 13. TOTAL PROJECT IMPACT RATIOS ($ IN MILLIONS) 24
TABLE 14. CASE STUDY DISTRIBUTION BY IMPACT RATIO AND LOCAL EXPENDITURE SHARE 27
BHiH^B
FIGURE 1. REGIONAL ECONOMIC IMPACT MODEL INPUTS AND OUTPUTS 8
FIGURE 2. DISTRIBUTION OF LOCAL EXPENDITURES BY MAJOR INDUSTRY GROUP 21
FIGURES. IMPACT RATIOS BY ARRA FUNDING SHARE, PROJECT SIZE, AND PROJECT TYPE 25
FIGURE 4. IMPACT RATIOS BY LOCAL DIRECT EXPENDITURE PROPORTION, PROJECT SIZE AND PROJECT TYPE .... 26
FIGURE 5. TOTAL ECONOMIC IMPACT BY LEVERAGE RATIOS, PROJECT SIZE AND PROJECT TYPE 28
F-ffBflBB^B
APPENDIX 1: WEST END DRINKING WATER RESERVOIR APPENDIX 1-1
APPENDIX 2: AMSTERDAM DRINKING WATER TREATMENT PLANT UPGRADES APPENDIX 2-1
APPENDIX 3: ATHENS DRINKING WATER DISTRIBUTION SYSTEM IMPROVEMENT APPENDIX 3-1
APPENDIX4: PINE BLUFFS METER INSTALLATION APPENDIX4-1
APPENDIX 5: CAPE CHARLES WASTEWATER TREATMENT PLANT UPGRADES APPENDIX 5-1
APPENDIX 6: CITY OF HEDRICK WASTEWATER TREATMENT PLANT UPGRADE APPENDIX 6-1
APPENDIX 7: GRANT COUNTY SANITARY SEWER DISTRICT EXTENSION APPENDIX 7-1
APPENDIX 8: SANTA CRUZ COUNTY REDUCTION OF NONPOINT SOURCE SEDIMENT AND
PESTICIDE POLLUTION APPENDIX 8-1
APPENDIX 9: ST. PAUL PORT AUTHORITY BEACON BLUFF ASSESSMENT AND CLEANUP APPENDIX 9-1
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EXECUTIVE SUMMARY
PURPOSE
To help the U.S. Environmental Protection Agency (EPA) better understand how the American Recovery
and Reinvestment Act (ARRA) funding was used to successfully leverage local resources to achieve short-
term and long-term economic benefits, Science Applications International Corporation (SAIC) studied the
impacts of several ARRA-funded projects. A crucial goal for ARRA enacted in 2009 was for local
communities to leverage funds in their local economies to stimulate economic activity during the
recession. To understand how particular programs leverage resources and expand local economic activity,
EPA sought to capture some of the successful examples of ARRA programs and funding recipients
leveraging resources and strengthening local economic activity.
EPA distributed the vast majority of its ARRA funding through programs designed to assist communities
making investments in infrastructure such as water treatment plant upgrades or pipeline replacements or
industrial site cleanups. These ARRA-funded investments potentially had two types of economic impact.
First, the infrastructure expenditures increased the demand for locally produced goods and services. This,
in turn, increased the demand for 'upstream' goods and services that produce the goods and services
needed by the infrastructure project. Thus, a dollar of infrastructure spending led to more than one dollar
of regional economic output. Second, the infrastructure investment may result in long-term economic
benefits by achieving environmental and/or development goals such as reducing health risks or
supporting local growth objectives.
Infrastructure investments such as water treatment plant upgrades to meet regulatory standards for
water quality can be expensive. For some municipalities, these kinds of infrastructure investments pose a
fiscal challenge when they have to raise fees and taxes to repay the capital construction loans or bonds.
ARRA funding provided an opportunity for these recipients to leverage local resources using federal
funding to implement such investments.
The study objectives are to quantitatively estimate the ratio of total regional economic growth relative to
the original project investment, called an 'impact ratio,' and to qualitatively address the long-term
benefits of the investment. To achieve these objectives, SAIC gathered information on nine ARRA-funded
projects in the Drinking Water State Revolving Fund (DWSRF), the Clean Water State Revolving Fund
(CWSRF) and the Brownfields program.
METHODOLOGY
For the qualitative analysis, SAIC used two information sources. SAIC interviewed local experts familiar
with the infrastructure projects and reviewed studies of economic benefits of environmental regulations
for projects that were part of a regulatory compliance plan.
For the quantitative analysis of regional economic impacts, SAIC collected detailed project expenditures
data and used the Regional Input-Output Modeling System (RIMS II) to estimate local economic impacts.
The RIMS II model was developed by the U.S. Bureau of Economic Analysis (BEA) to estimate the effect of
direct expenditures on indirect expenditures and induced expenditures in the region. Direct expenditures
are those paid to implement the project (e.g., laying a new pipeline), while indirect expenditures
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represent the additional economic impact of increases in the demand for 'upstream' goods and services
(e.g., from piping manufacturers or excavation companies), and induced expenditures represent the
additional economic impact of increased demand of consumer goods and services attributable to
'upstream' labor earnings. The longer each dollar of direct expenditure can remain within a local
community - going from vendor to vendor in the form of new revenue - the higher its regional impact will
be. This is the multiplier effect that the RIMS II estimates. The multiplier effect is limited by the tendency
for money to flow out of a region to pay for 'imported' goods and services, which is often called leakage.
These are not imports in the sense they are goods or services produced outside the United States; any
good or service that originates outside the local region is considered an import in RIMS II.
FINDINGS
Based on SAIC's interviews of individuals associated with nine ARRA projects and analysis of data, the
major case study findings regarding economic impacts are as follows.
The projects examined will provide the affected communities with a variety of medium-and long-term
environmental and economic benefits. SAIC's qualitative analysis shows that the environmental benefits
stem from meeting various regulatory compliance requirements. The benefits include human health risk
reductions and improvements in surface water quality from reduced nutrients, sediments and toxics in
wastewater discharges. The DWSRF projects will also reduce water use and/or energy production costs.
Both DWSRF and CWSRF projects will have some additional tangible financial benefits in the form of cost
savings for the utility and for customers. Two of the projects will also facilitate community economic
development objectives by increasing utility capacity to support residential and commercial growth. A
third project supports economic growth through the renewal and sale of urban land to industrial and
commercial businesses.
The case study project expenditures unambiguously achieved the objective of stimulating local
economies during the recession. The regional economic impact per dollar of project expenditure ranges
from $1.58 to $2.96 across the nine case study projects. These per-dollar estimates represent the
quantifiable direct, indirect and induced expenditures in the regional economies that can be attributed to
the infrastructure projects. These values are based on the impact ratios that SAIC estimated using RIMS II.
The regional economic impacts were higher for projects that could rely primarily on local sources of
goods and services. The projects that retained the highest proportion of direct expenditures in the local
community generally have higher impact ratios because the RIMS II multipliers applied to a majority of
total project expenditures. Projects that required imports of expensive materials tend to have lower
impact ratios. Because case study projects with treatment plant upgrades were more likely than other
project types to have expensive treatment equipment imports, these projects had lower regional
economic impacts.
These findings are subject to constraints that can lead to potential errors, uncertainties and biases. These
constraints arise from factors such as a having limited number of case studies, which restricts the extent
to which regional economic impact results can be generalized, and having a project mix that may be
atypical because of the 2009 to 2011 timeframe.
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SECTION 1. INTRODUCTION
In February of 2009, Congress passed ARRA, aimed primarily at making new jobs and saving old ones,
stimulating economic activity and long-term growth, and fostering accountability and transparency in
government spending. Of the $787 billion authorized in the Recovery Act, EPA was given $7.2 billion. EPA
distributed the majority of its ARRA funds to states in grants and contracts to support clean water and
drinking water projects, diesel emissions reductions, leaking underground storage tank cleanups,
Brownfields development and Superfund cleanups. This was a massive undertaking for EPA. The
administration of the funds, which were to be injected into the economy at an unprecedented pace,
required that EPA develop or revise policies, processes and automated information systems. In the Fall of
2011, EPA tasked SAIC, and its subcontractor Toeroek Associates, Inc., to design and conduct a study to
examine several components of EPA's implementation of ARRA. The SAIC Team studied three
management topics - Cost Estimating processes, Funds Management processes and Systems
enhancement and development. The Team also looked at three topics geared more towards outcomes
than management processes. These include the Green Project Reserve initiative, the use of ARRA funds to
spur Innovative Technologies and the use of ARRA funds to Leverage Local Economic Benefits. After
completion of the research phase, the SAIC Team produced a series of six reports, each covering one of
the six topics noted above. The Team also prepared a separate overarching summary report with an
Executive Summary, containing highlights of each of the six reports, as well as a description of the goals
and methodology for the entire study.
1.1 BACKGROUND AND OBJECTIVES OF THIS STUDY
A crucial goal for ARRA enacted in 2009 was for local communities to leverage funds in their local
economies to support economic activity during the recession. To understand how particular programs
leverage resources and expand local economic activity, EPA sought to capture and understand some
examples of ARRA programs and funding recipients leveraging resources and strengthening local
economic activity.
This chapter describes a review of the economic impacts of nine projects completed with ARRA funding
distributed through the CWSRF, DWSRF and Brownfields programs. These are projects that used ARRA
funding to leverage other resources to make infrastructure investments.
EPA awarded $7.2 billion of ARRA funding through programs such as the DWSRF, CWSRF and Brownfields
to contribute to the nation's economic stimulus and invest in environmental protection and infrastructure
that will provide long-term economic benefits (EPA, 2010a). Projects funded by these programs
potentially had two types of economic effects in a funding recipient's local economy that are relevant to
ARRA goals (EPA, 2010a). First, the federal funding affected the regional economy by increasing local
expenditures during the implementation phase (i.e., when the project expenditures occur). The phrase
'regional economic impact' refers to this type of effect.
The second type of effect is related to the goals of the funded projects. For many CWSRF and DWSRF
projects, the goals were to provide environmental and health risk reduction benefits. Brownfields projects
also achieved risk reductions and provided opportunities to revitalize areas affected by abandoned
infrastructure.
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SAIC conducted an analysis of both types of local economic impacts via a study of some examples of
funding recipients using ARRA funding to leverage other sources of financing to pay for large
infrastructure investments. In this context, leverage refers to the ratio of federal funding (including ARRA
funding) to other resources such as utility capital accounts, municipal bonds, or other grants or loans.
In general, infrastructure expenditures will have a regional economic impact regardless of whether federal
funds leverage money from other sources or pay for the entire project. Federal funding can be beneficial
by affecting the size or timing of these expenditures. When federal funds leverage state or local funds,
however, the project size can increase thereby leading to a larger overall impact. In addition, the
availability of federal funds might help borrowers implement project components that might otherwise be
unaffordable or deferred. The need for capital investments nationwide to replace aging infrastructure,
meet regulatory requirements and redevelop industrial areas is extensive. For example, according to a
recent infrastructure needs survey, the nationwide drinking water infrastructure need will cost $335
billion over the next 20 years (EPA, 2009); another study determined that the clean water infrastructure
need will cost $298 billion (EPA, 2010b). Despite these needs, the 2008 financial crisis and recession
resulted in many local governments canceling, delaying or scaling back projects because of budget cuts
and tight credit conditions (Copeland et al., 2009).
1.2 STUDY QUESTIONS
Table 1 presents the research questions for this study. SAIC developed these study questions to address
the factors motivating the study. Primarily, the questions pertain to the overall regional economic impacts
of the infrastructure investments and what factors such as project type or location might have affected
these impacts.
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TABLE 1. STUDY QUESTIONS AND CORRESPONDING RESEARCH QUESTIONS
OVERARCHING STUDY
QUESTIONS
What impact did the
selected projects have on
the local economies?
DETAILED RESEARCH QUESTIONS
What were the quantifiable direct, indirect and induced economic impacts of
the State Revolving Fund (SRF) or other program project on the regional
economy during the implementation phase (i.e., during the period when the
project funds were expended)?
What might the regional economic impacts of the project be during the post-
project period?
Do the quantitative impacts differ by technology or project type? How might
technology affect the relative success or effectiveness of ARRA funding on local
economic growth?
Do the quantifiable economic impacts vary by location (e.g., region or urban
versus rural)? How does location affect the relative success or effectiveness of
ARRA funding on local economic growth?
What kind of qualitative market and nonmarket impacts will the project have in
the intermediate- and long-term (e.g., environmental- or health-related
benefits)?
How do subsidy levels
affect the extent of local
impact?
Do the quantitative impacts differ by subsidy level?
How might the level and/or type of subsidy affect the relative success or
effectiveness of ARRA funding in terms of the regional economic impact?
How do leveraging levels
affect the extent of local
impact?
Do the quantitative impacts differ by degree of leveraging? How might different
leveraging schemes affect the relative success or effectiveness of ARRA funding
on local economic growth? (e.g., Did the presence of additional local or state
funds affect project type or project scope?)
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SECTION 2. METHODOLOGY AND DATA SOURCES
2.1 STUDY DESIGN
The study has two parts. The first part is a qualitative analysis of anticipated future economic and
environmental benefits, based on expert interviews as well as information in regulatory benefit studies.
The second part is a regional economic impact analysis, which is a quantitative analysis of how the project
expenditures - including the ARRA funding - affected total output in the local economy. Section 3.1
provides a summary of the case study results for the qualitative analysis while Section 3.2 provides the
results for this quantitative analysis. The qualitative and quantitative analyses for each of the case studies
are in the Appendices.
A regional economic impact analysis shows how expenditures for a project, such as constructing a new
wastewater treatment plant, can have a greater impact on total local output because of a multiplier
effect.1 This effect occurs because of linkages throughout the local economy-one industry's cost is
another industry's revenue. Therefore, increased direct expenditures made by the industry implementing
an ARRA-funded project lead to increased economic activity among its supplier industries and, in turn,
their supplier industries. The increased supplier or 'upstream' economic activity is called indirect
expenditures. The upstream economic activity includes wages to employees. When their expenditures
stimulate the local economy, it is called induced expenditures. The total impact of a project is the sum of
the direct, indirect and induced expenditures.
One widely used regional impact analysis model is the Regional Input-Output Modeling System (RIMS II)
developed by the U.S. Bureau of Economic Analysis (BEA). For example, the Housing and Urban
Development program used RIMS II multipliers to conduct a study of the regional economic impacts of
ARRA-funded Public Housing Authority (PHA) projects.2
Figure 1 shows that the RIMS II model uses a project's direct expenditures to generate estimates of
indirect and induced expenditures. It generates these estimates using industry-level multipliers. RIMS II
has multipliers for each of the 406 industries and these multipliers can vary by region. This variation
comes from differences in regional industrial mix as well as differences in the input-output linkages
among a region's industries.
1 In essence, a regional economic impact analysis is a comparison of two alternative scenarios -the local economy without the
project and the local economy with the project. The purpose of the comparison is to assess the net effect of the project in terms of
growth in economic output. The method in this study uses a modeling approach that estimates the change or growth in the
economy without having to estimate the level of economic output for both scenarios.
2 PHAs throughout the nation used $4 billion of ARRA funding to finance housing construction and renovation projects. A study of 20
PHAs that spent a total of $1.2 billion on capital investments, including $0.7 billion in ARRA funds that leveraged an additional $0.5
billion from other sources, estimated a total economic impact of almost $3.8 billion (Econsult Corporation, no date).
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FIGURE 1. REGIONAL ECONOMIC IMPACT MODEL INPUTS AND OUTPUTS
Expenditure Data
Direct
Expenditures
oject
Regional Economic
Impact Model
(RIMS II)
Model Estimates
Total Regional Economic Impact
Each multiplier indicates the aggregate indirect and induced spending expected to occur in a region for
each additional dollar of direct expenditure the industry receives.3 For example, a multiplier of 1.93 for
the construction industry means that each dollar of direct expenditure on goods and services provided by
this industry's businesses results in additional indirect and induced expenditures of $1.93. In the same
region, the wholesale industry may have a multiplier of 1.58, which means an additional dollar spent on
wholesale goods results in $1.58 of indirect and induced expenditures. Therefore, in this region, a project
that has a higher proportion of construction industry expenditures will have a larger overall economic
impact than one that has a higher proportion of wholesale industry expenditures.
To apply these multipliers, a project's direct expenditure data must be disaggregated into the various
industries that provide intermediate goods and services. Section 2.5 provides descriptions of these and
other data transformation needs for the RIMS II modeling effort.
2.2 CASE STUDY SELECTION
SAIC selected nine projects as case studies from the hundreds of projects funded through the CWSRF,
DWSRF and Brownfields programs. To obtain data for a variety of project types, SAIC categorized the
projects prior to selecting a sample of nine for study. Table 2 shows the characteristics used to categorize
the projects.
The multipliers come from an input-output matrix, which is a mathematical representation of expenditures through regressive
intra-industry relationships in which the direct expenditure industry's inputs are the outputs of several supplier industries and, in
turn, each supplier industry's inputs are outputs from several other industries. The multiplier is essentially a measure of how many
times a dollar of direct expenditure cycles as revenue through the local economy before it leaves via leakages such as businesses or
consumers purchasing goods from outside the region.
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TABLE 2. CHARACTERISTICS USED TO CATEGORIZE ARRA PROJECTS
FOR CASE STUDY SELECTION
CHARACTERISTIC
Program
Project size
Project type
Leverage- ratio A:B of
federal funds (A) to non-
federal funds (B) spent
on a project)1
VALUES
CWSRF
DWSRF
Brownfields
Small (<$1 million)
Medium ($1 to $10 million)
Large (>$10 million)
Varies by program
High leverage (>3:1)
Medium leverage (between
1:1 and 3:1)
Low leverage (< 1:1)
REASON TO CONSIDER CHARACTERISTIC
Regional economic impacts and benefits will differ by
type of program.
Regional economic impacts will differ by project size.
The types of expenditures and hence the types of
regional economic impacts will differ by project type.
Degree of leverage may affect post-project regional
economic impacts because leveraged funding will
presumably require repayment.
'The federal funding in this ratio includes ARRA funds as well as other federal sources such as the federal portion of traditional SRF
loans.
Table 3 provides a list of the selected case studies along with their locations and brief project descriptions.
Table 4 shows a summary of the case study programs, project sizes, project types and leverage intensities.
There are four case studies from each of the SRF funding programs, which account for the vast majority of
EPA's ARRA funding, and one from the Brownfields program. The DWSRF and CWSRF project types come
from the categories of projects most frequently funded (e.g., piping replacement/extensions and
treatment). Finally, project sizes vary, as well as leverage amounts.
TABLE 3. CASE STUDY PROJECT LOCATION AND DESCRIPTION
PROJECT DESCRIPTIOP
Drinking Water State Revolving Fund Projects
West End Drinking Water
Reservoir
Hagerstown,
Maryland
Partially replace 11 million gallon leaky, uncovered storage
reservoir with 6.8 million gallon storage tank.
Amsterdam Drinking Water
Treatment Plant Upgrades
Amsterdam,
New York
Implement multiple equipment upgrades to existing
conventional filtration plant to deal with drinking water
violations for disinfection byproducts and lead.
Athens Drinking Water
Distribution System
Improvement
Athens, Ohio
Replace frequently failing distribution main line and upgrade
related pump and electrical system.
Pine Bluffs Meter Installation
Pine Bluffs,
Wyoming
Replace failing manual meters with radio signal meters, add
meters to unmetered service lines, and move meter positions to
connection with main line to enhance leak detection.
Clean Water State Revolving Fund Projects
Town of Cape Charles
Wastewater Treatment Plant
Upgrades
Cape Charles,
Virginia
Retrofit existing wastewater treatment facility with advanced
treatment to reduce nitrogen and phosphorus concentrations in
discharge and also provide water suitable for nonpotable reuse
(e.g., irrigation).
City of Hedrick Wastewater
Treatment Plant Upgrades
Hedrick, Iowa
Construct new treatment plant to reduce ammonia discharges to
meet new permit limits, rehabilitate and increase lift station
capacity to prevent overflows during storm events, and replace
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P ROJ ECT N AM E LOCATIO N
Grant County Sanitary Sewer
District Extension
Santa Cruz County Reduction
of Nonpoint Source Sediment
and Pesticide Pollution
Grant County,
Kentucky
Santa Cruz
County,
California
PROJECT DESCRIPTION
conventional sludge drying bed with a reed bed.
Extend sewer service lines to new areas including a campground
with an aging treatment plant and a mobile home park with a
failing treatment plant.
Implement roadside integrated vegetation management plan to
reduce pesticide application, mowing and presence of invasive
species.
Brownfields Project
St. Paul Port Authority
Beacon Bluff Assessment and
Cleanup
St. Paul,
Minnesota
Conduct site assessment and cleanup activities for former 3M
production facilities and surrounding acreage and install 'Next
Generation' regional stormwater infiltration basin to treat runoff
from neighboring areas.
TABLE 4. CASE STUDY PROJECTS BY PROGRAM AND SELECTION CATEGORY
CATEGORY CATEGORY CATEGORY
Drinking Water State Revolving Fund Projects
West End Drinking Water Reservoir
Amsterdam Drinking Water Treatment Plant
Upgrades
Athens Drinking Water Distribution System
Improvement
Pine Bluffs Meter Installation
Storage
Treatment
Piping
Metering
Medium
Large
Small
Medium
High
High
High
High
Clean Water State Revolving Fund Projects
Town of Cape Charles Wastewater Treatment Plant
Upgrades
City of Hedrick Wastewater Treatment Plant
Upgrades
Grant County Sanitary Sewer District Extension
Santa Cruz County Reduction of Nonpoint Source
Sediment and Pesticide Pollution
Treatment
Treatment
Piping
Stormwater
Large
Medium
Medium
Small
Low
Medium
Medium
Low
Brownfields Project
St. Paul Port Authority Beacon Bluff Assessment
and Cleanup
Redevelopment
Medium
Medium
2.3 CASE STUDY DATA COLLECTION
The analysis method required data from a variety of sources. Table 5 provides an overview of the data
needs and sources. For each case study, SAIC obtained an expenditure breakdown that could be used to
disaggregate total project expenditures by the industries used to categorize the expenditures within RIMS
II. SAIC also obtained information to identify the local region for economic impact analysis. The region
included the county where the project was implemented and any surrounding counties that were major
suppliers of materials and labor. The Bureau of Economic Analysis developed custom RIMS II multipliers
for each of the nine project regions. SAIC also interviewed one expert per case study to learn more about
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the current and future impacts of the project on environmental quality and the local economy. Usually
this was a design engineer or utility official who understood the infrastructure needs that motivated the
project. Finally, to identify potential environmental benefits, SAIC consulted regulatory technical support
documents such as economic impact analyses for drinking water rules.
TABLE 5. DATA REQUIREMENTS AND SOURCES
TYPES OF REQUIRED DATA DATA SOURCES
Project expenditure breakdown
RIMS II multipliers for local region
Expert opinion
National environmental benefits information
Borrowers or finance documents, building contractors and RIMS II
industry list
Bureau of Economic Analysis
Local utility or engineering experts
EPA national regulatory technical support documents
2.4 QUALITATIVE ANALYSIS - ENVIRONMENTAL AND HEALTH RISK REDUCTION
BENEFITS
For the qualitative discussion of environmental and health risk reduction benefits, SAIC obtained
information from relevant regulatory analysis documents. These documents contained inventories of the
types of benefits that EPA attributes to actions taken to implement a particular rule. For example, the
Amsterdam, New York water treatment plant upgrades reduced levels of disinfection byproducts and lead
throughout its distribution system. Reducing the exposure to regulated disinfection byproducts can
reduce the risk of bladder, colon and rectal cancers, and also reduce the risk of reproductive and
developmental effects (EPA, 2006). In addition, improving corrosion control that reduces lead levels in
households that have lead service lines and plumbing can reduce risks of damage to the brain and
kidneys, and interference with the production of red blood cells that carry oxygen, especially among
infants, children and pregnant women (EPA, 2007). In addition to health risk reductions, the upgrades
may improve customer relations because the utility no longer has to notify its customers of health
standard violations.
Some case study projects do not have a direct link to a recent federal regulation. For example, many
projects are expenditures to replace aging and failing infrastructure such as water distribution pipes or
sewer collection mains. SAIC collected data for the qualitative benefits of these projects via expert
interview and literature review to identify the types of benefits that can be associated with these projects.
For example, replacement of aging drinking water pipes can have benefits associated with health risk
reduction (e.g., reducing infiltration of contaminated water into service lines) as well as improved water
delivery services (e.g., reduced risk of catastrophic pipe failure, temporary loss of water supply and risk of
flooding in low-lying areas).
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2.5 QUANTITATIVE ANALYSIS - REGIONAL ECONOMIC IMPACT MODELING
As a first step in the quantitative analysis, SAIC transformed the case study expenditure data into
quantities that match the data input requirements for a RIMS II modeling effort (BEA, 2012; BEA, 1997).
This required disaggregating expenditures into material purchases (i.e., revenues to supplier industries),
labor expenses (only those paid as direct expenditures in the primary industry; labor expenses incurred by
upstream industries remain in those industry totals because the supplier industry multipliers take induced
spending into account), and transportation expenses (these represent revenues to the transportation
industry).
In addition, SAIC identified the share of the direct expenditures that accrued to suppliers and businesses
located in the region. RIMS II framework presumes that the share accruing to non-regional sources
represents 'leakage,' i.e., dollars that leave the local region and therefore provide no additional indirect or
induced local economic benefit. For example, a water treatment project that includes a $1 million skid-
mounted membrane filter purchased from a vendor in another state will result in less regional economic
growth than a project that includes a $1 million sand filter built using locally sourced materials and
services such as excavation and concrete basin forming and pouring for the filter basin.
SAIC applied the region-specific RIMS II multipliers to the local expenditures using the recommended "bill-
of-goods" method (BEA, 2012; BEA, 1997). This method required that direct expenditures be
disaggregated by supplier industry category.
