Summary
This manuscript describes several years of work on improving input-output
models for use in regulatory analyses of fisheries management actions
in the Northeast U.S. Its primary intent is to provide a description
of the partial multiregional input-output modeling approach we developed
and to inform peers in how to make the required adaptations to the
widely distributed regional input-output modeling system known as IMPLAN
Pro. A hypothetical impact assessment is also conducted to illustrate
the types of impacts that can be generated with the model.
1. INTRODUCTION
The
economic effects of fishery management policies that alter commercial
harvesting levels in a regional economy can be far-reaching. As
commercial harvest levels change ancillary industries that supply the
commercial fishing industry with inputs in turn adjust their production
levels and expenditure patterns, initiating further rounds of backward
linked repercussions in the economy. Working up the marketing chain,
industries such as wholesale seafood dealers and seafood processors may
be required to adjust their production levels when the availability of
local seafood changes, thereby affecting input requirements from their
other suppliers, and triggering a whole series of additional multiplier
effects as the economy adapts to the policy action.
The National Environmental Policy Act (NEPA), Executive Order
12866, and National Standard 8 of the Sustainable Fisheries Act require
federal regulators to consider the impacts on businesses that are directly
and indirectly affected by proposed management actions. The scope of
most economic analyses of management actions in the Northeast Region
has been limited to analysis of impacts on directly affected businesses
and the preparation of benefit/cost analysis which have a national perspective. While
assessments of distributional effects are generally included in a Social
Impact Assessment (SIA), the focus of an SIA is on the social implications
of a management action on fishermen, fishing families, and social networks
in a community. This means that much of the discussion of the implications
of management actions on the larger fishing-related and regional economy
is left for vetting in public hearings.
In an attempt to resolve this shortcoming, we have developed a partial
multiregional input-output model that is capable of predicting the multiplier
effects of proposed fishery management actions in the Northeast. The
model is constructed at the regional level, but it has been designed
so that the multiplier effects, expressed in terms of sales by businesses,
average annual employment (both full and part-time), and personal income
(labor income) can be determined for 24 specific sub-regions within the
Northeast. While a full, disaggregated, multiregional model that
explicitly measures the economic connections between all business sectors
in 24 different regions is beyond possibility because of data availability,
the approach developed here can be described as a partial multiregional
model because it explicitly accounts for the interconnections between
the fishing-related businesses (commercial harvesters, wholesale seafood
dealers, bait suppliers, and seafood processors) in all 24 sub-regions
in the Northeast. The intra and inter-regional seafood linkages
are captured through the addition of up to 21 new fishing-related industry
sectors in each of the coastal sub-regions. All of the remaining
non-fishing business sectors in the model measure Northeast region-level
activity, but an allocation procedure is employed that apportions the
estimated region-wide effects for these businesses to the appropriate
sub-regions in the model based on the relative importance of a sub-region’s
economy to the total Northeast region’s economy.
The model was constructed using a ready-made regional input-output system
called IMPLAN Pro (Minnesota IMPLAN Group, Inc.). The IMPLAN Pro
system consists of software and data that may be purchased from Minnesota
IMPLAN Group. The software provides the mathematical algorithms
to estimate I/O models and their resulting multipliers, as well as providing
a user interface that makes conducting impact assessments and organizing
model outputs easier. Default data sets available for purchase
include county-level data for 509 economic sectors for every county in
the U.S. Data sets for each coastal state from Maine to North Carolina
were acquired to construct the I/O model for the Northeast region.
The IMPLAN Pro system has considerable appeal due to its extensive use
in practical applications and its readily available support literature. Further,
since the conceptual basis for input-output methodology is quite intuitive,
the results of impact assessments can be readily explained to fishery
managers and the Public. This is important because it is our hope
that the model’s multiplier effects will be used by managers to
compare and contrast the outcomes of proposed management strategies prior
to deciding upon a preferred alternative. The ability to determine
how policy-induced changes in sales, income, and employment will be distributed
among businesses in 24 different sub-regions in the Northeast should
provide regional decision makers with better information to make more
informed policy decisions.
The primary purpose of this paper is to provide an overview of the Northeast
Region input-output model’s (NERIOM) operations and model outputs. This
includes a comprehensive delineation of the adjustments that were made
to the default IMPLAN system, a description of the underlying data, and
the impact estimation approach we followed. A hypothetical impact
assessment is also conducted to illustrate the types of impacts that
can be generated with the model. Finally, the advantages and limitations
of using the NERIOM approach for assessing the multiplier effects of
fishery management actions are considered, along with a discussion of
potential model improvements through additional research.
2. INPUT-OUTPUT
MODELING
In addition to input-output analysis, there are a variety of other methods
available for analyzing the economic impacts of fishery management actions. These
range from simplistic approaches such as economic-base models to sophisticated
approaches such as computable general equilibrium models (CGEM) and models
that link economic and ecological or biological considerations. Although
these types of models produce total impact estimates that are similar
to those generated from input-output models, they either lack the detail
and complexity of those created with input-output analysis (i.e., economic-base
models) or are more data intensive and generally necessitate even greater
sectoral aggregation (i.e., CGEM and economic-ecological models).
In 1973, Professor Wassily Leontief received the Nobel Prize in Economic
Science for developing the analytical framework which came to be known
as input-output analysis. Input-output analysis is generally described
as a static general equilibrium approach to quantitative economic analysis. The
common fabric underlying all input-output models is a comprehensive accounting
system which records the sales and purchases of goods and services among
industries (manufacturers), final consumers (households, government,
and exports), and resource owners (land, labor, and capital) in a regional
economy during a specified time period (usually one year). As such,
input-output models are generally employed to predict the backward linked
ripple effects (i.e., multiplier effects) of changes in the economic
activity of a particular industrial sector. For example, a decrease
in the output of one industry decreases the demand for output in its
supplying industries, and in industries which support the suppliers,
and so on. However, forward linked industries may also experience
reductions in output through diminished supply of local production. If
one is able to exogenously determine how these forward linked sectors
might be impacted, the additional backward linked ripple effects associated
with these changes could also be estimated with input-output analysis. This
is discussed further in Section 6 - Impact Estimation.
