HomeMy WebLinkAbout040326 WWU Ferry Study Executive Summary1
Washington State Ferry
System Economic Impact
Study: Executive
Summary
December 2024
Dylan Braund
James Mark Gbeda
McK Mollner
Jennifer Noveck, Ph.D.
Tony Peterson
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About the Authors
The Center for Economic and Business Research is a research center at Western Washington
University located within the College of Business and Economics. In addition to publishing the
Puget Sound Economic Forecaster, the Center connects the resources found throughout the
University to assist for-profit, non-profit, government and quasi-government agencies, and
tribal communities in gathering and analyzing useful data to respond to specific questions. We
use a number of collaborative approaches to help inform our clients so that they are better able
to hold policy discussions and craft decisions.
The Center employs students, staff, and faculty from across the University as well as outside
resources to meet the needs of those we work with. Our work is based on academic
approaches and rigor that not only provide a neutral analytical perspective but also provide
applied learning opportunities. We focus on developing collaborative relationships with our
clients and not simply delivering an end product.
The approaches we utilize are insightful, useful, and are all a part of the debate surrounding the
topics we explore; however, none are absolutely fail-safe. Data, by nature, is challenged by how
it is collected and how it is leveraged with other data sources. Following only one approach
without deviation is ill advised. We provide a variety of insights within our work – not only on
the topic at hand but also the resources (data) that inform that topic.
We are always seeking opportunities to bring the strengths of Western Washington University
to fruition within our region. If you have a need for analysis work or comments on this report,
we encourage you to contact us at 360-650-3909 or by email at cebr@wwu.edu. The Center for
Economic and Business Research is directed by Hart Hodges, Ph.D. and James McCafferty.
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Key Terms and Definitions
This page provides a summary of key terms and definitions related to IMPLAN economic impact
analyses.
The economic impact of an industry consists of three types of effects, which sum to the total
effect: direct, indirect, and induced. Definitions are drawn from Slovachek (2023).
Direct
A direct effect is the initial exogenous change in the final demand in terms of industry output,
employment, or labor income. Put simply, direct effects are economic effects of the
Washington State Ferries (WSF) itself. This includes employment created by the WSF as well as
the wages and benefits paid to employees.
Indirect
An indirect effect is the economic effect on entities outside of WSF due to WSF’s activities.
Indirect effects are the business-to-business purchases in the regional supply chain that result
from the initial spending. Thus, changes in wages or jobs in sectors that are linked or related to
the WSF are considered indirect impacts. For example, when/if WSF purchases a new vessel
that is manufactured by a firm located within Washington State, that spending generates
additional jobs and wages within Washington State.
Induced
An induced effect is the economic effect of the additional spending that occurs due to labor
income derived directly from WSF or indirectly via additional supply chain spending. For
example, changes in household spending due to changes in economic activity or employment
by the WSF. Put another way, it demonstrates how the WSF’s economic activity causes ripple
effects through the local economy.
There are also four key economic indicators that IMPLAN reports as economic effects for U.S.
models: output, value-added, labor income, and employment. Each indicator is based on the
Leontief Production Function for a given industry in the selected region in a given year, which
demonstrates the interconnectedness of the economy. Additionally, IMPLAN reports impacts to
state and federal tax revenues. From these estimated effects, multipliers can also be calculated.
Definitions are drawn from Clouse (2019) and Nealy (2023).
Employment
Employment in IMPLAN analysis is an industry-specific mix of full-time, part-time, and seasonal
employment. It is an annual average that accounts for seasonality. IMPLAN follows the same
definition as Bureau of Economic Analysis (BEA) and Bureau for Labor Statistics (BLS), which
allows for full-time/part-time annual average. Thus, one job lasting 12 months is the same as
two jobs lasting six months each and three jobs lasting four months each, and so on. A job can
be either full-time or part-time. Similarly, a job that lasts one quarter of the year would be 0.25
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jobs. Note that a person can hold more than one job, so the job count is not the same as the
count of employed persons. Jobs in IMPLAN are not the same as a full-time equivalent (FTE)
number, but can be converted to FTEs (Clouse 2017).
Labor Income (LI)
Labor income is the sum of compensation paid to employees (e.g. wages and salaries, benefits,
and payroll taxes) and payments received by self-employed individuals and/or unincorporated
businesses in a given year.