Table 6 shows an example analysis for Santa Cruz County, California. It shows that after allocating each
expenditure line item to an industry in RIMS II, there are two industries that received revenues for
providing inputs to the project (construction and professional and technical services). In addition, direct
expenditures were paid as wages for Santa Cruz County employees who worked on the project; RIMS II
also has a "households" multiplier for this category of direct expenditure. The multipliers range from 0.88
for households to 1.62 for both construction and professional and technical services. According to the
multipliers for the local region, the direct expenditures of approximately $0.84 million resulted in an
additional $1.17 million of indirect and induced expenditures in the county. The total economic impact of
direct, indirect and induced expenditures was approximately $2.0 million ($0.84 million + $1.17 million).
The impact ratio is approximately 2.4 ($2.0 million divided by $0.84 million).
September 2013 12
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TABLE 6. EXAMPLE OF MULTIPLIER ANALYSIS INPUTS AND OUTPUTS
[SANTA CRUZ COUNTY)
INDUSTRIAL SECTOR
Construction
$16,225
1.62
INDIRECT AND
INDUCED
IMPACTS
$26,349
Professional and Technical Services
$570,475
1.62
$921,371
Households
$253,000
0.88
$222,387
Indirect and Induced Impacts
$1,170,107
Add Total Project Value
$839,700
Total Output Impact
$2,009,807
These are Type II multipliers, which means the multipliers for the construction and professional and technical services industries
include indirect expenditures for the intermediate goods used by these industries as well as induced expenditures for all associated
labor expenses. The multiplier that applies to direct expenditures in the households category (i.e., incomes earned by the Santa Cruz
County employees) includes indirect household purchases of local goods and services as well as induced expenditures of wages earned
by employees of those local businesses.
2.6 STUDY LIMITATIONS
As with any study, there are some limitations to the findings based on available data, resource and time
constraints and statutory limitations (specifically, the Paperwork Reduction Act). These conditions can
cause estimation difficulties, uncertainties and biases, arising from a number of factors, including those
described below:
The limited number of case studies restricts the extent to which regional economic impact results
can be generalized. The distribution of expenditures and regional multipliers are unique to each
project and, therefore, the regional economic impacts vary by project.
The regional economic impacts of projects implemented in the 2009 to 2011 timeframe may not
be typical of such investments because the underlying conditions (e.g., relatively tight credit
markets and high unemployment) are not typical. Thus, the degree of ARRA leverage among the
funded projects may be higher than under more typical economic circumstances.
SAIC's ability to disaggregate case study expenditures by industry affects the reliability of the
RIMS II multiplier analysis. Some expenditure data were more detailed and, therefore, more
readily allocated. In some cases, SAIC needed to estimate material and labor shares of direct
expenditures.
The discussion of qualitative environmental benefits, including health risk reductions, reflects the
types of benefits expected to occur nationwide as a result of meeting a regulatory standard. The
benefits realized in the region affected by a particular project might not include all of the types of
benefits identified.
September 2013
13
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September 2013 14
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SECTION 3. FINDINGS
This section contains two subsections of study results - qualitative economic and environmental impacts
and quantitative regional economic impact modeling results. Table 7 below summarizes the big picture
findings for each study question. The big picture findings are based on information gathered from
interviews with project recipient staff (local utility and engineers) for the qualitative impacts and analysis
of modeling results (for the quantitative results). The sections of the report following Table 7 include a
thorough discussion of the findings.
Whatii
TABLE 7. STUDY QUESTIONS WITH BIG PICTURE FINDINGS
OVERARCHING STUDY QUESTION - LOCAL ECONOMIC IMPACTS
What impact did the selected projects have on the local economies?
DETAILED RESEARCH QUESTIONS
BIG PICTURE FINDINGS
Quantifiable project impacts. What were the quantifiable
direct, indirect and induced economic impacts of the SRF
or other program project on the regional economy during
the implementation phase (i.e., during the period when
the project funds were expended)?
The quantifiable impacts were greater than the
expenditures. The quantifiable regional economic
impact per dollar of project expenditure ranges from
$1.58 to $2.96 across the nine case study projects.
Identifiable longer-term economic impacts. What might
the regional economic impacts of the project be during
the post-project period?
There are a variety of post-construction regional
economic impacts. They include cost savings for utilities
and customers of reduced water use and/or energy
production costs and enhanced capacity for residential
and commercial growth because of increased water and
wastewater utility capacity.
Impact variability by project type. Do the quantitative
impacts differ by technology or project type? How might
technology affect the relative success or effectiveness of
ARRA funding on local economic growth?
Project type affected local expenditure share, which
affected overall impact. The projects that retained the
highest proportion of direct expenditures in the local
community generally have higher impact ratios. For
example, treatment plant upgrades required outside
expenditures on treatment equipment, which reduced
regional economic impacts.
Impact variability by location. Do the quantifiable
economic impacts vary by location (e.g., region or urban
versus rural)? How does location affect the relative
success or effectiveness of ARRA funding on local
economic growth?
Location affects the magnitude of impact. The rural
regions tended to have lower industry and households
multipliers, which reduced the overall impact of local
expenditures.
Identifiable qualitative impacts. What kind of qualitative
market and nonmarket impacts will the project have in the
intermediate- and long-term (e.g., environmental- or
health-related benefits)?
All projects had multiple qualitative impacts. Most of
the projects had identifiable health risk reductions or
environmental benefits in addition to long-term cost
savings.
September 2013
15
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How d<
How do subsidy levels affect the extent of local impact?
DETAILED RESEARCH QUESTIONS
BIG PICTURE FINDINGS
Effect of subsidy level on economic impact. Do the
quantitative impacts differ by subsidy level?
How might the level and/or type of subsidy affect the
relative success or effectiveness of ARRA funding in terms
of the regional economic impact?
Hn\A/ rlf
ARRA subsidy was a key financial feature. For five of
the projects, all of the ARRA funding was subsidized and
for a sixth the ARRA funding was a grant. These six
projects will benefit in the future from lower future
capital financing costs. Some of the projects that were
highly leveraged by subsidized ARRA funds would not
have proceeded without the funding.
How do leveraging levels affect the extent of local impact?
DETAILED RESEARCH QUESTIONS
BIG PICTURE FINDINGS
Effect of leverage on economic impact. Do the
quantitative impacts differ by degree of leveraging?
How might different leveraging schemes affect the relative
success or effectiveness of ARRA funding on local
economic growth? (e.g., Did the presence of additional
local or state funds affect project type or project scope?)
L
Degree of leverage may have increased level of
economic impact. More highly leveraged projects
generally had higher regional economic impacts,
although not always. There is not enough variation in
the small sample to assess the relative success of
different leveraging schemes. Most of the projects -
regardless of type - relied heavily on federal resources
including ARRA funding.
3.1 RESULTS OF QUALITATIVE ECONOMIC AND ENVIRONMENTAL IMPACT
The case study projects were implemented to meet a variety of infrastructure objectives. Consequently,
they will provide a wide variety of mid-term and long-term economic and environmental benefits,
ranging from health risk reductions to surface water quality improvements to economic growth support.
Table 8 provides a list of common benefit categories and identifies which projects will provide each
benefit. It also shows that each project provides multiple benefits. The Appendices provide detailed
discussion of the benefits for each case study.
September 2013
16
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TABLE 8. MEDIUM- AND LONG-TERM BENEFITS OF PROJECT
SURFACE SURFACE
HUMA WATER WATER WATER
N QUALITY- SAVIN
HEALT QUALITY- Toxic G
BENEFIT
AND/OR
SUPPO
RTS COMMERCI
LOCAL AL COST
Mil
Drinking Water State Revolving Fund Projects
West End (Hagerstown, Maryland)
Amsterdam (New York)
Athens (Ohio)
Pine Bluffs (Wyoming)
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
Clean Water State Revolving Fund Projects
Cape Charles (Virginia)
Hed rick (Iowa)
Grant County (Kentucky)
Santa Cruz County (California)
V
V
V
V
V
V
V
V
V
V
Brownfields Project
St. Paul (Minnesota)
V
V
V
All of the DWSRF projects will reduce water use and production costs such as energy costs. The non-
metering projects will also reduce health risks. The Amsterdam project reduces health risks by improving
the quality of the water distributed to customers, while the West End and Athens projects help maintain
water quality in the distribution system. The West End project provided covered water storage, which
prevents drinking water contamination that might occur in an uncovered storage reservoir. The Athens
project replaced underground distribution piping that regularly failed, which prompted boil alerts to
ensure drinking water safety. Although the Pine Bluffs project helped identify leaking pipes, the health
benefits, if any, are probably minor compared to the reductions in water loss.
The four CWSRF projects will improve surface water quality by reducing the discharge of nutrients,
sediments or toxic substances to surface waters. The St. Paul redevelopment project similarly improves
surface water quality by reducing sediments in stormwater runoff. It will also improve groundwater
quality by removing contaminated soils from the site and by improving stormwater infiltration treatment
via the 'Next Generation' infiltration basin.
Although most of the projects have economic benefits in the form of reduced future utility costs, four
projects explicitly support community economic growth objectives. Two of the treatment projects
(Amsterdam and Cape Charles) increased utility capacity, which will support long-term residential and
commercial growth. Both also indirectly benefit commercial customers. Amsterdam's improvements in
water quality benefit a local manufacturer of baby food. Cape Charles' new capacity for water reuse will
benefit entities that can use nonpotable water for irrigation such as golf courses. By reducing water loss,
the Pine Bluffs project helps extend water supply and provides capacity for growth.
September 2013
17
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The St. Paul Port Authority redevelopment project provides competitively priced development property
that is centrally located in the urban core.
3.2 RESULTS OF QUANTITATIVE REGIONAL ECONOMIC IMPACT MODELING
The case studies encompass a wide range of financial conditions. Project size varies substantially-from
less than $1 million to almost $19 million (see Table 9). Similarly, the ARRA funding proportion ranges
widely from 16% (Grant County) to 90% (Pine Bluffs). Finally, financing conditions range from highly
leveraged (i.e., a ratio of ARRA and other SRF funding to other funding of more than 10:1) to very low
leverage (i.e., a ratio of less than 0.4:1).
Almost all of the ARRA funds are fully subsidized via principal forgiveness. This type of subsidy will
continue to benefit the local economies following the infrastructure investment. The ARRA funds that
have the principal forgiveness subsidy do not have to be repaid to either the SRFs or the federal
government. Normally, a utility that borrows funds for infrastructure investments would raise customer
water or wastewater rates to repay the borrowed funds. Furthermore, repayments made to state or
federal agencies immediately leave the local economy. Therefore, principal forgiveness subsidies will
reduce the amount of future rate increases and help keep money in the local economy.
TABLE 9. CASE STUDY FINANCIAL DATA ($ IN MILLIONS)
PROJECT NAM
FUNDING
RA ARRA LEVERAGE
FUNDING SUBSIDY* RATIO*
West End Drinking Water Reservoir
Amsterdam Drinking Water Treatment Plant Upgrades
Athens Drinking Water Distribution System Improvement
Pine Bluffs Meter Installation
Town of Cape Charles Wastewater Treatment Plant Upgrades
City of Hedrick Wastewater Treatment Plant Upgrades
Grant County Sanitary Sewer District Extension
Santa Cruz County Reduction of Nonpoint Source Sediment
and Pesticide Pollution
St. Paul Port Authority Beacon Bluff Assessment and Cleanup
$6.64
$10.65
$0.88
$1.11
$18.90
$4.29
$1.93
$0.84
$2.59
$5.31
$5.08
$0.32
$1.00
$6.08
$0.90
$0.30
$0.23
$1.40
$0
$5.08
$0.32
$0.76
$6.08
$0.90
$0.16
$0.23
$1.40
6.3:1
132:1
10.7:1
9.5:1
0.5:1
2.6:1
1.1:1
0.4:1
1.6:1
Source: DWSRF, CWSRF and Brownfields databases.
Subsidy amount shown is principal forgiveness except for the St. Paul Port Authority funding, which is a Brownfields grant. The
West End project financing did not include any principal forgiveness; the ARRA funding is a 30-year loan with a 0% interest rate.
2The leverage ratio shows the ratio of all federal funding to other resources. Several case studies received non-ARRA federal
funding.
September 2013
18
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3.2.1 VARIATIONS IN LOCAL EXPENDITURES
Table 10 shows the total expenditures for which line-item details were available and the portion
determined to accrue to local businesses and employees. The proportion of local spending suggests a
major difference in local spending patterns across project types. For five of the case studies, local
expenditures exceed 90% of enumerated expenditures. This outcome is not surprising given the nature of
these projects, which use materials and construction activities that can be locally provided in many areas
(e.g., laying pipe or roadside maintenance). In some cases, even though the project occurs in a rural
county, the local area includes a nearby major urban area that provides materials and skilled workers. For
example, the Cape Charles project occurred in one of Virginia's Eastern Shore counties, but most of the
labor and materials came from the Virginia Beach-Norfolk-Newport News metropolitan area.
The three treatment projects and the metering project, however, have substantially lower local
expenditure shares - 39 to 66 percent, compared to 94 to 100 percent for the five other projects. The
difference can be attributed to relatively large purchases of specialized treatment or meter installation
equipment from vendors located outside the local area. Consequently, ARRA funding for projects
requiring equipment sold by only a few U.S. manufacturers is likely to have had a lower local economic
impact because a larger portion of direct expenditures - and the resulting indirect and induced
expenditures - accrue elsewhere.
TABLE 10. CASE STUDY PROJECT TYPE AND LOCAL EXPENDITURES ($ IN MILLIONS)
PROJECT NAME
St. Paul Port Authority Beacon Bluff Assessment
and Cleanup
Santa Cruz County Reduction of Nonpoint Source
Sediment and Pesticide Pollution
Athens Drinking Water Distribution System
Improvement
West End Drinking Water Reservoir
Grant County Sanitary Sewer District Extension
Town of Cape Charles Wastewater Treatment
Plant Upgrades
Amsterdam Drinking Water Treatment Plant
Upgrades
City of Hedrick Wastewater Treatment Plant
Upgrades
Pine Bluffs Meter Installation
PROJECT TYPE TOTAL
CATEGORY EXPENDITURES1
Redevelopment
Stormwater
Piping
Storage
Piping
Treatment
Treatment
Treatment
Metering
$1.60
$0.84
$0.82
$5.22
$1.93
$15.16
$10.65
$3.36
$0.97
LOCAL EXPENDITURES
$1.60
$0.84
$0.82
$5.12
$1.82
$9.97
$6.74
$2.12
$0.38
100%
100%
100%
98%
94%
66%
63%
63%
39%
Expenditures for which industry detail was available, which may represent a portion of overall project expenditures reported in
Table 9. The quantitative analysis cannot include expenditures for which industry detail is not available.
The total based on available expenditure data is sometimes less than the total project cost (shown as Total Project Funding in Table
9. Case Study Project Data ($ in Millions)) because SAIC did not receive expenditure details for all project costs. The quantitative
analysis is based on the available expenditure data because the distribution of missing expenditures across industries or between
local and nonlocal categories is not known.
September 2013
19
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The distribution of local expenditures across industries also varies across the projects. Figure 2 shows local
expenditures broken into the percent accruing to the following major groups:
Production industries (e.g., construction and manufacturing).
Trade and transportation industries.
Service industries (e.g., professional and technical services).
Households.
This breakdown shows that two projects had expenditures dominated by service industries: the Santa
Cruz County vegetation management project and the St. Paul Brownfields redevelopment project.
Industry expenditures for the other projects tended to be dominated by production industries and trade
and transportation industries.
Direct expenditures paid to households ranged from none for the St. Paul project to almost 45% for the
Amsterdam project. The St. Paul project expenditures did not include any St. Paul Port Authority labor
expenses; all funds were expended on contracted services for site assessment and cleanup. The
household expenditures for the Amsterdam project comprise labor expenditures for treatment plant
construction activities. As the next section shows, direct expenditures to households tended to decrease
the total economic impact of a project because household expenditures can introduce a lot of leakage to
a local economy. Consequently, the ability to disaggregate construction-related expenditures into
material and labor expenditures is an important factor that affects the accuracy of the impact estimate.
September 2013 20
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FIGURE 2. DISTRIBUTION OF LOCAL EXPENDITURES BY MAJOR INDUSTRY GROUP
100.0%
90.0%
u 80.0%
i_
3
% 70.0%
c
| 60.0%
LLJ
15 50.0%
o
^ 40.0%
o
| 30.0%
u
£ 20.0%
10.0%
0.0%
iProduction Industries BTrade/Transportation Service Industries BHouseholds
3.2.2 VARIATIONS IN RIMS II MODELING RESULTS
Table 11 provides the RIMS II industry-level multipliers by region for the industries occurring most
frequently across the case studies. These are the multipliers that SAIC applied to the industry-level
expenditures to estimate indirect and induced economic impacts. Detailed information on the
expenditure distributions by industry for each project is in the Appendices.
Within a region, the RIMS II multipliers in Table 10 vary across industries. In general, the industry
multipliers within a study region are often closely grouped within a range of ± 0.2. This is true of the
service industries as well as the production industries such as mining, utilities, construction and
manufacturing (except for the Pine Bluffs and Athens projects). The consistent outlier across case studies
is the multiplier for household expenditures. This multiplier applies to direct expenditures identified as
labor expenditures. Factors that reduce the household expenditure multiplier include taxes and savings as
well as purchases of consumer goods that are imported to the region from elsewhere. Because there is
little variability across industries, but substantial variability between all industries and households, the
proportion of direct expenditures allocated to the households category tends to have a large effect on the
overall economic impact of a project. Therefore, a higher proportion of funding going directly to recipient
labor expenses will decrease regional economic impacts.
September 2013
21
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TABLE 11. VARIATIONS IN INDUSTRY-LEVEL RIMS II MULTIPLIERS
INDUSTRY
AL,
FlSHIN
HUNTI
WHO TRANSP PROFESS ADMINI
LESAL ORTATIO IONAL & STRATIV
N& E&
-\D TECHNIC WASTE
WAREH AL
SERVICE
OUSING SERVICE s
West End
Amsterdam
Athens
Pine Bluffs
Cape Charles
Hedrick
Grant County
Santa Cruz County
St. Paul
1.60
-
-
-
1.57
1.50
-
-
1.69
-
-
-
1.72
1.62
1.71
-
-
1.93
1.87
-
-
1.92
1.77
1.87
1.62
2.01
1.74
1.68
1.59
1.76
1.67
1.61
1.73
-
-
1.58
1.65
0.14
0.95
1.72
1.54
1.64
-
-
1.82
1.83
1.35
1.52
1.97
1.74
1.89
-
-
1.66
1.78
1.46
1.84
1.88
1.61
-
1.62
1.99
1.69
-
1.44
-
-
-
-
-
1.86
0.95
0.96
0.73
0.88
1.09
0.88
1.02
0.88
-
Source: RIMS II industry-level multipliers from BEA. The Appendices contain multipliers for additional industries that were
affected in only one or two case studies.
'--' = no local direct expenditures tabulated for this industry so the multiplier is excluded from the analysis.
Within an industry, the variation of multipliers across the case study regions is relatively small with the
exception of the multipliers for the Athens Ohio project. The relatively low industry-level multipliers for
this region indicate that direct expenditures with local businesses tend to leak rapidly out of the local
region compared to the other regions. This is especially true of expenditures in the manufacturing
industry with a multiplier of 0.14, which indicates that most of the inputs for this industry come from
business located outside the local region.
Table 12 shows a summary of the RIMS II modeling results. It contains the local portion of direct
expenditures for each project and the estimates of indirect and induced expenditures from the RIMS II
model. It also shows project-specific multipliers that SAIC calculated by dividing the total indirect and
induced expenditure by the local direct expenditures for each project. The project-specific multiplier
shows the average impact of a dollar of local direct expenditure, given each project's unique distribution
of expenditures across local industries. Thus, it is an expenditure-weighted average of the RIMS II
industry-level multipliers for a given region (i.e., the multipliers in Table 11).
September 2013
22
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TABLE 12. DIRECT EXPENDITURES AND SUBSEQUENT INDIRECT
AND INDUCED EXPENDITURES ($ IN MILLIONS)
PROJECT NAME
OCAL DIRECT
EXPENDITURES (A)
INDIRECT AND
INDUCED
EXPENDITURES (B)
PROJECT-SPECIFIC
MULTIPLIER1
West End Drinking Water Reservoir
Amsterdam Drinking Water Treatment Plant
Upgrades
Athens Drinking Water Distribution System
Improvement
Pine Bluffs Meter Installation
Town of Cape Charles Wastewater Treatment
Plant Upgrades
City of Hedrick Wastewater Treatment Plant
Upgrades
Grant County Sanitary Sewer District Extension
Santa Cruz County Reduction of Nonpoint Source
Sediment and Pesticide Pollution
St. Paul Port Authority Beacon Bluff Assessment
and Cleanup
$5.12
$6.74
$0.82
$0.38
$9.97
$2.12
$1.82
$0.84
$1.60
$8.37
$9.24
$0.55
$0.56
$14.68
$2.98
$2.91
$1.17
$3.14
1.63
1.37
0.67
1.49
1.47
1.41
1.60
1.39
1.96
These are not the industry-level RIMS II multipliers. They are weighted averages across the industries that experienced increased
demand because of the project expenditures. Ratios shown may vary from detail because of independent rounding.
The project-specific multipliers in Table 12 show a wide range of local economic interdependences. At the
low end, the value of 0.67 for the Athens project indicates that a $1 of incremental direct expenditures
led to only $0.67 of additional indirect and induced expenditures. For this region, all direct expenditures
accrued to businesses in two rural counties. SAIC retained this as a definition for the local area to evaluate
an example of multipliers for highly localized direct expenditures in rural areas.
At the opposite end of the spectrum is the multiplier of 1.96 for the St. Paul Port Authority project. This
high value reflects a region that can produce much of the supply chain needed for the funded project. The
outcome may be typical of expenditures in a large urban area.
One result to note is that the project-specific multipliers in Table 12 tend to be smaller than the
construction industry multipliers in Table 11. For example, the project-specific multiplier for the Pine
Bluffs project is 1.49, which is lower than the region's construction industry multiplier of 1.76. If SAIC had
not used the bill-of-goods method shown above in Table 6 to estimate the regional economic impact and
had simply applied the construction industry multiplier to the local expenditures for the infrastructure
projects, the RIMS II output would generate higher impact estimates, which would have overstated the
impact of expenditure pattern for this particular project. This result illustrates the importance of using a
bill-of-goods approach.
Table 13 shows the combined effect of local expenditure shares and the regional multipliers. The
estimates of total project impact are the sum of the indirect and induced expenditures shown in Table 12
and the total project expenditures (from Table 10). The final column shows the impact ratio, which is the
September 2013
23
-------
ratio of total project economic impact to total project expenditures. These ratios demonstrate that the
case study project expenditures unambiguously achieved the objective of stimulating local economies
during the recession.
TABLE 13. TOTAL PROJECT IMPACT RATIOS ($ IN MILLIONS)
P^CTN..
West End Drinking Water Reservoir
Amsterdam Drinking Water Treatment Plant
Upgrades
Athens Drinking Water Distribution System
Improvement
Pine Bluffs Meter Installation
Town of Cape Charles Wastewater Treatment
Plant Upgrades
City of Hedrick Wastewater Treatment Plant
Upgrades
Grant County Sanitary Sewer District Extension
Santa Cruz County Reduction of Nonpoint Source
Sediment and Pesticide Pollution
St. Paul Port Authority Beacon Bluff Assessment
and Cleanup
TOTAL PROJECT
$13.59
$19.89
$1.38
$1.53
$29.84
$6.34
$4.85
$2.01
$4.74
$5.22
$10.65
$0.82
$0.97
$15.16
$3.36
$1.93
$0.84
$1.60
IMPACT RATio3
(A)/(B)
2.60
1.87
1.67
1.58
1.97
1.89
2.51
2.39
2.96
'Total Project Impact equals the sum of Total Expenditures and Indirect and Induced Expenditures.
Expenditures for which industry detail was available, which may represent a portion of overall project expend tures reported in
Table 9. The quantitative analysis cannot include expenditures for which industry detail is not available.
3 Ratios shown may vary from detail because of independent rounding.
Figure 3 shows the impact ratios along with three other project dimensions. The y-axis contains the scale
for the impact ratio, while the x-axis contains the scale for the ARRA proportion of total funding. The size
of the bubbles and data labels indicate overall project size, while the bubble color indicates project type -
blue for treatment projects, red for piping and storage projects, green for land use (vegetation and
redevelopment) projects and orange for the metering project.
September 2013
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FIGURE 3. IMPACT RATIOS BY ARRA FUNDING SHARE, PROJECT SIZE,
AND PROJECT TYPE
^Treatment
^Piping/storage
^Land use
^Metering
20%
40% 60% 80%
ARRA Funding Share(%)
100%
120%
Piping and land use projects (red and green bubbles) tend to
outperform treatment and metering projects (blue and orange bubbles).
This display format shows that, of the nine case studies included in the study, the smaller piping and land
use (vegetation or redevelopment) projects (red and green bubbles) tend to have higher impact ratios,
regardless of the wide variations in ARRA funding shares; the one exception is the Athens project ($0.88),
which has the second lowest impact ratio. In contrast, the treatment plant projects (blue bubbles) have
ARRA funding shares in a narrower 20-to-50 percent range and had consistently lower impact ratios than
four of the piping and landscape projects. The metering project had a high ARRA funding share, but a low
impact ratio.
Figure 4 shows the same impact ratio and project type and size dimensions, but the x-axis now shows the
proportion of project direct expenditures spent in the local area. There are three distinct categories of
results.
First, there are four projects that have high local spending shares and impact ratios above 2.3. These
projects have aggregate multipliers in Table 12 that are in the 1.39 to 1.96 range, but the high local
expenditure share boosts the impact ratio above 2.30. The second category has one project with a high
local expenditure share, but an impact ratio well below 2.00. This is the Athens project, which has a very
low aggregate multiplier, as noted above.