Mathematically, the Leontief input-output approach derives sectoral
outputs from exogenously specified final demands as
(1)
where X is a n x 1 column vector denoting output; I is a n x n identity
matrix; A is a n x n direct input coefficient matrix; and is a
n x 1 column vector denoting exogenous final demand. The elements
of A ( ) are called direct
input coefficients and are denoted as
![equation 2](eq2.gif)
where zij is the level of
sales from sector i to sector j; and Xj is the total
output of sector j. The (I-A)-1 matrix is often referred
to as the Leontief inverse, comprised of the interdependence coefficients
alpha ij ( ij)or
what are commonly referred to as industry output multipliers. These
multipliers indicate how much the output of each row sector would change
if the final demand for sector j’s
output changed by one dollar. In a regional input-output model
that treats household income and spending as endogenous1,
the column sum of the industry specific output multipliers measures the
total direct, indirect, and induced backward linked multiplier effects
from each sector of the economy required to satisfy a one dollar change
in final demand for sector j’s output. The direct industry
multiplier measures the initial effect of sector j’s one dollar
final demand change on itself, which implies a multiplier value of 1. The
total indirect multiplier represents the additional region-wide output
changes necessitated by the one dollar change in final demand, and the
total induced multiplier measures the economy-wide output effects of
changes in household spending generated by the direct and indirect effects. Total
output multipliers are used to estimate the economy-wide backward linked
output effect associated with exogenously specified changes in final
demand.2
The standard Leontief input-output approach must be modified before
it can be applied to fishery management actions, however. From
equation (1) it is clear that the model is designed to ascertain the
economy-wide effects of changes in the demand for a product at the final
consumption level. However, fishery management policies act by
controlling gross industry output at the point of production, rather
than operating to control the sale of outputs in final markets. For
example, fisheries policies are generally implemented to control landings
and thus directly affect the production of seafood at the harvesting
level. Therefore, the basic form of equation 1 must be modified
to assess the effects of management policies that induce gross changes
in industry output.
To accommodate the problem of handling constraints on sectoral output
within an input-output context, a procedure was developed that explicitly
transforms the traditional Leontief model into one that is capable of
accepting gross output changes as entries as opposed to only final demand
changes (Johnson and Kulshreshtha 1982). This technique, now commonly
referred to as a mixed exogenous/endogenous variables model (Miller and
Blair 1985, p. 325), was used by Leung and Pooley (2002) in assessing
the total economic impacts of a reduction in output of the Hawaii-based
longline fishery. Unfortunately, the number of sectors contained
in mixed exogenous/endogenous variables models is generally not sufficient
to derive detailed estimates of indirect multiplier effects. In
part, this is because all of the existing ready-made commercial regional
input-output models, such as IMPLAN Pro, which provide considerable sectoral
detail to trace backward linkages, are based on traditional Leontief
techniques and it is not possible to incorporate the modified relationships
embodied in the mixed exogenous/endogenous variables approach into these
ready-made models (see Steinback 2004). As a consequence, mixed
exogenous/endogenous variables models are usually derived from condensed
versions of ready-made models and therefore offer rather limited evaluations
of how total estimated impacts are dispersed throughout a particular
region.
In an attempt to provide fishery policymakers with a higher level of
sectoral detail than that contained in a mixed exogenous/endogenous variables
model, another modification technique which provides the same aggregate
solution as the mixed exogenous/endogenous variables model was adopted
for this study. The procedure, first introduced by Tanjuakio, Hastings,
and Tytus (1996), can accept gross output changes as entries within a
traditional Leontief model by setting the directly impacted sectors’ regional
purchase coefficients (RPCs) to zero and then by modeling the changes “as
if” they originated from final demand.3 In contrast
to the mixed exogenous/endogenous variables model, Tanjuakio, Hastings,
and Tytus’ (1996) approach is based on the traditional Leontief
relationships shown in equation (1) so it can be incorporated into ready-made
input-output models such as IMPLAN Pro. This is important because
ready-made models reduce the cost and complexity of model formulation
and the time required to generate impact estimates.4
The ready-made IMPLAN Pro system that was used in this study provides
a user-friendly media for customizing input-output models to a specific
application, and offers the capability to create custom sectors such
as commercial fishing that are not well configured in the default IMPLAN
Pro data base. In fact, constructing an IMPLAN Pro model capable
of calculating the multiplier effects of proposed fishery management
actions on businesses in 24 different sub-regions in the Northeast required
creating many additional fishing-related sectors. These adjustments
are explained in detail in the next section.
3. COMPONENTS
OF THE NORTHEAST REGION INPUT-OUTPUT MODEL (NERIOM)
Fishing Regions in the Northeast U.S.
A distinguishing feature of the NERIOM is its ability to assess the
impacts of management alternatives on the entire Northeast Region’s
economy and on the economies of 24 specific sub-regions. The 24
sub-regions represent semi self-sufficient fishing areas that have similar
economic networks and fishing-related attributes. The sub-regional
designations were based on several criteria. First, data on fishing
and non-fishing industrial sectors were generally only available at a
county-level. Therefore, the sub-regional impact area designations
represent either individual counties or groups of counties within each
of the 11 Northeast Region States. Data obtained from federal Northeast
vessel trip reports, Northeast dealer weigh-out slips, Northeast permit
applications, and County Business Patterns were used to classify sub-regions
that have similar economic networks and fishing-related attributes. These
data provide an indication of regional distribution channels of seafood
as it flows from harvesters through dealers and finally on to processors
in the Northeast. The sub-regional designations consist mainly
of a coastal county or groups of coastal counties, for these are the
counties where the majority of the impacts accrue and where the employees
and owners of fishing businesses, seafood dealers, and processors reside. However,
if it were determined that fish are regularly being sold to dealers and
processors in adjacent non-coastal counties, the sub-regional designations
were expanded to account for these transactions. There are 23 coastal
and one non-coastal sub-region contained in the NERIOM (see Figure 1 and Table 1).
