The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania

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The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania
The Economic Impact of Letterkenny Army
Depot in Franklin County, Pennsylvania

Spring 2016

Kurt Fuellhart
Professor of Geography

George Pomeroy
Professor of Geography and Co-Director, Center for Land Use & Sustainability

Michael Benham
Undergraduate Geography & Earth Science Major

John Kooti
Dean, John L. Grove College of Business
The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania
Executive Summary

The goal of this document is to provide the Franklin County Area Development
Corporation (FCADC) and the Franklin County community with estimates of the
economic value that Letterkenny Army Depot (LEAD) provides to the region. To
make these estimates, we have used a common analytical technique: (1) to first
measure key county economic characteristics with LEAD fully functioning as it is
today; (2) to hypothetically remove the base from the county’s economy; and (3) to
calculate the differences.

After providing some contextual and background comments, we examine several
major regional economic and demographic characteristics and their relationship to
LEAD. These assessments include calculations related to: employment, value added
(income); earnings, labor force, population, and capital stock.

Using the REMI PI+ modeling system, we estimate LEAD’s impact on the county. As
would be expected, the effect of the base is very large, both in absolute terms and
relative to Franklin County’s economy as a whole. Among the results, we found that
LEAD – directly or indirectly – is related to:
     About 8.1% of county employment;
     About 8.5% of county value added;
     About 9% of county earnings;
     A significant portion of the labor force and population;
     A potentially large percentage of residential and non-residential property

This report is organized as follows. In the next section, Section A, we provide a bit of
context for the region and for the study. Section B provides the details and results of
the economic modeling, assuming the hypothetical removal LEAD from the economy
in 2016.


Franklin County Area Development Corporation (FCADC)
1900 Wayne Road
Chambersburg, PA 17202 U.S.A.

Department of Geography & Earth Science
Shippensburg University
Shippensburg, PA 17257 U.S.A.

We are grateful for the assistance by the following:
 Mike Ross and David Mackley, Franklin County Area Development Corporation, and staff
 Nadine Stoler, Chief of Staff, Letterkenny Army Depot, and her staff
 John Van Horn, Letterkenny Industrial Development Authority, and his staff
 Joseph Spielbauer, Pennsylvania Military Community Enhancement Commission
 Scott Nystrom, Regional Economic Models, Inc.

All findings and interpretations are those of the authors, as are all mistakes, omissions etc.

The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania
Section A. Overview

Ever since operations began 74 years ago, Letterkenny Army Depot (LEAD) has and
continues to play a substantial economic role in Franklin County, Pennsylvania. It
remains, by a substantial margin, the county’s largest employer, largest source of
personal income, and the largest single contributor to the county’s gross regional
product. These substantial impacts come because of the depot’s activities in
supporting the nation’s defense mission. LEAD, in pursuing that mission, provides
an extensive array of repair, maintenance, and storage services for a wide array of
military equipment and war materiel, including the Patriot, Avenger, and other
missile systems.
Franklin County, with LEAD placed near the geographic center of the county, is
located in South Central Pennsylvania and situated at the western edge of the major
metro areas of the east coast. These geographical circumstances are compounded by
the fact that Interstate 81 bisects the county and provides excellent access to major
metropolitan centers of the Eastern Seaboard via the interstate system. Thus, in
recent years, the county has ranked among the fastest growing counties in the state.
Due to this growth the county, with a current population of 153,638 (2015 est.), was
designated a metropolitan area by the U.S. Census Bureau in 2013.

Letterkenny Army Depot: Contemporary Activities, General Historical
Background, and the Impacts of Prior BRAC Cycles
The 18,000-acre depot is located 5 miles north of Chambersburg and straddles rural
Greene and Letterkenny townships. Total federal civilian employment at the depot
is 2,800 along with some contractors and a small number of military personnel.
These numbers are detailed later in this report. Taken together, LEAD is easily the
largest single employer in Franklin County. LEAD is distinctive from many military
facilities as its percentage of military personnel is relatively small compared to its
federal civilian employment.
There are two sets of operations at LEAD. The leading activity is the repair,
maintenance, and storage of missile systems and other military equipment. Over
time, specific missions have included the Patriot Launcher, High-Mobility
Multipurpose Wheeled Vehicle (HUMVEE) repair, Biological Integrated Detection
Systems (BIDS), Aviation Ground Power Units (AGPU), and Soldier Support
Systems. The depot has been recognized with nine Shingo Awards for its
manufacturing excellence, with the most recent being awarded in 2013. An
expansion of the depot’s main shop incorporating a Component Rebuild Facility was
announced in March 2016 to support the repair and recapitalization of Patriot
missile systems and ground support equipment. In the last several years, contract
employees have become an increasingly large share of the workforce.
A secondary activity is ammunition storage and disposal. These activities, organized
as Letterkenny Munitions Center (LEMC), are distributed across 16,000 of the
depot’s 18,000 acres. Ammunition is stored in 902 earth sheltered igloos, 10 above
ground igloos and 100 inert storage locations. The actual demolition grounds
comprise 16 acres.
General operations at LEAD were initiated in 1941 in anticipation of increased
demand for war materiel. Situated in reasonable proximity to the eastern seaboard
and Washington, D.C., site characteristics were suitable for anticipated operations.
The next year, acreage was acquired and activities commenced.
In subsequent decades, a wide range of military support activities occurred at the
depot, with operations generally expanding in times of conflict or with the expansion
of federal military budgets. Since the 1980s, activities have gradually come to
resemble those at the depot today.

The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania
In 1995, the Federal Base Realignment and Closure Commission (BRAC)
recommendations led to full or partial closure of over 100 military installations
scattered across the country, including a realignment at LEAD. As a result, over
2,000 jobs were lost and the county’s economy suffered a severe shock. Another
round of BRAC related activities in 2005 lead to a realignment that subsequently
added jobs at LEAD.
An additional component of the realignment and closure (BRAC) activities of 1995
was the conveyance of 1,450 acres of excess army property to local governments for
redevelopment. A Franklin County Reuse Committee (FCRC) was appointed to
determine what would happen to this property. From this, three entities were
1) Letterkenny Industrial Development Authority (LIDA). LIDA is charged with real
estate redevelopment and electrical distribution operations across the newly
conveyed areas. LIDAs goals are to generate high quality jobs, attract private
investment, and minimize financial risk to Franklin County, as well as enhance
Department of Defense missions.
2) Franklin County General Authority (FCGA). With a mission to manage and
provide resources for the Cumberland Valley Business Park, FCGA is responsible for
the Letterkenny reservoir, water distribution systems, sanitary sewer collection &
treatment, and rail infrastructure.
3) Cumberland Valley Business Park Association (CVBPA). Administered by LIDA,
the mission of the association is to protect the value and desirability of the
Cumberland Valley Business Park through use of covenants, conditions, and deed
restrictions, as well as management of common area maintenance.
The BRAC transfer process initiated in 1995 is still incrementally unfolding. Seven
transfers of property have included 1007 to date, with 346 remaining. Additionally,
environmental assessments and other studies were prepared to examine
environmental issues, economic development, and real estate implications of the