Multipliers
Multipliers are rates of change that indicate the implications a specific change a sector or
agency has on the economy. In this case, this is the rate of change WSF has on the Washington
State’s economy. For example, an output multiplier of 2.25 means that for an additional $1.00
in output in a sector, the overall economy sees an additional $1.25 of output in other sectors
(for a total increase of $2.25). There are two types of Multipliers: Type 1 Multipliers and Social
Accounting Matrix (SAM) Multipliers. Both are further described in the Methodology section
below.
Output
Output is the value of the economic activity within a given time period. In this case, it is the
value of the economic activity of the WSF’s economic activity in Fiscal Year 2023 (FY23).
State and Federal Tax
IMPLAN also includes a tax impact report, which captures all tax revenue in the study area(s)
across all levels of government that exist in that study area for the specific Industries and
Institutions affected by an Event or group of Events. State and federal tax impacts are not
reported in this study because WSF is a public agency rather than an industry. Put another way,
WSF is not a traditional tax-paying sector as it is a state-operated, managed, and funded agency
within the transportation sector.
Value Added (VA)
This is a metric used to quantify how much an industry contributes to the Gross Domestic
Product (GDP). It is the difference between the overall output of the industry and the expenses
they incur on intermediate inputs. Value added is not reported in this study because WSF
system is not a typical production-industry or sector. It is a publicly operated, managed, and
funded agency within the transportation sector with connections to many other Washington
State sectors such as tourism, recreation, and retail.
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Section 1: Introduction and Background
“We are connected by water.”
– The Maritime Washington National Heritage Area Management Plan (2022)
This study was conducted at the request of the Washington State Legislature and is funded by
the Multimodal Transportation Account as a State Appropriation. According to the
appropriation description:
The study must examine the impacts on a statewide and systemwide basis,
on all 10 routes of service provided by the Washington state ferries.
Specifically, the study must analyze the direct economic impact of
Washington state ferry system spending, along with peer-reviewed,
estimated ranges of indirect impacts on economic activities supported by the
ferry fleets’ movement of passengers and freight as it relates to tourism,
labor, and commerce. The study must also include a review of key factors
that impact the overall economy of both ferry-served communities and the
state economy, which may include impacts on housing, health care costs and
access, emergency response, climate resilience, and small business.
To say that the Washington State Ferries (WSF) are an integral part of Washington State’s
economy and culture is an understatement. The WSF system is the largest ferry system in the
United States and the second largest vehicular ferry system in the world, transporting over nine
million vehicles in 2023 (WSF System Facts 2024).
Created in 1951, WSF is a core component of the Washington State Department of
Transportation (WSDOT). The Assistant Secretary (Executive Director) of Washington State
Ferries, currently Steve Nevey, reports to the Deputy Secretary of Transportation, Mike Gribner.
WSF operates 20 terminals on 10 routes (with service to Sidney, British Columbia currently
suspended) with a fleet of 21 auto-passenger vessels. Importantly, WSF is part of the state
highway network, serving eight Western Washington counties: Island, Jefferson, King, Kitsap,
Pierce, San Juan, Skagit, and Snohomish. They provide a connection for emergency services on
and off several islands, including but not limited to Vashon Island, the San Juan Islands, and
Whidbey Island. The ferries were constructed specially to accommodate commercial vehicles
and as such they give priority loading for freight. The agency has nearly 2,000 employees and
works with 16 unions to manage 13 collective bargaining agreements. It is the 3rd largest
transit system in Washington (WSF System Facts 2024).
WSDOT’s WSF division operates on the homelands of the Coast Salish Peoples, who have lived
in the Salish Sea basin, throughout the San Juan Islands and North Cascades and Olympic
watersheds, since time immemorial. Washington State Ferries is devoted to setting precedence
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for acknowledgment and gratitude to the first inhabitants of the Salish Sea region as leaders for
their continuing care and protection of our shared lands and waterways.
It is important to note that this study is the first comprehensive economic impact analysis of
WSF. The study’s commission by the Washington State Legislature and funding via the
Multimodal Transportation Account underscores the strategic importance of WSF to
Washington State, Washington State’s economy and cultural fabric.