IMPLAN Pro Adjustments
The IMPLAN Pro sector classification system is based on the U.S. Census
Bureau’s North American Industry Classification System (NAICS)
and contains county-level estimates of business activity for up to 509
industry sectors. Included in the coding system are four industry
sectors that either directly produce seafood or are directly involved
in the seafood marketing chain. These sectors are: animal production
including aquaculture (sector 13), fishing (sector 16), seafood product
preparation and packaging (sector 71), and wholesale trade (sector 390). These
four sectors, however, are constructed from aggregate industrial sector
level data which do not distinguish businesses of different types and
sizes. For example, all commercial fishing harvesters, regardless
of gear type or size, are included in one aggregate “catch-all” fishing
sector (sector 16). This level of aggregation is too gross for
conducting impact assessments of fishery management actions on specific
fisheries or gear types. Furthermore, since businesses that produce
similar types of products or services are generally pooled into a single
IMPLAN Pro sector, the underlying data may not accurately portray the
establishments of interest. For instance, businesses engaged in
the wholesale distribution of seafood comprise only a small portion of
the wholesale activity included in the single default “wholesale
trade sector”. The production functions, tradeflows, and
value-added estimates associated with seafood wholesalers that deal with
a perishable product may be different from those of wholesalers that
distribute other durable or nondurable goods. Therefore, the fishing-related
sectoral designations and underlying data must be refined before IMPLAN
Pro can be used to describe the economic activity associated with fishery
management actions.
To the extent that the businesses most impacted by fishery management
actions are commercial fishing, wholesale distribution of seafood,
and seafood processing, our refinements focused on improving on the aggregate
information contained in IMPLAN Pro sectors 16 (fishing), 390, (wholesale
trade), and 71 (seafood product preparation and packaging). Since
the National Marine Fisheries Service (NMFS) is not directly responsible
for managing the activities associated with aquaculture establishments
no adjustments were made to sector 13 (animal production).
We began by deleting IMPLAN Pro’s single commercial fishing sector
and adding new harvesting sectors based on gear type and vessel size
class. Consideration was given to classifying sectors according
to primary species landed, but since management regulations often target
specific gear sectors or vessels of a given size class it was necessary
to incorporate these characteristics into the NERIOM. Further,
the number of modifications necessary to construct a species-specific
model would have been much larger than was necessary to develop gear-based
sectors. Commercial fishing businesses were grouped into 18 distinct
gear sectors in 22 of the 23 coastal county sub-regions. No harvesting
sectors were added for the North Carolina north sub-region or for inshore
and offshore lobster vessels in the North Carolina central sub-region. An
IMPLAN Pro model is limited to 1,000 industry sectors and this constraint
would have been exceeded after adding all of the new harvesting sectors
and the remaining fisheries-related sectors to be discussed below. Therefore,
since there were no recording landings in the North Carolina north sub-region
in 2001 (the base year of the NERIOM) nor for the North Carolina central
inshore and offshore lobster harvesting sectors in 2001, these industries
were excluded from the model. A total of 394 new harvesting sectors
were added to the NERIOM. Table 2 delineates all of the new sectors
that were added to the default IMPLAN Pro model. The data used
in the construction of the fisheries-related sectors are discussed in
the next section. Additional modifications included separating the wholesale
seafood dealer component from the default wholesale trade sector and
adding 23 new wholesale seafood dealer sectors (one for each coastal
sub-region). Since seafood also passes through fish exchanges/auctions
in four sub-regions in New England a fish exchange sector was created
in each of these sub-regions (Lower Mid-Coast Maine, Massachusetts North
Shore, Massachusetts Boston Area, and Massachusetts New Bedford Coast).5 Purchases
of bait by longline, hand gear, and lobster vessels were accounted for
by assigning the purchases directly to 23 new midwater trawl bait supplying
sectors and 23 medium bottom trawl bait supplying sectors (one in each
coastal sub-region). In the Northeast, midwater and bottom trawls
land the majority of the species used as bait by the longline, handgear,
and lobster sectors. Lastly, we removed the default seafood processing
sector (sector 71) and added a sub-regional processing sector in each
of the 23 coastal sub-regions.
Product Flow Assumptions
The distribution patterns of seafood among harvesters, wholesalers,
processors, and final demand establishments in the Northeast U.S. are
complex. Product flow patterns are influenced by product type,
market demand (both domestic and foreign), location of buyers and sellers,
sales agreements/contracts between buyers and sellers, management regulations,
and a host of other immeasurable factors that present challenges to modeling. In
a regional input-output model each successive level of sale among seafood
industry establishments adds value to the product, thereby generating
additional economic impacts. Thus, it is important to account for
the origin and destination of purchases and sales that will be impacted
(either directly or indirectly) by management regulations.
Unfortunately, limited available data on this complicated product flow
within the Northeast region called for some simplifying assumptions. In
the NERIOM, harvesters are assumed to sell all of their output to wholesale
dealers via direct sales or through fish exchanges/auctions (see Figure 2). Since federally permitted harvesters are required to sell to
establishments that hold a valid federal dealer permit, this assumption
is reasonable. For state water fisheries or where some vessels
may engage in direct sales to final consumers this assumption is tenable. Wholesale
dealers, in turn, are then assumed to sell their output to final consumers,
intermediate demand industries (including seafood processors) and to
businesses located outside of the Northeast region. However, due
to the number of available seafood substitutes at the retail level, the
impacts of most fishery management actions on final consumers and intermediate
demand industries other than seafood processors are likely to be negligible. Therefore,
impacts that may accrue beyond the processor level are not incorporated
in the NERIOM (see Section 7 for more detail).
4. BACKGROUND
DATA
Sales (Output)
Estimates of sales by the harvesting sectors (i.e., ex-vessel revenues)
were derived from 2001 Northeast dealer weigh-out slips for each sub-region. In
cases where the value of landings for a given state was not assigned
to a county, sales were prorated to each sub-region within the state
based on calculated revenue shares by gear sector and sub-region for
data that was assignable.
Total wholesale seafood dealer sales in each sub-region were derived
from 2002 Economic Census data (U.S. Census Bureau 2004), 2001 County
Business Patterns data (U.S. Census Bureau 2003) and the average 2001
Fulton Market margin. The Economic Census estimates of National
wholesale seafood dealer sales and employment were used to calculate
a value for average sales per employee. This value was then multiplied
by the County Business Patterns estimates of number of employees in each
sub-region to obtain sales estimates for the sub-regions. Note
that in the NERIOM the wholesale seafood dealer sectors are treated as
margin sectors. That is, the value of sales excludes the “cost
of goods sold.” In so doing, the sales estimate for a margin
sector includes only the value added to the product being sold (i.e.,
total value of sales less the cost of the purchased raw material). Therefore,
the sub-regional sales estimates were multiplied by the average Fulton
Market mark-up (i.e., margin) in 2001 (40%).