September 2013
25
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FIGURE 4. IMPACT RATIOS BY LOCAL DIRECT EXPENDITURE PROPORTION,
PROJECT SIZE AND PROJECT TYPE
3.30
3.10
2.90
2.70
o
£ 2.50
-5
g.2.30
_E
2.10
1.90
1.70
1.50
$259
$1.93
iTreatment
IPipinglstorage
tLand use
t Metering
$0.84
$18.90
$1.11 $10.65
$0.88
0% 20% 40% 60% 80% 100%
Local Direct Expenditure Proportion (%)
120%
Piping and land use projects (red and green bubbles) tend to boost local economies
more than treatment and metering projects (blue and orange bubbles).
The third category includes the projects with local expenditure proportions below 80%. Despite having
aggregate multipliers in the 1.37 to 1.47 range, the impact ratios for the three treatment projects are
below 2.30 because of the low local expenditure shares. The metering project has a slightly higher
aggregate multiplier (1.49), but a smaller impact ratio because of the low local expenditure proportion.
Table 14 shows a summary of the case studies grouped by impact ratio and local expenditure share. This
table suggests that in the nine case studies included in this study, the impact ratio is highly correlated with
local expenditure share. The exception, however, is the Athens case study. None of the case studies were
in the fourth category: high impact ratio and low local expenditure share. This outcome is theoretically
possible, however. For example, the St. Paul case study has a high enough weighted average industry
multiplier (1.97) that the project impact ratio would exceed 2.30 even if the local expenditure share were
as low as 67%. Nevertheless, the results point to the importance of keeping a high proportion of direct
expenditures in the local economy to achieve a high overall impact.
September 2013
26
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TABLE 14. CASE STUDY DISTRIBUTION BY IMPACT RATIO AND LOCAL EXPENDITURE SHARE
HIGHER LOCAL EXPENDITURE SHARE
HIGHER IMPACT RATIO (>2.3) LOWER IMPACT RATIO (<2.3)
West End
Grant County
Santa Cruz County
St. Paul
Athens
LOWER LOCAL EXPENDITURE SHARE
no case study
Amsterdam
Pine Bluffs
Cape Charles
Hedrick
3.2.3 VARIATIONS IN FUNDING LEVERAGE
This section returns to the concept of how leveraging resources helped local economies during the
recession. To quantify the degree to which a project used federal funding to leverage other resources,
SAIC calculated a ratio of federal funding (ARRA funding and other federal sources) to local resources
funding the project. A higher leverage ratio means that more resources for the project are coming from
outside the local area in the form of ARRA funds or other SRF funds. A lower leverage ratio means that the
recipient has other financial resources such as annual maintenance funds for pipe replacement.
There are three observations to make about leverage based on the case study information. First, higher
leverage generally led to higher regional economic impacts. Figure 5 illustrates this outcome. The figure
shows four data dimensions for each case study: leverage ratio (shown on the x-axis), total economic
impact (shown on the y-axis), project size (bubble size reflects dollar value), and project type (bubble
color). For six of the nine projects, larger leverage ratios and higher total economic impacts are positively
correlated. These six projects include three types: two treatment projects (blue bubbles), two piping and
storage projects (red bubbles), and both land use projects (green bubbles).
There are three outliers. The first is a large $18.9 million treatment project that was not highly leveraged,
but had a larger economic impact than other projects with the same degree of leverage. The other two
are the small $0.88 million piping project and the $1.11 million metering project that were more highly
leveraged than the other projects that had economic impacts of similar sizes.
September 2013
27
-------
FIGURE 5. TOTAL ECONOMIC IMPACT BY LEVERAGE RATIOS,
PROJECT SIZE AND PROJECT TYPE
(/>
^Treatment
^Pipinglstorage
Land use
Metering
$0.00
0.1
1.0 10.0 100.0
Leverage Ratio (log scale)
1000.1
The second observation comes from an unusual ARRA funding condition. Usually, high leverage has a
disadvantage of creating high indebtedness, which can increase future debt-servicing costs. For most of
these projects, however, the ARRA funding was subsidized by principal forgiveness. This condition means
that some portion of the loan principal does not need to be repaid. For five of the projects, all of the ARRA
funding was subsidized; for a sixth project, the ARRA funding was a grant, which has the same effect as
principal forgiveness. Thus, the presence of principal forgiveness results in the opposite outcome - more
highly leveraged projects benefit from lower future capital financing costs.
The third observation is that the ability to leverage local resources with federal funding was either an
important catalyst in moving projects forward during the recession or a major contributor to the project
existing at all. For example, ARRA funding and additional SRF funding accounted for almost all the cost of
the Amsterdam treatment plant and more than 70% of the Hedrick treatment plant cost. These cities
might have had to choose different regulatory compliance strategies if they had to rely solely on local
financing. The Santa Cruz County Integrated Vegetation Management project was placed on hold because
the recession depleted the expected funding from California, but ARRA funding allowed Santa Cruz
County to complete the project (Project Manager, Santa Cruz County, 2012). For the St. Paul
redevelopment project, ARRA funding was critical to keep the redevelopment project going forward and
provided a "big shot in the arm" for the regional economy (Vice President of Redevelopment, St. Paul Port
Authority, 2012).
September 2013
28
-------
Bureau of Economic Analysis. Regional Multipliers: A User Handbook for the Regional Input-Output
Modeling System (RIMS II). Washington, DC: U.S. Department of Commerce. 1997.
Bureau of Economic Analysis. RIMS II: An Essential Tool for Regional Developers and Planners.
Washington, DC: U.S. Department of Commerce. 2012.
C. Copeland, L Levine, W. Mallett, and N. Carter. "The Role of Public Works Infrastructure in Economic
Stimulus." Congressional Research Service 7-5700. 2009.
DiScenza, B., Chief Operator, Amsterdam Water Treatment Plant. 2012 Interview with SAIC and EPA.
September 25, 2012
Econsult Corporation. "Public Housing Stimulus Funding: A Report on the Economic Impact of Recovery
Act Capital Improvements." Report prepared for Council of Large Public Housing Authorities, the
National Association of Housing and Redevelopment Officials and the Public Housing Authority
Directors Association. Philadelphia, PA: Econsult Corporation.
EPA. "National Primary Drinking Water Regulations: Stage 2 Disinfectants and Disinfection Byproducts
Rule." 71 Federal Register 388 (January 4, 2006).
EPA. "National Primary Drinking Water Regulations for Lead and Copper: Short-Term Regulatory Revisions
and Clarifications." 72 Federal Register 57782 (October 10, 2007).
EPA. "Drinking Water Infrastructure Needs Survey and Assessment: Fourth Report to Congress." EPA 816-
R-09-001. 2009.
EPA. "American Recovery and Reinvestment Act of 2009, Environmental Protection Agency Recovery Act
Plan: A Strong Economy and a Clean Environment" 2010a.
EPA. "Clean Watershed Needs Survey: 2008 Report to Congress." EPA 832-R-10-002. 2010b.
Hilleman, M., Vice President of Redevelopment, St. Paul Port Authority. 2012 Interview with SAIC and
EPA. December 20, 2012.
Silva, C., Project Manager, Santa Cruz Department of Public Works. 2012. Interview with SAIC and EPA.
November 26, 2012.
September 2013 29
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September 2013 30
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APPENDIX 1: WEST END DRINKING WATER RESERVOIR
-------
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I. PROJECT DESCRIPTION
The City of Hagerstown is located about 75 miles northwest of the Washington, D.C. It is in Washington
County, which is located in the northwest corner of Maryland in close proximity to Pennsylvania and West
Virginia. The project area consists of eight counties, three of which are within the Hagerstown-
Martinsburg, MD-WV metropolitan statistical area (MSA).
In 2010, the Hagerstown public water system (PWS) received $5.3 million in ARRA funding to support the
planning, design and construction for a $6.6 million project to partially replace the West End Reservoir.
The 11 million gallon (MG) reservoir was built in 1906 for drinking water storage. Because it was not
covered, water stored in it did not meet drinking water standards after the covered storage requirement
became effective on April 1, 2009. Furthermore, the reservoir's condition had deteriorated beyond repair
from exposure to the elements and outdated plumbing. The ARRA funding helped construct the 6.8 MG
Hellane Park storage tank and appurtenances.
FIGURE 1. HELLANE PARK TANK UNDER CONSTRUCTION
Source: City of Hagerstown, photo of tank construction
II. REGION DESCRIPTION
The study region (Figure 2) contains eight counties in three states. The counties are: Frederick, MD;
Washington, MD; Adams, PA; Franklin, PA; Fulton, PA; Berkeley, WV; Jefferson, WV; and Morgan, WV. It is
assumed that the labor to construct the storage tank is from within the study region and that most of the
earnings are spent within the study region.
September 2013
Appendix 1-1
-------
FIGURE 2. AMSTERDAM DRINING WATER PLANT ECONOMIC IMPACT STUDY REGION
< " Mtlin. PA , '
r, PA
Huntingdon, PA
Jumafa. PA .
< t
AHegany. MD\
f I
«V -^
Bedford, PA
Legend
Study Area
Places
MD Counties
PA counties
VA Counties
WV Counties
Franklin, PA
Fulton, PA
%* Cumberland. PA
?
c Adams, PA.
V^
~
.- *
Morgan, WV
^ |S V / -."
^^ y* 4"
Washington', MD \~
IT f, H« ' 's
f
/. MD
Ffed&ick, VA
-: ^
-/ - ;
September 2013
Appendix 1-2
-------
POPULATION
Table 1 reports population data for the study region components. In aggregate, the population in the
communities within the study area grew by 1.7 percent annually between 2000 and 2010 largely driven by
the relatively high growth rates in Berkeley, Jefferson and Frederick Counties, which were 3.2 percent, 2.4
percent and 1.8 percent, respectively. Population in the remaining five counties increased at rates below
the average growth rate for the combined study region.
TABLE 1. POPULATION CHANGES IN SELECTED AREAS, 2000-2010
^mm
Frederick, MD
Washington, MD
Adams, PA
Franklin, PA
Fulton, PA
Berkeley, WV
Jefferson, WV
Morgan, WV
Total
POPULATION PERCENT CHANGE, 2000-2010
2000
196,563
132,051
91,457
129,745
14,296
76,357
42,485
15,021
697,975
2010
234,188
147,586
101,443
149,850
14,860
104,664
53,643
17,519
823,753
TOTAL
19.1%
11.8%
10.9%
15.5%
3.9%
37.1%
26.3%
16.6%
18.0%
ANNUAL
1.8%
1.1%
1.0%
1.5%
0.4%
3.2%
2.4%
1.6%
1.7%
LOCAL ECONOMY
Within the study region, retail trade employs a significant portion of the full- and part-time workers,
accounting for 12.2 percent of total employment in 2010 (Table 2). Next, state and local government and
health care and social assistance account for 9.9 percent and 9.2 percent of employment, respectively.
Employment trends varied substantially across sectors. Overall, there was an increase of approximately
44,999 jobs or 12 percent of 2001 employment. The manufacturing sector experienced the largest decline
with nearly 12,700 fewer employees in this sector and a reduction in the share of total employment of 4.5
percentage points (declining from 12.1 percent to 7.6 percent). The sector that experienced the next
largest gain between 2001 and 2010 was the professional and technical services sector, which added
more than 8,000 new employees for a 1.4% gain in the share of total employment (from 5.0 percent to
6.4 percent). The state and local government sector and administrative and waste services sector
experienced gains of 6,688 (19.9 percent) and 6,103 (47.2 percent), respectively.
September 2013
Appendix 1-3
-------
TABLE 2. EMPLOYMENT BY INDUSTRIAL SECTOR, 2001 AND 2010
iNDUST^SECTOR
otal Employment
Farm Employment
Forestry, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate, Rental and Leasing
Professional and Technical Services
Management of Companies
Administrative and Waste Services
Educational Services
Health Care and Social Assistance
Arts, Entertainment and Recreation
Accommodation and Food Services
Other Services
Federal Government, Civilian
Military
State and Local Government
360,259
10,085
491
301
338
27,301
43,605
8,457
46,889
10,043
6,560
16,890
9,789
18,045
800
12,937
5,854
33,734
6,462
22,781
20,752
9,463
4,087
33,535
404,263
9,128
1,272
420
161
26,278
30,919
7,267
49,227
11,482
6,215
22,171
15,041
26,058
1,701
19,040
6,693
37,106
10,125
28,032
22,857
12,846
3,888
40,223
PERCENT OF TOTAL
2001 2010
100.0%
2.8%
0.1%
0.1%
0.1%
7.6%
12.1%
2.3%
13.0%
2.8%
1.8%
4.7%
2.7%
5.0%
0.2%
3.6%
1.6%
9.4%
1.8%
6.3%
5.8%
2.6%
1.1%
9.3%
100.0%
2.3%
0.3%
0.1%
0.0%
6.5%
7.6%
1.8%
12.2%
2.8%
1.5%
5.5%
3.7%
6.4%
0.4%
4.7%
1.7%
9.2%
2.5%
6.9%
5.7%
3.2%
1.0%
9.9%
Source: BEA, 2012b
Note: Totals include employment that is not displayed in the sector breakout because of non-disclosure issues.
September 2013
Appendix 1-4
-------
Net income growth over the period was low. Real per capita income in the study area increased at
average annual rate of 0.5 percent between 2000 and 2010 (Table 3). The variation in per capita incomes
among counties within the study region is substantial with Frederick, MD having the highest 2010 real per
capita income of $46,057 and Fulton, PA having the lowest of $30,132. Growth in real per capita income
between 2000 and 2010 increased at the fastest rate in Washington, MD at average annual rate of 1.0
percent followed by Frederick, MD and Jefferson, WV at 0.8 percent and 0.6 percent, respectively. Income
growth in the remaining counties was essentially flat with growth in incomes ranging between a loss of
0.3 to a gain of 0.1 percent per year. For perspective, during the same period, per capita income in the
United States increased at average annual rate of 0.2 percent while the State of Maryland experienced an
annual average increase of 0.9 percent.
TABLE 3. REAL PER CAPITA INCOME FOR SELECTED AREAS, 2000 AND 2010
^^H COUNTY/CITY
Frederick, MD
Washington, MD
Adams, PA
Franklin, PA
Fulton, PA
Berkeley, WV
Jefferson, WV
Morgan, WV
Weighted Average
PER CAPITA INCOME
2000
$42,451
$32,715
$33,319
$32,660
$29,708
$30,496
$34,532
$30,550
$35,285
2010
$46,057
$36,140
$32,459
$32,898
$30,132
$30,644
$36,792
$30,829
$37,174
PERCENT CHANGE, 2000-2010 1
TOTAL
8.5%
10.5%
-2.6%
0.7%
1.4%
0.5%
6.5%
0.9%
5.4%
ANNUAL
0.8%
1.0%
-0.3%
0.1%
0.1%
0.0%
0.6%
0.1%
0.5%
Source: BEA, 2012b
Note: Values are in 2010 dollars.
III. QUANTITATIVE ANALYSIS INPUTS
The data collection efforts for this analysis focused on obtaining complete, accurate and descriptive data
related to the West End Reservoir Tank construction project. The Maryland Water Quality Financing
Administration provided a detailed invoice with line items for each component. The costs in the document
are bid data provided by the general contractor. Any changes between the bid costs and actual costs were
documented in change orders, which SAIC used to revise the original cost data.
SAIC made the following adjustments to transform the component cost data into inputs for the RIMS II
model (BEA, 2012c):
Assign each cost component to an industrial category.
Split item costs into material and labor categories.
Identify which material and labor line items were not local purchases.
Disaggregate local material costs into transportation costs, wholesaler costs and wholesaler
profit.
September 2013
Appendix 1-5
-------
For each line item of the expenditure data, SAIC assigned one of the 406 RIMS II industrial categories that
best matched the component description. SAIC excluded expenditures for nonlocal materials and labor
using information in the provided cost sheets. Where the source of purchase was not readily identified,
SAIC consulted Census County Business Patterns (U.S. Census Bureau, 2012) data to determine whether
there were businesses within the study region that could supply the item in question. This adjustment is
necessary because materials provided by vendors outside the region (e.g., specialized equipment)
represent leakage of dollars that are spent outside of the study region. All earnings accrue to local
workers. Finally, SAIC used National Income and Product Accounts (NIPA) data to disaggregate
expenditures on materials (i.e., purchaser cost) into cost components for wholesaler value, wholesaler
markup and transportation costs.
Table 4 displays the itemized expenditures for the reservoir tank construction project. After taking into
account change orders that reduced the construction cost to $5,218,597, the ARRA funding was sufficient
to cover the itemized expenditures. Of the total contract amount, $4,153,053 was used to purchase
materials and $1,065,544 went to labor. Not all of the purchases were made locally, with an estimated
$94,674 spent outside of the study region. The amount that is input into the model to calculate the
multiplier effects is $5,123,923.
TABLE 4. TOTAL EXPENDITURES FOR RESERVOIR TANK CONSTRUCTION
Wholesale Purchases
(includes transportation costs)
Household Income (labor)
Total
$4,153,053
$1,065,544
$5,218,597
LEAKAGE, SAVINGS, AND
INPUT INTO MODEL _ ...
OTHER NON-INPUTS
$4,058,379
$1,065,544
$5,123,923
$94,674
$0
$94,674
IV. REGIONAL ECONOMIC IMPACTS
The regional economic impacts measure the increase in total economic output for the study region as a
result of the West End Reservoir Tank construction spending attributable to ARRA funding. For the study
region, the total economic output increased by $13,587,424. The total project value is $5,218,597 and the
indirect and induced impacts are a combined $8,368,826, which implies a total impact-to-project value
ratio of 2.60:1 (i.e., each dollar spent on the project resulted in a regional economic impact of
approximately $2.60 including the initial expenditures and the indirect and induced demand changes).
The multipliers vary by industry with construction having the highest at 1.93 and the households sector
having the lowest at 0.95 (Table 5). A higher multiplier indicates that direct expenditures on the products
of that industry have a higher tendency to cycle throughout the regional economy multiple times via
input-output linkages in local industries. The household multiplier of 0.95 means that household
expenditures are more likely to leak outside the regional economy because of purchases of goods that are
manufactured elsewhere and services purchased from suppliers outside the region. In addition, the
household multiplier reflects leakages in the form of taxes and savings.
September 2013
Appendix 1-6
-------
TABLE 5. TOTAL OUTPUT BY INDUSTRY BASED ON RIMSII ANALYSIS
DUSTRIALSECT
Forestry, Fishing and Hunting
$13,052
1.60
$20,860
Mining
$21,465
1.69
$36,373
Construction
$1,743,120
1.93
$3,360,909
Manufacturing
$1,812,089
1.74
$3,151,775
Wholesale Trade
$218,522
1.58
$344,587
Transportation and Warehousing
$158,706
1.82
$289,311
Professional and Technical Services
$75,276
1.66
$125,279
Administrative and Waste Services
$16,149
1.69
$27,359
Households
$1,065,544
0.95
$1,012,373
Indirect and Induced Impacts
$8,368,826
Total Project Value (including direct impact)
$5,218,597
Total Output Impact
$13,587,424
V. OTHER ENVIRONMENTAL AND ECONOMIC BENEFITS
The Hagerstown PWS provides drinking water for approximately 90,000 customers. It serves over 28,000
residences and businesses throughout the city and county (City of Hagerstown, no date). The PWS
operates two conventional filtration plants (the R.C. Willson Water Treatment Plant and the W.M.
Breichner Water Treatment Plant) to treat surface water from two sources (the Potomac River and the
Edgemont Reservoir) (City of Hagerstown, 2012). The PWS has a peak production capacity of 15 million
gallons per day (MGD), but produces an average of 11 MGD. It uses eight water storage tanks and four
pump stations to distribute water throughout its 400 miles of water mains (City of Hagerstown, no date).
The ARRA-funded project partially replaced the 11 MG West End Reservoir with the 6.8 MG Hellane Park
Water Tank (Spiker, 2012). This project was needed to meet drinking water regulations pertaining to
covered storage of treated water. EPA required that all water storage facilities newly constructed after
February 16,1999 by large systems such as the Hagerstown PWS be covered under the Interim Enhanced
Surface Water Treatment Rule (EPA, 1998). EPA then extended similar requirements to pre-existing
storage facilities in the Long-Term 2 Interim Enhanced Surface Water Treatment Rule (EPA, 2006), which
included storage facilities such as the West End Reservoir. Systems with existing uncovered storage
needed to either provide covered storage or treat the water leaving the uncovered storage facility to
levels that meet the standards for Cryptosporidium parvum by April 1, 2009.
Because the West End Reservoir was not covered, treated water stored in it was at risk of re-
contamination from a variety of sources. Environmental risks included direct contact with water fowl, wet
deposition of air contaminants during precipitation events, and direct storm water runoff into the
reservoir. Man-made risks included illegal waste dumping, illegal swimming and intentional sabotage.
Because the enclosed Hellane Park Water Tank protects treated water against these risks, it prevents
exposure to contaminants that could cause a wide range of adverse health effects among customers.
September 2013
Appendix 1-7
-------
In addition to ensuring drinking water quality, the Hagerstown PWS may also benefit from reduced
operating costs. First, it avoids the added expense of having to re-treat all of the stored water to drinking
water standards prior to distribution. In addition, it may be able to reduce chemical addition to the
reservoir. The West End Reservoir had deteriorated to the extent that leakages led to daily losses of a
minimum of 0.3 MG treated water (Spiker, 2012). This is the equivalent of the amount of water used by
3,000 people assuming an average daily per capita consumption of 100 gallons. The Hellane Park Water
Tank ends a portion of this water loss. The related water savings reduces overall water production costs
including costs for treatment chemicals, energy and sludge residuals that are the result of conventional
filtration processes.
Water utility customers also benefit because the project funding included ARRA funds with a principal
forgiveness. The City of Hagerstown originally planned to issue bonds to finance a portion of the project.
After replacing that source with the ARRA funds, the City was able to reduce the long-term debt
associated with the overall Hagerstown PWS capital investment plan. This savings can either reduce utility
rates or it can allow the utility to incur debt for other projects that will continue to improve and expand
overall service. Michael Spiker, Director of Utilities for Hagerstown, says that by reducing the amount of
long term-debt incurred for the project, the favorable financing conditions have aided in allowing the
utility to establish a Repair, Renewal and Replacement (3R) Reserve Fund to address aging infrastructure.
September 2013 Appendix 1-8
-------
Bureau of Economic Analysis (BEA). 2012a. Data from U.S. Census Bureau accessed on BEA website on
November 9, 2012. http://www.bea.gov/regional/
BEA. 2012b. "Decennial Census, 2000 and 2010". "Local Area Personal Income & Employment" Interactive
tables on website. Accessed November 9, 2012. http://www.bea.gov/regional/
BEA. 2012c. RIMS II: An essential tool for regional developers and planners. Washington, D.C.; U.S.
Government Printing Office.
BEA. 2012d. RIMS II Multipliers 2002/2010: Table 1.5, Total Multipliers for Output by Detailed Industry,
Custom Study Region (Type II). November 2012.
City of Hagerstown. 2012. Consumer Confidence Report. Available online at
http://www.hagerstownmd.org/DocumentCenter/Home/View/1183, accessed November 2012.
City of Hagerstown. No date. Utilities Department slide presentation. Available online at
http://www.hagerstownmd.org/DocumentCenter/Home/View/1205, accessed November 2012.
City of Hagerstown. Photo of tank construction. Available online at
http://www.hagerstownmd.org/PhotoViewScreen.aspx?PID=42, accessed November 2012.
EPA. 1998. National Primary Drinking Water Regulations: Interim Enhanced Surface Water Treatment;
Final Rule. Federal Register 63 (69478), December 16,1998.
EPA. 2006. National Primary Drinking Water Regulations: Long Term 2 Enhanced Surface Water Treatment
Rule; Final Rule. Federal Register 71 (654), January 5, 2006.
Spiker, M. 2012. Personal communication with SAIC. November 30, 2012.
U.S. Census Bureau. 2012. County Business Patterns, 2010 for Frederick, MD; Washington, MD; Adams,
PA; Franklin, PA; Fulton, PA; Berkeley, WV; Jefferson, WV; and Morgan, WV counties.
http://www.census.gov/econ/cbp/, accessed August 15, 2012.
September 2013
Appendix 1-9
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September 2013 Appendix 1-10
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APPENDIX 2: AMSTERDAM DRINKING WATER TREATMENT PLANT UPGRADES
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I. PROJECT DESCRIPTION
The City of Amsterdam is located in Montgomery County, New York about 35 miles northwest of the City
of Albany. The project area consists of the Albany-Schenectady-Troy metropolitan statistical area (MSA)
plus Fulton County and Montgomery County.
In 2009, the City of Amsterdam received $10.6 million to support the planning, design and construction
fora major upgrade of the city's existing drinking water treatment plant in order to meet current
standards. The funding comprised $10.5 million in total SRF support including almost $5.1 million in ARRA
funding. Therefore, the nonfederal funding of $100,000 is highly leveraged.
The original drinking water plant, built in 1974, consisted of direct filtration. It could not meet the water
quality standards for haloacetic acid (HAAS) levels and total trihalomethanes (TTHM) (City of Amsterdam,
2009). Furthermore, the plant provided wholesale water to a purchasing system, the Town of Amsterdam,
which had a violation for TTHM in 2009 (Town of Amsterdam, 2010). Finally, in 2008, the plant was also
unable to meet standards for lead in the City of Amsterdam (City of Amsterdam, 2009).
The upgrade included a new sedimentation/clarification process to improve pre-filter solids removal, a
new ultraviolet (UV) disinfection system, a new carbon contact system for taste and odor control,
corrosion control treatment, and a multi-level intake system at the city's Steele Reservoir. In addition, the
existing degrading filter backwash water supply tank was demolished and replaced with a new bolted
steel tank. Other facility improvements include emergency standby generators, a new high-efficiency
space conditioning system, existing roof repairs, and upgrades to the existing computer control system.