Given this important role, this study was subject to both WSF and peer review. WSF and the
Association for University Business and Economic Research (AUBER) were both provided with a
draft for edits and comments in November 2024. Comments and edits were received from WSF
after internal review and this report was updated accordingly. Two sets of comments were
received from AUBER reviewers, both of which highlighted the extensive detailed nature of the
report and deserving of the AUBER seal of approval. AUBER comments and suggested edits
were carefully reviewed and incorporated. All reviewers agreed that this study used
methodologies and analytical frameworks that reflect industry and academic best practices.
The rest of the Executive Summary proceeds as follows. Section 1.1 describes and defines
economic impact analysis. Section 1.2 describes the methodology behind IMPLAN analysis,
including the Input-Output Models and the Outputs and Multipliers generated by IMPLAN.
After that, the data used for the economic impact is described, followed by a section describing
the Washington State model inputs. Section 2 presents the estimated economic impacts of WSF
expenditures in 2023 on Washington State economy. It also discusses the impact of WSF on
freight movement. Due to space considerations Other Key Factors impacting Washington
State’s economies and WSF are only presented in the full-length report.
Section 3 focuses on Route Specific Economic Impacts and Ferry Community Profiles. Section
3.1 describes the model inputs for the route-specific models, including how the expenditures
were split between the counties. County profile can also be found in the full-length report.
County profiles include a very brief summary of the County’s basic geography, demography,
employment, industry, wages, healthcare, and housing. They also include a detailed description
of the ferry routes within the county and summary details of the estimated route-specific
impacts.
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1.1 About Economic Impact Analysis
Economic impact analyses are an important tool used to make decisions. However, they are
often misused, overestimated, or generally misunderstood. An economic impact analysis
measures the ripple effects of an action taken by a government, industry, household, or other
entity. The impacts include output, employment, labor income, and can also include state, local,
and federal taxes. Economic impacts reported in this study include direct, indirect, and induced
effects for three economic indicators: employment, labor income, and output. All of these
terms are defined in the Key Terms and Definitions section.
1.2 Methodology
IMPLAN Analysis
This analysis was conducted using IMPLAN, an input-output software that is widely used to
conduct impact analyses. We used two sources of data as inputs. First, we use data procured
from WSF’s budget management and accounting system, which produced WSF specific
spending by the Agency in FY2023 and FY2024. Second, we used publicly available data from
WSF route statements to model the impact of specific WSF routes on the counties they are
located within. The results provide an overview of the flow of jobs, dollars, and impacts from
WSF’s general activity on the state and each of the counties that WSF operates within. The data
for each set of models is further described in the Washington State model inputs and Route
specific model inputs sections that follow.
Input-Output Models
This study is founded on a regional input-output model (I-O model) design, used to measure a
sector or agency’s economic activity in the regional economy. These types of models are based
on the premise that economic activity (e.g. agency spending) creates additional changes in an
economy, usually represented by an increase in jobs or dollars.
The results of I-O models are dependent on both the defined region and the scope of the sector
or agency’s activities. The counties and cities that have WSF ferry routes within them are all
directly impacted by WSF’s activities. However, interagency work within WSDOT, as well as
partnerships with neighboring agencies, businesses, cities, counties, and industries means that
the WSF’s influence is broader not only than the counties that they operate within, but also
broader than Washington State, even if most of the effects still occur within the bounds of
Washington State.
Regional boundaries help to account for leakage of money outside of the defined boundaries.
For example, if WSF imports goods from other states, such as new equipment or vessels, the
economic impact of WSF expenditures on that new equipment or vessel to Washington State is
lower than it would be if WSF had bought that good from a Washington State manufacturer. It
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is important to note therefore that I-O models produce regional multipliers, which indicate the
impact of a dollar amount spent within the defined region.
Outputs and Multipliers
The four outputs produced by the IMPLAN analysis are employment, labor income, and total
value-added and output. From this information multipliers can be calculated. All of these
outputs are defined in the Key Terms and Definitions section above. It is important to note that
all outputs are estimations. This report analysis focuses on employment, labor income and
output estimations for both the state-wide impacts and route-specific impacts.
There are two types of multipliers that can be calculated from the economic impact outputs:
Type 1, which excludes induced effects, and Social Accounting Matrix (SAM) Multipliers, which
includes induced effects. Type 1 multipliers look specifically at business-to-business (B2B)
transactions (calculated as (Direct Effects + Indirect Effects) / (Direct Effects)). SAM Multipliers
take into account all effects generated by WSF spending, including induced effects. Type 1
Multipliers also do not include induced household spending impacts. If one assumes that WSF’s
major impacts are via capital related expenditures, which are primarily B2B and thus unlikely to
raise incomes and therefore stimulate household spending, then Type 1 multipliers may be
more appropriate.