Fish exchange/auction houses are also required to hold a valid federal
dealer permit so it was possible to obtain sales estimates for seafood
that passed through these establishments from the 2001 Northeast dealer
weigh-out data. Fish exchange/auction houses were also treated
as margin sectors in the NERIOM, however, so the weigh-out data had to
be refined so that it represented only the value added by the auction
houses. Value added by auction houses is generally associated with
a small per-pound commission to unload, sort, ice, and display the seafood. The
fees cover employee expenses and operating costs, which, in turn, generate
additional economic impacts in the Northeast region. Average handling
fees vary slightly across species and auction houses, but were assumed
to be 11 cents per pound (7 cents paid by the boat and 4 cents by the
buyer (R. Ciulla, co-manager Gloucester Seafood Display Auction, personal
communication). Given handling fees, the sales estimates (i.e.,
the value of production) for the 4 fish exchange sectors were calculated
by first dividing the average handling fee by the average annual price
per pound of seafood sold at each exchange in 2001, and then multiplying
the result by the total annual value of seafood sold at each exchange.
IMPLAN Pro default values for sector 17 (seafood product preparation
and packaging) in each of the 23 coastal sub-regions were used to determine
the value of the processing sectors’ sales. These values
were obtained by constructing separate default IMPLAN Pro models for
each of the 23 coastal sub-regions and then incorporating the resulting
23 processor sales values into the NERIOM.
Employment
Employment estimates for fishermen are typically not compiled according
to type of harvesting gear. In fact, because self-employed fishermen
are generally not included in U.S. labor statistics, there is often a
great deal of uncertainty even in aggregate estimates of commercial fishing
employment. However, all federally permitted harvesters in the
Northeast are required to fill out vessel trip reports in which they
indicate the type of gear employed and the number of crew (including
captain) that participated on each trip. This data provided the
basis for calculating the majority of the harvesting sector employment
estimates in each sub-region. Limited Northeast vessel trip reports
were available for the hand gear trips, dive gear trips, and ocean quahog
dredge gear trips, so another method for estimating employment was employed
for those gear sectors. Employment calculations for both methods
are shown below.
The steps used to calculate employment from vessel trip reports (VTR)
were as follows:
(1) Identify a gear sector for all VTR trips in each sub-region;
(2) Calculate
the average number of crew by vessel for all vessels that participated
in a gear sector in each sub-region;
(3) Sum total annual value
of landings by vessel for all vessels that participated in a gear sector
in each sub-region;
(4) Sum total annual value by gear sector
and sub-region;
(5) Sum average crew by gear sector and sub-region;
(6) Calculate
the value/labor ratio by dividing the result of step 4 by the result
of step 5; and
(7) Divide the value/labor ratio from step 6 into
the total value of production (i.e., sales values discussed above) by
gear sector and sub-region to obtain employment estimates.
Employment numbers for the hand gear sectors, dive gear sectors, and
ocean quahog dredge gear sectors were estimated by dividing the sectoral
production values (sales values) by IMPLAN Pro’s default value/labor
ratios for sector 16 (Fishing) in each sub-region. The value/labor
ratios were obtained by creating 23 default sub-regional IMPLAN Pro models.
Wholesale seafood dealer employment estimates were obtained from
2001 County Business Patterns Data (NAICS sector 42246; U.S. Census Bureau
2003). Employment values for counties with suppressed seafood dealer
data were estimated by multiplying the average state-level employment
per establishment by the total establishments contained in the county
(County Business Patterns provides estimates of number of establishments
even if employment data is suppressed).
Employment estimates for the four fish exchange/auction sectors were
obtained by making several simplifying assumptions as follows:
(1) Average earnings per employee in each of the four
sub-regions where the fish exchange/auction sectors are located were
assumed to be equal to the wholesale seafood dealer average earnings
values in those sub-regions.
(2) Calculate total employee
earnings for each fish exchange/auction sector by multiplying the sectoral
production values by the percent of total value that is used to pay
employee earnings (the derivation of these employee compensation percentages
is described in the next section).
(3) Divide total employee
earnings for each fish exchange/auction sector by the average earnings
per employee from step 1 to obtain employment estimates.
Cost-Earnings
The validity of impact estimates generated by input-output models depends
to a large degree on the underlying industry cost data, and in identifying
which combination of IMPLAN Pro sectors is most likely to represent the
distribution of these costs. In the development of the NERIOM,
a significant effort was made to incorporate the best available cost
and earnings data for harvesters, wholesale dealers, fish exchanges/auctions,
and seafood processors. Cost and earnings data are the heart of
an input-output model because they are used to construct production functions
that link the purchasing activities of a particular sector to all other
sectors in the model. This section describes the origin of these
data and explains how the production functions were created.
We also extended considerable effort in developing a bridge that was
used to allocate each industry expenditure item to its appropriate IMPLAN
Pro sector or sectors. The description of this bridge is quite
lengthy, however, so it was placed in Appendix A. Nonetheless,
readers are encouraged to review the bridging process shown in Appendix A because input-output model results are particularly sensitive to how
industry expenditures are distributed among sectors.
Cost and earnings estimates for harvesters were derived from Northeast
Observer data, Northeast Dealer data, and surveys of specific gear sectors
in the Northeast. A detailed description of these data sources
by gear type is provided below and a summary is shown in Table 3.
Inshore and Offshore
Lobster Trap
As part of a larger project to develop a simulation model of the lobster
fishery researchers at the University of Rhode Island conducted a series
of focus groups with lobster vessel owners (Anonymous, 1995). These
focus groups were conducted in selected ports throughout New England
and were conducted with both inshore and offshore participants. In
addition to gathering data on trap management and notable changes in
the fishery, the researchers collected information on operating costs,
fixed costs, and crew remuneration. Since these data were originally
collected in 1993-1994 the cost estimates were adjusted to 2001 dollars
using IMPLAN Pro price deflators.