The project also incorporated improvements to the city's raw water source including rehabilitation of a
portion of the raw water transmission main. The upgrades are expected to double the existing system's
capacity to 12 million gallons per day and improve drinking water quality for the city's approximately
19,000 residents (John M. McDonald Engineering, 2012).
II. REGION DESCRIPTION
The study region (Figure 1) consists of seven jurisdictions in upper New York, five of which are within the
Albany-Schenectady-Troy MSA. The City of Amsterdam is located in Montgomery County about 35 miles
northwest of the City of Albany. The market area reaches the boundaries of Massachusetts and Vermont,
but only includes counties within the State of New York to remain consistent with the economically
independent defined MSA.
September 2013
Appendix 2-1
-------
FIGURE 1. CITY OF AMSTERDAM PWS ECONOMIC IMPACT STUDY REGION
Legend
Study Area
Cry Boundary
County Boundary
Water Body
I
r
Mamrfton
Fulton
Potto*. VT
I
SarafOga
Montgomery
Benmngion. VT
Schoncclady
Rensselaer
Sctioftane
MA
>mtota
September 2013
Appendix 2-2
-------
The study region includes Fulton County and Montgomery County plus all of the Albany-Schenectady-Troy
MSA: Albany County, Rensselaer County, Saratoga County, Schenectady County and Schoharie County.
POPULATION
Table 1 reports population data for the study region components. In aggregate, the population in the
communities within the study area increased 4.8 percent between 2000 and 2010. The growth has been
strong in three of the counties with Saratoga, Schenectady and Rensselaer gaining above 4 percent.
Population growth in Fulton and Montgomery was essentially flat and Schoharie and Albany increased at
a modest 3.7 and 3.0 percent, respectively.
TABLE 1. POPULATION CHANGES IN SELECTED AREAS, 2000-2010
^J^^^^^H
Albany, NY
Fulton, NY
Montgomery, NY
Rensselaer, NY
Saratoga, NY
Schenectady, NY
Schoharie, NY
Total
POPULATION PERCENT CHANGE, 2000-2010
2000
295,106
54,976
49,605
152,684
201,514
146,581
31,514
931,980
2010
303,889
55,471
50,260
159,465
219,988
154,932
32,692
976,697
TOTAL
3.0%
0.9%
1.3%
4.4%
9.2%
5.7%
3.7%
4.8%
ANNUAL
0.3%
0.1%
0.1%
0.4%
0.9%
0.6%
0.4%
0.5%
Source: BEA, 2012a
LOCAL ECONOMY
Within the study region, local and state government is a significant portion of the full- and part-time
employment accounting for 17.7 percent of total employment (Table 2). Next, healthcare and social
assistance each account for 13 percent of employment, and retail trade accounts for 10.7 percent. The
presence of Albany, the state capital of New York, results in a higher concentration of state and federal
government employees within the Albany MSA.
Montgomery County is more reliant on manufacturing than other jurisdictions in the region. The
manufacturing sector accounts for more than 14 percent of countywide employment, but this sector
makes up less than 5 percent of total employment throughout the region. Furthermore, Montgomery
County accounts for more than 12 percent of regional employment in the manufacturing sector, despite
having less than 3 percent of regional jobs.
September 2013
Appendix 2-3
-------
TABLE 2. EMPLOYMENT BY INDUSTRIAL SECTOR, 2010
INDUSTRY SECTOR
Total Employment
Farm Employment
Forestry, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate, Rental and Leasing
Professional and Technical Services
Management of Companies
Administrative and Waste Services
Educational Services
Health Care and Social Assistance
Arts, Entertainment and Recreation
Accommodation and Food Services
Other Services
Federal Government, Civilian
Military
State and Local Government
27,093
256
(D)
(D)
(D)
1,815
1,938
(D)
3,472
1,586
469
883
1,316
971
182
816
217
4,118
451
1,323
1,774
121
89
4,347
MONTGOMERY "*%££*
22,485
774
79
101
(D)
937
3,327
(D)
3,102
1,445
329
541
410
437
303
291
89
4,321
201
1,352
941
118
80
2,743
535,201
2,832
(D)
(D)
(D)
25,036
21,544
13,514
56,071
(D)
10,261
32,183
20,884
41,455
6,877
20,647
22,537
67,842
10,795
31,509
25,259
7,240
3,117
96,340
584,779
3,862
79
101
-
27,788
26,809
13,514
62,645
3,031
11,059
33,607
22,610
42,863
7,362
21,754
22,843
76,281
11,447
34,184
27,974
7,479
3,286
103,430
Source: BEA, 2012b
Note: Totals include employment that is not displayed in the sector breakout because of non-disclosure issues.
1 Includes the following counties: Albany, Rensselaer, Saratoga, Schenectady and Schoharie.
September 2013
Appendix 2-4
-------
Real per capita income in the study area increased at average annual rate of 0.8 percent between 2000
and 2010 to $41,439 (Table 3). Albany has the highest per capita income at $45,764 whereas Montgomery
has the lowest at $31,887. Growth in per capita income between 2000 and 2010 increased at the fastest
rate in Schoharie County at average annual rate of 1.3 percent with all of the other counties increasing
between 0.7 and 1.0 percent except for Montgomery, which increased at a more modest 0.3 percent. For
perspective, during the same period per capita income in the United States increased at average annual
rate of 0.2 percent while the State of New York experienced an annual average increase of 0.9 percent.
TABLE 3. REAL PER CAPITA INCOME FOR SELECTED AREAS, 2000 AND 2010
COUNTY/CITY
Albany, NY
Fulton, NY
Montgomery, NY
Rensselaer, NY
Saratoga, NY
Schenectady, NY
Schoharie, NY
Weighted Average
PER CAPIT
$42,601
$30,659
$30,863
$35,124
$39,257
$37,426
$29,950
$38,082
A INCOME
$45,764
$33,997
$31,887
$37,956
$43,428
$41,025
$34,120
$41,439
PERCENT CHAP
7.4%
10.9%
3.3%
8.1%
10.6%
9.6%
13.9%
8.8%
JGE, 2000-2010
0.7%
1.0%
0.3%
0.8%
1.0%
0.9%
1.3%
0.8%
Source: BEA, 2012b
Note: Values are in 2010 dollars
QUANTITATIVE ANALYSIS INPUTS
The data collection efforts for this analysis focused on obtaining complete, accurate and descriptive data
related to the Amsterdam PWS upgrades. The New York SRF provided several scanned cost sheets with
line items for each component. The costs in the cost sheets are bid data provided by the general
contractor. Any changes between the bid costs and actual costs were documented in change orders,
which SAIC used to revise the original cost data.
SAIC made the following adjustments to transform the component cost data into inputs for the RIMS II
model:
Assign each cost component to an industrial category.
Split item costs into material and labor categories.
Identify which material and labor line items were not local purchases.
Disaggregate local material costs into transportation costs, wholesaler costs and wholesaler
profit.
For each line item of the expenditure data, SAIC assigned one of the 406 RIMS II industrial categories that
best matched the component description. SAIC excluded expenditures for nonlocal materials and labor
using information in the provided cost sheets. Where the source of purchase was not readily identified,
SAIC applied Census County Business Patterns data to determine whether there were businesses within
September 2013
Appendix 2-5
-------
the study region that could supply the item in question. This adjustment is necessary because materials
provided by vendors outside the region (e.g., filtration package plant) represent leakage of dollars that are
spent outside of the study region. Finally, SAIC used National Income and Product Accounts (NIPA) data to
disaggregate expenditures on materials (i.e., purchaser cost) into cost components for wholesaler value,
wholesaler markup and transportation costs.
Table 4 displays the total expenditures for the PWS Upgrade project. The total contract amounts to
$10,647,863 including change orders. ARRA funding accounted for nearly half of the value of the project
with $5,081,049 provided in the form of a principal-forgiven loan. Of the total contract amount,
$7,077,431 was used to purchase materials, $3,020,432 went to labor costs, and $550,000 was applied to
insurance, bonding, and mobilization. Not all of the purchases were made locally, with an estimated
$3,908,613 spent outside of the study region. The amount that is input into the model to calculate the
multiplier effects is $6,739,250.
TABLE 4. TOTAL EXPENDITURES FOR PWS UPGRADE
Wholesale Purchases
(includes transportation costs)
$7,077,431
UT INTO LEAKAGE, SAVINGS AND
MODEL OTHER NON-INPUTS
$3,718,817
$3,358,613
Household Income (labor)
$3,020,432
$3,020,432
$604,086
Other
$550,000
$0
$550,000
Total
$10,647,863
$6,739,250
$4,512,700
Note: Values are in 2008 dollars
IV. REGIONAL ECONOMIC IMPACTS
The regional economic impacts measure the increase in total economic output for the study region as a
result of the Amsterdam PWS upgrades. For the study region, the total economic output increased by
$19,889,366. The total project value is $10,647,863 and the indirect and induced impacts are a combined
$9,241,503, which implies a total impact-to-project value ratio of 1.87:1 (i.e., each dollar spent on the
project resulted in a regional economic impact of approximately $1.87 including the initial expenditures
and the indirect and induced demand changes). The region applied in this analysis represents the area
where the expenditures and labor were procured. However, many of the materials used in the project
were manufactured outside of the region. The smaller study region most likely results in a lower total
impact-to-project value ratio because spending leakages are likely given the size of the local economy.
The multipliers vary by industry with construction and finance and insurance sectors having the highest at
1.87 and the households sector having the lowest at 0.96 (Table 5). A higher multiplier indicates that
direct expenditures on the products of that industry have a higher tendency to cycle throughout the
regional economy multiple times via input-output linkages in local industries. The household multiplier of
0.96 indicates that household expenditures are more likely to leak outside the regional economy because
of purchases of goods that are manufactured elsewhere and services purchased from suppliers outside
the region. In addition, the households multiplier reflects leakages in the form of taxes and savings.
September 2013
Appendix 2-6
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TABLE 5. TOTAL OUTPUT BY INDUSTRY BASED ON RIMSII ANALYSIS
INDUSTRY SECTOR
Construction
Manufacturing
Wholesale Trade
Transportation and Warehousing
Finance and Insurance
Professional and Technical Services
Households
^^?
$228,708
$2,564,973
$286,555
$122,472
$17,390
$498,719
$3,020,432
1.87
1.68
1.65
1.83
1.87
1.78
0.96
°u;;cu;
$426,998
$4,303,207
$472,873
$223,861
$32,516
$887,266
$2,894,782
Indirect and Induced Impacts
Total Project Value (including direct impact)
Total Output Impact
$9,241,503
$10,647,863
$19,889,366
Note: Values are in 2008 dollars
Table 6 displays the total output attributable to ARRA funding. It is not known which items were
purchased with the ARRA funding. The simplifying assumption had to be applied that the ARRA-funded
proportion of the total project was spent in the same proportions as the non-ARRA funded portion. The
ARRA funding on the project results in an increase in total output of $9,490,997.
TABLE 6. TOTAL OUTPUT ATTRIBUTABLE TO ARRA FUNDING BASED ON RIMSII ANALYSIS
INDIRECT
OTAL OUTPUT
Total Output
$5,081,049
$4,409,948
$9,490,997
Note: Values are in 2008 dollars
V. OTHER ENVIRONMENTAL AND ECONOMIC BENEFITS
Built in the 1970s, the Amsterdam PWS was no longer able to comply with state and federal water quality
standards for TTHM and HAAS levels (City of Amsterdam, 2009). In addition, the PWS exceeded the lead
action level in 2008 (City of Amsterdam, 2009). The plant also supplies water to the Towns of Florida and
Amsterdam, which were found in violation of TTHM levels in 2009 (Town of Amsterdam, 2010). Finally,
Robert DiScenza (Chief Operator for the City of Amsterdam PWS) and Tom Bates (Project Engineer for
John M. McDonald Engineering, P.C.) noted that the PWS would have had difficulty meeting its original
design flow of 10 million gallons per day (MGD) and shortages of treated water had adversely affected
businesses in the region.
Following the PWS upgrades, "the treatment plant is phenomenal." according to Robert DiScenza. The
plant is fully compliant with all drinking water standards (John M. McDonald Engineering, 2012). In
meeting the standards, the PWS reduces the health risks associated with exposure HAAS, TTHM and lead.
September 2013
Appendix 2-7
-------
Exposure to disinfection byproducts such as HAAS and TTHM can increase the risk of bladder, colon and
rectal cancers, and also increase the risk of reproductive and developmental effects (EPA, 2006). In
addition, the new corrosion control system should reduce lead levels in households that have lead service
lines and plumbing. Reduced exposure to lead in drinking water reduces risks of damage to the brain and
kidneys, and interference with the production of red blood cells that carry oxygen, especially among
infants, children and pregnant women (EPA, 2007). The PWS also benefits from no longer having to notify
its customers of health standard violations.
Maintaining compliance with drinking water quality standards improves customer relations, and DiScenza
and Bates commented that it also benefits efforts to attract businesses to the region. In addition to
providing short-term benefits for the local economy, the PWS upgrades contribute to medium- and long-
term economic benefits and support future economic development. Local businesses will also benefit
from improved water quality. For example, in June 2010, Beech-Nut opened a new $124 million plant in
Montgomery County (Beech-Nut, 2012). This manufacturer initially requested up to 1.5 MGD of the
treated water for its baby food production plant, which would have been a difficult demand to meet prior
to the upgrades. Having a reliable source of high quality water reduces Beech-Nut's cost for on-site water
treatment. The plant may also result in indirect economic benefits in the local community if it fulfills its
plan to use locally sourced food products.
The capacity constraints of the original plant also adversely affected growth potential. With higher
production capacity, the municipalities served by the Amsterdam PWS can consider extending their
service areas, which encourages economic growth.
The upgrades also have some environmental benefits related to water efficiency and renewable energy
use. The PWS has lower filter backwash discharges because fewer solids reach the filters; the upflow
clarifiers remove solids prior to filtering. The PWS was also able to upgrade a hydro-powered turbine that
generates enough power to operate pumps, saving the utility up to $80,000 in avoided annual power
expenses (DiScenza and Bates, 2012).
Beech-Nut. www.beechnut.com/About%20Us/itn_newProductionFacility.asp. Accessed August 22, 2012.
Bureau of Economic Analysis (BEA). 2012a. Data from U.S. Census Bureau accessed on BEA website on
Augusts, 2012. http://www.bea.gov/regional/
BEA. 2012b. "Decennial Census, 2000 and 2010". "Local Area Personal Income & Employment" Interactive
tables on website. Accessed Augusts, 2012. http://www.bea.gov/regional/
BEA. 2012c. RIMS II: An essential tool for regional developers and planners. Washington, D.C.; U.S.
Government Printing Office.
BEA. 2012d. RIMS II Multipliers 2002/2008: Table 1.5, Total Multipliers for Output by Detailed Industry,
Custom Study Region (Type II). July 2012.
The Buzz: Business news- timesunion.com. blog.timesunion.com/business/ticker-beech-nuts-new-plant-
now-operating/20004. Accessed August 22, 2012.
September 2013 Appendix 2-8
-------
City of Amsterdam. 2009. 2009 Annual Report for 2008 Drinking Water Quality.
DiScenza, Robert and Tom Bates. Personal communication with SAIC, September 25, 2012.
EPA. 2006. "National Primary Drinking Water Regulations: Stage 2 Disinfectants and Disinfection
Byproducts Rule." 71 Federal Register 388 (January 4, 2006).
EPA. 2007. "National Primary Drinking Water Regulations for Lead and Copper: Short-Term Regulatory
Revisions and Clarifications." 72 Federal Register 57782 (October 10, 2007).
John M. McDonald Engineering, P.C. Project Descriptions, www.mcdonaldengineers.com/past-
projects/water. Accessed June 13, 2012.
Perez, D. 2010. "Amsterdam prepares for Beech-Nut's arrival." Your News Now, June 9, 2010.
http://capitalregion.ynn.com/content/headlines/507487/amsterdam-prepares-for-beech-nut-s-
arrival/. Accessed September 6, 2012.
Town of Amsterdam. 2010. Annual Drinking Water Quality Report for 2009.
September 2013 Appendix 2-9
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This page intentionally blank.
September 2013 Appendix 2-10
-------
APPENDIX 3: ATHENS DRINKING WATER DISTRIBUTION SYSTEM IMPROVEMENT
-------
This page intentionally blank.
-------
I. PROJECT DESCRIPTION
Athens County is located in eastern Ohio with the county seat in the City of Athens, which is located about
72 miles southeast of the City of Columbus, Ohio. The City received $320,000 in ARRA funding to repair
and replace part of the drinking water utility's distribution system. The ARRA funding included $320,000
of principal forgiveness. Additional funding for the project included $480,000 in other State Revolving
Fund financing and up to $75,000 in other resources to cover the initial project cost of $875,000.
In 2010, the ARRA-funded Curtis Street and Mulligan Road project replaced 3,700 feet of 8- and 12-inch
service lines that deliver water to a storage tower as well as distribute water in a residential area. The
project also replaced a pump station. The service line for the tower in the Curtis Street and Mulligan Road
area was old and prone to breakage. In addition, the electrical system that supports that part of the
distribution network is older and prone to electrical fluctuations. These fluctuations could alter pumping
pressures and cause water hammer - a surge in water pressure that can damage pipes. Thus, the project
addressed both sources of frequent line breaks, which were almost a monthly occurrence (Stone, 2012).
II. REGION DESCRIPTION
Based on a recommendation of staff at the Athens Department of Public Works, the study region (Figure
2) contains two counties in Ohio: Athens and Washington (Adine, 2012). This recommendation was based
on the belief that all labor and materials came from these counties. These two rural counties share a
contiguous boundary with West Virginia and are located within 1.5 hours of Columbus, OH. Because both
counties are rural and located near a major urban area, it is possible that some household expenditures
occur outside the region and, therefore, will not be captured in the analysis.
September 2013
Appendix 3-1
-------
FIGURE 1. ATHENS DRINKING WATER DISTRIBUTION SYSTEM
ECONOMIC IMPACT STUDY REGION
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September 2013
Appendix 3-2
-------
POPULATION
Table 1 reports population data for the study region components. In aggregate, the population in the
communities within the study area grew by 0.1 percent annually between 2000 and 2010. All of the
growth occurred in Athens County where population increased at an average annual rate of 0.4 percent.
Population declined, however, in Washington County at an average annual rate of 0.2 percent.
TABLE 1. POPULATION CHANGES IN SELECTED AREAS, 2000-2010
Athens, OH
Washington, OH
Total
Source: BEA, 2012a
POPULATION
PERCENT CHANGE, 2000-2010
2000
62,324
63,180
125,504
2010
64,774
61,716
126,490
TOTAL
3.9%
-2.3%
0.8%
ANNUAL
0.4%
-0.2%
0.1%
LOCAL ECONOMY
Within the study, region, state and local government accounts for over one-third of the full- and part-time
workers in 2010 (Table 2). This share remained almost constant over the period. Other key employment
industries in 2010 include retail trade (12.1 percent) and health care and social assistance (11.2 percent).
Together, these three industries account for almost 60 percent of total employment.
Overall, there was a net gain of 2,025 jobs or approximately 7.3 percent of 2001 employment.
Employment trends varied substantially across sectors. Employment growth rates ranged from net losses
above 22 percent in the farming industry and the forestry, fishing and hunting industry to net gains of
almost 50 percent in the real estate, rental and leasing industry. There is insufficient data to make a valid
comparison among sectors such as the construction and manufacturing sectors that do not have data
available for one of the years. The state and local government sector added the most employees between
2001 and 2010 with over 835 new employees (an increase of 8.6 percent).
September 2013
Appendix 3-3
-------
TABLE 2. EMPLOYMENT BY INDUSTRIAL SECTOR, 2001 AND 2010
Total Employment
Farm Employment
Forestry, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate, Rental and Leasing
Professional and Technical Services
Management of Companies
Administrative and Waste Services
Educational Services
Health Care and Social Assistance
Arts, Entertainment and Recreation
Accommodation and Food Services
Other Services
Federal Government, Civilian
Military
State and Local Government
27,834
709
119
83
90
1,144
1,111
293
3,279
331
387
640
568
935
(D)
(D)
291
2,975
495
2,262
1,338
253
181
9,755
29,859
548
92
(D)
83
(D)
(D)
400
3,609
305
401
674
847
1,061
86
613
335
3,339
440
2,502
1,372
265
174
10,590
PERCENT OF TOTAL
2001 2010
100.0%
2.5%
0.4%
0.3%
0.3%
4.1%
4.0%
1.1%
11.8%
1.2%
1.4%
2.3%
2.0%
3.4%
(D)
(D)
1.0%
10.7%
1.8%
8.1%
4.8%
0.9%
0.7%
35.0%
100.0%
1.8%
0.3%
(D)
0.3%
(D)
(D)
1.3%
12.1%
1.0%
1.3%
2.3%
2.8%
3.6%
0.3%
2.1%
1.1%
11.2%
1.5%
8.4%
4.6%
0.9%
0.6%
35.5%
Source: BEA, 2012b
(D) = not displayed because of non-disclosure issues.
Note: Totals include employment that is not displayed in the sector breakout because of non-disclosure issues.
September 2013
Appendix 3-4
-------
Real per capita income in the study area increased at average annual rate of 1.0 percent between 2000
and 2010 (Table 3). For perspective, during the same period per capita income in the United States
increased at average annual rate of 0.2 percent while the State of Ohio experienced an annual average
decrease of 0.3 percent. In 2010, Washington County has a substantially higher real per capita income of
$32,134 compared to Athens County's real per capita income of $26,296.
TABLE 3. REAL PER CAPITA INCOME FOR SELECTED AREAS, 2000 AND 2010
COUNTY/CITY
Athens, OH
Washington, OH
Weighted Average
H^
$23,477
$29,395
$26,456
»
2010
$26,296
$32,134
$29,144
PERCENT CHANGE, 2000-2010
TOTAL
12.0%
9.3%
10.2%
ANNUAL
1.1%
0.9%
1.0%
Source: BEA, 2012b
Note: Values are in 2010 dollars.
QUANTITATIVE ANALYSIS INPUTS
The data collection efforts for this analysis focused on obtaining complete, accurate and descriptive data
related to the Curtis Street and Mulligan Road construction project. The City of Athens Department of
Public Works provided a detailed invoice with line items for each component. The costs in the document
are bid data provided by the general contractor.
SAIC made the following adjustments to transform the component cost data into inputs for the RIMS II
model (BEA, 2012c):
Assign each cost component to an industrial category.
Split item costs into material and labor categories.
Identify which material and labor line items were not local purchases, if any.
Disaggregate local material costs into transportation costs, wholesaler costs and wholesaler
profit.
For each line item of the expenditure data, SAIC assigned one of the 406 RIMS II industrial categories that
best matched the component description. Where the source of purchase was not readily identified, SAIC
applied Census County Business Patterns (U.S. Census Bureau, 2012) data to determine whether there
were businesses within the study region that could supply the item in question. This adjustment is
necessary because materials provided by vendors outside the region (e.g., specialized equipment)
represent leakage of dollars that are spent outside of the study region. Finally, SAIC used National Income
and Product Accounts (NIPA) data to disaggregate expenditures on materials (i.e., purchaser cost) into
cost components for wholesaler value, wholesaler markup and transportation costs.
Table 4 displays the itemized expenditures for the water line and pump station construction. For the total
contract amount of $821,832, $528,129 was used to purchase materials and $293,703 went to labor.
Because all expenditures for this project are local, the entire project value of $821,832 is considered as a
September 2013
Appendix 3-5
-------
direct impact and is input into the model to calculate the multiplier effects.
TABLE 4. TOTAL EXPENDITURES FOR WATER LINE AND PUMP STATION
Wholesale Purchases
(includes transportation costs)
Household Income (labor)
Total
Note: Values are in 2010 dollars.
AMOUNT
$528,129
$293,703
$821,832
IV. REGIONAL ECONOMIC IMPACTS
The regional economic impacts measure the increase in total economic output for the study region as a
result of the Curtis Street and Mulligan Road construction spending attributable to ARRA funding. For the
study region, the total economic output increased by $1,375,984. The total project value is $821,832 and
the indirect and induced impacts are a combined $554,153, which implies a total impact-to-project value
ratio of 1.67:1 (i.e., each dollar spent on the project resulted in a regional economic impact of
approximately $1.67 including the initial expenditures and the indirect and induced demand changes).
The region applied in this analysis represents the area where the expenditures and labor were procured.
However, many of the materials used in the project were manufactured outside of the region. The smaller
study region most likely results in a lower total impact-to-project value ratio because spending leakages
are likely given the size of the local economy.
The multipliers vary by industry with the construction sector having the highest at 1.59 and the
manufacturing sector having the lowest at 0.14 (Table 5). A higher multiplier indicates that direct
expenditures on the products of that industry have a higher tendency to cycle throughout the regional
economy multiple times via input-output linkages in local industries. The manufacturing multiplier of 0.14
indicates that most of the materials used in this project use inputs that were manufactured outside of the
study region. This lack of second-order industry linkages results in almost no recirculation of initial
manufacturing expenditures within the study region. Additionally, the household multiplier of 0.73 means
that household expenditures are more likely to leak outside the regional economy because of purchases
of goods that are manufactured elsewhere and services purchased from suppliers outside the region. In
addition, the household multiplier reflects leakages in the form of taxes and savings.