Franklin County: Geographic Context, Demographic Overview, and
General Economic Profile
As noted in the outset, Franklin County is located in South Central Pennsylvania.
The county occupies a cross-section of the Cumberland Valley and lies at the western
edge of Megalopolis. Franklin County’s accessibility to major metropolitan centers
has become even more pronounced over time due to regional urban growth, the
increased importance of truck transportation along I-81, and integration with rail via
multi-modal facilities. Cities and other transportation centers such as New York,
Cleveland, and Norfolk are within 300 miles. Philadelphia, Baltimore, Washington,
D.C. and Pittsburgh are all even closer. Additional speculation is that the widening
of the Panama Canal will only enhance this regional accessibility. As a result of this
relative location, the county and the wider region are experiencing sustained,
consistent, and comparatively rapid economic and population growth.
Historically a rural and agricultural area, the county’s economic profile has been
augmented by LEAD since the 1940s, selected manufacturing activities from the
1960s, and, in the last several decades, trucking and warehousing. Apart from
LEAD, which is discussed throughout this report, leading economic sectors in terms
of employment relative to other regions are trucking and warehousing (4,106;
location quotient = 2.37) and manufacturing (8,448, location quotient = 1.85).
As of January 2016, the county has a civilian labor force of close to 80,000.
Unemployment rates have been lower than the state and national averages for
several years. As of January 2016, the county unemployment rate of 4.3% compared

The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania
favorably to the state (4.6%) and national (5.6%) rates. Average hourly wages locally
($19.13) are, however, lower than for the state ($24.03).
The county grew by 15.7% between 2000 and 2010 to 149,618, with subsequent
Census Bureau population estimates (153,696, 2015 est.) showing that growth
continuing. This growth played a role in the transition of the county from a Census
Bureau defined Micropolitan Statistical Area to a Metropolitan Statistical Area in
2013. Two projections each show this growth continuing at least through 2025 to
2040, placing it among the 15 fastest growing counties in the state. The more
conservative Pennsylvania State Data Center projection forecasts a population of
173,765 in 2040. This growth is consistent with other counties in the wider area of
south-central Pennsylvania and ranks the county among the fastest growing in the
Median age sheds insight onto the potential social and economic ramifications of
workforce change. The county’s median age of 40.6 closely mirrors that of the state
(40.4) and is lower than all of the surrounding counties, which range between 40.7
for Cumberland to the northeast and 42.8 for Fulton to the west. Consistent with
national and state trends, the median age in the county has increased 2.4 years since
2000. Most Pennsylvania counties are easily above the national median age of 37.8
Age composition and profile is generally helpful in understanding workforce
characteristics and economic dynamics. Franklin County is distinctive for having a
much higher Age Dependency Ratio than Pennsylvania and the country at large. The
county’s ratio is 68.9 (29.4 for the old age dependency ratio, and 39.5 for the child
dependency ratio). For the United States the numbers are 59.3 (21.9, 37.4) and for
Pennsylvania, 59.9 (25.6, 34.4), respectively. In other words, the county
simultaneously has higher proportions of people who are older (65 and above) and
younger (0-17), relative to those aged 18-64. This number is also much higher than
nearly all surrounding counties, which largely resemble the numbers for the state.
Educational attainment also serves as one general measure of workforce skill
development and is also closely tied to economic development. Estimates for 2010-
14 find that 19.1% of Franklin County adults aged 25 and above possess a bachelor’s
degree or higher, which lags behind the state’s 28.1%. The county rate shows a
substantial growth from 2000, when only 14.8% of those aged 25 and over possessed
a bachelor’s degree or higher. This change from 2000 was comparable to the change
for the state generally. Currently, the two largest neighboring counties, Adams and
especially Cumberland, both fare better on this measure, with 21.2% and 32.8% of
adults aged 25 and older possessing a bachelor’s degree or higher respectively.
Differences and fluctuations in labor force participation rates carry serious
implications for local economic development. Higher levels of labor force
participation correlate positively with economic growth. In Franklin County, 63.2%
of the population aged 16 years and over is in the labor force, placing the county
roughly at the midpoint between the state average percentage of 62.8% and the
national average of 63.5%. The labor force participation rate for the country as a
whole suffered during the recession and dropped 4% over the course of the decade
before only beginning to recover over the last year.
Commuting data provides information about the jobs / housing balance within
communities. For Franklin County, the data provide contradictory indications of
this balance. Workers in Franklin County get to work more quickly in terms of mean
travel time (23.4 minutes) than either Pennsylvanians (26.1 minutes) or people in
the US at large (25.7 minutes). Mean travel time to work for commuting residents
within the county is roughly the same today as it was in 2000 (25.5 minutes). While
mean travel times are quicker compared to the country or state, workers in Franklin
County are more likely to have to leave the county to work. Only 69.1% of Franklin
County residents work within the county, whereas the rates for working in the
county of residence are 70.7% for the state and 72.4% for the country. This is

The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania
partially explained by a large employment center, Hagerstown, being located just
across the state line in Maryland and with one community, Shippensburg, straddling
the county boundary. Like commuters elsewhere, workers in Franklin County have
become increasingly likely to leave the county for work. In 1990, 77% of employed
residents worked within the county and in 2000, this had dropped to 72%.
Per capita income levels in Franklin County ($25,540) lag, falling 11% behind the
national ($28,555) and 12 % behind the state ($28,912). Yet, median household
income - $53,394 for Franklin County - is comparable between the three and falls
within the statistical margin error for each value.

Given the prominent role LEAD in the county, the next section quantifies the specific
economic contributions of Letterkenny Army Depot with Franklin County, PA

Section B. Modeling the Economic Contributions of
Letterkenny Army Depot in Franklin County
In this section we model the estimated economic impacts of Letterkenny Army Depot
(LEAD) in Franklin County.

While there are several modeling software programs to estimate changes within a
region as a result of economic “shocks” or “events”, we utilize REMI PI+ (version
1.7.11) developed by Regional Economic Models, Inc. The REMI model is arguably
the most comprehensive and complete modeling package, and includes the ability to
assess both economic and demographic variables as a result of regional change. The
model adds econometric and other analyses to the input-output methodologies. A
more complete description of these issues, the model, and methods is available in
REMI’s Model Equations Guide (2014).

Generally speaking, input-output (IO) analysis is a technique that follows economic
flows and waves created by some change within a region as it moves through various
industrial sectors based upon supply and demand linkages, regional purchase
coefficients and other connections. The costs or benefits of such changes are
computed until the regional economic system has returned to a new and different
state of equilibrium. An important aspect of these techniques is that they happen
“somewhere”; that is, rather than generic changes unconstrained by geography, the
model computes the effects on a specific region taking into account the particular
regional economic structure of that area. The model results that follow are all
specific to Franklin County, Pennsylvania.

Results from the model explicitly and implicitly take into account direct effects of
change, indirect effects of change, and induced effects of change. Generically
speaking, direct effects are those related to an economic shock itself – for example,
the loss of 20 jobs due to a retail store closing. Indirect effects are those caused to
other economic sectors as a result of the direct effects – for example, a lost contract
to a supplier to the store. Induced effects are those related to local individuals and
households based upon the changed circumstances of direct and indirect effects.
REMI examines all of these, and links them to a series of other potential changes
such as migration, the value of capital stock, the labor force pool and the like. It
should be noted that the method used here does not attempt to model more
qualitative aspects of the county’s “health” such as quality of life, environmental
conditions, etc. although these too are important regional considerations.