Linear production functions for offshore lobster, Northern inshore lobster
(Maine and New Hampshire) and Southern inshore lobster (Massachusetts
southward) were then developed by calculating the proportion of total
costs (including payments to crew and profits) each expenditure item
represented (Table 4). Before these production functions could
be incorporated into the NERIOM, however, several of the proportions
(i.e., absorption coefficients) had to be adjusted for wholesale and
retail trade margins. Commodities and services purchased directly
from manufacturers (i.e., mooring fees, repair and maintenance services,
permit fees, etc.) have no transportation, wholesale, or retail markups
so no adjustments were made to these absorption coefficients (Table 4). However,
expenditures for commodities obtained from wholesalers or retailers (i.e.,
groceries, fuel, trip supplies, etc.) must be subdivided into the portion
going to the retailer, wholesaler, transportation industries, and the
manufacturer. The IMPLAN Pro system provides margin tables for
each manufactured commodity that is purchased at the wholesale and retail
level, to determine how a dollars worth of consumer expenditures is distributed
among the manufacturing, wholesale, transportation, and retail industries
that contribute to its production and distribution. These margin
tables were used to distribute the absorption coefficients in Table 4 that pertained to commodities purchased from wholesalers or retailers,
into the amount paid to the retailer, wholesaler, transportation industries,
and the manufacturer. The adjusted values were then incorporated
into the production functions shown in Table 4 and entered into the NERIOM. The
adjusted production functions were too large to present in this document,
but the margined proportions used to adjust the absorption coefficients
shown in Table 4 are contained in Appendix A.
Bottom Trawl
Cost and earnings data for the small (< 50 feet), medium (50 to 70
feet), and large (> 70 feet) bottom trawl vessels were obtained from
three sources. Average variable cost estimates (e.g., ice, fuel,
oil, water, food, bait, and supplies) were obtained through the Northeast
Fisheries Science Center’s sea sampling program. These data
are collected for all commercial fishing trips that carry onboard observers
and represent the best available trip-level data for many fisheries. Northeast
observer data from 2003 were used to estimate average trip-level expenditures
because the coverage in 2003 was considerably higher than in 2001. IMPLAN
Pro’s deflators, which are derived from the Bureau of Labor Statistics
Growth Model, were used to adjust these average values to their 2001
equivalent.
All fixed cost expenditures were derived from studies conducted by researchers
at the University of Rhode Island (URI). The data for the studies
were collected from mail surveys of Northeast fishing vessels whose primary
gear was an otter trawl. Results of the studies were published
in two reports: one for small trawlers (Lallemand et al. 1998) and one
for large trawlers (Lallemand et al. 1999). Small trawlers were
defined as vessels of 65 feet or less and large trawlers consisted of
all vessels greater than 65 feet. These length categories are different
than those contained in the NERIOM, however, so we obtained access to
the raw survey data and recalculated average fixed cost estimates for
the three categories of vessels contained in our model (i.e., small,
medium, and large trawlers). All data were then adjusted to their
2001 equivalent using IMPLAN Pro inflators.
Average gross stock estimates were derived from 2001 Northeast dealer
data and pertained only to those vessels that were surveyed in the URI
reports. In combination with average crew and vessel remuneration
percentages obtained from the raw URI survey data, the gross stock estimates
were used to calculate the value of crew payments (employee compensation)
and boat profits (proprietary income). Linear production functions
for the three categories of bottom trawl vessels could then be developed
by calculating the proportion of total costs (including payments to crew
and profits) each expenditure item represented (Table 4). Appendix A contains the adjustments made for commodities purchased at the wholesale
or retail level.
Scallop Dredge
The cost and earnings data for scallop dredge vessels were obtained
from a study conducted by researchers at the University of Massachusetts
Dartmouth (see Georgianna, Cass, and Amaral 1999). The study design
involved a mail survey to all vessels that held a federal sea scallop
permit in the Northeast in 1997. The researchers categorized the
data by small (< 50 feet), medium (50 to 70 feet), and large (> 70
feet) vessels, so the average variable and fixed cost estimates published
in the report were directly applicable to the vessel size classifications
used for scallop vessels in the NERIOM. Average earnings estimates
for crewmen, captains, and vessel owners were also determined from the
survey data for each of the vessel size categories. After all of
the average expenditures were obtained (including payments to crew and
profits) linear production functions were created to be incorporated
into the NERIOM (Table 4). Appendix A contains the adjustments
made for commodities purchased at the wholesale or retail level.
Surfclam, Ocean Quahog Dredge
Cost and earnings data for this gear sector were unavailable so the
production function created for the medium scallop dredge sector was
used a placeholder until estimates for clam dredge vessels are obtained.
Sink Gillnet
Variable costs for sink gillnet vessels were derived from 2003 Northeast
observer data and adjusted to their 2001 equivalent using IMPLAN Pro
deflators. The availability of fixed costs and earnings data for
these vessels was quite limited, however, so values that pertained to
bottom longline vessels (described below) were used as placeholders until
these data are collected. Fishing costs for bottom longline vessels
are likely to be similar to vessels fishing with sink gillnets because
they generally operate in the same manner. Nonetheless, the production
function created for sink gillnet vessels was derived from a combination
of observer data and bottom longline proxy data (see Table 4). Appendix A contains the adjustments made for commodities purchased at the wholesale
or retail level.
Diving Gear
The southern inshore lobster production function was used as a proxy
to describe the purchasing activities of the diving gear sectors until
cost and earnings estimates for vessels fishing with this gear are obtained. Note
that purchases unique to the lobster fishery such as bait and traps were
excluded from the dive gear production function. Expenditures for
these items were reallocated to the remaining cost categories according
to the relative shares contained in the production function.
Midwater Trawl
Northeast observer coverage for this fishery is quite limited and information
on fixed costs and earnings could not be found for vessels that fish
with midwater trawls. Therefore, the production function created
for the medium bottom trawl fishery was used as a placeholder until these
data become available.
Fish Pots and Traps
The operating characteristics of inshore lobster fishing are very similar
to fishing with fish pots and traps. Since specific cost and earnings
data for this gear sector could not be located, the production function
created for southern inshore lobster fishing was used to represent the
purchasing activities associated with the fish pots and traps sectors
in the NERIOM.
Bottom Longline
Average variable cost estimates were generated with data obtained from
the Northeast observer program in 2003. IMPLAN Pro deflators were
used to estimate 2001 equivalent values. Data collected by researchers
at the University of Massachusetts Dartmouth, for a study of longline
vessel owners in the Northeast in 1997, were used to calculate average
fixed costs and value-added estimates (see Georgianna and Cass 1998). After
these estimates were adjusted to 2001 values, they were combined with
the Northeast observer variable cost data and linear production functions
were created by calculating the proportion of total costs attributable
to each expenditure item (Table 4). Appendix A contains the adjustments
made for commodities purchased at the wholesale or retail level.