We report both multipliers as a possible range for the state-wide economic impact results,
however, we recommend focusing on the smaller Type 1 multipliers because SAM Multipliers
can be high whenever capital expenditures are high and thus may overestimate the impact of a
public agency. It is also important to consider that not all of the expected labor will be long
term (i.e. construction jobs may only last the duration of the project). Furthermore,
government spending is typically not strongly associated with job growth. In this case, we can
expect some new positions can be expected based on new vessels coming online and expanded
ferry capacity over time. Some expansion of management positions is expected as well. We do
not report multipliers for the route-specific impacts because multipliers make the most sense in
the context of regional supply chains rather than distinct geographic areas.
Model Limitations
While an accurate measure of the outputs of the ferry system's economic impact, I-O models
do not include the social or cultural value or impact of transportation systems.
Economic impact analyses also do not consider opportunity costs, environmental costs-
benefits, or social costs-benefits. Another commonly ignored issue with economic impact
analyses is crowding out. For example, if the WSF hires a ferry worker who resides in Thurston
County and who works in Pierce County on the Route 40 ferry, the economic impact analysis for
the route-specific model may completely miss the spending that is occurring outside of the area
of interest (Pierce and King Counties) and therefore ultimately underestimates the impact of
WSF spending on Route 40.
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It is important to note that for interpretation purposes when one county has multiple routes,
the economic impact totals cannot be added to create a “county total impact.” This is because
model inputs vary for each route and inputs drive the output results. If the route-specific
output totals are added together, the totals will never add up to the total economic impact for
Washington State in FY23 (the year of route-specific model data) because the economic
impacts of WSF spending impacts different regions with different economic structures and
features. Each route’s individual impact must be discussed as separate and distinct economic
activity of WSF.
Economic impact analysis is a helpful tool, but it is important to keep in mind its limitations. The
analysis is highly dependent on the data quality and its user. Impact analysis does not and
cannot account for all possible outcomes.
IMPLAN analysis and economic impact analysis generally do not include model outputs that
specify impacts on housing, healthcare, climate resiliency, emergency response and many other
topics of interest and importance. Those topics are therefore reviewed in the community
profiles and other key factors sections.
Data
After reviewing the data available, two sources of data were used for this analysis. For overall
agency spending and modeling impacts to Washington State, WSF accounting data for Fiscal
Years 2023 (FY23) and 2024 (FY24) was used. This data was drawn directly from WSF’s budget
management system.
Route data was pulled from the publicly available data from the WSF Fiscal Year 2023 Route
Statements. The route statements include details on traffic (passenger, vehicle and driver)
revenue (fares, miscellaneous), as well as expenses related to vessel and terminal operation,
maintenance, and management and support. The FY23 route statement data was used only for
the route-specific impact models. The data is further described in the model inputs sections for
both the state-wide and route-specific models. In reviewing the route specific data in
comparison to the system-wide data produced from the accounting system we note that the
agency appears to have applied overhead (non-route specific) costs to the routes so that the
sum total of all routes is similar to the total agency operational expenses. This results in our
work overstating the route specific economic impacts based on the route statements.
Washington State model inputs
Using WSF’s accounting data for FY23 and FY24 expenditures, this impact analysis was
conducted using the following categories of spending data as model inputs: WSF’s Operations
and Management Program and Ferries Division Construction. WSF’s Chart of Accounts
describes these spending categories as follows.
WSF Operations and Maintenance Program
This program provides for the operations and maintenance of the WSF boats and terminals. It
contains three major categories: Daily operations of the boats and terminals, maintenance of
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both, and administrative staff support. In FY23 Operations and Maintenance expenditures
totaled $330 million. In FY24 Operation and Maintenance expenditures totaled $347 million.
Ferries Division Construction
This program implements the construction projects for terminals and vessels that are intended
to preserve or enhance WSF assets and keep them safe. This includes all capital costs of
providing ferry and terminal assets for the WSF operations and maintenance program. In FY23
Construction expenditures totaled $150 million. In FY24 Construction expenditures totaled
$209 million.