This study is designed to estimate the economic effect of LEAD in the economy of
Franklin, County Pennsylvania alone. While the effects of an installation such as this
go beyond the boundaries of a county, for the purposes of this economic analysis,
those results “don’t count”. They may be added, however, if the study region were to
be enlarged beyond county boundaries.

The rest of this section is organized as follows. First we detail some current
characteristics of the county by examining portions of the standard regional control
model (SRC -- i.e., the status quo) and current employment multipliers. Next, we

The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania
detail the inputs used to create a model of LEAD’s effects on the county. Last, we
examine the results of the modeling process, describing the effects of LEAD in terms
of county employment, value added, net earnings, population, labor force, and
capital stock.

Status Quo: Standard Regional Control Model

Before modeling the impact of LEAD, it is worthwhile to examine the current status
of the county along several important economic variables according to the REMI PI+
model, since it is from this starting point the effects of LEAD can be calculated. The
REMI PI+ model uses a variety of government data sources, custom inputs, and
mathematical techniques that combine to form the basis of the model. Here, we
specifically detail three basic economic components of the county: employment and
its distribution by economic sector, value added (income) and the contribution by
economic sector; and the current sectoral employment multipliers. While the first
two provide a snapshot of the employment and income in the county at the current
time, employment multipliers provide a sense of the degree to which county
industries are interconnected. This latter point is important in assessing how
changes in one economic sector may have reverberations through others.

Table 1 provides a snapshot of the SRC view of estimated county employment and
value added for 2016 (note that the table is sorted by total employment, but that the
two data items are highly correlated). Employment is simply measured by the
approximate number of individuals employed within each sector, while value added
(similar to gross regional product) is defined by REMI as: “The gross output of an
industry or a sector less its intermediate inputs…”.

Table 1.
Franklin County: Employment & Value Added, 2016
                                                     Employment      (millions,
Sector                                               (individuals)     2016 $)
Manufacturing                                               9,856      1,396.0
Health Care and Social Assistance                           9,816        647.8
Retail Trade                                                9,450        496.5
Government                                                  9,042        759.2
Transportation and Warehousing                              6,383        408.1
Other Services, except Public Administration                5,330        205.0
Accommodation and Food Services                             4,890        152.2
Construction                                                4,243        245.1
Administrative and Waste Management Services                3,897        178.9
Professional, Scientific, and Technical Services            2,999        256.3
Finance and Insurance                                       2,138        230.8
Real Estate and Rental and Leasing                          2,050        232.4
Farm                                                        1,994        171.5
Wholesale Trade                                             1,936        224.8
Arts, Entertainment, and Recreation                         1,470          37.8
Educational Services                                        1,386          53.2
Information                                                   518          65.8
Management of Companies and Enterprises                       483          36.4
Forestry, Fishing, and Related Activities                     412          16.1
Mining                                                        174           9.7
Utilities                                                      71          29.6
Totals                                                     78,538      $5,853
Source: Authors' calculations on data from REMI.

The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania
In aggregate the county employs over 78,500 workers and has a gross value added of
more than $5.8billion dollars. As the table shows, about 57% of employment and
63% of value added can be accounted for by just the top five sectors: manufacturing,
health care and social assistance, retail, government, and transportation and
warehousing. This indicates relative regional specializations in these areas. From a
value added perspective, the manufacturing, government and health care sectors
make the largest contributions to the county. Retail, with substantial levels of
employment, is proportionally less important to income.

A hierarchical tree diagram (Figure 1, below) provides a slightly different view of the
components of the county’s value added profile where each individual block/text is
sized approximately by the relative contribution of that sector to county value added.

Figure 1.

Source: Authors’ calculations on data from REMI

The Economic Impact of Letterkenny Army Depot in Franklin County, Pennsylvania
Table 2 shows the relative importance of the various economic sectors to Franklin
County in a slightly different way: through employment multipliers. While the
aggregate values of employment and value added give a static view of the overall
contributions of sectors to the regional economy and provide a window into the parts
of the economy upon which the county largely “stands”, multipliers provide a more
dynamic look at the potential impacts of change to the region.

Multipliers are a mathematical representation of the extent to which economic
sectors are interlinked within a region. The essential idea is that as economic change
occurs in one sector, the connections between that sector and others will result in net
cumulative changes (positive or negative) that are larger than the initial change.
This is due to the fact each sector may rely on others for goods and services. This
means that as one sector grows, there will likely be an increased need for goods and
services from various suppliers and related industries who will also grow. The
growth of these suppliers will result in further changes based upon their economic
connections … and so on. While there are different types of multipliers, perhaps the
most straightforward are employment multipliers (such as those shown in Table 2).

The multipliers show, that for an addition of 1 (one) job created in each of the listed
sectors on the left, the approximate total number of jobs (including the initial job)
that would be created in the county. The median (or mid-point) multiplier level for
the county is 1.29. Thus, in a macro sense, one new job created in the Franklin
County results in approximately 1.29 total jobs (comprised of the 1 new employment
opportunity plus 0.29 jobs across the sectors due to multiplier effects). Or, put in
round numbers, 100 new jobs in Franklin County would result in an estimated total
of 129 jobs overall (at the median). The actual effect varies by industrial sector.

The exact multiplier numbers are perhaps less important than the relative
magnitudes. As the table shows, sectors such as manufacturing, federal civilian (the
bulk of LEAD’s employment) and transportation and warehousing all have relatively
high multipliers. This means that a change in one of those sectors will have a larger
overall impact on the county than some other sectors with lower multipliers (such as
retail, and accommodation and food services). It is important to recognize that the
impact would work both ways. Positive changes in sectors with high multipliers
would result in relatively larger overall impact; however negative change would work
similarly in the opposite direction.

Importantly, it is also critical to note the connections between employment, value
added and the multipliers. While sectors such as manufacturing, government, and
transportation and warehousing have relatively high values on all, other sectors
show different patterns. For example, retail, which has relatively high employment
within the county, has one of the lowest multipliers.

Modeling the Economic Contributions of Letterkenny Army Depot

The previous section established that the relative importance of several industries
including the government / federal civilian sectors (the core of LEAD’s employment)
within the county both from the static perspectives of current employment and
contribution to regional value added, but also in their connections to other industries
via multipliers. This combination of facts suggests that changes in these sectors
would have substantial overall effects on the region.

Modeling Technique and Model Inputs

A typical way to calculate the economic value of a component of a regional economy
is essentially to model a forecast of the status quo (referred to above as a Standard
Regional Control [SRC] Model), and then remodel the region under a “changed”
scenario, and finally, to calculate the differences. To determine the overall effect of a
firm, industry sector, or other entity (like LEAD), it is common to re-model the

region under a changed scenario where the focus-element hypothetically
“disappears.” In the present case, this means Letterkenny Army Depot, its
employment, associated contracts and other regional connections are eliminated
from the region and the model. The economic impacts of the base are then
estimated by calculating the differences between the SRC and the new results
without the presence of LEAD.