September 2013
Appendix 3-6
-------
TABLE 5. TOTAL OUTPUT BY INDUSTRY BASED ON RIMSII ANALYSIS
DUSTRIAL SECT
Construction
REGIONAL
IRCHASES
$72,415
MULTIPLIE
1.59
$114,994
Manufacturing
$360,840
0.14
$52,010
Wholesale Trade
$108,974
1.35
$147,246
Transportation and Warehousing
$26,576
1.46
$38,861
Professional and Technical Services
$7,663
1.44
$11,052
Administrative and Waste Services
$17,000
1.36
$23,147
Households
$228,364
0.73
$166,843
Indirect and Induced Impacts
$554,153
Total Project Value (including direct impact)
$821,832
Total Output Impact
$1,375,984
Note: Values are in 2010 dollars. Totals may not add to detail because of independent rounding.
Table 6 displays the total output attributable to ARRA funding. It is not known which items were
purchased with the ARRA funding. The simplifying assumption had to be applied that the industry
allocation of the ARRA funds is the same as the overall project allocation shown in Table 5. Therefore, the
ratio of total impacts-to-ARRA funding is also the same. The ARRA funding on the project results in an
increase in total output of $535,772.
TABLE 6. TOTAL OUTPUT ATTRIBUTABLE TO ARRA FUNDING BASED ON RIMSII ANALYSIS
Total Output
$320,000
$215,772
OTAL OUTPUT
$535,772
Note: Values are in 2010 dollars
V. OTHER ENVIRONMENTAL AND ECONOMIC BENEFITS
The Athens Water Department has provided drinking water to residents since 1894 (AOWD, 2012).
According to Andrew Stone, Director of Public Works, the system has grown over time throughout a hilly
area, and as a result has a variety of pressure zones as well as aging infrastructure that deliver 3 million
gallons per day (MGD) to its customers. One of the older parts of the distribution system, in particular,
was a source of frequent pipeline breaks.
The highest point of the city has a small water storage tower, Longview Tower, which serves southwest
Athens. At 0.2 million gallons (MG), the tower capacity is small relative to demand and, therefore 'turns
over' several times a day. The service line for the tower in the Curtis Street and Mulligan Road area was
old and prone to breakage. In addition, the electrical system that supports that part of the distribution
network is older and prone to electrical fluctuations. These fluctuations affect pumping pressures, which
can cause water hammer - a surge in water pressure that can damage pipes. Thus, there are multiple
causes of frequent pipe breaks.
September 2013
Appendix 3-7
-------
These breaks are problematic. They can result in temporary service interruptions for up to 500
households. Depending on the extent of the pressure loss, the repair can be followed by mandatory boil
alerts. Finally, depending on the break location and severity, up to 0.3 MG of water can flood low-lying
residential areas when the water from the tower and supply lines empties through the break (Stone,
2012).
The ARRA-funded Curtis Street and Mulligan Road project replaced 3,700 feet of 8- and 12-inch service
lines for the tower and replaced the pump station. Thus, the project addressed both sources of frequent
line breaks, which were almost a monthly occurrence. The ARRA funding was important to successfully
implement this particular line upgrade because the cost exceeded the system's typical annual budget of
$0.4 million for line replacement (Stone, 2012). The system had nominated the project for the State
Revolving Fund for several years and finally received support including ARRA funding.
There are several different kinds of economic benefits because the project has ended pipe breaks in this
portion of the water distribution system. First and foremost, ending pipe breaks avoids any potential for
health risk of drinking water contaminated via the break. It also reduces the nuisance of complying with
boil alerts; secondary benefits of not having to issue boil alerts consist of improved customer relations
and reduced administrative costs of handling the alerts and any subsequent complaints about the break,
service loss, flooding and boil alerts. Additional benefits include avoided property damages to the
residences at risk of flooding caused by pipeline breaks. Finally, the system no longer incurs the repair
costs and saves on production costs because it avoids the large water losses that could accompany a pipe
break in this area. For example, if a break results in a loss of 0.3 MG of water, then the loss is equivalent
to 10% of one day's production. Given an annual electricity cost of $375,000 for plant operation and
distribution pumping, a break represents approximately $100 in wasted electricity expenditures (Stone,
2012).
The Curtis Street and Mulligan Road area of the distribution system serves a residential area. Therefore,
there are no known benefits in terms of current economic benefits for commercial or industrial customers
aside from the production cost savings associated with reductions in annual water loss. All customers,
however, potentially benefit from lower water rates for two reasons. First, the principal forgiveness
subsidy of the ARRA funding reduces the utility's debt and payback requirements, which lowers rates.
Also, lower production costs contribute to lower rates.
There are indirect environmental benefits associated with the savings in production costs that are
associated with avoiding large water losses. An energy loss on the order of $100 per break noted above
translates into 1400 kWh (kilowatt hour) of wasted treatment plant and pumping energy at an average
power cost of $0.07 per kWh. There are most likely environmental externalities associated with power
production (e.g., air emissions from coal or natural gas generation).
There are no other readily identifiable environmental benefits associated with the project (Stone, 2012).
The water loss associated with breaks is not known to enter surface waters soon enough for the chlorine
residual to have an ecosystem effect. The leaking water most likely infiltrates in pervious areas or is
sufficiently diluted in the separate storm water system before it reaches discharge points. If it did
discharge to surface waters, the chlorinated water could have adverse effect on aquatic ecosystems. For
example, a recent fish kill occurred after chlorinated water flushed from a line elsewhere in the system
entered a stream.
September 2013 Appendix 3-8
-------
Adine, J. P2012. Project and Development Manager, Engineering and Public Works. Personal
communication with SAIC. November, 2012.
Athens Ohio Water Department (AOWD). 2012. News About Your Water. [2011 Consumer Confidence
Report.] Available online at http://www.ci.athens.oh.us/DocumentCenter/View/782, accessed
November 2012.
Bureau of Economic Analysis (BEA). 2012a. Data from U.S. Census Bureau accessed on BEA website on
December 7, 2012. http://www.bea.gov/regional/
BEA. 2012b. "Decennial Census, 2000 and 2010". "Local Area Personal Income & Employment" Interactive
tables on website. Accessed December 7, 2012. http://www.bea.gov/regional/
BEA. 2012c. RIMS II: An essential tool for regional developers and planners. Washington, D.C.; U.S.
Government Printing Office.
BEA. 2012d. RIMS II Multipliers 2002/2010: Table 1.5, Total Multipliers for Output by Detailed Industry,
Custom Study Region (Type II). December 2012.
Stone, A. City Engineer and Director of Public Works, City of Athens. 2012. Interview with SAIC and EPA.
November 29, 2012.
U.S. Census Bureau. 2012. County Business Patterns, 2010 for Santa Cruz County, CA.
http://www.census.gov/econ/cbp/, accessed August 15, 2012.
September 2013
Appendix 3-9
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September 2013 Appendix 3-10
-------
APPENDIX 4: PINE BLUFFS METER INSTALLATION
-------
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I. PROJECT DESCRIPTION
The Town of Pine Bluffs is located in Laramie County, Wyoming. It is about 42 miles east of the City of
Cheyenne and lies adjacent to the Wyoming-Nebraska border. The project area consists of Laramie
County, which corresponds with the Cheyenne, WY metropolitan statistical area (MSA).
In 2009, the Town of Pine Bluffs received approximately $1.0 million in ARRA funding, most of which was
used for a meter replacement project. The town needed to replace substantially malfunctioning water
meters that were no longer working properly or were not used. The ARRA funding included $755,304 of
principal forgiveness and a 20-year loan of $251,768 with 0% interest.
The project consisted of replacing substantially malfunctioning or nonfunctioning water meters. Poor
meter reliability prevented the town from implementing conservation measures because it could not bill
customers based on water consumption. Instead, the town had to charge a flat rate for water, which
resulted in overconsumption. Facing water supply shortages, the town's goal for the project was to
improve conservation via a new use-based water billing system and leak detection monitoring. The
project qualified for Green Project Reserve funding because of the expected energy savings and water
savings associated with future reductions in unaccounted water losses and water conservation among
customers.
II. REGION DESCRIPTION
The study region (Figure 1) consists of Laramie County which is in the southeast corner of Wyoming. The
study area was limited to one county because the labor for the project and the locally supplied materials
are all confined to Laramie County. A large portion of the expenditures, however, paid for metering
materials that are manufactured outside of Wyoming. In addition, the wholesaler of the metering
equipment (the only major wholesaler of such equipment in the state) is located in Casper, which is more
than 200 miles northwest of the Town of Pine Bluffs in Natrona County, which is not part of the Cheyenne
MSA. Therefore, Casper is too far away to be considered part of the local economy for the study region.
September 2013
Appendix 4-1
-------
FIGURE 1. PINE BLUFFS METER REPLACEMENT PROJECT
ECONOMIC IMPACT STUDY REGION
Legend
S;udy Area
WYCOunhw
CO Count**
ME Count**
Slate Boundary
Cheyenne
Yoder
-fr.-,
, ,m-
Burn*
LB Grange
Pine BiuHi
:''
Nunn
September 2013
Appendix 4-2
-------
POPULATION
Table 1 reports population data for the study region. The population in Laramie County increased from
81,825 to 92,130 between 2000 and 2010. Thus, the annual average growth rate is 1.2 percent.
TABLE 1. POPULATION GROWTH IN LARAMIE COUNTY, 2000 TO 2010
^HJ^^^^^^H
Laramie, WY
POPULATION PERCENT CHANGE. 2000-2010
2000
81,825
2010
92,130
TOTAL
12.6%
ANNUAL
1.2%
Source: BEA, 2012a
LOCAL ECONOMY
Within the study region, state and local government accounts for 18.2 percent of the full- and part-time
workers in 2010 (Table 2). This share remained almost constant over the period. Other key employment
industries in 2010 include retail trade (11.0 percent) and health care and social assistance (7.8 percent).
Together, these three industries account for approximately 37 percent of total employment.
September 2013
Appendix 4-3
-------
TABLE 2. EMPLOYMENT BY INDUSTRIAL SECTOR, 2001 AND 2010
INDUSTRY SECTOR
Total Employment
Farm Employment
Forestry, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate, Rental and Leasing
Professional and Technical Services
Management of Companies
Administrative and Waste Services
Educational Services
Health Care and Social Assistance
Arts, Entertainment and Recreation
Accommodation and Food Services
Other Services
Federal Government, Civilian
Military
State and Local Government
53,220
950
(D)
172
112
3,202
1,683
921
6,937
2,385
(D)
2,197
1,847
2,236
395
2,467
315
3,159
805
3,793
2,611
2,393
3,753
9,542
61,984
913
(D)
(D)
138
3,620
1,618
1,009
6,814
3,439
1,233
3,399
3,286
2,807
129
2,518
571
4,811
771
4,236
2,640
2,682
3,556
11,251
PERCENT OF TOTAL
100.0%
1.8%
(D)
0.3%
0.2%
6.0%
3.2%
1.7%
13.0%
4.5%
(D)
4.1%
3.5%
4.2%
0.7%
4.6%
0.6%
5.9%
1.5%
7.1%
4.9%
4.5%
7.1%
17.9%
100.0%
1.5%
(D)
(D)
0.2%
5.8%
2.6%
1.6%
11.0%
5.5%
2.0%
5.5%
5.3%
4.5%
0.2%
4.1%
0.9%
7.8%
1.2%
6.8%
4.3%
4.3%
5.7%
18.2%
Source: BEA, 2012b
(D) = not displayed because of non-disclosure issues.
Note: Totals include employment that is not displayed in the sector breakout because of non-disclosure issues.
Over the decade, there was a net gain of 8,764 jobs. This represents an overall growth rate of more than
16 percent above 2001 employment or an annual average of 1.5 percent. Employment trends varied
substantially across sectors, however. Employment growth rates ranged from net losses of almost 4
percent in the farming and manufacturing sectors to net gains of approximately 13 percent in the
construction sector. The state and local government sector added the most employees between 2001 and
2010 with over 1,700 new employees (an increase of about 18 percent).
September 2013
Appendix 4-4
-------
Real per capita income in the study area increased at average annual rate of 1.9 percent between 2000
and 2010 (Table 3). For perspective, during the same period per capita income in the United States
increased at average annual rate of 0.2 percent while the State of Wyoming experienced an annual
average increase of 1.9 percent.
TABLE 3. REAL PER CAPITA INCOME GROWTH IN LARAMIE COUNTY, 2000 TO 2010
Laramie, WY |
PER CAPITA INCOME PERCENT CHANGE, 2000-2010
2000
$36,703
2010
$44,285
TOTAL
20.7%
ANNUAL
1.9%
Source: BEA, 2012b
Note: Values are in 2010 dollars.
QUANTITATIVE ANALYSIS INPUTS
The data collection efforts for this analysis focused on obtaining complete, accurate and descriptive data
related to the Pine Bluffs meter replacement project. The Town Engineer for Pine Bluffs provided a
detailed invoice with line items for each expenditure component. The expenditures in the document are
final invoice data provided by the general contractor.
SAIC made the following adjustments to transform the component cost data into inputs for the RIMS II
model (BEA, 2012c):
Assign each cost component to an industrial category.
Split item costs into material and labor categories.
Identify which material and labor line items were not local purchases.
Disaggregate local material costs into transportation costs, wholesaler costs and wholesaler
profit.
For each line item of the expenditure data, SAIC assigned one of the 406 RIMS II industrial categories that
best matched the component description. Based on final invoice information, SAIC identified which
expenditures represented income to businesses within the study region. The nonlocal expenditures
comprised purchases of metering equipment and related materials. A local contractor installed all of the
equipment, however. This adjustment is necessary because materials provided by vendors outside the
region (e.g., new metering equipment) represent leakage of dollars that are spent outside of the study
region. Finally, SAIC used National Income and Product Accounts (NIPA) data to disaggregate expenditures
on materials (i.e., purchaser cost) into cost components for wholesaler value, wholesaler markup and
transportation costs.
Table 4 displays the itemized expenditures for the metering replacement project. For the total contract
amount of $967,259, $816,714 was used to purchase materials and $61,885 went to labor. Because
approximately 39 percent of expenditures for this project are local, $375,777 is considered as a direct
impact and is input into the model to calculate the multiplier effects.
September 2013
Appendix 4-5
-------
TABLE 4. TOTAL EXPENDITURES FOR METER REPLACEMENT
Wholesale Purchases
(includes transportation costs)
Household Income (labor)
Total
Note: Values are in 2010 dollars.
$816,714
$61,885
$88,660
$967,259
INPUT INTO
$313,891
$61,885
$0
$375,777
LEAKAGE, SAVINGS AND
OTHER NON-INPUTS
$502,822
$0
$88,660
$591,483
IV. REGIONAL ECONOMIC IMPACTS
The regional economic impacts measure the increase in total economic output for the study region as a
result of the project spending attributable to ARRA funding. For the study region, the total economic
output increased by $1,526,860, which is all attributable to the ARRA funding. The total project
expenditure included in the analysis is $967,259 and the indirect and induced impacts are a combined
$559,601, which implies a total impact-to-project value ratio of 1.58:1 (i.e., each dollar spent on the
project resulted in a regional economic impact of approximately $1.58 including the initial expenditures
and the indirect and induced demand changes). The affected region in this analysis represents the area
where the locally produced materials and labor were procured. The metering materials, which accounted
for a large fraction of overall expenditures, were manufactured outside of the region. This metering
project had fairly large leakages because of the equipment purchases.
The multipliers vary by industry with the transportation and warehousing sector having the highest at
1.84 and the households sector having the lowest at 0.88. A higher multiplier indicates that direct
expenditures on the products of that industry have a higher tendency to cycle throughout the regional
economy multiple times via input-output linkages in local industries. The manufacturing multiplier of 0.95
indicates that there are limited second-order linkages (i.e., supplier industries acquire their inputs from
outside the region). Additionally, the household multiplier of 0.88 means that household expenditures are
more likely to leak outside the regional economy because of purchases of goods that are manufactured
elsewhere and services purchased from suppliers outside the region. In addition, the household multiplier
reflects leakages in the form of taxes and savings.
September 2013
Appendix 4-6
-------
TABLE 5. TOTAL OUTPUT BY INDUSTRY BASED ON RIMSII ANALYSIS
STRIAL 5 EC
Construction
REGIONA-
$244,654
1.76
PUT IMPACTS
$431,521
Manufacturing
$56,885
0.95
$53,762
Wholesale Trade
$9,808
1.52
$14,947
Transportation and Warehousing
$2,544
1.84
$4,676
Households
$61,885
0.88
$54,694
Indirect and Induced Impacts
$559,601
Total Project Value (including direct impact)
$967,259
Total Output Impact
$1,526,860
Note: Values are in 2010 dollars. Totals may not add to detail because of independent rounding.
V. OTHER ENVIRONMENTAL AND ECONOMIC BENEFITS
The Pine Bluffs Department of Public Works used ARRA funding to implement a meter replacement
project. Meters in the town were mostly installed in the 1970s. Many of these meters had failed or were
too inaccurate to use for billing purposes (Wyoming State Revolving Fund, 2009). The project replaced all
of the town's water meters with new smart meters that can be read remotely by vehicle.
In addition to replacing the meters, the town moved the new meter locations from inside residential or
commercial buildings to the point at which the individual customer service lines connected to main
distribution lines. This move allowed the city to meter all water entering each service line, which has
enhanced leak tracking capabilities substantially (Miller and McDonough, 2013). Because the original
meters were located some distance away from the service line connections, a service line leak before the
meter could not be detected.
Water conservation is an important issue for the Town of Pine Bluffs. Its water comes from several ground
water wells. Water levels in the aquifer that supplies the wells are declining because consumption
outpaces infiltration rates. Estimated water losses of 25 percent (Wyoming State Revolving Funds, 2009)
exacerbate the aquifer drawdown. These losses can be attributed to leaks and malfunctioning meters that
do not accurately account for all water usage.
Reduced water consumption is the primary environmental benefit of the project. Following meter
installation, the Town of Pine Bluffs changed its billing from flat rate to use-based. This change led to
improvements in residential water conservation. The movement of meters toward the distribution
connections has helped the city identify and repair leaks. Both features of the program have helped
reduce consumption from over 1 million gallons per day (MGD) to 0.5 MGD (Miller and McDonough,
2013).
Reducing water losses also leads to one of the economic benefits of the project. The system can reduce its
treatment costs and pumping costs if it can meet customer demands while treating and distributing less
water.
September 2013
Appendix 4-7
-------
Another economic benefit of the project is reduced operating costs for meter reading and billing. Before
installing the smart meters, the town collected data manually from each customer site and entered it into
the billing system. With an estimated 80 percent of old meters located in household or business
basements (Wyoming State Revolving Fund, 2009), the manpower requirements to read over 500 meters
were extensive and made more complicated by access issues. With the smart meters, a single meter
reader can collect digital meter readings while driving through the service area in a fraction of the time it
took to do the manual readings. Employees also no longer encounter building access issues (Miller and
McDonough, 2013). The system also reduces fuel costs and air emissions because meter reading trucks
are not left idling as they were when meter readers had to access buildings (Wyoming State Revolving
Fund, 2009).
Bureau of Economic Analysis (BEA). 2012a. Data from U.S. Census Bureau accessed on BEA website on
May 3, 2013. http://www.bea.gov/regional/
BEA. 2012b. "Decennial Census, 2000 and 2010". "Local Area Personal Income & Employment" Interactive
tables on website. Accessed May 3, 2013. http://www.bea.gov/regional/
BEA. 2012c. RIMS II: An essential tool for regional developers and planners. Washington, D.C.; U.S.
Government Printing Office.
BEA. 2012d. RIMS II Multipliers 2002/2010: Table 1.5, Total Multipliers for Output by Detailed Industry,
Custom Study Region (Type II). May 2013.
Miller, C. and T. McDonough. Pine Bluffs Department of Public Works. 2013. Personal communication
with SAIC. April 30, 2013.
Wyoming State Revolving Funds. 2009. Pine Bluffs Meter Project and Telemetry Business Cases.
Document prepared for U.S. Environmental Protection Agency. Dated July 8, 2009.
September 2013
Appendix 4-8
-------
APPENDIX 5: CAPE CHARLES WASTEWATER TREATMENT PLANT UPGRADES
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I. PROJECT DESCRIPTION
Located in Northampton County on Virginia's Eastern Shore, the Town of Cape Charles is connected to
Virginia Beach by the 17.6 mile long Chesapeake Bay Bridge-Tunnel. In 2010, Cape Charles had a
population of 1,009 residents in 516 households (U.S. Census Bureau. 2010). The town is a noted resort
destination with over 30 percent of the houses designated as seasonal or recreational homes (U.S. Census
Bureau. 2010).
In 2009, the Town of Cape Charles undertook a project to retrofit an existing Wastewater Treatment Plant
(WWTP) for enhanced nutrient removal to reduce pollutants into the Chesapeake Bay. The tertiary
treatment expansion is part of a brownfields redevelopment plan for a former landfill and abandoned
industrial operations (EPA, 2000). Cape Charles received funding from the Virginia Department of
Environmental Quality (VDEQ) including funds made available through ARRA. The ARRA funding - in the
form of a loan with 100% principal forgiveness - was executed on September 28, 2009. In the CWSRF
database, the total cost of the project is listed as $18.9 million with almost $6.1 million provided in ARRA
assistance. The ARRA funding leveraged funds from the Virginia Water Quality Improvement Fund and the
Town of Cape Charles.
The project consisted of installing nutrient reduction technology for nitrogen and phosphorus removal in
a new facility capable of handing an average flow of 0.25 million gallons per day (MGD) and meeting
treatment goals of 3.0 milligrams per liter (mg/l) total nitrogen and 0.3 mg/l total phosphorus. The
upgraded plant employs a membrane bioreactor system and is designed to be expanded to a second
phase of 0.5 MGD with an ultimate build-out of 0.75 MGD factored into site design. The retrofitted WWTP
will allow the Town of Cape Charles to comply with its nutrient removal agreement with VDEQ and
thereby contribute to the nutrient reduction efforts throughout the Chesapeake Bay watershed as
outlined in the Total Maximum Daily Load (TMDL) for the Bay adopted in 2010 (EPA, 2010). Additional
benefits result from the membrane technology's production of higher quality discharge water that is
suitable for reuse (Town of Cape Charles, 2009).
II. REGION DESCRIPTION
The study region (Figure 1) consists of eight jurisdictions in southeastern Virginia, six of which are within
the Virginia Beach-Norfolk-Newport News metropolitan statistical area (MSA). The town of Cape Charles
is located in Northampton County on the Virginia's eastern shore and is accessible from Virginia Beach by
crossing a 17.6 mile long toll bridge. Accomack County is also on the eastern shore located north of
Northampton County. The eastern shore's economy relies on the Virginia Beach-Norfolk-Newport News
MSA to provide goods and services that are not available locally.
September 2013
Appendix 5-1
-------
FIGURE 1. TOWN OF CAPE CHARGES WWTP ECONOMIC IMPACT STUDY REGION
'
Stny
X
^^ Hampton
)
Norfolk
Portsmouth
MD
' .' .<; -
Atlantic Ocean
Virginia Beach
SuflbA
LegeixJ
Study ATM
10
September 2013
Appendix 5-2
-------
The study region includes only the MSA components that are closest to Northampton County. The Virginia
Beach-Norfolk-Newport News MSA comprises nine counties and seven cities that are independent of the
adjacent counties. For this study, six independent cities from within the MSA are included in the study
region based on proximity to the eastern shore via the Chesapeake Bay Bridge-Tunnel: Chesapeake,
Hampton, Newport News, Norfolk, Portsmouth and Virginia Beach.
POPULATION
Table 1 reports population data for the study region components. In aggregate, the population in the
communities within the study area increased about 0.2 percent annually between 2000 and 2010. The
growth has been varied with Accomack County, Northampton County, and the cities of Hampton and
Portsmouth losing population and Chesapeake City growing by 11.6 percent over the decade. Population
growth in Newport News was essentially flat and Norfolk and Virginia Beach increased at a modest 3.6
and 3.0 percent, respectively.
TABLE 1. POPULATION CHANGES IN SELECTED AREAS, 2000-2010
^^g
Accomack County
Northampton County
Chesapeake City
Hampton
Newport News
Norfolk
Portsmouth
Virginia Beach
Total
;ooo
38,305
13,093
199,184
146,437
180,150
234,403
100,565
425,257
1,337,394
,
2010
33,164
12,389
222,209
137,436
180,719
242,803
95,535
437,994
1,362,249
PERCENT CHAP
-13.4%
-5.4%
11.6%
-6.1%
0.3%
3.6%
-5.0%
3.0%
1.9%
JGE, 2000-2010
-1.4%
-0.6%
1.1%
-0.6%
0.0%
0.4%
-0.5%
0.3%
0.2%
Source: BEA, 2012a
LOCAL ECONOMY
The military has a strong presence in the Virginia Beach-Norfolk-Newport News MSA. There are nine
headquartered military commands in the area. Within the study region, federal civilian and military
employment is a significant portion of the full- and part-time employment accounting for 16.6 percent of
total employment (Table 2). The distribution of federal employees is highly concentrated in Norfolk and
Virginia Beach with over 45 and 18 percent of the total federal employees, respectively. Accomack and
Northampton Counties have a small federal presence with federal employees comprising only 5.2 and 1.6
percent of employment within the respective counties.
The employment profile for Northampton County reflects a more rural character compared to the other
jurisdictions in the region. The farm and forestry, fishing and hunting industries account for more than 17
percent countywide employment, but these industries make up less than 0.4 percent of total employment
September 2013
Appendix 5-3
-------
throughout the region. Furthermore, Northampton County accounts for nearly 46 percent of regional
employment in these industries, despite having less than 1 percent of regional jobs.