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Section 2: Washington State Economic Impacts
Due to the nature of I-O models, here we present two different sets of results. The first set of
results are based on WSF’s FY23 expenditures drawn agency budget management data. Based
on WSF’s FY23 spending ($330 million in Operations and $150 million in Construction), the total
estimated economic impact on Washington State was over $711 million in the same year, at an
estimated $711.28 million in total output. Out of the total output, approximately $420.26
million is the estimated direct impact, while the remainder is split between indirect and
induced impacts. WSF’s total estimated impact on labor income was more than $327 million.
WSF spending also could be viewed as creating an estimated total of 3,632 jobs (Full-Time
Equivalent (FTE)) in FY23.
Details of the estimated direct, indirect, and induced impacts on the State of Washington by
WSF’s FY23 spending can be found in Table 1 below.1 Note that Tables found in Section 2 (this
section) are all rounded to the nearest significant digit. Dollar amounts for labor income and
output are reported in millions with two decimal points, as we assume that impacts over
$100,000 are significant to Washington State. Tables found in Section 3 report the actual dollar
amounts for both labor income and output as some communities and economy’s are smaller
and thus even $10,000 may be significant. Employment is rounded to the nearest significant
whole number. Therefore, the rounded numbers reported in the tables in both sections may
not match the Totals when added together.
Table 1: Washington State Ferries: Estimated Economic Impact of FY23 Expenditures on
Washington State’s economy (in 2023 dollars).
Impact Employment Labor Income (millions) Output (millions)
Direct 2,405 $236.57 $420.26
Indirect 303 $25.75 $84.97
Induced 925 $65.07 $206.05
Total 3,632 $327.38 $711.28
Type 1 Multiplier 1.13 1.11 1.2
SAM Multiplier 1.51 1.38 1.69
1IMPLAN provides results to the tenth decimal point. This level of detail should not be
misinterpreted as a more precise or exact estimate. This is just simply the result of a computer
doing the math. For clarity and to increase user friendliness, we present rounded numbers in
Section 2.
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As for multipliers, the output multipliers include direct, indirect, and induced impacts. The Type
1 had a very small range from 1.11 to 1.2. That means for every dollar spent by the ferry
system, $1.11 to $1.20 of estimated activity is generated in the local economy. The SAM
Multiplier indicates more of an estimated impact, ranging from 1.38 to 1.69. For every dollar
spent by the ferry system, up to $1.69 in estimated economic activity is generated. The original
dollar plus .69 cents. The two multipliers, 1.11 – 1.69, offered here should be considered to be
a range of the actual economic impacts of WSF operations.
IMPLAN analysis does not calculate confidence intervals, however, readers can estimate
confidence in these numbers by taking into consideration data quality, model complexity,
region size, and other variability in the data or model. While we are confident in the data
quality, as previously noted these models are intended for use in measuring the economic
impacts of private industry not public agencies. Therefore, we assume that these models are
likely overestimating the economic impact of WSF and the resulting estimates should be
interpreted and used with caution.2
Table 2: Washington State Ferries: Estimated Economic Impact of FY24 Expenditures on
Washington State’s economy (in 2024 dollars)
Impact Employment Labor Income Output
Direct 2,779 $273.41 $485.71
Indirect 350 $29.76 $98.20
Induced 1,069 $75.20 $238.14
Total 4,198 $378.36 $822.05
Type 1 Multiplier 1.13 1.11 1.2
SAM Multiplier 1.51 1.38 1.69
2 It is also important to note that confidence intervals (CIs) or margins of error are not calculated
by this model because the inputs for IMPLAN analysis are discrete and do not vary. Confidence
intervals are the degree to which there is uncertainty around an estimated value based on a
sample data set. IMPLAN estimates are not based on a data set or population sample. There is no
distribution of variables. There are only two input numbers for the model: the operations and
construction total spending for FY23 and FY24. Therefore, even if one were to calculate the
degree of confidence in the estimates based upon the factors described, it would still not be a
confidence interval according to the statistical definition of the word.
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Based on WSF’s FY24 spending ($347 million in Operations and $209 million in Construction),
the total estimated economic impact on Washington State was nearly a billion dollars, at an
estimated $822.05 million in total output. Out of the total output, $485.71 million is the
estimated direct impact. WSF’s total impact to labor income based on FY24 expenditures was
estimated to be more than $378 million. WSF also impacted an estimated total of 4,198 jobs in
FY24.