Table 2.
Franklin County Employment Multipliers (2016)
Sector                                             Multiplier
  Utilities                                             2.93
  Mining                                                1.72
  Manufacturing                                         1.61
  Information                                           1.55
  Federal Civilian                                      1.47
  Transportation and Warehousing                        1.37
  Finance and Insurance                                 1.36
  State & Local Government                              1.35
  Wholesale Trade                                       1.34
  Health Care and Social Assistance                     1.33
  Farm                                                  1.30
                                            MEDIAN      1.29
  Professional, Scientific, and Technical Services      1.28
  Management of Companies and Enterprises               1.28
  Construction                                          1.25
  Real Estate and Rental and Leasing                    1.24
  Educational Services                                  1.22
  Other Services, except Public Administration          1.21
  Retail Trade                                          1.20
  Administrative and Waste Management Services          1.20
  Forestry, Fishing, and Related Activities             1.16
  Accommodation and Food Services                       1.15
  Arts, Entertainment, and Recreation                   1.11
  Source: Authors' calculations on data from REMI

For the purposes of this analysis, we have calculated the effects of LEAD by assuming
an instantaneous loss of the base in 2016. This is clearly unlikely if not impossible in
reality, where a more gradual removal of the base would likely occur if the base were
to be shut down. However, with no specific alternative scenario available reflecting a
slower reduction in base activity, an immediate shutdown provides the best estimate
of the overall impact of LEAD.

As described above, we utilized REMI PI+ software to make the calculations. In
addition to the model’s built-in characteristics for Franklin County, we added ten
(10) custom changes based upon information provided by LEAD. This information

was provided in close cooperation with LEAD management and initially requested
under the Freedom of Information Act. All inputs were provided to LEAD and
FCADC for review prior to estimating the results. Table 3 summarizes the inputs.

Table 3.
REMI Model Input Assumptions
   1,680 direct LEAD jobs (which is reduced by about 16% by the model
   for non‐locals). Source: LEAD.
     1,100 federal civilian tenants at the average salary of lead employees
     (reduced for non‐locals as above). Source: LEAD.

     Approximately $40m adjustment in wage levels within the REMI
     data to match model and actual salary data. Source: LEAD, REMI.

     A yearly average of 1,433 contractors in machinery, repair, etc.
     Source: LEAD.
     A yearly average of 29 contractors in information technologies.
     Source: LEAD.

5)   $130,200 in annual water fees. Source: LIDA.

6)   $140,008 in annual sewer fees. Source: LIDA.

7)   $3,843,000 in annual electric fees. Source: LIDA.

8)   $108,999 in annual fire protection costs. Source: LIDA.

    $542,915 in visitation (LEAD & Tenant visitation: Average of
9) 2013/2014 ‐ 4,208 (nights/days) * $129 GSA expenses. Source:
    LEAD, GSA.
    An average of $35,539,311 in goods and services purchases which
10) we distributed across industries according to a typical input‐output
    scheme for a similar facility. Source: REMI.

As the table shows, there are close to 1,700 federal LEAD employees on the base.
These jobs are assumed to be lost under a base-removal scenario. The actual average
wage of LEAD employees is approximately $54,000 (without benefits) and
adjustments were made to the modeling software to insure that both were in
agreement. The approximately 1,100 additional government tenants on the base
were assumed to have similar salaries. A wage increase of 10% was included for all
employees as representative of recent wage changes (based on data provided by
LEAD). The model assumes an approximately 16% commuting rate (that is, workers
that are employed at LEAD but live elsewhere). This is important since, as discussed
above, the model “counts” only those elements (including employees) that work and
reside in Franklin County.

Other inputs included estimated effects from visitation to the base, use of regional
services, average use of specialized contractors by the base (using a several year
average), and a typical suite of goods and services used by similar organizations. In
cooperation with FCADC, LEAD, and LIDA it was agreed that these inputs provided

the representative information to “remove” LEAD from the county, calculate the
differences in the model in relation to the SRC, and to determine estimates of the
economic contributions of the Depot to the regional economy.

Model Results

We modeled the economic effects of a current removal of LEAD out to 2026 – a ten-
year span. The results described here focus on the immediate 2016 impacts, as the
future predictions have no way to take into account other unforeseen changes to the
community that would surely come (e.g., a new firm enters the county, an existing
firm expands, etc.). Therefore, the years beyond 2016 should be looked upon as
purely illustrative of a region that suffers the loss of the base but has little
intermediate change over the next decade – again, a scenario that is unlikely. Thus
these long term forecasts should be used with some caution.

Based upon the inputs described above, we modeled the effects of an immediate
closure of LEAD in 2016. As expected, given both the current contributions of LEAD
to the community in terms of employment and value added, as well as relatively high
employment multiplier for federal civilian workers, the contributions and impact of
LEAD to Franklin County is very large. Below we describe the results of the model
according to the following general outline: employment, value added, net earnings,
labor force, population, and capital stock.


Perhaps the most cited number reflecting economic change at any regional level is
employment. REMI employment estimates include impacts on both full and part-
time jobs, as well as employees and proprietors.

In the case of LEAD, we estimate that the total contribution of the base to county
jobs, inclusive of the multiplier effect, is nearly 6,400. Put differently, the model
predicts a total possible job loss of well over 6,000 positions with a closure of the
base. This includes the direct loss of more than 2,700 federal civilian workers at the
base and an additional (roughly) 3,600 jobs from multiplier effects.

Table 4 shows the overall employment change estimate. In percentage terms, LEAD
contributes to more than 8.1% of county employment. Yet as important as the
aggregate losses of 6,400 jobs is, perhaps just as vital is a description of the sectors
from which those jobs will be lost. Figure 2 and Table 5 provide summaries of these
estimated changes.

As Figure 2 shows via proportional areas and text, the largest effect will be on
government sector jobs, owing largely to the direct loss of these positions with a
removal of the LEAD from the county economy. “Other Services” (a major sector
which includes machinery repair and for which Letterkenny contracts) also shows a
large decline with retail, construction, healthcare, and professional services also
being affected significantly. Table 5 provides more specific detail.

Several points are worth elaboration. The reduction in employment in sectors such
as retail and accommodation and food service will likely occur as income falls as a
result of reduced wage-earning opportunities. However, an interesting and
somewhat encouraging result is that two of the county’s major employment and
value added drivers appear to be little affected by the removal of the base.
Transportation and warehousing, and, manufacturing are estimated to feel only
small reductions in employment of 67 and

 Table 4.
LEAD Effect on Franklin County Employment ‐ Forecast 2016‐2026 (individuals)
Scenario                                          2016        2017        2018         2019      2020      2021      2022      2023      2024      2025      2026
   Regional control forecast                    78,538      79,338      79,411       79,489    79,633    79,787    79,944    79,995    80,020    80,133    80,163
   Subtract LEAD effect (2016)                   ‐6,387      ‐6,561      ‐6,612       ‐6,573    ‐6,491    ‐6,389    ‐6,282    ‐6,189    ‐6,105    ‐6,024    ‐5,957
Result                                          72,151      72,776      72,799       72,917    73,142    73,397    73,662    73,807    73,915    74,108    74,206
Percentage reduction without LEAD               ‐8.13%      ‐8.27%      ‐8.33%       ‐8.27%    ‐8.15%    ‐8.01%    ‐7.86%    ‐7.74%    ‐7.63%    ‐7.52%    ‐7.43%
Source: Authors' calculations on data from REMI and sources described in the text.
59 jobs respectively. This points to a relatively minor set of connections between
LEAD and these other important sectors for the county - suggesting that there would
be only a minor cascading effect to these critical generators of employment and

Figure 2.