Other Mobile Gear
This gear sector includes all mobile gear except trawls and dredges. Vessels
fishing with purse seines, scottish seines, danish seines, stop seines,
bag nets, lift nets, shrimp beam trawls, stop nets, runaround gillnets,
and trammel nets were classified as other mobile gear in the NERIOM. The
production function created for medium bottom trawls was used to represent
the purchasing activities of these other mobile gear sectors.
Other Fixed Gear
Landings from fixed gears such as pound nets, weirs, harpoons, fyke
nets, pelagic longlines, box traps, and drift/anchor gillnets were combined
into an other fixed gear sector. The sink gillnet production function
was used to characterize industry purchases for these gear types.
Hand Gear
Cost and earnings data for vessels fishing with hand gear were estimated
from surveys of jig and pole vessel owners that fished for groundfish
in New England in 1997 (see Georgianna, Cass, and Brough 1998). IMPLAN
Pro’s inflators were used to adjust mean expenditure values to
their 2001 equivalent. The predominant gear in this sector is hand
lines, but also includes dip nets, cast nets, tongs and grabs, oyster
rakes, picks, shovels, forks, hoes, and common and long seines. Linear
production functions were also generated for these gears by calculating
the proportion of total costs each expenditure item represented (Table 4). Appendix A contains the adjustments made for commodities purchased
at the wholesale or retail level.
Small Dredge
This sector is comprised of oyster, clam, crab, conch, mussel, and urchin
dredge gear. The production function created for small scallop
dredges was used to represent the purchasing activities of these dredge
sectors.
Wholesale Seafood Dealers
A production function developed for wholesale seafood dealers in the
Mid-Atlantic region by Kirkley, Ryan, and Duberg (2004) was used to characterize
the purchasing behavior of the 23 wholesale seafood sectors in the NERIOM
(Table 5). As previously mentioned, the wholesale seafood dealer
sectors are treated as margin sectors so the production function does
not include the cost of purchasing seafood from the commercial fishing
sectors. Appendix A contains the adjustments made to the production
function for commodities purchased at the wholesale or retail level.
Fish Exchange/Auctions
Expenditures by the 4 fish exchange/auction sectors contained in the
NERIOM were modeled with a production function developed by Kirkley,
Ryan, and Duberg (2004) for Fulton Fish Market in New York (Table 5). The
data utilized in their report were obtained from TechLaw, Inc. (2001)
and represent the operating costs associated with the provision of services
offered by the Fulton Market. These services are similar to those
offered by the fish exchange/auction houses in the NERIOM. See Appendix A for the adjustments made to the production function for commodities
purchased at the wholesale or retail level.
Seafood Processors
The default IMPLAN Pro production functions for Sector 71 Seafood Product
Preparation and Packaging, in the 23 coastal sub-regions, were used to
allocate purchases to their appropriate IMPLAN Pro sectors in the NERIOM. The
default sub-regional production functions were too large to include in
this document.
Exports
Foreign trade data by species and product type were obtained from the
Fisheries Statistics and Economics Division of the National Marine Fisheries
Service (NMFS). These data provide information on trade (value
and pounds) through specific U.S. customs districts such as New England
and the Mid-Atlantic regions. The procedures used to incorporate
these trade data into the NERIOM are delineated in the next section.
5. MODEL
CONSTRUCTION
An IMPLAN Pro model consists of more than 60 underlying
Microsoft Access tables (Table 6). These tables
can be grouped into four general categories. The tables in the
first category contain unique code numbers for industries, commodities,
margins, value-added sectors, household expenditure groups, institutions,
transfer payments, and trade. Tables
in the second category contain raw input data used in the impact assessment
portion of the program. Default model building information about
the study area and model specs are contained in the third category of
tables; and the remaining tables contain report data that are created
during the model construction stage, the impact analysis stage, and for
viewing final impact runs. In constructing the NERIOM, changes
were made to the majority of the Access tables in the first and second
categories and a few of the Access tables in the fourth category (modified
Access tables are indicated with an * in Table 6).
The modification procedure generally consisted of exporting the data
contained in the relevant Microsoft Access tables to Microsoft Excel,
adding new data, and then importing the modified tables back into Microsoft
Access. A brief summary of the modification procedure is shown
in Table 7, and the detailed steps involved in building the Northeast
Region input-output model are presented below. This section also
includes a description of the adjustments made to each of the individual
Microsoft Access tables.
Model Construction Steps
Step 1 -- A default Northeast Region input-output model with a set of
estimated multipliers was first created by opening the IMPLAN Pro software,
importing the default 2001 IMPLAN data for all of the counties in Table 1, and then constructing a default IMPLAN model along with a set of estimated
multipliers.
Step 2 -- The default model was then opened using Microsoft Access 2000. IMPLAN Pro
data bases require this version of Access.
Step 3 -- The three US tables and the Observed RPCs table were
then deleted. This
step was necessary because all IMPLAN Pro models share the following
five tables (as indicated by black arrows to the left of the tables when
the model is opened in Access):
- US Absorption Table
- US Absorption Totals
- US Byproducts Table
- Observed RPCs
- Margin Codes
Deletion of these tables “breaks” the link so that any subsequent
changes will not affect other IMPLAN models. No changes were made
to the Margin Codes table so it was not necessary to remove the link
to this table.
Step 4 -- The deleted tables (the three US tables and the Observed RPCs
table) were then replaced with the same tables contained in the 2001
IMPLAN Pro structural matrix file 01NAT509.IMS through the import feature
in Access.
Step 5 -- In Access, the default data in the 16 tables that needed to
be modified were exported to Excel. Note that since the US Absorption
table consisted of over 80,000 rows of data, it was necessary to create
two Excel tables to house this information since Excel has a capacity
of 65,536 rows.
Step 6 -- All of the default data in the 16 tables to be modified were
deleted. The
table layout including column and table names was not changed so that
the updated data could be imported back into the original Access file
structure.
Step 7 -- Data in these 16 tables were modified to better reflect the
sectoral linkages among fisheries-related industries. Consistent
with Step 6, the variable names and record formats were maintained so
that the files were compatible with the original Access file structure.
Step 8 -- Once all 16 data files had been modified they were imported
into Access. An
append query in Access was used to combine the two US Absorption Excel
files into one US Absorption table.
Step 9 -- The modified model was then opened in IMPLAN Pro, the model
was reconstructed and multipliers were re-estimated. This step
was necessary because IMPLAN Pro is not capable of recognizing the direct
changes made to the underlying Access tables, so the model must be reconstructed
to use the updated data.