Details of the estimated direct, indirect, and induced impacts on the State of Washington by
WSF’s FY23 spending can be found in Table 2. While WSF’s economic impact to Washington
State’s Economy has increased significantly from FY23 to FY24, the output multipliers for Type 1
and SAM Multipliers, including direct, indirect, and induced impacts, were the same in FY24 as
it was in FY23.
The potential costs of disruption to service in the WSF system for any reason are not
insignificant. Disruptions could include but are not limited to delays in services, sailing
cancellations, and service reductions due to lack of funding, delays in maintenance, ferry
availability, crew availability, or weather. A complete loss or closure of all WSF operations puts
an estimated $711 million in total estimated economic activity at risk, based on FY23 spending,
and $822 million at risk based on FY24 spending. More likely and common route-specific
disruptions and their real and potential impacts are further discussed in Section 3.
Freight
In addition to the estimated economic impact of WSF’s expenditures on Washington State’s
economy, WSF also impacts the Washington State economy and local economies through
movement of freight. To try to capture the value of freight moved on average by WSF, we used
data to calculate an estimate of the Average Daily Payload (or value of the cargo/freight) that
trucks are moving via WSF routes.
To calculate the impact of freight on commerce, calculations were performed using publicly
available WSDOT Freight Data to obtain the Average Daily Number of Trucks and Annual Truck
Tonnage for each route, which includes the estimated weight of the truck and the cargo. To
calculate Average Annual Truck Traffic, the Average Daily Number of Trucks was multiplied by
356. To calculate the Average Daily Truck Tonnage, the Annual Truck Tonnage was divided by
365.
Average Daily Freight Tonnage was calculated by multiplying the average weight of an empty
truck (14,000 pounds) (drawn from LoadUp 2024) by the Average Daily Number of Trucks for
each route. This value was then subtracted from the Average Daily Truck Tonnage for each
route to produce the Average Daily Freight Tonnage.
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Finally, the Average Daily Payload Value was calculated by multiplying the Average Daily Freight
Tonnage by the Average Value of Freight transported by trucks, which was estimated to be
$926 per ton (Bureau of Transportation Statistics 2024).
A final additional consideration to the calculation is how many trucks might be full or empty.
Studies have found that approximately 65-80% of all trucks on the road were not hauling cargo
(Uber 2023). However, there is significant variation in truck usage of WSF routes, suggesting
that it is possible mostly full trucks use some WSF routes according to WSDOT’s Freight Data.
As shown in Table 3, Route 10 (Seattle-Bremerton) only has six trucks per day on average, the
least of all routes. Route 60 (Mukilteo-Clinton) has the most average trucks per day of all ferry
routes at 190 trucks.
This data highlights how Vashon Island and the San Juan Islands are reliant upon WSF for
transportation of goods available on island (including basic necessities such as food and
medicine, as well as other consumer goods, construction materials, and so on). Unlike
Bremerton, Vashon Island and San Juan County have no alternative driving routes or bridges.
Unsurprisingly, Vashon Island and San Juan Island routes comprise the second and third most
trucks per day. 157 trucks were moved on average, daily, in the San Juan Islands (Route 80), and
145 trucks moved through Vashon Island on average daily (Routes 30 and 40 combined).
To account for potential variation in the reason for truck usage between the routes as well as
the conservative possibility that half of all trucks on WSF are empty (only full one way) we
report both the average daily payload value if all trucks were full and an average daily payload if
only half the trucks were full.
Table 3 summarizes these calculations by route.
Table 3: Washington State Ferries: Estimated Freight Impacts (WSDOT Freight Data).