Source: Authors’ calculations on data from REMI

Table 5.

LEAD Effect on Franklin County Employment by Industy Sector (individuals)
Industry Sector                                    2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
Government                                       ‐2,884 ‐2,941 ‐2,977 ‐2,999 ‐3,013 ‐3,022 ‐3,028 ‐3,032 ‐3,035 ‐3,037 ‐3,038
Other Services, except Public Administration     ‐1,584 ‐1,573 ‐1,562 ‐1,550 ‐1,537 ‐1,525 ‐1,515 ‐1,506 ‐1,500 ‐1,493 ‐1,489
Construction                                       ‐415   ‐573    ‐636    ‐644 ‐621   ‐583   ‐537   ‐494   ‐455   ‐419   ‐389
Retail Trade                                       ‐365   ‐369    ‐375    ‐374 ‐369   ‐362   ‐354   ‐348   ‐342   ‐335   ‐329
Professional, Scientific, and Technical Services   ‐273   ‐272    ‐269    ‐264 ‐258   ‐253   ‐248   ‐243   ‐239   ‐236   ‐234
Health Care and Social Assistance                  ‐274   ‐267    ‐261    ‐253 ‐244   ‐236   ‐228   ‐226   ‐222   ‐215   ‐208
Accommodation and Food Services                    ‐155   ‐158    ‐161    ‐162 ‐161   ‐160   ‐159   ‐158   ‐157   ‐156   ‐156
Administrative and Waste Management Services       ‐112   ‐111    ‐109    ‐105 ‐102    ‐98    ‐95    ‐91    ‐88    ‐86    ‐84
Transportation and Warehousing                      ‐67    ‐55      ‐42    ‐27  ‐14     ‐1     10     20     28     35     40
Manufacturing                                       ‐59    ‐51      ‐40    ‐29  ‐18     ‐8      1      9     15     20     24
Information                                         ‐40    ‐39      ‐38    ‐36  ‐35    ‐34    ‐33    ‐32    ‐31    ‐30    ‐29
Finance and Insurance                               ‐37    ‐34      ‐31    ‐28  ‐24    ‐21    ‐18    ‐16    ‐14    ‐12    ‐10
Real Estate and Rental and Leasing                  ‐32    ‐34      ‐35    ‐35  ‐34    ‐33    ‐32    ‐31    ‐30    ‐29    ‐29
Mining                                              ‐17    ‐17      ‐17    ‐17  ‐16    ‐16    ‐15    ‐15    ‐14    ‐14    ‐14
Wholesale Trade                                     ‐18    ‐17      ‐16    ‐14  ‐12    ‐10     ‐9     ‐7     ‐6     ‐5     ‐4
Arts, Entertainment, and Recreation                 ‐19    ‐17      ‐15    ‐12  ‐10     ‐8     ‐6     ‐4     ‐2     ‐1      0
Management of Companies and Enterprises             ‐15    ‐14      ‐12    ‐11   ‐9     ‐8     ‐6     ‐5     ‐4     ‐3     ‐3
Utilities                                           ‐11    ‐11      ‐10    ‐10   ‐9     ‐9     ‐8     ‐8     ‐8     ‐7     ‐7
Educational Services                                 ‐5     ‐5       ‐5     ‐4   ‐4     ‐3     ‐3     ‐2     ‐2     ‐2     ‐2
Forestry, Fishing, and Related Activities            ‐3     ‐2       ‐1     ‐1    0      1      1      2      2      2      2
Farm                                                  0      0        0      0    0      0      0      0      0      0      0
TOTALS                                           ‐6,387 ‐6,561 ‐6,612 ‐6,573 ‐6,491 ‐6,389 ‐6,282 ‐6,189 ‐6,105 ‐6,024 ‐5,957
Source: Authors' calculations on data from REMI and sources described in the text.
Value Added

After employment, income (value added) is probably the second most cited regional
economic statistic. In the REMI model, value added is an industry’s “gross output
minus its intermediate purchases.” Summed across industries, this is largely
equivalent to gross regional product. Table 6 provides a summary of the value added
results and Figure 3 and Table 7 show additional detail on the impacts to value
added across specific sectors in Franklin County if LEAD were to be removed from
the local economy.

As Table 6 shows, Letterkenny and its connections account for approximately 8.5%
of the total county value added ($5.9 Billion) or approximately $495 million. This
value is inclusive of the multiplier effects that the removal of the base would have on
other county economic sectors.

As with employment, it is important to examine the sectoral interconnections to see
what other industries would be most affected by a stoppage of operations at the
Depot. Figure 3 shows these relationships using a hierarchical tree diagram, and
Table 7 provides the detail. The figure makes clear that in addition to the
government sector (which will show the largest decline since it is the sector facing an
immediate loss), other services, professional technical, healthcare, construction, and
retail sectors would be most impacted. These are mostly service industries that
would be greatly impacted by the loss of wages and benefits at LEAD.

Table 7 confirms this. Again, the links to “other services” by the base are reflected
here, and the loss of jobs and disposable income will certainly hit the retail sector
hard. As with before, despite the massive overall economic losses that the removal of
the base would create, there is very little impact on the county’s critical
transportation and warehousing or manufacturing industries. This points to a level
of insulation or separation between LEAD operations and activities and these

Net Earnings

To look at the impact of income differently, the model calculates changes in net
earnings which include wages, salaries, and proprietor’s income. The effects of
LEAD on net earnings is shown in Table 8. Overall, a loss of about $340 million is
estimated should Letterkenny cease operations. Although not all county residents
would be affected equally, this equates to more than $2,200 per person, a substantial
amount both in absolute terms and relative to total per capita earnings for many
families. In total the losses would amount to about 9% of Franklin County’s total
earnings by place of residence.

 Table 6.
LEAD effect on Franklin County Value Added ‐ Forecast 2016‐2026 (millions of 2016 dollars)
Scenario                                       2016       2017      2018        2019         2020                                     2021          2022           2023          2024           2025           2026
   Regional control forecast                  5,853      6,022     6,146       6,269        6,403                                    6,540         6,684          6,826         6,969          7,122          7,271
   Subtract LEAD effect (2016)                 ‐495       ‐512       ‐522       ‐526         ‐527                                     ‐527          ‐527           ‐527          ‐527           ‐527            ‐529
Result                                        5,358      5,510     5,624       5,743        5,875                                    6,013         6,158          6,299         6,442          6,594          6,742
Percentage reduction without LEAD            ‐8.46%     ‐8.50%    ‐8.49%      ‐8.39%       ‐8.23%                                   ‐8.06%        ‐7.88%         ‐7.71%        ‐7.56%         ‐7.40%         ‐7.27%
REMI PI Note: "The gross output of an industry or a sector less its intermediate inputs; the contribution of an industry or sector to gross domestic product (GDP). Value added by industry can also be measured as
the sum of compensation of employees, taxes on production and imports less subsidies, and gross operating surplus." Similar to GRP or income.
Source: Authors' calculations on data from REMI and sources described in the text.
Figure 3.