IMPLAN Pro Table Adjustments
The following provides a more detailed discussion of modifications to
certain Access tables.
Industry/Commodity Codes
This table contains unique code numbers for industries and commodities. Industries
and commodities share the same name and number in an IMPLAN Pro model. Modifications
began by removing the default commercial fishing sector (IMPLAN sector
number 16) and adding 394 new harvesting sectors to the model (sectors
510-903 in Table 2). These industry sectors
designate different commercial fishing gear and vessel length categories
in each of the sub-regions in the Northeast.
Additional changes included adding 23 wholesale seafood dealer sectors
(sector’s 904-926 in Table 2), 4 fish exchange sectors (sector’s
927-930 in Table 2), 23 midwater trawl bait supplying sectors, and 23
medium bottom trawl bait supplying sectors (sectors’s 931-976 in Table 2). Finally, we removed the default seafood processing sector
(IMPLAN sector number 71) and added 23 sub-regional processing sectors
(sector’s 977 - 999 in Table 2).
Type Codes
The Type Codes table contains coding information on all transaction
types in the data sets. For this table, we added the 490 industry/commodity
code designations as assigned above and the associated 490 SAM Commodity
codes. Transaction codes associated with Factors, Households, Institutions,
Transfers, Employment, Output, and Trade remained the same.
US Absorption
This table contains the United States absorption matrix which, in input-output
terminology, is the coefficient form of the use table. The default
2001 table contains 80,285 rows of data that show the proportions of
commodities each industry uses in its production process (i.e., its production
function). We removed the 213 rows of data contained in the production
functions for the default commercial fishing sector (sector 16) and the
default seafood processing sector (sector 71), and then added 55,425
rows of data that contained the production functions of each of the 490
fisheries-related sectors that were added to the model.
Sectors that purchase commodities from the default commercial fishing
sector (i.e., seafood) and the default seafood processing sector required
adjustments to their production functions, since these two default sectors
were removed from the Northeast Region input-output model. Adjustments
were made by first identifying the industries that purchase seafood from
these sectors. The commodity balance sheet report option in IMPLAN
Pro provides information on all industry sectors that purchase from a
particular sector. Using this option, we were able to identify
the 11 industry sectors that purchase seafood from the default commercial
fishing sector, and the 27 industries that purchase from the default
seafood processing sector in the 2001 data (Table 8 and Table 9). However,
in our Northeast Region model wholesale seafood dealers are assumed to
purchase 100% of the commercial fishing output. Thus, it was necessary
to change the assignment of seafood purchases for the 11 industry sectors
that purchase commercial fishing output. We assumed that 7 of the
11 sectors would purchase seafood from seafood processors and the remaining
4 would purchase from wholesale seafood dealers (see Table 8).
This reassignment strategy, however, entailed an additional step because
the Northeast Region model includes 23 sub-regional wholesale seafood
dealer sectors and 23 sub-regional seafood processing sectors. A
method had to be developed to determine from whom of the sub-regional
dealers and processors the industries in Table 8 and Table 9 would
purchase their seafood. At the suggestion of the software vendor,
we decided to allocate purchases according to output proportions in each
sub-region. In
other words, we used the sub-regional shares of total wholesale seafood
dealer output and seafood processing output in the Northeast Region to
allocate purchases. These shares were then multiplied by the default
commercial fishing sector’s and/or the default seafood processing
sector’s gross absorption coefficient contained in the production
functions of each of the sectors shown in Table 8 and Table 9. The
resulting vector of absorption coefficients was then inserted into the
US Absorption Table in place of the default values for each of the sectors
shown in Table 8 and Table 9. Note
that the sum of the values contained in the vector of absorption coefficients
must sum exactly to the absorption coefficient contained in the default
data or the model will not build correctly.
US Absorption Totals
The US Absorption Totals table contains the sum of the absorption coefficients
for each industry sector. We removed the default commercial fishing
sector’s total absorption coefficient and the default seafood processing
sector’s total absorption coefficient, and then added the appropriate
absorption coefficients for the 490 new sectors in the Northeast Region
model. The sum of the coefficients from each sector in the US Absorption
table must match the coefficients in the US Absorption Totals table.
US Byproducts
This table contains US estimates of the proportions of each commodity
an industry produces. In input-output terminology it is the coefficient
form of the “make” table derived by dividing each element
by the make table row totals. Industries often produce more than
one commodity. Commodities other than primary commodities are called
secondary commodities or byproducts. For this table, we first examined
if any of the 509 default industry sectors produced commercial fishing
seafood or processed seafood as a byproduct. The commodity balance
sheet report option in IMPLAN Pro provides information on commodity production
by all industries. From this report we found that no other industries
produced commercial fishing seafood as a byproduct in the 2001 data. However,
there were three industries (Frozen food manufacturing, Fruit and vegetable
canning and drying, and Meat processed from carcasses) that produced
processed seafood as a byproduct. If we allowed these sectors to
produce processed seafood as a byproduct, the model would not construct
properly because the default seafood processing sector was removed from
the model. Therefore, we removed the proportion associated with
processed seafood for these three sectors and added it to their primary
commodity share, so that the sum of each sector’s byproducts coefficients
remained equal to one. The byproducts coefficients, which include
the primary commodity share coefficient, must sum to one for each sector
in the model in order for the model to construct properly.
We also assumed that each of the 490 new sectors that were added to
the model would produce only primary commodities. Thus, we added
a single record to the US Byproducts table for each of the 490 new sectors
and set each sector’s primary commodity share coefficient to one.
SACommodity Sales
This table shows sales of commodities by households and institutions
in the study area. There were no sales of seafood by households
in the default data so no changes were made to the commodity sales rows
for the nine household expenditure sectors (i.e., Type Codes 10001 -
10009). There were also no institutional sales of raw harvested
seafood or processed seafood. However, zeros are entered into the commodity
sales field when no sales are present so we had to remove the records
that pointed to these commodity codes in the default institutional sales
data. In addition, since we assumed that there are no institutional
sales of commodities produced by the 490 new industries that were added
to the model, rows with zeros in the commodity sales field were inserted
into the table.
We also removed inventory additions (Type Code 14002) that existed in
the default data for the seafood processing sector (Sector 71) because
we eliminated this sector from the Northeast Region model. Sales
estimates for the 23 new sub-regional seafood processing sectors in the
model were estimated by first constructing 23 separate sub-regional IMPLAN
Pro models to obtain the default inventory seafood processing values
in each sub-region, and then assigning them to the 23 sub-regional seafood
processing commodity codes in the SA Commodity Sales table.