Route # (Name) Average
Daily
Number of
Trucks
Average
Daily Truck
Tonnage
Average
Daily
Freight
Tonnage
Average
Daily
Payload
Value (all
trucks full)
(dollars)
Average
Daily
Payload
Value (half
trucks full)
(dollars)
Route 10: Seattle-
Bremerton 6 71 29 $26,562 $13,281
Route 30: Fauntleroy
–Southworth 7 83 34 $31,222 $15,611
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Route 30:
Southworth-Vashon 14 166 68 $62,871 $31,435
Route 40: Point
Defiance-Tahlequah 58 691 285 $263,973 $131,986
Route 70: Port
Townsend-Coupeville 60 718 298 $276,399 $138,199
Route 20: Seattle-
Bainbridge Island 62 749 315 $291,900 $145,950
Route 30: Fauntleroy-
Vashon 66 796 334 $309,050 $154,525
Route 50: Edmonds-
Kingston 134 1,614 676 $625,960 $312,980
Route 80: Anacortes-
San Juan Islands 157 1,884 785 $727,244 $363,622
Route 60: Mukilteo-
Clinton 190 2,277 947 $877,264 $438,632
The cost to disruptions to freight is hard to fully capture because delays for certain goods have
different effects, impacts, and consequences (e.g. late prescription medication delivery versus a
late delivery of new furniture versus a late delivery of asphalt). Like positive economic impacts,
negative economic impacts also ripple through economies.
As shown in Table 3, WSF is responsible for moving up to $3.49 million in freight daily between
ferry communities. Even if half of the trucks are empty, WSF is still moving an estimated $1.75
million in freight daily. Of those routes, the five routes with the largest average daily payloads
are Route 60 (Mukilteo-Clinton), Route 80 (Anacortes-San Juan Islands), and Route 50
(Edmonds-Kingston), Route 30 (Fauntleroy-Vashon-Southworth), and Route 20 (Seattle-
Bainbridge Island).
Disruptions curtail this significant economic activity and that is likely to have ripple effects
throughout supply chains. The routes with the largest average daily payloads are also the
routes to be most negatively economically impacted by freight disruptions. In most cases
disruptions are unlikely to create severe economic impact losses but do delay economic activity
and impact businesses and customers relying on those items. Because there is significant
variation in the use of routes for freight movement, a deeper discussion of freight impacts and
the potential costs of disruptions are further elucidated in Section 3 of the full report as part of
the in-depth county profiles.
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Section 3: Route Specific Economic Impacts
Due to space considerations, this section only provides summary route-specific impacts. The
potential costs of disruption to each route, as well as basic route characteristics are also
discussed.
For this additional detail, as well as the tables that breakdown the specific impact estimates for
each route in each county, please consult the full-length report.
3.1 Route Specific Model Inputs
Route-specific impact analysis was conducted using two categories of spending data as model
inputs based off of the FY23 Route Statements: labor and goods and services. Labor includes all
wages, salaries, and benefits paid to employees and non-employees.
On the route statements labor is an expense related to: direct vessel operating, direct terminal
operating, and management and support. The remainder of the route statement categories,
which we call goods and services, includes all non-labor expenses for the same three
categories. It also includes fuel for vessel operation and direct maintenance expenses on both
vessels and terminals.
We note here that our analysis of the route data in comparison to system-wide financial data
indicates that the agency has likely included non-route spending within the management and
support. Due to this, our calculations of economic impacts per route is likely overstated in
terms of localized economic impacts.
WSF routes connect two geographically disconnected places. Importantly for this study, these
geographically disconnected entities are also separate statistical areas. Therefore, while all
models in the study use FY23 Route Statements as their data input, model inputs had to be
divided between the distinct geographic and statistical units, in this case at the county level, in
order to generate route-specific outputs and results.
Ferry routes in Washington fall within the eight Western Washington counties: Island,
Jefferson, King, Kitsap, Pierce, San Juan, Skagit, and Snohomish Counties. To determine how to
split the inputs between these counties, WSF’s Gray Notebook Ridership & Revenue data for
ridership, which is publicly available, was reviewed from FY2019 – FY2024.
First, Q3 (January – March) values were subtracted from Q1 (July – September) values in order
to assess which of the routes were most heavily impacted by tourism in peak seasons. In order
to standardize the calculation, the Q3-Q1 difference was then divided by the total number of
riders for that fiscal year on that route. While there are certainly impacts to all routes in the
summer months (Q1), the two with the largest difference between Q1 and Q3 as a percent of
total riders in the same year are Route 80 (Anacortes-San Juan Islands) and Route 70 (Port
Townsend – Coupeville).
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The threshold to be considered a tourism impacted route is the difference between ridership in
Q1 and Q3 accounts for 20% or more of the total rides on that route in at least one fiscal year
FY2019 - FY2024.