Source: Authors’ calculations on data from REMI

  Table 7.
Industry Sector                                                             2016      2017      2018      2019      2020      2021      2022      2023      2024      2025      2026
Government                                                              ‐304.111 ‐311.104 ‐316.627 ‐321.307 ‐325.541 ‐329.534 ‐333.436 ‐337.045 ‐340.329 ‐343.679 ‐347.163
Other Services, except Public Administration                             ‐60.923   ‐61.445   ‐62.042   ‐62.518   ‐62.994   ‐63.471   ‐63.983   ‐64.633   ‐65.358   ‐66.063   ‐66.848
Construction                                                             ‐24.012   ‐33.858   ‐38.498   ‐39.879   ‐39.382   ‐37.815   ‐35.687   ‐33.554   ‐31.616   ‐29.830   ‐28.351
Professional, Scientific, and Technical Services                         ‐23.370   ‐23.703   ‐23.855   ‐23.880   ‐23.846   ‐23.794   ‐23.741   ‐23.746   ‐23.811   ‐23.942   ‐24.142
Retail Trade                                                             ‐19.196   ‐20.012   ‐21.004   ‐21.558   ‐21.956   ‐22.202   ‐22.348   ‐22.623   ‐22.908   ‐23.081   ‐23.321
Health Care and Social Assistance                                        ‐18.087   ‐17.877   ‐17.671   ‐17.295   ‐16.902   ‐16.522   ‐16.178   ‐16.170   ‐16.069   ‐15.763   ‐15.407
Manufacturing                                                             ‐8.507    ‐7.706    ‐6.574    ‐5.214    ‐3.817    ‐2.466    ‐1.214    ‐0.108     0.866     1.734     2.455
Administrative and Waste Management Services                              ‐5.166    ‐5.186    ‐5.154    ‐5.075    ‐4.981    ‐4.886    ‐4.801    ‐4.707    ‐4.629    ‐4.569    ‐4.534
Information                                                               ‐5.126    ‐5.237    ‐5.342    ‐5.445    ‐5.545    ‐5.654    ‐5.772    ‐5.892    ‐6.021    ‐6.159    ‐6.306
Accommodation and Food Services                                           ‐4.835    ‐5.022    ‐5.204    ‐5.299    ‐5.367    ‐5.413    ‐5.450    ‐5.494    ‐5.542    ‐5.580    ‐5.634
Utilities                                                                  ‐4.558   ‐4.568    ‐4.571    ‐4.557    ‐4.538    ‐4.518    ‐4.501    ‐4.486    ‐4.473    ‐4.462    ‐4.459
Transportation and Warehousing                                            ‐4.341    ‐3.678    ‐2.898    ‐2.024    ‐1.146    ‐0.307     0.465     1.225     1.894     2.484     2.975
Finance and Insurance                                                     ‐4.016    ‐3.823    ‐3.594    ‐3.304    ‐3.003    ‐2.706    ‐2.424    ‐2.182    ‐1.970    ‐1.781    ‐1.630
Real Estate and Rental and Leasing                                        ‐3.674    ‐3.982    ‐4.164    ‐4.236    ‐4.253    ‐4.237    ‐4.204    ‐4.170    ‐4.135    ‐4.094    ‐4.063
Wholesale Trade                                                           ‐2.162    ‐2.106    ‐2.028    ‐1.905    ‐1.768    ‐1.623    ‐1.479    ‐1.371    ‐1.278    ‐1.192    ‐1.125
Management of Companies and Enterprises                                   ‐1.158    ‐1.096    ‐1.012    ‐0.914    ‐0.812    ‐0.714    ‐0.621    ‐0.532    ‐0.453    ‐0.382    ‐0.322
Mining                                                                    ‐0.945    ‐0.971    ‐0.974    ‐0.962    ‐0.944    ‐0.925    ‐0.906    ‐0.883    ‐0.860    ‐0.842    ‐0.829
Arts, Entertainment, and Recreation                                        ‐0.489   ‐0.441    ‐0.391    ‐0.334    ‐0.276    ‐0.221    ‐0.170    ‐0.126    ‐0.087    ‐0.052    ‐0.023
Educational Services                                                       ‐0.205   ‐0.197    ‐0.185    ‐0.169    ‐0.150    ‐0.132    ‐0.115    ‐0.101    ‐0.090    ‐0.080    ‐0.073
Forestry, Fishing, and Related Activities                                 ‐0.115    ‐0.091    ‐0.064    ‐0.034    ‐0.006     0.021     0.045     0.067     0.087     0.104     0.119
Farm                                                                        0.000    0.000     0.000     0.000     0.000     0.000     0.000     0.000     0.000     0.000     0.000
TOTALS                                                                 ‐$494.996 ‐$512.103 ‐$521.852 ‐$525.909 ‐$527.227 ‐$527.119 ‐$526.520 ‐$526.531 ‐$526.782 ‐$527.229 ‐$528.681
Source: Authors' calculations on data from REMI and sources described in the text.
 Table 8.
LEAD Effect on Franklin County Net Earnings by Place of Residence ‐ Forecast 2016‐2026 (millions of 2016 dollars)
Scenario                                       2016        2017        2018      2019       2020        2021        2022                                          2023          2024           2025          2026
   Regional control forecast                  3,784       3,901       3,990     4,076      4,167       4,263       4,362                                         4,461         4,560          4,661         4,753
   Subtract LEAD effect (2016)                  ‐339        ‐361       ‐380      ‐392        ‐400       ‐405        ‐408                                          ‐411          ‐414            ‐416          ‐417
Result                                        3,444       3,540       3,610     3,684      3,767       3,858       3,954                                         4,050         4,146          4,245         4,336
Percentage reduction without LEAD            ‐8.96%      ‐9.25%      ‐9.52%    ‐9.62%    ‐9.59%       ‐9.50%      ‐9.36%                                        ‐9.22%        ‐9.07%         ‐8.92%        ‐8.78%
REMI PI+ Note: Equals ... "the sum of wages and salaries, supplements to wages and salaries, and proprietors' income ‐ less contributions for government social insurance, plus an adjustment to convert earnings by
place of work to a place‐of‐residence basis."
Source: Authors' calculations on data from REMI and sources described in the text.
Demographics: Labor Force

The cessation of activities LEAD would have substantial impacts on the regional
labor force, which is defined as those individuals that are currently working or
seeking work.

While other industrial sectors would certainly be able to soak up some of the best
and most highly skilled workers if they lost positions at Letterkenny, in the short
run, it is almost impossible to imagine that there would be jobs for all displaced
LEAD workers. Table 9 shows the labor force impact that LEAD has on the region as
modeled by REMI.