TABLE 2. EMPLOYMENT BY INDUSTRIAL SECTOR, 2010
iNDUSTKiALSEOOR
Total Employment
Farm Employment
Forestry, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate, Rental and Leasing
Professional and Technical Services
Management of Companies
Administrative and Waste Services
Educational Services
Health Care and Social Assistance
Arts, Entertainment and Recreation
Accommodation and Food Services
Other Services
Federal Government, Civilian
Military
State and Local Government
ACCOMACK NORTHAMPTON
18,121
490
(D)
(D)
87
1,087
3,526
304
1,668
233
119
304
627
997
133
903
(D)
(D)
204
1,274
1,119
647
287
2,201
7,135
713
512
0
(D)
343
441
89
700
(D)
22
202
318
(D)
(D)
194
(D)
(D)
(D)
(D)
363
44
69
907
VA BEACH
MSA AREAS1
822,480
741
228
301
173
41,004
45,440
13,787
78,188
15,987
13,147
30,709
33,545
52,271
8,371
48,459
16,296
75,869
11,612
58,698
37,515
47,927
92,094
87,497
TOTAL
847,736
1,944
740
301
260
42,434
49,407
14,180
80,556
16,220
13,288
31,215
34,490
53,268
8,504
49,556
16,296
75,869
11,816
59,972
38,997
48,618
92,450
90,605
Source: BEA, 2012b
Note: Totals include employment that is not displayed in the sector breakout because of non-disclosure issues.
Includes the following cities: Chesapeake, Hampton, Newport News, Norfolk, Portsmouth and Virginia Beach.
September 2013
Appendix 5-4
-------
Real per capita income in the study area increased at average annual rate of 1.7 percent between 2000
and 2010 to $39,447 (Table 3). Virginia Beach has the highest per capita income at $44,857 whereas
Newport News has the lowest at $32,921. Growth in real per capita income between 2000 and 2010
increased at the fastest rate in Accomack County and Portsmouth at an average annual rate of 3.1 percent
and 2.9 percent, respectively. For perspective, during the same period per capita income in the United
States increased at average annual rate of 0.2 percent while the State of Virginia experienced an annual
average increase of 0.9 percent.
TABLE 3. REAL PER CAPITA INCOME FOR SELECTED AREAS, 2000 AND 2010
COUNTY/CITY
PER CAPITA INCOME
Accomack County
Northampton County
Chesapeake city
Hampton city
Newport News city
Norfolk city
Portsmouth city
Virginia Beach city
Weighted Average
2000
$24,515
$28,785
$35,051
$31,464
$28,686
$30,060
$27,744
$39,349
$33,380
2010
$33,403
$35,498
$40,812
$38,678
$32,921
$35,816
$36,762
$44,857
$39,447
RCENT CHANGE, 2000-2010
TOTAL
36.3%
23.3%
16.4%
22.9%
14.8%
19.1%
32.5%
14.0%
18.2%
Source: BEA, 2012b
Note: Values are in 2010 dollars
ANNUAL
3.1%
2.1%
1.5%
2.1%
1.4%
1.8%
2.9%
1.3%
1.7%
QUANTITATIVE ANALYSIS INPUTS
The data collection efforts for this analysis focused on obtaining complete, accurate and descriptive data
related to the Cape Charles WWTP upgrades. The Town of Cape Charles provided a Schedule of Values
spreadsheet with line items for each component, with costs further identified as material or labor. The
costs in the spreadsheet are bid data provided by the general contractor. Any changes between the bid
costs and actual costs were documented in change orders, which SAIC used to revise the spreadsheet.
SAIC made the following adjustments to transform the component cost data into inputs for the RIMS II
model:
Assign each cost component to an industrial category.
Identify which material and labor line items were not local purchases.
Disaggregate local material costs into transportation costs, wholesaler costs and wholesaler
profit.
For each line item of the expenditure data, SAIC assigned one of the 406 RIMS II industrial categories that
best matched the component description. Based on information provided by Bob Panek (Cape Charles
WWTP Project Manager and the Assistant Town Manager) about which contractors and materials
suppliers were located in the region, SAIC excluded expenditures for nonlocal materials and labor. This
September 2013
Appendix 5-5
-------
adjustment is necessary because materials provided by vendors outside the region (e.g., membranes)
represent leakage of dollars that are spent outside of the study region. Finally, SAIC used National Income
and Product Accounts (NIPA) data to disaggregate expenditures on materials (i.e., purchaser cost) into
cost components for wholesaler value, wholesaler markup and transportation costs.
Table 4 displays the total expenditures for the WWTP upgrade project. The original contract specified
$14,737,000 with an additional $425,491 in change orders for a total amount of $15,162,491. Of the total
amount, $10,823,056 was used to purchase materials, $3,850,435 went to labor costs and $489,000 was
applied to insurance, bonding and mobilization. Not all of the purchases were made locally, with an
estimated $5,196,972 spent outside of the study region. The amount that is input into the model to
calculate the multiplier effects is $9,965,519.
TABLE 4. TOTAL EXPENDITURES FOR WWTP UPGRADE
AMOUNT To LOERE'N^|NNGPSUATSND
Wholesale Purchases
(includes transportation costs)
Household Income (labor)
Other
Total
$10,823,056
$3,850,435
$489,000
$15,162,491
$6,115,084
3,850,435
$0
$9,965,519
$4,707,972
$0
$489,000
$5,196,972
IV. REGIONAL ECONOMIC IMPACTS
The regional economic impacts measure the increase in total economic output for the study region as a
result of the Cape Charles WWTP upgrades. For the study region, the total economic output increased by
$29,843,832. The total project value is $15,162,491 and the indirect and induced impacts are a combined
$14,681,341, which implies a total impact-to-project value ratio of 1.97:1 (i.e., each dollar spent on the
project resulted in a regional economic impact of approximately $1.97 including the initial expenditures
and the indirect and induced demand changes). The region applied in this analysis represents the area
where the expenditures and labor were procured. However, many of the materials used in the project
were manufactured outside of the region. The smaller study region most likely results in a lower total
impact-to-project value ratio because spending leakages are likely given the size of the local economy.
The multipliers vary by industry with the transportation and warehousing sector having the highest at
1.97 and the households sector having the lowest at 1.09 (Table 5). A higher multiplier indicates that
direct expenditures on the products of that industry have a higher tendency to cycle throughout the
regional economy multiple times via input-output linkages in local industries. The households multiplier of
1.09 indicates that household expenditures are more likely to leak outside the regional economy because
of purchases of goods that are manufactured elsewhere and services purchased from suppliers outside
the region. In addition, the household multiplier reflects leakages in the form of taxes and savings.
September 2013
Appendix 5-6
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TABLE 5. TOTAL OUTPUT BY INDUSTRY BASED ON RIMSII ANALYSIS
INDUSTRY SECTOR
Agriculture, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Transportation and Warehousing
Professional and Technical Services
Households
^^?
$48,059
$157,549
$1,000
$249,642
$4,455,431
$607,532
$361,871
$234,000
$3,850,435
1.57
1.72
1.80
1.92
1.67
1.72
1.97
1.88
1.09
°u;;cu;
$75,506
$271,013
$1,797
$478,988
$7,441,813
$1,045,137
$713,053
$439,733
$4,214,301
Indirect and Induced Impacts
Total Project Value (including direct impact)
Total Output Impact
$14,681,341
$15,162,491
$29,843,832
Note: Values are in 2008 dollars
Table 6 displays the total output attributable to ARRA funding. It is not known which items were
purchased with the ARRA funding. The simplifying assumption had to be applied that the ARRA-funded
proportion of the total project was spent in the same proportions as the non-ARRA funded portion. The
ARRA funding on the project results in an increase in total output of $11,959,866.
TABLE 6. TOTAL OUTPUT ATTRIBUTABLE TO ARRA FUNDING BASED ON RIMSII ANALYSIS
Total Output
DIRECT
$6,076,343
INDIRECT AND
INDUCED
$5,883,523
TOTAL OUTPUT
$11,959,866
Note: Values are in 2008 dollars
IV. OTHER ENVIRONMENTAL AND ECONOMIC BENEFITS
Water quality throughout the Chesapeake Bay watershed is impaired because excess nutrients and
sediment loadings adversely affect the fish, shellfish and plants that are indigenous to the Bay. For
example, nutrients cause algae blooms that deplete dissolved oxygen levels, block sunlight needed by
underwater grasses, and smother aquatic life on the bottom (EPA, 2010).
Among the many sources of nutrient loadings are numerous WWTPs that discharge to various waterways
throughout the Bay's 64,000-square mile watershed. The Cape Charles WWTP is one of these sources. It
has a National Pollutant Discharge Elimination System (NPDES) permit that limits the amount of
contaminants such as nutrients that it can discharge to the Bay in its treated sewage flows.
September 2013
Appendix 5-7
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A recent Chesapeake Bay TMDL establishes nutrient and sediment waste load allocations to help restore
water quality. By 2025, the TMDL should reduce loadings of nitrogen by 25 percent and phosphorus
loadings by 24 percent (EPA, 2010). The wasteload allocations for Virginia's portion of the Eastern Shore
are 1.31 million pounds per year for nitrogen, 0.14 million pounds per year for phosphorus, and 11.31
million pounds per year for sediment (EPA, 2010). These loads are further allocated among point sources
such as WWTPs and nonpoint sources such as runoff from agricultural areas.
Cropper and Isaac (2011) identify the following types of use-related benefits of achieving the water
quality standards in the Chesapeake Bay5:
Commercial and recreational fishery benefits of improved fish and shellfish stocks.
Boater and swimmer recreational benefits of improved water clarity.
Property value increases of improved aesthetic and recreational values.
They also identify nonuse benefits in the form of higher existence values for improved water quality (i.e.,
higher willingness-to-pay for water quality improvements because people place intrinsic value on the
quality of water resources). There are, however, no quantitative benefit estimates for the TMDL
In addition to providing environmental benefits associated with reduced nutrient loadings, the WWTP
upgrades should provide medium and long-term benefits to the Town of Cape Charles by increasing
economic growth potential. The upgrades will allow water reuse for nonpotable use such as golf course
irrigation and process water for a concrete supplier (Town of Cape Charles, 2009). This reuse capability
reduces the current WWTP discharge volume to the Bay as well as aquifer withdrawals (Town of Cape
Charles, 2009). Furthermore, the excess discharge capacity under the NPDES permit remains available to
accommodate future population growth and related economic development (Panek, 2012).
5 Categories of benefits can be divided into those associated with resource use and those that do not
require direct or indirect resource use. Use-related benefits categories include human health benefits
such as reduced risk of mortality or morbidity; resource use for commercial purposes; resource use for
recreational purposes or aesthetic enjoyment; and indirect resource use via its support for ecosystem
functions. Nonuse benefits arise when natural resources or environmental quality have intrinsic value
aside from their ability to directly or indirectly provide goods and services.
September 2013 Appendix 5-8
-------
Bureau of Economic Analysis (BEA). 2012a. Data from U.S. Census Bureau accessed on BEA website on
May 14, 2012. http://www.bea.gov/regional/
BEA. 2012b. "Decennial Census, 2000 and 2010". "Local Area Personal Income & Employment" Interactive
tables on website. Accessed May 14, 2012. http://www.bea.gov/regional/
BEA. 2012c. RIMS II: An essential tool for regional developers and planners. Washington, D.C.; U.S.
Government Printing Office.
BEA. 2012d. RIMS II Multipliers 2002/2008: Table 1.5, Total Multipliers for Output by Detailed Industry,
Custom Study Region (Type II). May 2012.
Cropper, M.L, and W.S. Isaac. 2011. "The Benefits of Achieving the Chesapeake Bay TMDLs (Total
Maximum Daily Loads): A Scoping Study." RFF Discussion Paper 11-31. Available online at
http://www.rff.org/RFF/Documents/RFF-DP-ll-31.pdf.
EPA. 2000. Brownfields Assessment Demonstration Pilot: Cape Charles-Northampton County, VA. EPA
500-F-00-261.
EPA. 2010. The Chesapeake Bay TMDL Available online at
http://www.epa.gov/reg3wapd/tmdl/ChesapeakeBay/tmdlexec.html.
Panek, Bob. 2012. Personal communication with Walter Gills (VA DEQ), Kelly Ward (VA DEQ), Ed Hopkins
(EPA/R3), Hamilton Humes (EPA/OCFO), and SAIC staff. May 8, 2012.
Town of Cape Charles. 2009. "Cape Charles WWTP Nutrient Removal Upgrade Groundbreaking Ceremony
- November 12, 2009." Electronic file: Groundbreaking Handout.pdf
September 2013
Appendix 5-9
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September 2013 Appendix 5-10
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APPENDIX 6: CITY OF HEDRICK WASTEWATER TREATMENT PLANT UPGRADES
-------
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I. PROJECT DESCRIPTION
The City of Hedrick is located in Keokuk County, Iowa, which is about 90 miles southwest of the City of
Cedar Rapids. The project area consists of eight counties in southeastern Iowa, two of which are within
the Cedar Rapids, IA metropolitan statistical area (MSA).
In 2009, the City of Hedrick received $2.3 million to support the planning, design and construction for a
new $4.6 million wastewater treatment facility. The SRF funding of $2.3 million included an ARRA loan of
$899,000 with 100% principal forgiveness.
The project consisted of replacing the wastewater lift station with one of greater capacity and
constructing a new wastewater treatment facility. The increased capacity in lift station is expected to
eliminate uncontrolled discharges during heavy rain events. The wastewater treatment system will
include an Aeromod activated sludge plant to allow for larger flows and effective removal of ammonia
from the wastewater before discharge into stream resulting in a higher quality effluent being introduced
to receiving stream. The treatment plant will also employ reed bed technology for sludge storage (State of
Iowa, 2012). This goal of the project was to maximize job creation and economic benefit by investing in
infrastructure that provides long-term economic benefits to the community while avoiding reductions in
essential services.
II. REGION DESCRIPTION
The study region (Figure 1) consists of eight counties in southeastern Iowa, two of which are within the
Cedar Rapids, IA MSA. The City of Hedrick is located in Keokuk County about 90 miles southwest of the
City of Cedar Rapids, which is located in Linn County. The market area reaches the Illinois boundary, but
only includes counties within the State of Iowa to remain consistent with the economically independent
defined MSA.
September 2013
Appendix 6-1
-------
FIGURE 1. HEDRICK WWTP ECONOMIC IMPACT STUDY REGION
Btad>. Ha.-
Tama
'..-"' Q
Bemon
Iowa
'-- Una
Johnson
Washington
Jeffensoa
r;
*"
Legend
Study Area
CityScundares
County Boundaries
Jones L-.. ,
u -.-,
.
ScoH
Muscattne
-"ni.
Louisa
'ties
Hemletson IL
September 2013
Appendix 6-2
-------
The study region includes the following counties within the State of Iowa: Benton, Iowa, Johnson, Keokuk,
Linn, Louisa, Muscatine and Washington.
POPULATION
Table 1 reports population data for the study region components. In aggregate, the population in the
communities within the study area increased 9.4 percent between 2000 and 2010 largely driven by the
growth in Johnson and Linn counties, which increased by 17.7 percent and 10 percent respectively.
Population declined in Keokuk and Louisa counties and increased modestly in the remaining four counties.
TABLE 1. POPULATION CHANGES IN SELECTED AREAS, 2000-2010
Benton, IA
Iowa, IA
Johnson, IA
Keokuk, IA
Linn, IA
Louisa, IA
Muscatine, IA
Washington, IA
Total
Source: BEA, 2012a
25,326
15,729
111,455
11,418
192,365
12,174
41,791
20,718
430,976
26,073
16,338
131,238
10,501
211,564
11,374
42,732
21,712
471,532
2.9%
3.9%
17.7%
-8.0%
10.0%
-6.6%
2.3%
4.8%
9.4%
0.3%
0.4%
1.6%
-0.8%
1.0%
-0.7%
0.2%
0.5%
0.9%
LOCAL ECONOMY
Within the study region, local and state government is a significant portion of the full- and part-time
employment accounting for 15.8 percent of total employment, up from 15.0 percent in 2001 (Table 2).
Next, manufacturing and retail trade account for 11.7 percent and 10.5 percent of employment,
respectively. The manufacturing sector experienced the largest decline with over 4,200 fewer employees
in this sector and a loss of over 2.2% in the share of total employment. The health care and social
assistance sector experienced the largest increase with over 8,200 new employees and a 2.1% gain in the
share of total employment.
September 2013
Appendix 6-3
-------
TABLE 2. EMPLOYMENT BY INDUSTRIAL SECTOR, 2001 AND 2010
Total Employment
Farm Employment
Forestry, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate, Rental and Leasing
Professional and Technical Services
Management of Companies
Administrative and Waste Services
Educational Services
Health Care and Social Assistance
Arts, Entertainment and Recreation
Accommodation and Food Services
Other Services
Federal Government, Civilian
Military
State and Local Government
306,462
9,997
185
74
1,552
16,611
42,554
8,961
35,013
10,567
10,936
12,225
8,239
10,100
372
15,834
5,828
21,490
4,340
17,892
14,185
3,194
2,022
45,901
326,793
8,519
183
191
1,878
16,381
38,288
8,365
34,202
13,807
8,378
16,734
9,941
12,486
1,369
15,835
7,547
29,702
5,343
20,383
14,438
3,363
2,001
51,623
PERCENT OF TOTAL
2001 2010
100%
3.3%
0.1%
0.0%
0.5%
5.4%
13.9%
2.9%
11.4%
3.4%
3.6%
4.0%
2.7%
3.3%
0.1%
5.2%
1.9%
7.0%
1.4%
5.8%
4.6%
1.0%
0.7%
15.0%
100%
2.6%
0.1%
0.1%
0.6%
5.0%
11.7%
2.6%
10.5%
4.2%
2.6%
5.1%
3.0%
3.8%
0.4%
4.8%
2.3%
9.1%
1.6%
6.2%
4.4%
1.0%
0.6%
15.8%
Source: BEA, 2012b
Note: Totals include employment that is not displayed in the sector breakout because of non-disclosure issues.
September 2013
Appendix 6-4
-------
Real per capita income in the study area increased at average annual rate of 2.4 percent between 2000
and 2010 to $39,514 (Table 3). Linn has the highest per capita income at $41,062 whereas Louisa has the
lowest at $32,197. Growth in per capita income between 2000 and 2010 increased at the fastest rate in
Benton County at average annual rate of 1.2 percent with the other counties increasing between 0.2 and
0.7 percent except for Linn, which was essentially flat, and Iowa County, which experienced a small
decrease. For perspective, during the same period per capita income in the United States increased at
average annual rate of 0.2 percent while the State of Iowa experienced an annual average increase of 0.8
percent.
TABLE 3. REAL PER CAPITA INCOME FOR SELECTED AREAS, 2000 AND 2010
COUNTY/CITY
Benton, IA
Iowa, IA
Johnson, IA
Keokuk,IA
Linn, IA
Louisa, IA
Muscatine, IA
Weighted Average
PER CAPITA INCOME PERCENT CHANGE. 2000-2010
2000
$34,681
$38,798
$38,890
$30,713
$40,891
$30,733
$33,857
$38,583
2010
$39,066
$37,797
$39,607
$32,770
$41,062
$32,197
$36,100
$39,514
TOTAL
12.6%
-2.6%
1.8%
6.7%
0.4%
4.8%
6.6%
2.4%
ANNUAL
1.2%
-0.3%
0.2%
0.7%
0.0%
0.5%
0.6%
0.2%
Source: BEA, 2012b
Note: Values are in 2010 dollars
QUANTITATIVE ANALYSIS INPUTS
The data collection efforts for this analysis focused on obtaining complete, accurate and descriptive data
related to the Hedrick WWTP construction project. The Iowa SRF provided several scanned cost sheets
with line items for each component. The costs in the cost sheets are bid data provided by the general
contractor. Any changes between the bid costs and actual costs were documented in change orders,
which SAIC used to revise the original cost data.
SAIC made the following adjustments to transform the component cost data into inputs for the RIMS II
model:
Assign each cost component to an industrial category.
Split item costs into material and labor categories.
Identify which material and labor line items were not local purchases.
Disaggregate local material costs into transportation costs, wholesaler costs and wholesaler
profit.
For each line item of the expenditure data, SAIC assigned one of the 406 RIMS II industrial categories that
best matched the component description. SAIC excluded expenditures for nonlocal materials and labor
using information in the provided cost sheets. Where the source of purchase was not readily identified,
September 2013
Appendix 6-5
-------
SAIC applied Census County Business Patterns data to determine whether there were businesses within
the study region that could supply the item in question. This adjustment is necessary because materials
provided by vendors outside the region (e.g., filtration package plant) represent leakage of dollars that are
spent outside of the study region. Finally, SAIC used National Income and Product Accounts (NIPA) data to
disaggregate expenditures on materials (i.e., purchaser cost) into cost components for wholesaler value,
wholesaler markup and transportation costs.
Table 4 displays the enumerated expenditures for the WWTP upgrade project. The total contract amounts
to $3,356,043 including change orders. ARRA funding accounted for about 27 percent of the value of the
project with $899,000 provided in the form of a principal-forgiven loan. Of the total amount, $2,665,016
was used to purchase materials, $636,027 went to labor costs, and $55,000 was applied to insurance,
bonding and mobilization. Not all of the purchases were made locally, with an estimated $1,233,424 spent
outside of the study region. The amount that is input into the model to calculate the multiplier effects is
$2,122,619
TABLE 4. TOTAL EXPENDITURES FOR WWTP CONSTRUCTION
Wholesale Purchases
(includes transportation costs)
Household Income (labor)
Other
Total
$2,665,016
$636,027
$55,000
$3,356,043
$1,486,592
$636,027
$0
$2,122,619
LEAKAGE, SAVINGS AND
OTHER NON-INPUTS
$1,178,424
$0
$55,000
$1,233,424
Note: Values are in 2008 dollars
IV. REGIONAL ECONOMIC IMPACTS
The regional economic impacts measure the increase in total economic output for the study region as a
result of the Cape Charles WWTP upgrades. For the study region, the total economic output increased by
$6,339,383. The total project value is $3,356,043 and the indirect and induced impacts are a combined
$2,983,340, which implies a total impact-to-project value ratio of 1.89:1 (i.e., each dollar spent on the
project resulted in a regional economic impact of approximately $1.89 including the initial expenditures
and the indirect and induced demand changes). The region applied in this analysis represents the area
where the expenditures and labor were procured. However, many of the materials used in the project
were manufactured outside of the region. The smaller study region most likely results in a lower total
impact-to-project value ratio because spending leakages are likely given the size of the local economy.
The multipliers vary by industry with the construction sector having the highest at 1.77 and the
households sector having the lowest at 0.88 (Table 5). A higher multiplier indicates that direct
expenditures on the products of that industry have a higher tendency to cycle throughout the regional
economy multiple times via input-output linkages in local industries. The household multiplier of 0.88
indicates that household expenditures are more likely to leak outside the regional economy because of
purchases of goods that are manufactured elsewhere and services purchased from suppliers outside the
region. In addition, the households multiplier reflects leakages in the form of taxes and savings.
September 2013
Appendix 6-6
-------
TABLE 5. TOTAL OUTPUT BY INDUSTRY BASED ON RIMSII ANALYSIS
INDU^LSECTOK
Agriculture, Fishing and Hunting
Mining
Construction
Manufacturing
Wholesale Trade
Transportation and Warehousing
Professional and Technical Services
Households
Indirect and Induced Impacts
Total Project Value (including direct impact)
Total Output Impact
REGIONAL
PURCHASES
$4,997
$2,165
$200,062
$1,082,715
$135,403
$57,968
$3,281
$636,027
1.50
1.62
1.77
1.61
1.54
1.74
1.61
0.88
mmm
$7,489
$3,503
$354,651
$1,747,003
$207,925
$100,888
$5,294
$556,587
$2,983,340
$3,356,043
$6,339,383
Note: Values are in 2008 dollars
Table 6 displays the total output attributable to ARRA funding. It is not known which items were
purchased with the ARRA funding. The simplifying assumption had to be applied that the ARRA-funded
proportion of the total project was spent in the same proportions as the non-ARRA funded portion. The
ARRA funding on the project results in an increase in total output of $1,698,162.
TABLE 6. TOTAL OUTPUT ATTRIBUTABLE TO ARRA FUNDING BASED ON RIMSII ANALYSIS
Total Output
$899,000
INDIRECT AND
INDUCED
$799,162
TOTAL OUTPUT
$1,698,162
Note: Values are in 2008 dollars
V. OTHER ENVIRONMENTAL AND ECONOMIC BENEFITS
The Hedrick treatment plant project includes constructing a new treatment facility that includes an
activated sludge plant, headworks, pumping stations, reed beds and a new lift station adjacent to the
plant to replace an existing lift station (Krewson, 2009). The new plant has a design capacity of 1.878
million gallons per day (MGD; peak hour wet weather flow) and average flows of 0.465 MGD (wet
weather) and 0.103 (dry weather) (Leopold, 2009). The plant was designed to meet national pollution
discharge elimination system (NPDES) permit limits for ammonia, biological oxygen demand, total
suspended solids and bacteria. These upgrades will improve the wastewater utility's effluent quality and
reduce loadings of nutrients and sediments in the receiving waters.
One of the green components of the project is using reed beds for sludge dewatering instead of more
conventional options such as drying pads. Although there is no performance data available for the Hedrick
facility, a study of another site shows that reed beds have the potential to dewater a greater volume of
sludge per square foot of drying area, which reduces the land area needed for sludge drying (NYSERDA,
September 2013
Appendix 6-7
-------
2006). The plants in a reed bed dewater sludge via water uptake, which is more efficient than dewatering
via evaporation on a drying pad. The reed bed in a demonstration project achieved a 78% reduction in
sludge volume compared to a 60% reduction for a drying bed (NYSERDA 2008). The reed bed also allows a
higher loading rate per square foot of drying area and extended sludge storage. The same demonstration
site showed that a reed bed could reduce annualized operating costs for sludge handling by over 70%
because the sludge can remain in the reed bed for up to 10 years, but drying pads incur annual costs for
sludge cake removal and disposal (NYSERDA, 2008). In addition, the plants may remove some
contaminants such as metals.