Three routes are on the border of being classified as tourism impacted in at least one year:
Route 50 (Edmonds-Kingston), Route 10 (Seattle-Bainbridge), and Route 20 (Seattle-
Bremerton). However, none fall above 20% in any year. Route 30 (Fauntleroy – Vashon-
Southworth), Route 40 (Point Defiance-Tahlequah), and Route 60 (Mukilteo-Clinton) are all
solidly below 20% over time, indicating they are the routes with the least variation between
quarters over time. Put another way, those three routes are most likely to be routes that are
regularly used by residents and businesses year-round. Only on Routes 80 and 70 did 20% or
more of total rides in a year occur in summer months (Q1) in at least one fiscal year from
FY2019 to FY2024.
Table 4 shows the total riders for each route in the past five years the range of the percent of
total rides that could be attributed to tourism, and the category the route was assigned as a
result. The final column (fourth column on the far right) shows how the expenditure inputs
were split between the counties.
We distributed the expenditures between the counties that the routes connect. For commuter-
routes, we distributed the expenditures equally between the counties. On the other hand, we
assigned higher expenditures to counties considered to have tourist destinations such as
Jefferson (75%) and 25% to others (e.g. Island County). This is based on our assumption of
where the impacts are most likely to be as a result of the ferry ride. In the case of Route 80, the
split is 75% San Juan County, 25% Skagit County. For Route 70, Island County’s split was 25%
and Jefferson County’s 75%.
The remaining routes all fall below the 20% threshold for summer month rides and therefore
were all categorized as commuter routes. Commuter designated routes are all split evenly 50-
50 between the two counties. Output results should therefore be interpreted within the
context of the input splits between the counties.
Table 4: This table summarizes the ridership data used to inform the route-specific model
inputs.
Route #: Route
Name
Total Riders
(FY19-FY24)
Percent of
Total
Range
(FY19-
FY24)
Route
Type
Category
Route
Expenditure
Inputs
Labor
Services
(Total)
County
(Expenditure
Inputs %)
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Route 80:
Anacortes- San
Juan
11,226,44 14%-29% Tourism $44,773,000
$28,406,000
($73,179,000)
Skagit (25%)
San Juan (75%)
Route 70: Port
Townsend -
Coupeville
4,158,071 13%-28% Tourism $12,698,000
$5,761,000
($18,459,000)
Island (25%)
Jefferson (75%)
Route 10:
Seattle -
Bremerton
8,334,314 -.7%-19%3 Commuter $20,461,000
$12,550,000
($33,011,000)
King (50%)
Kitsap (50%)
Route 20:
Seattle –
Bainbridge
27,281,659 2%-18% Commuter $35,014,000
$19,890,000
($54,904,000)
King (50%)
Kitsap (50%)
Route 50:
Edmonds -
Kingston
20,668,722 7%-17% Commuter $31,132,000
$17,497,000
($48,629,000)
Snohomish (50%)
Kitsap (50%)
Route 60:
Mukilteo -
Clinton
21,717,399 6%-13% Commuter $27,595,000
$13,040,000
($40,635,000)
Snohomish (50%)
Island (50%)
Route 30:
Fauntleroy –
Vashon-
Southworth
14,089,134 5%-12% Commuter $32,257,500
$13,698,500
($45,956,000)
King (50%)
Kitsap (50%)
Route 40: Point
Defiance-
Tahlequah
5,048,470 .065-.11 Commuter $10,857,000
$4,378,000
($15,235,000)
Pierce (50%)
King (50%)
The estimated economic impact to of WSF FY23 Expenditures on Puget Sound Counties by
routes is summarized in Map 1. For additional detail see Section 3 of the full-length report.
It is important to note that for interpretation purposes when one county has multiple routes,
the economic impact totals cannot be added to create a “county total impact.” This is because
model inputs vary for each route; they cannot be added together. Each route’s individual
impact must be discussed as separate and distinct economic activity of WSF. The same
3 The low range value for the Seattle-Bremerton route is negative because there was a year in
which the winter quarter (Q3) ridership actually exceeded summer quarter (Q1) ridership due to
COVID-19 and related issues.
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limitations apply to the route-specific model results as the statewide and electrification impact
results.
Map 1. This map summarizes the estimated route-specific impacts of the WSF on Washington
State Counties based on WSF FY23 expenditures.
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