The model estimates a total labor force loss (which includes persons 16 years of age
and older) of over 1,300 persons. Table 10 provides more detail by showing these
losses by age cohort, and several features of this table are worth noting. First, there
are substantial reductions in important 22-54 year-old cohorts. This likely reflects
the fact that young and mid-career workers will see fewer opportunities in the region
and be “pushed” to other places for employment. The loss is concerning because this
group is likely to have substantial work experience which would be lost from the
county. Interestingly, the over 60 age cohorts also show substantial reductions. It is
much more likely that these groups will simply drop out of the labor force as retirees
as opposed to moving out of the county for a new job.

It should be noted that these estimates can’t account for new job opportunities in
other parts of the economy should LEAD cease operations. Thus, while the labor
force losses are somewhat limited in the first year, absent new employment
opportunities, the figures climb substantially in subsequent years. This would reflect
continued movements out of the county as well as those who simply drop out of the
labor force, discouraged that suitable employment prospects are not available.

Demographics: Population

It follows that a reduction in labor force, linked in some ways to workers leaving the
county for new job opportunities, would also have negative population effects.
Tables 11 and 12 estimate these possible changes.

Population effects generally follow the labor force changes rather closely, however
their magnitude is a bit larger. This is mostly because while the labor force statistics
only include persons 16 years of age and older, population includes everyone.
Importantly, as young and mid-career workers are disproportionately affected by the
reductions in labor force – many of whom will move away – these are also age
groups that are likely to have started families and have young children. These
children obviously move with the parents creating the larger overall numbers.
From a modeling perspective, population is estimated to drop by nearly 1,500 in the
short-run, but by much larger figures five and ten years down the road absent the
replacement of job opportunities for area workers.

Table 9.
LEAD effect on Franklin County Labor Force ‐ Forecast 2016‐2026 (individuals)
Scenario                                            2016        2017         2018      2019      2020      2021      2022      2023      2024      2025      2026
   Regional control forecast                      82,359      82,529       82,727    82,968    83,207    83,460    83,724    84,214    84,649    85,046    85,462
   Subtract LEAD effect (2016)                     ‐1,311      ‐2,283       ‐2,952    ‐3,463    ‐3,854    ‐4,155    ‐4,375    ‐4,541    ‐4,692    ‐4,817    ‐4,909
Result                                            81,048      80,246       79,775    79,505    79,354    79,305    79,349    79,673    79,957    80,229    80,554
Percentage reduction without LEAD                 ‐1.59%      ‐2.77%       ‐3.57%    ‐4.17%    ‐4.63%    ‐4.98%    ‐5.23%    ‐5.39%    ‐5.54%    ‐5.66%    ‐5.74%
Source: Authors' calculations on data from REMI and sources described in the text.
  Table 10.
LEAD Effect on Labor Force by Age Cohort ‐ Forecast 2016‐2026 (individuals)
Age Cohort           2016        2017        2018     2019     2020     2021     2022     2023     2024     2025     2026
Ages 16‐19             ‐83        ‐116        ‐132     ‐139     ‐142     ‐145     ‐148     ‐153     ‐158     ‐163     ‐168
Ages 20‐21             ‐82        ‐103        ‐115     ‐117     ‐114     ‐109     ‐105     ‐105     ‐107     ‐108     ‐110
Ages 22‐24            ‐142        ‐242        ‐314     ‐354     ‐369     ‐365     ‐350     ‐328     ‐308     ‐294     ‐284
Ages 25‐29            ‐202        ‐365        ‐504     ‐622     ‐718     ‐786     ‐823     ‐833     ‐812     ‐766     ‐702
Ages 30‐34            ‐162        ‐285        ‐389     ‐482     ‐567     ‐646     ‐718     ‐785     ‐844     ‐892     ‐923
Ages 35‐44            ‐241        ‐414        ‐556     ‐676     ‐780     ‐873     ‐960   ‐1,048   ‐1,136   ‐1,223   ‐1,308
Ages 45‐54            ‐173        ‐325        ‐421     ‐501     ‐559     ‐617     ‐657     ‐676     ‐703     ‐746     ‐789
Ages 55‐59             ‐47        ‐114        ‐146     ‐166     ‐179     ‐187     ‐191     ‐189     ‐199     ‐215     ‐231
Ages 60‐61             ‐45         ‐84        ‐100     ‐106     ‐110     ‐109     ‐104      ‐93      ‐86      ‐80      ‐71
Ages 62‐64             ‐12         ‐43         ‐53      ‐58      ‐61      ‐60      ‐57      ‐58      ‐62      ‐67      ‐71
Ages 65‐69             ‐86        ‐119        ‐122     ‐121     ‐118     ‐113     ‐112     ‐118     ‐117     ‐104      ‐88
Ages 70‐74             ‐24         ‐46         ‐62      ‐72      ‐79      ‐82      ‐80      ‐80      ‐81      ‐83      ‐85
Ages 75+               ‐12         ‐26         ‐39      ‐49      ‐57      ‐63      ‐71      ‐75      ‐77      ‐78      ‐78
Totals              ‐1,311      ‐2,283      ‐2,952   ‐3,463   ‐3,854   ‐4,155   ‐4,375   ‐4,541   ‐4,692   ‐4,817   ‐4,909
Source: Authors' calculations on data from REMI
 Table 11.
LEAD Effect on Franklin County Population ‐ Forecast 2016‐2026 (individuals)
Scenario                                        2016      2017        2018     2019       2020       2021       2022       2023       2024       2025       2026
   Regional Control Forecast                 153,970 154,705 155,470 156,272           157,105    157,973    158,888    159,810    160,749    161,728    162,739
   Subtract LEAD effect (2016)                 ‐1,491    ‐2,596      ‐3,584   ‐4,453     ‐5,216     ‐5,880     ‐6,462     ‐6,978     ‐7,428     ‐7,814     ‐8,149
Result                                       152,479 152,109 151,886 151,819           151,889    152,093    152,426    152,832    153,320    153,913    154,589
Percentage reduction without LEAD             ‐0.97%    ‐1.68%      ‐2.31%   ‐2.85%     ‐3.32%     ‐3.72%     ‐4.07%     ‐4.37%     ‐4.62%     ‐4.83%     ‐5.01%
Source: Authors' calculations on data from REMI and sources described in the text.
                                                                                                                               within a relatively short period, driving prices down. Table 13 presents the estimated

                                                                                                                                                                                                                        residents who wish to sell to actually do so because of the normal lags in disposing of
                                                                                                                               The final set of estimations made, assuming that LEAD was no longer functioning in

                                                                                                                                                                                                                        assumes that property sales and values are sticky. That is, it will take some time for
                                                                                                                               (described above) it is likely that many will move from the county in search of other
                                                                                                                               the economy, relates to capital stock, or put more simply, property values. With a
                                                                                                                               large reduction in job opportunities and a reduction in labor force and population

                                                                                                                                                                                                                        property, attempts to find other employment locally, and other issues such as not
                                                                                                                                                                                                                        wishing to remove children from the school system. As a result, the drop in both
                                                                                                                                                                                                                        In the scenario presented here, we take a conservative modeling approach which