The financial subsidies will benefit the utility's customers. Of the total project, only $1.6 million will need
to be repaid. Although the utility raised rates to repay the 20-year loan, the fee increases would have
been substantially higher if the full amount had been financed via loans or bonds. The base household fee
increased from $12.45 to $27.50, and the usage fees per 1,000 gallons over 3,000 per month increased
from $4.15 to $5.30 (Davis, 2011). The principal forgiveness provision of the ARRA funding contributed to
the affordability of the plant upgrades.
Bureau of Economic Analysis (BEA). 2012a. Data from U.S. Census Bureau accessed on BEA website on
September 7, 2012. http://www.bea.gov/regional/
BEA. 2012b. "Decennial Census, 2000 and 2010". "Local Area Personal Income & Employment" Interactive
tables on website. Accessed May 14, 2012. http://www.bea.gov/regional/
BEA. 2012c. RIMS II: An essential tool for regional developers and planners. Washington, D.C.; U.S.
Government Printing Office.
BEA. 2012d. RIMS II Multipliers 2002/2008: Table 1.5, Total Multipliers for Output by Detailed Industry,
Custom Study Region (Type II). September 2012.
Davis, C. 2011. "Hedrick Shoulders Sewer Rate Hikes." Ottumwa Courier Online. October 25, 2011.
Krewson, J. 2009. Iowa State Revolving Fund - Categorical Exclusion. Prepared by Iowa Department of
Natural Resources.
Leopold, R. 2009. Construction Permit. Prepared by Iowa Department of Natural Resources.
New York State Energy Research and Development Authority (NYSERDA). 2006. Energy Efficient Sludge
Treatment with Reed-Bed Technology Demonstration Project. Final Report 06-12.
State of Iowa - Reporting on Iowa's Economic Recovery.
https://reporting.iowa.gov/?cmd=AwardReports&AwardlD=09-CDR-002. Accessed September 27,
2012.
U.S. Census Bureau. County Business Patterns, 2010 for Benton, Iowa, Johnson, Keokuk, Linn, Louisa,
Muscatine and Washington counties
September 2013 Appendix 6-8
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APPENDIX 7: GRANT COUNTY SANITARY SEWER DISTRICT EXTENSION
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I. PROJECT DESCRIPTION
Grant County is located in northern Kentucky with the county seat in the City of Williamstown about 46
miles north of the City of Lexington. The project area consists of eight counties in northern Kentucky, six
of which are within the Lexington-Fayette, KY metropolitan statistical area (MSA).
In 2010, the Grant County Sanitary Sewer District received $300,000 in ARRA funding to support the
planning, design and construction for phase 1 of the extension to the County's sanitary sewer, which cost
approximately $1.9 million. Of the total ARRA loan of $300,000, principal forgiveness amounted to
$156,300; the remainder is a loan to be repaid over 20 years at 3% interest.
The project extended sanitary sewer service to 50 residential customers and two larger customers who
operated their own sewage package plants: a commercial campground that had a functioning plant, but
was beginning to have problems; and a mobile home park (MHP) that had an old package plant that was
not meeting National Pollutant Discharge Elimination System (NPDES) permit limits. It also reaches a
second MHP that has not tied in yet. According to the design engineer for the project, Kerry Odle, the
existing sewer treatment plant has excess capacity, so the added wastestream does not affect the plant
(Odle, 2012).
II. REGION DESCRIPTION
The study region (Figure 1) consists of eight counties in northern Kentucky, six of which are within the
Lexington-Fayette, KY MSA. The market area reaches both the Indiana and Ohio borders, but only includes
counties within the State of Kentucky to remain consistent with the economically independent defined
MSA. Kerry Odle, the Project Engineer, confirmed that materials were purchased from the Lexington-
Fayette, KY MSA rather than the Cincinnati, OH MSA. The study region includes the following counties
within the State of Kentucky: Boone, Bourbon, Clark, Fayette, Grant, Jessamine, Scott and Woodford.
September 2013
Appendix 7-1
-------
FIGURE 1. GRANT COUNTY SANITARY SEWER ECONOMIC IMPACT STUDY REGION
Legend
, SludyArea
"] City Boundaries
i KY Counties
, IN Counties
' ] OH Counties
Men/fee
ngton
September 2013
Appendix 7-2
-------
POPULATION
Table 1 reports population data for the study region components. In aggregate, the population in the
communities within the study area grew by 1.7 percent annually between 2000 and 2010 driven by the
growth in Scott, Boone and Jessamine counties, which increased by 3.6 percent, 3.2 percent and 2.2
percent, respectively. Population in the remaining five counties increased at rate below the average
growth rate for the combined study region.
TABLE 1. POPULATION CHANGES IN SELECTED AREAS, 2000-2010
^^^^^^1
Boone, KY
Bourbon, KY
Clark, KY
Fayette, KY
Grant, KY
Jessamine, KY
Scott, KY
Woodford, KY
Total
POPULATION PERCENT CHANGE, 2000-2010
2000
87,108
19,366
33,234
261,408
22,485
39,216
33,422
23,278
519,517
2010
119,314
19,972
35,623
296,792
24,689
48,729
47,441
25,011
617,571
TOTAL
37.0%
3.1%
7.2%
13.5%
9.8%
24.3%
41.9%
7.4%
18.9%
ANNUAL
3.2%
0.3%
0.7%
1.3%
0.9%
2.2%
3.6%
0.7%
1.7%
Source: BEA, 2012a
LOCAL ECONOMY
Within the study region, local and state government is a significant portion of the full- and part-time
employment, accounting for 12.8 percent of total employment in 2010, up from 10.9 percent in 2001
(Table 2). Next, retail trade and manufacturing account for 10.5 percent and 9.8 percent of 2010
employment, respectively. Despite overall employment growth between 2001 and 2010, employment in
the manufacturing sector declined by nearly 11,800, which is a loss of almost 23 percent. After local and
state government, the sector that experienced the next largest gain between 2001 and 2010 was the
health care and social assistance sector, which increased by over 5,100 new employees or approximately
20 percent compared to the 2001 employment level.
September 2013
Appendix 7-3
-------
TABLE 2. EMPLOYMENT BY INDUSTRIAL SECTOR, 2001 AND 2010
INDUSTRY SECTOR
Total Employment
Farm Employment
Forestry, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate, Rental and Leasing
Professional and Technical Services
Management of Companies
Administrative and Waste Services
Educational Services
Health Care and Social Assistance
Arts, Entertainment and Recreation
Accommodation and Food Services
Other Services
Federal Government, Civilian
Military
State and Local Government
386,295
12,103
1,923
411
1,222
22,803
51,997
14,585
44,899
21,334
8,131
13,695
11,397
17,837
3,057
20,166
4,124
25,390
6,668
26,701
16,820
5,502
1,760
42,200
409,599
10,049
1,934
392
609
18,221
40,200
16,712
43,086
20,913
7,052
16,086
15,747
21,717
4,484
24,547
7,521
30,522
8,973
30,632
19,108
6,175
2,046
52,337
PERCENT OF TOTAL
100.0%
3.1%
0.5%
0.1%
0.3%
5.9%
13.5%
3.8%
11.6%
5.5%
2.1%
3.5%
3.0%
4.6%
0.8%
5.2%
1.1%
6.6%
1.7%
6.9%
4.4%
1.4%
0.5%
10.9%
100.0%
2.5%
0.5%
0.1%
0.1%
4.4%
9.8%
4.1%
10.5%
5.1%
1.7%
3.9%
3.8%
5.3%
1.1%
6.0%
1.8%
7.5%
2.2%
7.5%
4.7%
1.5%
0.5%
12.8%
Source: BEA, 2012b
Note: Totals include employment that is not displayed in the sector breakout because of non-disclosure issues.
September 2013
Appendix 7-4
-------
Real per capita income in the study area decreased at average annual rate of 1.0 percent between 2000
and 2010 to $35,098 as wages failed to keep pace with inflation primarily due the loss of higher-paying
manufacturing jobs (Table 3). The variation in per capita incomes among counties within the study region
is substantial with Woodford having the highest 2010 real per capita income of $40,483 and Grant having
the lowest of $28,058. The rate of decline in real per capita income between 2000 and 2010 was fastest in
Bourbon County, where per capita income declined at average annual rate of 2.2 percent. Other counties
experienced declines ranging between 0.1 and 1.6 percent per year. For perspective, during the same
period per capita income in the United States increased at average annual rate of 0.2 percent while the
State of Kentucky experienced an annual average increase of 0.2 percent.
TABLE 3. REAL PER CAPITA INCOME FOR SELECTED AREAS, 2000 AND 2010
r_/r, PER CAP.TA INCOME
Boone, KY
Bourbon, KY
Clark, KY
Fayette, KY
Grant, KY
Jessamine, KY
Scott, KY
Woodford, KY
Weighted Average
2000
$39,215
$38,428
$34,590
$40,514
$28,244
$33,594
$38,717
$46,977
$38,960
2010
$34,043
$30,903
$32,697
$37,874
$28,058
$29,863
$32,995
$40,483
$35,098
PERCENT CHANGE, 2000-2010
TOTAL
-13.2%
-19.6%
-5.5%
-6.5%
-0.7%
-11.1%
-14.8%
-13.8%
-9.9%
ANNUAL
-1.4%
-2.2%
-0.6%
-0.7%
-0.1%
-1.2%
-1.6%
-1.5%
-1.0%
Source: BEA, 2012b
Note: Values are in 2010 dollars
QUANTITATIVE ANALYSIS INPUTS
The data collection efforts for this analysis focused on obtaining complete, accurate and descriptive data
related to the Grant County sanitary sewer construction project. The Kentucky Infrastructure Authority
provided two detailed spreadsheets with line items for each component and information regarding the
location and payrolls of the contractors. The costs in the cost sheets are bid data provided by the general
contractor. Any changes between the bid costs and actual costs were documented in change orders,
which SAIC used to revise the original cost data.
SAIC made the following adjustments to transform the component cost data into inputs for the RIMS II
model:
Assign each cost component to an industrial category.
Split item costs into material and labor categories.
Identify which material and labor line items were not local purchases.
Disaggregate local material costs into transportation costs, wholesaler costs and wholesaler
profit.
September 2013
Appendix 7-5
-------
For each line item of the expenditure data, SAIC assigned one of the 406 RIMS II industrial categories that
best matched the component description. SAIC excluded expenditures for nonlocal materials and labor
using information in the provided cost sheets. Where the source of purchase was not readily identified,
SAIC applied Census County Business Patterns (U.S. Census Bureau, 2010) data to determine whether
there were businesses within the study region that could supply the item in question. This adjustment is
necessary because materials provided by vendors outside the region (e.g., filtration package plant)
represent leakage of dollars that are spent outside of the study region. Finally, SAIC used National Income
and Product Accounts (NIPA) data to disaggregate expenditures on materials (i.e., purchaser cost) into
cost components for wholesaler value, wholesaler markup and transportation costs.
Table 4 displays the total expenditures for the sanitary sewer construction project. The total contract was
$1,933,558. ARRA funding of $300,000 accounted for about 15.5 percent of the value of the project and
included principal forgiveness for $156,300; the remainder is a loan to be repaid over 20 years at 3%
interest. Of the total contract amount, $1,532,459 was used to purchase materials and $401,100 went to
labor. Not all of the purchases were made locally, with an estimated $110,000 spent outside of the study
region. The amount that is input into the model to calculate the multiplier effects is $1,823,558.
TABLE 4. TOTAL EXPENDITURES FOR SANITARY SEWER CONSTRUCTION
Wholesale Purchases
(includes transportation costs)
Household Income (labor)
Total
1 INPUT INTO LEAKAGE, SAVINGS AND
AMOUNT .. .. .
MODEL OTHER NON-INPUTS
$1,532,459
$401,100
$1,933,558
$1,422,459
$401,100
$1,823,558
$110,000
$0
$110,000
Note: Values are in 2010 dollars
IV. REGIONAL ECONOMIC IMPACT
The regional economic impacts measure the increase in total economic output for the study region as a
result of the Grant County sanitary sewer construction spending (BEA, 2012c). For the study region, the
total economic output increased by $4,847,230. The total project value is $1,933,558 and the indirect and
induced impacts are a combined $2,913,671, which implies a total impact-to-project value ratio of 2.51:1
(i.e., each dollar spent on the project resulted in a regional economic impact of approximately $2.51
including the initial expenditures and the indirect and induced demand changes). The region applied in
this analysis represents the area where the expenditures and labor were procured. If, however, workers
living in the northern counties (Boone and Grant) tend to spend earnings in the Cincinnati, OH MSA
instead of the Lexington-Fayette, KY MSA, then the households multiplier may overstate the impact of
some portion of the household earnings portion of total project expenditures.
The multipliers vary by industry with the transportation and warehousing sector having the highest at
1.89 and the households sector having the lowest at 1.02 (Table 5). A higher multiplier indicates that
direct expenditures on the products of that industry have a higher tendency to cycle throughout the
regional economy multiple times via input-output linkages in local industries. The households multiplier of
1.02 indicates that household expenditures are more likely to leak outside the regional economy because
September 2013
Appendix 7-6
-------
of purchases of goods that are manufactured elsewhere and services purchased from suppliers outside
the region. In addition, the households multiplier reflects leakages in the form of taxes and savings.
TABLE 5. TOTAL OUTPUT BY INDUSTRY BASED ON RIMSII ANALYSIS
INDUSTRY SECTOR ^^
Mining
Construction
Manufacturing
Wholesale Trade
Transportation and Warehousing
Households
Indirect and Induced Impacts
Total Project Value (including direct impact)
Total Output Impact
$4,39£
$358,99?
$898,62:
$123,42C
$37,02:
$401,10C
1.71
1.87
1.73
1.64
1.89
1.02
$7,522
$671,651
$1,552,419
$202,47C
$69,845
$409,764
$2,913,67^
$1,933,55£
$4,847,230
Note: Values are in 2010 dollars
Table 6 displays the total output attributable to ARRA funding. It is not known which items were
purchased with the ARRA funding. The simplifying assumption had to be applied that the ARRA-funded
proportion of the total project was spent in the same proportions as the non-ARRA funded portion. The
ARRA funding on the project results in an increase in total output of $752,069.
TABLE 6. TOTAL OUTPUT ATTRIBUTABLE TO ARRA FUNDING BASED ON RIMSII ANALYSIS
Note: Values are in 2010 dollars
V. OTHER ENVIRONMENTAL AND ECONOMIC BENEFITS
The Grant County Sanitary Sewer District (the District) provides sanitary sewer service to approximately
1,500 customers via approximately 30 miles of sewer lines. Its customers are primarily located in and
around the city of Crittenden, KY. The District also operates a sanitary sewer treatment plant, which was
operating at 60% of its maximum treatment capacity of 0.3 million gallons per day (MGD) (Grant County
Sanitary Sewer District, 2010).
The project extended sanitary sewer service along the US-25 corridor between Crittenden and Dry Ridge.
This sewer extension brings sanitary sewer service to the Grant Mobile Home Park and the Cincinnati
South Campground recreational vehicle park, both of which operated package sewage treatment plants. It
September 2013
Appendix 7-7
-------
also provides service to residences, businesses and churches that previously used septic systems.
Furthermore, service will also be available to three additional mobile home parks serviced by package
treatment plants, should they choose to connect to the sewer system. Finally, the US-25 corridor is
believed to be the area with the greatest potential for growth and development in Grant County.
Therefore, the project provides service for future development and growth (Grant County Sanitary Sewer
District, 2010).
An immediate environmental benefit of the project comes from switching the Grant County Mobile Home
Park (56 mobile home pads) and Cincinnati South Campground (12 acres in size) from package
wastewater treatment plants to centralized treatment. The average daily flow from these plants is 17,000
gallons (Grant County Sanitary Sewer District, 2010). Although the campground's plant is meeting its
discharge permit limits, the mobile home park's plant is older and is no longer meeting its permit limits
for chlorine residual (Odle, 2012) and (EPA ECHO, 2012). Therefore, shifting treatment to the Grant
County plant will improve surface water quality in the receiving streams for discharges from the two
package plants. Although the connections will increase the flow of treated effluent from the District's
treatment plant, larger centralized treatment plants can have more advanced treatment capabilities
compared to small package plants.
Another environmental benefit pertains to the new customers who switched from septic systems to the
sewer service. The original grant proposal estimated that at least 15 of the replaced septic systems had
failed. Untreated sewage from these systems may not affect ground water quality, but because of the clay
soils, sewage can seep to the surface causing health risks and odor problems (Odle, 2012).
The future economic benefits will primarily be realized when growth occurs along the US-25 corridor.
Investors in new commercial operations will have readily available sewer connections. A wood truss
factory that closed during the recession now has sewer service, which may make the industrial site easier
to sell. Among the medium-term benefits, the connecting campground and mobile home park avoid the
costs of maintaining and replacing their own package treatment plants, which is likely to be more
expensive than the sewer connection and service fees.
Bureau of Economic Analysis (BEA). 2012a. Data from U.S. Census Bureau accessed on BEA website on
November 9, 2012. http://www.bea.gov/regional/
BEA. 2012b. "Decennial Census, 2000 and 2010". "Local Area Personal Income & Employment" Interactive
tables on website. Accessed November 9, 2012. http://www.bea.gov/regional/
BEA. 2012c. RIMS II: An essential tool for regional developers and planners. Washington, D.C.; U.S.
Government Printing Office.
BEA. 2012d. RIMS II Multipliers 2002/2010: Table 1.5, Total Multipliers for Output by Detailed Industry,
Custom Study Region (Type II). October 2012.
September 2013 Appendix 7-8
-------
EPA ECHO. 2012. Available online at http://www.epa-echo.gov/cgi-
bin/effluents.cgi?permit=KY0083631&charts=viols&monlocn=all&outt=all accessed October 2012.
Grant County Sanitary Sewer District. 2010. Application of the Grant County Sanitary Sewer District for
Certificate of Public Convenience and Necessity to Construct Proposed Sanitary Sewer Line
Improvements and Approval of the Proposed Plan to Finance the Improvements. Available online at
http://psc.ky.gov/pscscf/2009%20cases/2009-00488/20091210_grant_application.pdf accessed
October 2012.
Odle, Kerry. 2012. Personal communication with SAIC. October 26, 2012.
U.S. Census Bureau. County Business Patterns, 2010 for Boone, Bourbon, Clark, Fayette, Grant, Jessamine,
Scott and Woodford counties.
September 2013 Appendix 7-9
-------
This page intentionally blank.
September 2013 Appendix 7-10
-------
APPENDIX 8: SANTA CRUZ COUNTY REDUCTION OF NONPOINT SOURCE
SEDIMENT AND PESTICIDE POLLUTION
-------
This page intentionally blank.
-------
I. PROJECT DESCRIPTION
Santa Cruz County is located along the California coastline about 73 miles south of the City of San
Francisco. The County of Santa Cruz Department of Public Works (CSCDPW) utilized $226,089 in ARRA
funding to partially finance activities under its integrated vegetation management plan (IVMP). The
CSCDPW is responsible for maintaining 600 miles of roadway in the county. In addition to road
construction and repair, the CSCDPW controls roadside vegetation to maintain good visibility along the
roadways and reduce fire risk of either on-road vehicles or fire spread across roadways. Roadside
vegetation is also a vector for the spread of invasive plants. Roadways managed by the CSCDPW have
gravel pullouts that are subject to soil erosion. The CSCDPW adopted the IVMP to replace historical
control and management measures such as pesticide application, frequent mowing and gravel addition
with more sustainable practices.
According to Connie Silva of the CSCDPW, the ARRA funding replaced approximately $200,000 in expected
State Water Resources Control Board (SWRCB) grant funds that could not be allocated to the County
because of California's fiscal crisis (Silva, 2012). The SWRCB supported the initial project phase, during
which the IVMP was developed and partially implemented at top priority sites. The ARRA funding helped
complete the IVMP project. The ARRA funding of $226,089 included 100 percent principal forgiveness.
II. REGION DESCRIPTION
Based on information provided by Connie Silva, the CSCDPW Project Manager, the project used materials
from local nurseries and employed local labor increasing the benefits to the local community (Silva, 2012).
Therefore, the study region (Figure 2) consists solely of Santa Cruz County. The main cities are Santa Cruz
and Watsonville.
September 2013
Appendix 8-1
-------
FIGURE 1. SANTA CRUZ VEGETATION MANAGEMENT ECONOMIC IMPACT STUDY REGION
\ vU ,_ ,:..». c )! i N - i ^i r ^_t
&& \ s J>>v r **'&*?* v i -r-'
vag; f^r;vr-%.
-Safmarrascb " ^ < V ', *,**-
"'-/ ^ I1 a^y » '
"' S\ i/ \ * V 1
^-. ^x v J1^ ",-J^,^ j i ^
<* J* ", V ''-^ -i,,^>
. ;
1C-
j- t J'~
^<%
Legend
ShKty jV*B
~ Places
Ooun
-------
POPULATION
Table 1 reports population and real per capita income data for the study region. The population within the
study area grew by 0.3 percent annually between 2000 and 2010. Real per capita income growth
decreased at an average annual rate of 1.2 percent between 2000 and 2010 from $52,611 to $46,586. For
perspective, during the same period per capita income in the United States increased at average annual
rate of 0.2 percent while the State of California experienced an annual average decrease of 0.2 percent.
TABLE 1. POPULATION AND REAL PER CAPITA INCOME FOR SANTA CRUZ, CA, 2000-2010
^K^^^^^^^l
Population
Per Capita Income
POPULATION PERCENT CHANGE, 2000-2010
2000
255,835
$52,611
2010
262,880
$46,586
TOTAL
2.8%
-11.5%
ANNUAL
0.3%
-1.2%
Source: BEA, 2012a and 2012b
Note: Values are in 2010 dollars
LOCAL ECONOMY
Within the study region, state and local government employs a significant portion of the full- and part-
time workers, accounting for 12.8 percent of total employment in 2010, up from 12.1 percent in 2001
(Table 2). Next, health care and social assistance account for 10.7 percent and retail trade accounts for
10.6 percent of employment.
Employment trends varied substantially across sectors. Overall, there was a loss of 8,952 jobs or
approximately 6 percent of 2001 employment. The manufacturing sector experienced the largest decline
with 3,800 fewer employees - a loss of over one-third of sector jobs. Construction industry losses were
also high with a reduction of 23 percent from 2001 to 2010 - a loss of 2,053 jobs. The largest percentage
loss, however, accrued to the information sector, which lost 1,724 jobs, or over 50 percent. Employment
grew in some sectors. The sector that experienced the largest gain between 2001 and 2010 was the
health care and social assistance sector, which increased by over 1,800 new employees. The educational
services sector grew by more than 50 percent with a gain of over 1,400 jobs.
September 2013
Appendix 8-3
-------
TABLE 2. EMPLOYMENT BY INDUSTRIAL SECTOR, 2001 AND 2010
INDUSTRY SECTOR
Total Employment
Farm Employment
Forestry, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate, Rental and Leasing
Professional and Technical Services
Management of Companies
Administrative and Waste Services
Educational Services
Health Care and Social Assistance
Arts, Entertainment and Recreation
Accommodation and Food Services
Other Services
Federal Government, Civilian
Military
State and Local Government
147,338
7,912
1,074
140
(D)
8,820
10,317
4,322
17,561
(D)
3,260
3,926
6,743
12,522
2,222
6,928
2,451
12,955
4,600
11,403
8,913
562
472
17,893
138,386
8,463
(D)
(D)
186
6,767
6,517
4,575
14,610
1,873
1,536
4,566
7,587
11,105
1,921
6,977
3,861
14,789
4,869
10,127
8,259
548
425
17,775
PER2ZTOF
100.0%
5.4%
0.7%
0.1%
(D)%
6.0%
7.0%
2.9%
11.9%
(D)
2.2%
2.7%
4.6%
8.5%
1.5%
4.7%
1.7%
8.8%
3.1%
7.7%
6.0%
0.4%
0.3%
12.1%
EQ|
100.0%
6.1%
(D)
(D)
0.1%
4.9%
4.7%
3.3%
10.6%
1.4%
1.1%
3.3%
5.5%
8.0%
1.4%
5.0%
2.8%
10.7%
3.5%
7.3%
6.0%
0.4%
0.3%
12.8%
Source: BEA, 2012b
(D) = Not reported for non-disclosure purposes.
Note: Totals include employment that is not displayed in the sector breakout because of non-disclosure issues.
September 2013
Appendix 8-4
-------
QUANTITATIVE ANALYSIS INPUTS
The data collection efforts for this analysis focused on obtaining complete, accurate and descriptive data
related to the vegetation management project. CSCDPW provided a detailed invoice with line items for
each component.
SAIC made the following adjustments to transform the component cost data into inputs for the RIMS II
model (BEA, 2012c):
Assign each cost component to an industrial category.
Split item costs into material and labor categories.
Identify which material and labor line items were not local purchases, if any.
Disaggregate local material costs into transportation costs, wholesaler costs and wholesaler
profit.
For each line item of the expenditure data, SAIC assigned one of the 406 RIMS II industrial categories that
best matched the component description. Where the source of purchase was not readily identified, SAIC
applied Census County Business Patterns (U.S. Census Bureau, 2012) data to determine whether there
were businesses within the study region that could supply the item in question. This adjustment is
necessary because materials provided by vendors outside the region (e.g., specialized equipment)
represent leakage of dollars that are spent outside of the study region. Finally, SAIC used National Income
and Product Accounts (NIPA) data to disaggregate expenditures on materials (i.e., purchaser cost) into
cost components for wholesaler value, wholesaler markup and transportation costs.
Table 3 displays the itemized expenditures for the water line and pump station construction. For the total
contract amount of $839,700, $586,700 was used to purchase materials and $253,000 went to labor.
Because all expenditures for this project are local, the entire project value of $839,700 is considered as a
direct impact and is input into the model to calculate the multiplier effects.
September 2013
Appendix 8-5
-------
TABLE 3. TOTAL EXPENDITURES FOR VEGETATION MANAGEMENT
Wholesale Purchases
(includes transportation costs)
AMOUNT
$586,700
Household Income (labor)
$253,000
Total
$839,700
Note: Values are in 2010 dollars.
IV. REGIONAL ECONOMIC IMPACTS
The regional economic impacts measure the increase in total economic output for the study region as a
result of the vegetation management spending attributable to ARRA funding. For the study region, the
total economic output increased by $2,009,807. The total project value is $839,700 and the indirect and
induced impacts are a combined $1,170,107, which implies a total impact-to-project value ratio of 2.39:1
(i.e., each dollar spent on the project resulted in a regional economic impact of approximately $2.39
including the initial expenditures and the indirect and induced demand changes).
The labor and materials applied in this project are attributable to three industrial sectors: construction,
professional and technical services and households. The multipliers for construction and professional and
technical services are both 1.62 whereas the households sector has a multiplier of 0.88 (Table 4). A higher
multiplier indicates that direct expenditures on the products of that industry have a higher tendency to
cycle throughout the regional economy multiple times via input-output linkages in local industries. The
households multiplier of 0.88 means that household expenditures are more likely to leak outside the
regional economy because of purchases of goods that are manufactured elsewhere and services
purchased from suppliers outside the region. In addition, the households multiplier reflects leakages in
the form of taxes and savings.
TABLE 4. TOTAL OUTPUT BY INDUSTRY BASED ON RIMSII ANALYSIS
INDUSTRIAL SECTOR
Construction
Professional and Technical Services
Households
Indirect and Induced Impacts
Total Project Value (including direct impact)
Total Output Impact
$16,225
$570,475
$253,000
1.62
1.62
0.88
_L
$26,349
$921,371
$222,387
$1,170,107
$839,700
$2,009,807
Note: Values are in 2010 dollars. Totals may not add to detail because of independent rounding.
September 2013
Appendix 8-6
-------
Table 5 displays the total output attributable to ARRA funding. It is not known which items were
purchased with the ARRA funding. The simplifying assumption had to be applied that the ARRA-funded
proportion of the total project was spent in the same proportions as the non-ARRA funded portion. The
ARRA funding on the project results in an increase in total output of $541,141.
TABLE 5. TOTAL OUTPUT ATTRIBUTABLE TO ARRA FUNDING BASED ON RIMSII ANALYSIS
DIRECT
Total Output
$226,089
INDIRECT AND
INDUCED
$315,052
TOTAL OUTPUT
$541,141
Note: Values are in 2010 dollars.
V. OTHER ENVIRONMENTAL AND ECONOMIC BENEFITS
The County of Santa Cruz Department of Public Works (CSCDPW) utilized $226,089 in ARRA funding to
partially finance activities under its integrated vegetation management plan (IVMP). According to the
CSCDPW Project Manager, Connie Silva, the ARRA funding replaced approximately $200,000 in expected
State Water Resources Control Board (SWRCB) grant funds that could not be allocated to the County
because of California's fiscal crisis (Silva, 2012).
The IVMP seeks to address water quality problems in perennial streams that are caused by pesticide
runoff and soil erosion from areas along county-maintained roads. Historically, CSCDPW roadside
maintenance practices along over 600 miles of roadway include mowing and herbicide application to
roadside plants. The objectives of these practices were to improve driver visibility, reduce fire risk, and
reduce the spread of invasive species. Such practices can, however, adversely affect water quality in
surface waters that receive sediment loads and pesticide runoff from the maintained areas. Therefore,
the IVMP alters maintenance practices for roadside management areas that are within 150 feet of
perennial waters - defined as including streams, ponds, lakes or inundated wetlands (URS, 2008).
URS (2008) reports that nine stream segments in the County are impaired because of sedimentation and
siltation and lists road construction and nonpoint sources among the potential sources of impairment.
These segments account for more than 53 stream miles. Impaired waters are those that do not meet
water quality standards (WQS) adopted by California pursuant to Section 303 of the Clean Water Act (40
CFR Part 131 and Part 132). WQS are adopted to protect designated uses for a water body such as
supporting aquatic life.
Water quality impairment for aquatic life is a particular concern in streams and wetlands throughout the
County that are designated as critical habitat for several species listed as threatened or endangered
species: Coho salmon (Oncorhynchus kisutch), steelhead (Oncorhynchus mykiss), the tidewater goby
(Eucyclogobius newberryi)], and the California red-legged frog (Rana aurora draytonii). Sediment
adversely affects fisheries by reducing the amount of habitat suitable for eggs and juveniles as well as
their typical food sources (URS, 2008). Pesticides such as herbicides can be toxic to nontarget organisms in
aquatic ecosystems. Even if pesticide concentrations are not high enough to cause lethal or sublethal
effects among fish species, they may be high enough to adversely affect the food chain. Historically, the
September 2013
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CSCDPW used the pesticide RoundupTM to treat roadside vegetation (Silva, 2012). This product contains
glyphosate - a broad-spectrum herbicide that is regulated as a contaminant under the Safe Drinking
Water Act.
The intent of the IVMP is to "plan and implement roadside maintenance activities to discourage or
eliminate unwanted vegetation and promote desirable vegetation" (URS, 2008). It identifies a variety of
alternatives to control roadside vegetation including invasive species:
Mechanical controls (e.g., manual/mechanical weed removal, weed burning and weed barriers).
Cultural controls (e.g., preventing inadvertent spread of weed seeds via vehicles, disposing of
weed timely and properly, and planting appropriate native species that need less maintenance).
Chemical controls in limited circumstances when other control methods are not acceptable or
feasible.
The plan also identifies several erosion control measures (e.g., mulching, erosion control blankets and
wattles).
After implementing the IVMP measures at the ten high-priority sites, monitoring efforts show that the
measures have substantially reduced invasive plant species without extensive pesticide use. Furthermore,
the sediment runoff from project sites has been reduced. For example, URS (no date) indicates that the
run-off control measures along Upper Zayante Creek have reduced sediment runoff by 98% (i.e., from 0.5
to 0.01 tons per year). The expected benefits include improved aquatic habitat in the affected streams as
well as downstream waters. These improvements may help fisheries recover.
Removing invasive plant species and replacing them with indigenous species also has benefits
independent of water quality concerns. First, these controls will improve riparian habitats. Furthermore,
removing and destroying invasive plant species helps reduce the risk of their spread to public and private
property throughout the County and, consequently, reduces future control costs.
There are several economic benefits of the IVMP implementation effort. First, the CSCDPW should benefit
from lower roadside maintenance costs in the affected areas. Following the intensive management phase
funded by the ARRA and state grants, future costs to control invasive species should be lower. In addition,
replacing tall road-side vegetation with low-growing species can help reduce future mowing costs
incurred to maintain visual and fire protection benefits.
Additional benefits are associated with the implementation program, which emphasized multiple training
components. First, several adults from the Community Action Board received training in species
identification and in proper nuisance species removal techniques. Similarly, CSCDPW road maintenance
crews learned new vegetation management and sediment control measures that they can apply as
needed in other locations throughout the County. Finally, the CSCDPW generated several outreach
materials including the IVMP and follow-up site reports and a video that shows the IVMP measures for a
variety of sites. Ms. Silva reports broad interest in these outreach materials by neighboring counties that
face similar challenges, as well as other agencies that are interested in improving sediment management
practices.
September 2013 Appendix 8-8
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Bureau of Economic Analysis (BEA). 2012a. Data from U.S. Census Bureau accessed on BEA website on
December 7, 2012. http://www.bea.gov/regional/
BEA. 2012b. "Decennial Census, 2000 and 2010". "Local Area Personal Income & Employment" Interactive
tables on website. Accessed December 5, 2012. http://www.bea.gov/regional/
BEA. 2012c. RIMS II: An essential tool for regional developers and planners. Washington, D.C.; U.S.
Government Printing Office.
BEA. 2012d. RIMS II Multipliers 2002/2010: Table 1.5, Total Multipliers for Output by Detailed Industry,
Custom Study Region (Type II). November 2012.
Silva, C. Santa Cruz Department of Public Works. 2012. Personal communication with SAIC. November 26,
2012.
URS Corporation (URS). 2008. County of Santa Cruz Integrated Vegetation Management Plan for Roads
Near Perennial Waters. Prepared for County of Santa Cruz Department of Public Works. Available
online at http://www.dpw.co.santa-cruz.ca.us/Operations/IVMP_Feb08.pdf, accessed November
2012.
URS. no date. Upper Zayante Creek Sedimentation Reduction Project Results. Available online at
http://www.dpw.co.santa-
cruz.ca.us/Operations/HerbicideReductionProject/U pper_Zayante_Creek_Rd-Complete_Report.pdf.
Accessed December 2012.
U.S. Census Bureau. 2012. County Business Patterns, 2010 for Santa Cruz County, CA.
September 2013
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APPENDIX 9: ST. PAUL PORT AUTHORITY BEACON BLUFF ASSESSMENT AND
CLEANUP
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I. PROJECT DESCRIPTION
In 2009, the St. Paul Port Authority (SPPA) received $1.6 million in ARRA funding through four grants to
support the assessment and cleanup of urban brownfields sites contaminated with petroleum and other
hazardous substances. Most of the funding, which was awarded through the EPA Brownfields Program,
supported the Beacon Bluff redevelopment project. This project transformed the former 11.4-acre 3M
manufacturing site located along Phalen Avenue in St. Paul. Prior to redevelopment, the site had
contaminated soils and over 200,000 square feet of contaminated industrial structures (SPPA, 2009).
The ARRA-funded redevelopment project comprised multiple site assessments and extensive cleanup
efforts to make the brownfields site construction-ready for new businesses. Although the assessments
were initiated before the recession, the ARRA funding was critical to keep the project going during the
recession. The cleanup phase could not have gotten underway without the ARRA funding (Hilleman,
2012). As a result of the ARRA-funded cleanup efforts, the former 3M industrial site is ready for
redevelopment and lots are being sold to companies that can meet hiring requirements designed to
maximize the benefit of SPPA's investment to the St. Paul economy.
II. REGION DESCRIPTION
The St. Paul study region (Figure 1) consists of four counties in eastern Minnesota, all of which are within
the Minneapolis-St. Paul-Bloomington metropolitan statistical area (MSA). The market area reaches the
Wisconsin border, but only includes counties within Minnesota to remain consistent with the
economically independent defined MSA. The study region counties are: Dakota, Hennepin, Ramsey and
Washington. According to the St. Paul Port Authority, the labor employed in the brownfields assessment
and cleanup project is from within the study region and that most of the earnings are likely to be spent
within the study region.
September 2013
Appendix 9-1
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FIGURE 1. ST. PAUL PORT AUTHORITY BROWNFIELDS ASSESSMENT AND CLEANUP
PROJECT ECONOMIC IMPACT STUDY REGION
Sherbume
Wnght
Carver
Anoka
Hennepin
Ramsey
Chisago
' Polk, Wl
Washington
'St. Croix. Wl
Pierce. Wl
Scoff
Legend
Study Area
, MN Counties
, Wl Counties
Rice
Dakota
\
' \
Goodhue
September 2013
Appendix 9-2
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POPULATION
Table 1 reports population data for the study region components. In aggregate, the population in the
communities within the study area grew by 0.5 percent annually between 2000 and 2010 driven by the
growth in Washington and Dakota counties, which increased by 1.7 percent and 1.1 percent, respectively.
Population in Hennepin and Ramsey counties increased at a rate below the average growth rate for the
combined study region.
TABLE 1. POPULATION CHANGES IN SELECTED AREAS, 2000-2010
Dakota, MN
Hennepin, MN
Ramsey, MN
Washington, MN
Total
POPULATION PERCENT CHANGE, 2000-2010
2000
357,848
1,117,775
511,520
202,686
2,189,829
2010
399,155
1,154,067
509,259
238,983
2,301,464
TOTAL
11.5%
3.2%
-0.4%
17.9%
5.1%
ANNUAL
1.1%
0.3%
0.0%
1.7%
0.5%
Source: BEA, 2012a
LOCAL ECONOMY
Within the study region, employment in the health care and social assistance sector is substantial,
accounting for 11.7 percent of total employment in 2010, up from 9.2 percent in 2001 (Table 2). Next,
local and state government and retail trade account for 9.4 percent and 8.9 percent of 2010 employment,
respectively.
Employment trends varied substantially across sectors. Employment growth rates ranged from a net loss
of more than 27 percent in the construction industry to a net gain of almost 49 percent in the educational
services industry. The health care and social assistance sector added the most employees between 2001
and 2010 with over 46,000 new employees. Over the same period, employment in the manufacturing
sector declined by over 41,700 representing a loss of almost 25 percent.
September 2013
Appendix 9-3
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TABLE 2. EMPLOYMENT BY INDUSTRIAL SECTOR, 2001 AND 2010
INDUSTRY SECTOR
Total Employment
Farm Employment
Forestry, Fishing and Hunting
Mining
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate, Rental and Leasing
Professional and Technical Services
Management of Companies
Administrative and Waste Services
Educational Services
Health Care and Social Assistance
Arts, Entertainment and Recreation
Accommodation and Food Services
Other Services
Federal Government, Civilian
Military
State and Local Government
1,726,401
3,724
867
958
4,549
77,482
170,829
89,608
177,177
53,688
54,520
120,678
55,839
133,167
59,965
101,678
36,235
158,619
37,833
102,613
86,649
19,610
9,267
158,422
1,745,504
3,331
955
977
4,734
56,182
129,038
79,261
155,868
54,657
42,833
136,975
76,211
143,369
61,916
104,510
53,933
205,016
46,131
107,415
85,927
20,412
9,366
163,927
PERCENT OF TOTAL
100.0%
0.2%
0.1%
0.1%
0.3%
4.5%
9.9%
5.2%
10.3%
3.1%
3.2%
7.0%
3.2%
7.7%
3.5%
5.9%
2.1%
9.2%
2.2%
5.9%
5.0%
1.1%
0.5%
9.2%
100.0%
0.2%
0.1%
0.1%
0.3%
3.2%
7.4%
4.5%
8.9%
3.1%
2.5%
7.8%
4.4%
8.2%
3.5%
6.0%
3.1%
11.7%
2.6%
6.2%
4.9%
1.2%
0.5%
9.4%
Source: BEA, 2012b
Note: Totals include employment that is not displayed in the sector breakout because of non-disclosure issues.
September 2013
Appendix 9-4
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Real per capita income in the study area decreased at average annual rate of 0.7 percent between 2000
and 2010 to $44,453 as wages failed to keep pace with inflation (Table 3). The variation in per capita
incomes among counties within the study region is substantial with Hennepin having the highest 2010
real per capita income of $55,122 and Ramsey having the lowest of $43,787. Real per capita income
between 2000 and 2010 decreased at the fastest rate in Dakota County at average annual decline of 0.7
percent. For perspective, during the same period per capita income in the United States and in Minnesota
both increased at average annual rate of 0.2 percent.
TABLE 3. REAL PER CAPITA INCOME FOR SELECTED AREAS, 2000 AND 2010
COUNTY/CITY
Dakota, MN
Hennepin, MN
Ramsey, MN
Washington, MN
Weighted Average
PER CAPIT
$47,535
$56,268
$43,263
$47,229
$50,967
A INCOME
$44,453
$55,122
$43,787
$47,033
$49,923
PERCENT CHAP
-6.5%
-2.0%
1.2%
-0.4%
-2.0%
JGE, 2000-2010
-0.7%
-0.2%
0.1%
<0.0%
-0.2%
Source: BEA, 2012b
Note: Values are in 2010 dollars
QUANTITATIVE ANALYSIS INPUTS
The data collection efforts for this analysis focused on obtaining complete, accurate and descriptive data
related to the SPPA brownfields assessment and cleanup projects. The SPPA provided a detailed invoice
with line items for each component. The costs in the document are bid data provided by the general
contractors.
SAIC made the following adjustments to transform the component cost data into inputs for the RIMS II
model:
Assign each cost component to an industrial category.
Split item costs into material and labor categories.
Identify which material and labor line items were not local purchases, if any.
For each line item of the expenditure data, SAIC assigned one of the 406 RIMS II industrial categories that
best matched the component description. Where the source of purchase was not readily identified, SAIC
applied Census County Business Patterns (U.S. Census Bureau, 2010) data to determine whether there
were businesses within the study region that could supply the item in question. This adjustment is
necessary because materials provided by vendors outside the region (e.g., specialized equipment)
represent leakage of dollars that are spent outside of the study region.
Table 4 displays the itemized expenditures for the brownfields assessment and cleanup. The total contract
amount of $1,600,000 was used to purchase contract services of various types. None of the ARRA funding
was allocated to labor at SPPA. Therefore, the table shows household income for the direct demand
September 2013
Appendix 9-5
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business as zero; induced demand attributable to workers in the contracted industries are embedded in
those industries' multipliers. Because all expenditures for this project are local, the entire project value of
$1,600,000 is considered as a direct impact and is input into the model to calculate the multiplier effects.
TABLE 4. TOTAL EXPENDITURES FOR BROWNFIELDS ASSESSMENT AND CLEANUP
Production and Services
Household Income (labor)
Total
$1,600,000
$0
$1,600,000
Note: Values are in 2010 dollars
IV. REGIONAL ECONOMIC IMPACTS
The regional economic impacts measure the increase in total economic output for the study region as a
result of the SPPA brownfields assessment and cleanup spending attributable to ARRA funding (BEA,
2012c). For the study region, the total economic output increased by $4,739,245, which is attributable to
funding made available through ARRA. The total direct expenditure is $1,600,000 and the indirect and
induced impacts are a combined $3,139,245, which implies a total impact-to-project value ratio of 2.96:1
(i.e., each dollar spent on the project resulted in a regional economic impact of approximately $2.96
including the initial expenditures and the indirect and induced demand changes).
The multipliers vary by industry with other services having the highest at 2.07 and the utilities sector
having the lowest at 1.50 (Table 5). A higher multiplier indicates that direct expenditures on the products
of that industry have a higher tendency to cycle throughout the regional economy multiple times via
input-output linkages in local industries. The Beacon Bluff project was a "big shot in the arm" for the
regional economy because many earth and utility workers were unemployed and ARRA funding played an
important role in leveraging other funding to keep the project on pace: "Folks can say about ARRA what
they want, but I saw it feed families" (Hilleman, 2012).
TABLE 5. TOTAL OUTPUT BY INDUSTRY BASED ON RIMSII ANALYSIS
1
Utilities
Construction
Professional and Technical Services
Administrative and Waste Services
Other Services
Indirect and Induced Impacts
Total Project Value (including direct impact)
Total Output Impact
$1,128
$236,019
$943,666
$392,637
$26,550
1.50
2.01
1.99
1.86
2.07
$1,689
$473,784
$1,880,034
$728,734
$55,004
$3,139,245
$1,600,000
$4,739,245
Note: Values are in 2010 dollars. Totals may not add to detail because of independent rounding.
Consists of various services including government-owned enterprises
September 2013
Appendix 9-6
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V. OTHER ENVIRONMENTAL AND ECONOMIC BENEFITS
The SPPA obtained ARRA funding in the form of four grants or loans (EPA, 2009):
Two $200,000 assessment grants to perform multiple environmental site assessments to
characterize hazardous substance and petroleum contamination at two brownfields sites
including Beacon Bluff.
A $200,000 cleanup grant for hazardous substances to clean up the Minehaha Lanes site, a
vacant bowling center and parking lot that was previously an unpermitted dump with
contaminants including metals, vinyl chloride and other volatile organic compounds.
A $1,000,000 revolving loan fund grant to support cleanup activities at the Beacon Bluff site.
Because the Beacon Bluff project received a majority of ARRA funding, the discussion in this section
focuses on the environmental and economic benefits of that redevelopment project.
The redevelopment site that is now called Beacon Bluff served most recently as 3M's global headquarters
and production facility, but supported other industries including a foundry over the past century. These
industrial activities left the soils contaminated with hazardous substances such as benzo(a)pyrene,
polychlorinated biphenyls, iron, lead and arsenic. Some contaminants posed a risk to ground and surface
waters via stormwater leaching and run-off (SPPA, 2009). In addition, the older on-site buildings were
contaminated with lead paint and asbestos (SPPA, 2009).
The SPPA acquired the 3M property with the intent of removing the contaminated soils and structures
and preparing the site for sale to commercial ventures. Thus, the medium- and long-term benefits of the
project are two-fold: a variety of environmental and health risk reductions because of the cleanup and
economic growth from attracting new companies to the business center.
The SPPA routinely acquires and cleans up brownfields sites that are 'shovel-ready' building sites suitable
for developing new industrial or business centers to attract commercial and industrial investment to St.
Paul. SPPA's process also encourages green redevelopment efforts. For example, SPPA provides an
incentive to recycle demolished structures by requiring demolition contractors to submit salvage credits
for the nonferrous materials that are recycled by the contractor (e.g., building materials crushed and used
for site fill material). For the Beacon Bluff project, SPPA estimates that 80% to 90% of the materials from
the demolished structures have been recycled (Hilleman, 2012).
The ARRA-funded Beacon Bluff project also provided a demonstration site for stormwater handling
innovations. Sitework included designing, installing and continued monitoring of a 'Next Generation'
stormwater infiltration basin. The basin was a collaborative effort between SPPA, the City of Saint Paul,
the Capitol Region Watershed District, Loucks Associates and the University of Minnesota (Enterprise
Minnesota, 2010). It included several innovative approaches to constructing and monitoring an
engineered infiltration basin that captures and treats stormwater runoff from 143.6 acres of neighboring
residential and brownfields areas (Shopek, 2012). The infiltration basins demonstrate innovative use of
recycled materials and engineered soil to remove contaminants from stormwater flows that are small
enough to percolate through the soil. For larger flows, there are three 10-foot diameter culverts that
convey stormwater underground, away from the site. A sump manhole that conveys water to the culverts
contains the first field installation of a Saint Anthony Falls Laboratory (SAFL) baffle, designed by the Saint
September 2013 Appendix 9-7
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Anthony Falls Laboratory at the University of Minnesota with support from the Minnesota Department of
Transportation. The SAFL baffle is the result of a multi-year research and development process to design a
simple vertical-mounted perforated metal plate that almost completely reduces sediment concentrations
in high stormwater flows that would normally wash out the settled sediments in sumps (e.g., from 100-
500 milligrams per liter (mg/L) without the baffle to almost 0 mg/L with the baffle) (Mclntire et al., 2012).
These innovative technologies may lead to improvements in stormwater management in other urban
areas and, thereby, have indirect environmental benefits beyond the study area.
In addition to improving environmental quality, the redevelopment project should improve the city's
economic conditions. Because the Beacon Bluff project is located in one of the poorer St. Paul
neighborhoods, bringing businesses and jobs to the neighborhood has social benefits associated with
replacing a source of blight with a productive business center. If future business center buyers are new
businesses to the St. Paul area, then they will also bring new industrial and commercial employment
opportunities to the region. These jobs tend to have higher-than-average wages. For example, employees
in the industrial sector earn an average of $4,400 more than the average for all employees in St. Paul (ICIC
et al., 2012). Furthermore, ICIC et al (2012) show that industrial sector growth can also improve municipal
finances because the city earns a dollar of industrial sector revenue for every $0.60 to $0.70 spent to
support this sector.
September 2013 Appendix 9-8
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Bureau of Economic Analysis (BEA). 2012a. Data from U.S. Census Bureau accessed on BEA website on
December 7, 2012. http://www.bea.gov/regional/
BEA. 2012b. "Decennial Census, 2000 and 2010". "Local Area Personal Income & Employment" Interactive
tables on website. Accessed December 22, 2012. http://www.bea.gov/regional/
BEA. 2012c. RIMS II: An essential tool for regional developers and planners. Washington, D.C.; U.S.
Government Printing Office.
BEA. 2012d. RIMS II Multipliers 2002/2010: Table 1.5, Total Multipliers for Output by Detailed Industry,
Custom Study Region (Type II). December 2012.
Enterprise Minnesota. 2010. "Eco-Development: The St. Paul Port Authority's Beacon Bluff
redevelopment project features a state-of-the-art storm water treatment system." Enterprise
Minnesota Magazine. December, 2010. Available online at
http://www.enterpriseminnesota.org/resources/magazine-enewsletter/enterprise-minnesota-
magazine/2010-december/eco-development.html, accessed January, 2013.
EPA. 2009. Brownfields 2009 Assessment, Cleanup and Revolving Loan Fund Grant Fact Sheet, St. Paul
Port Authority, MN.
http://cfpub.epa.gov/bf_factsheets/gfs/index.cfm?event=factsheet.display&display_type=PDF&xpg_id
=6894
Hilleman, Monte. 2012. St. Paul Port Authority. Interview with SAIC and EPA. December 20, 2012.
Initiative for a Competitive Inner City (ICIC), Interface Studio, and Penn School of Design. 2012. An
Industrial Strategy for Saint Paul. Report prepared for the Saint Paul Port Authority. May 2012.
Mclntire, K., A. Howard, O. Mohseni, and J. Gulliver. 2012. Standard Sumps as Best Management Practices
for Stormwater Treatment (Volume 2). Report prepared for Minnesota Department of Transportation.
MN/RC 2012-13. Available online at http://www.dot.state.mn.us/research/TS/2011/201108.pdf,
accessed January, 2013.
St. Paul Port Authority (SPPA). 2009. Draft USEPA Brownfields Cleanup Grant - Narrative Proposal 3M
Parcel 5. Available online at http://www.sppa.com/wp-content/uploads/2009/10/DRAFT-10-2-
Cleanup-Grant-3M-Parcel-5.pdf, accessed January, 2013.
Shopek, J. 2012. Beacon Bluff Next Generation Storm Water System. Presentation for the 2012 American
Planning Association Minnesota Conference. Available online at
http://www.plannersconference.com/pdf/sessions/t900_Next%20Generation%20Stormwater%20Ma
nagement.pdf, accessed January, 2013.
U.S. Census Bureau. 2012. County Business Patterns, 2010. http://www.census.gov/econ/cbp/, accessed
August 15, 2012.
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