                                                                                                                                                                                                                        residential (-0.39%) and non-residential (-0.24%) values are modest at first, but
                                                                                                                               options. This will result in a significant quantity of property hitting the market
    Table 12.
LEAD Effect on Population by Age Cohort ‐ Forecast 2016‐2026 (individuals)
Age Cohort      2016   2017    2018     2019     2020     2021     2022    2023       2024     2025     2026
Ages 0‐4        ‐156    ‐278    ‐391     ‐495    ‐592     ‐663     ‐725    ‐777       ‐819     ‐849     ‐870
Ages 5‐9        ‐116    ‐205    ‐286     ‐359    ‐424     ‐498     ‐566    ‐632       ‐695     ‐754     ‐794
Ages 10‐14       ‐93    ‐164    ‐229     ‐289    ‐343     ‐393     ‐439    ‐482       ‐521     ‐557     ‐605
Ages 15‐19       ‐97    ‐155    ‐202     ‐243    ‐282     ‐320     ‐356    ‐390       ‐422     ‐452     ‐480
Ages 20‐24      ‐234    ‐382    ‐485     ‐543    ‐563     ‐558     ‐543    ‐525       ‐510     ‐499     ‐494

                                                                                                                                                                                                                        quickly increase over time, all things being equal.
                                                                                                                               effects of LEAD on property to Franklin County.
Ages 25‐29      ‐214    ‐379    ‐528     ‐661    ‐773     ‐858     ‐909    ‐925       ‐907     ‐861     ‐798
Ages 30‐34      ‐157    ‐280    ‐397     ‐508    ‐611     ‐706     ‐794    ‐875       ‐948    ‐1007    ‐1046
Ages 35‐39      ‐117    ‐206    ‐290     ‐369    ‐442     ‐512     ‐580    ‐648       ‐714     ‐779     ‐841
Ages 40‐44      ‐102    ‐178    ‐247     ‐306    ‐359     ‐406     ‐450    ‐493       ‐536     ‐578     ‐621
Ages 45‐49       ‐74    ‐133    ‐188     ‐240    ‐289     ‐334     ‐373    ‐409       ‐440     ‐467     ‐492
Ages 50‐54       ‐55     ‐98    ‐138     ‐175    ‐209     ‐242     ‐273    ‐304       ‐335     ‐365     ‐394
Ages 55‐59       ‐41     ‐71    ‐100     ‐127    ‐152     ‐177     ‐199    ‐221       ‐243     ‐263     ‐284
Ages 60‐64       ‐35     ‐63     ‐88     ‐109    ‐127     ‐142     ‐156    ‐169       ‐183     ‐197     ‐211
Ages 65‐69         0      ‐6     ‐16      ‐30      ‐49     ‐72      ‐93    ‐111       ‐127     ‐140     ‐151
Ages 70‐74         0       0       0        0        0       0       ‐5     ‐15        ‐28      ‐46      ‐67
Ages 75‐79         0       0       0        0        0       0        0       0          0        0        0

                                                                                                               Capital Stock
Ages 80‐84         0       0       0        0        0       0        0       0          0        0        0
Ages 85+           0       0       0        0        0       0        0       0          0        0        0
TOTALS        ‐1,491 ‐2,596 ‐3,584 ‐4,453 ‐5,216 ‐5,880 ‐6,462 ‐6,978               ‐7,428   ‐7,814   ‐8,149
Source: Authors' calculations on data from REMI and sources described in the text
  Table 13.
Change in Franklin County capital stock due to LEAD effect ‐ Forecast 2016‐2026 (millions of 2016 dollars)
Scenario                                                                 2016          2017           2018         2019         2020        2021        2022        2023        2024        2025        2026
      Regional control forecast: Residential capital stock         10,744.43      10,804.27      10,890.76    11,004.10    11,142.81   11,304.50   11,486.87   11,688.51   11,908.77   12,149.38   12,410.85
        Subtract LEAD effect (2016)                                    ‐41.95       ‐103.51        ‐173.25      ‐244.23      ‐312.61     ‐376.32     ‐434.41     ‐487.11     ‐534.90     ‐578.10     ‐617.35
    Result                                                         10,702.48      10,700.76      10,717.51    10,759.87    10,830.20   10,928.18   11,052.46   11,201.40   11,373.87   11,571.28   11,793.51
    Percentage reduction without LEAD                                 ‐0.39%         ‐0.96%         ‐1.59%       ‐2.22%       ‐2.81%      ‐3.33%      ‐3.78%      ‐4.17%      ‐4.49%      ‐4.76%      ‐4.97%
        Regional control forecast: Nonresidential capital stock     6,187.01       6,207.26       6,238.79     6,279.64     6,327.83    6,380.88    6,436.24    6,493.60    6,553.03    6,614.86    6,679.11
        Subtract LEAD effect (2016)                                    ‐15.15         ‐35.93         ‐58.82       ‐81.63     ‐103.07     ‐122.42     ‐139.33     ‐153.83     ‐166.05     ‐176.17     ‐184.47
    Result                                                          6,171.86       6,171.33       6,179.97     6,198.01     6,224.76    6,258.46    6,296.91    6,339.78    6,386.98    6,438.68    6,494.64
    Percentage reduction without LEAD                                 ‐0.24%         ‐0.58%         ‐0.94%       ‐1.30%       ‐1.63%      ‐1.92%      ‐2.16%      ‐2.37%      ‐2.53%      ‐2.66%      ‐2.76%
Combined capital stock percentage reduction without LEAD              ‐0.34%         ‐0.82%         ‐1.35%      ‐1.89%       ‐2.38%      ‐2.82%      ‐3.20%      ‐3.53%      ‐3.80%      ‐4.02%      ‐4.20%
Source: Authors' calculations on data by REMI
Concluding Comments

Letterkenny Army Depot has a long and important past within the Franklin County
community from an economic standpoint and arguably from a social and cultural
perspective as well. The highly skilled workers at the base have long formed a key
component of the region’s labor force. As we noted in a similar study a decade ago,
the base not only provides the region with solid jobs, but indirectly with workforce
training and links with other industrial sectors. The central position of LEAD in the
community has been clearly expressed over the years in the changes that have
occurred in the region relative to various fluctuations in base operations

It is obvious from the results presented above that LEAD plays a fundamental role in
the local Franklin County community, from perspectives that span employment,
income, demographics, and property values. While the model results shown here are
somewhat unrealistic from a contextual perspective in that it is unlikely that the base
would close immediately and in its entirety, the estimates provide one window into
the contributions of the base to the region.

In sum, we found that the base is associated with more than 8% of both county
employment and value added. Net earnings would drop substantially (by 9%) across
the region and the area would likely suffer substantial labor force and population
declines. Importantly, the loss of both the general population and the labor force
would fall most heavily within the younger and mid-career cohorts – typically those
with significant skill sets. The community at large would suffer measurable property
value declines with a loss of base operations, an impact that would affect both those
directly associated with the base as well as others.

It is worth noting that it was beyond the scope of the present report to model the
effects of other potential aspects and effects of the base: traffic, noise, environmental
concerns, philanthropy and the like. Each of these issues, and many others, might be
seen to have either positive or negative impacts on the overall contributions of the
base to the area. While each of these may be important to some members of the
community, this report has focused on the core regional economic measures that are
largely common to all regions. Research that moves beyond the core aspects covered
here is an avenue for future analysis.

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