SAEmployment
The SAEmployment table delineates average annual jobs for each industry
in the study area. Jobs are measured in terms of both full-time
and part-time workers combined. For this table, we removed the
employment estimates for the default commercial fishing sector (Sector
16) and the default seafood processing sector (Sector 71), and then inserted
our employment estimates for the 490 new sectors.
SAFinal Demands
The final demand table consists of purchases of commodities for final
consumption by households and institutions. Several modifications
were made to this table. The first step entailed removing final
demands associated with the default commercial fishing sector and the
default seafood processing sector for all nine of the household expenditure
type codes and for all of the institution type codes. We then used
these default values in combination with output data and the dealer transaction
matrix (see Section 6) to estimate final demands by type codes for the
new harvesting sectors, seafood dealer sectors, and seafood processing
sectors.
Final demands for each of the 394 new harvesting sectors were estimated
by first calculating the proportion of a sector’s output to the
total output across all 394 sectors, and then multiplying this proportion
by each default commercial fishing sector’s (Sector 16) final demand
value contained in a Northeast Region-level model across type codes.
Final demands at the wholesale seafood dealer level were more difficult
to calculate because the wholesale seafood dealer sector is lumped into
in an all-encompassing wholesale trade sector in an IMPLAN Pro model. Thus,
there were no default final demand values specifically for seafood purchased
at the wholesale level to use as a benchmark in our new model. Therefore,
we used the average 2001 wholesale seafood mark-up from Fulton fish market
(40%) in combination with the dealer transaction matrix to determine
final demands by type code for all 23 wholesale seafood dealers in the
model. Assuming that all commercially landed seafood flows through
wholesale dealers, we first divided the commercial fishing final demand
sales calculated above by 0.4 to calculate the wholesale dealer final
demand sales associated with each of the 394 new harvesting sectors. We
then multiplied these values by the proportions contained in the dealer
transaction matrix to determine final demand sales by wholesale dealer
and type code associated with each new harvesting sector. Lastly,
we summed the final demand sales across harvesting sectors for each wholesale
dealer by type code. The resulting final demand vector contained
final demand sales for all 23 wholesale seafood dealers by type code
and was incorporated into the SAFinal Demands table. These final
demands were also subtracted from the default wholesale trade final demand
values since they were reassigned to 23 new sectors in our model.
Last, final demands for the 23 seafood processing sectors were calculated
in the same manner as the harvesting sector’s final demands. The
proportion of a sector’s output to the total output across all
23 sectors was multiplied by each default seafood processing sector’s
(Sector 71) final demand value contained in a Northeast Region-level
model across type codes.
SAForeign Exports
The SAForeign Exports table shows demands made for goods and services
by consumers and industries outside the US. For this table, we
removed the foreign export estimates for the default commercial fishing
sector (Sector 16), re-estimated foreign exports for raw seafood from
new data (see below) and assumed that foreign exports of raw seafood
occur at the wholesale level and not the harvesting level. Additionally,
foreign exports for the 23 new processing sectors added to the model
were estimated by constructing 23 separate sub-regional IMPLAN Pro models
to obtain the default inventory seafood processing export values in each
sub-region.
Foreign exports of seafood produced at the wholesale-level in the Northeast
Region were calculated according to the following six steps.
Step 1 -- Northeast dealer reports were used to calculate the value
of landings by species for the top three to five species landed (by value)
for each harvesting sector gear type in the model.
Step 2 -- The proportion of total value by species and product
type that is exported out of the US was calculated. New England
and Mid-Atlantic foreign trade data by species and product type is available
from the Fisheries Statistics & Economics Division of the National
Marine Fisheries Service (NMFS) at http://www.st.nmfs.gov/st1/trade/index.html. These
data are purchased from the Foreign Trade Division of the U.S. Census
Bureau and provide information on trade ($s and lbs) through specific
U.S. customs districts such as the New England and the Mid-Atlantic regions. We
divided the value exported by species and product type by the region-wide
export value of total landings (average export price multiplied by total
landings) to obtain the proportion of total sales exported out of the
U.S. by product type.
Step 3 -- Fresh fillets and fresh and frozen whole fish were assumed
to be exported by wholesalers. The remaining product type categories
(frozen fillets, salted, dried, minced frozen, smoked, and an other product
type category) were assumed to be exported by seafood processors. We
summed the export proportions from Step 2 across the three wholesale
product type categories to estimate total Northeast Region export proportions
(in terms of value) by species.
Step 4 -- Weighted average wholesaler export proportions were
calculated from the harvesting gear types. The export proportions
from Step 3 were weighted by the proportions of landed value by species
(for the top three to five valued species) to total landed value for
each of the harvesting gear types. These species-level proportions
for each gear type were then summed across species to obtain the proportions
of wholesaler output that will be exported out of the U.S., originating
from the 18 different gear types in the model. Note that the export
rates derived from each gear type are assumed to be the same across the
23 sub-regions.
Step 5 -- The wholesale export rates from Step 4 were then multiplied
by the output values contained in the dealer transaction matrix (adjusted
upward assuming a margin of 40%) to obtain the wholesale dealer export
values across the 23 wholesale sectors associated with each harvesting
sector in the model.
Step 6 -- We then summed the wholesale export values from Step 5 across
harvesting sectors for each of the 23 wholesalers and divided this value
by the total sales from each wholesale sector. The resulting values
show the average proportion of total seafood sales that is exported out
of the U.S. by each wholesaler. The average amount exported by
seafood dealers ranges from a low of 18% in the VA North sub-region to
70% in the Southern ME sub-region. These proportions were then
multiplied by the margined wholesale output values contained in the SAOutput
table (the wholesale sectors are treated as margin sectors in the model)
to determine the value of foreign exports for each of the 23 wholesale
sectors in the model.
SAOutput
The SAOutput table is a vector of output values in millions of dollars
that represents an industries total production. There is a single
value for each of the 997 sectors in the model. We removed the
default commercial fishing sector’s output value and the default
seafood processing sector’s output value, and then added the appropriate
production values for the 490 new sectors in the Northeast Region model.
SAValue Added
This table details payments made/received by each industry to employee
compensation (wage and salary payments, insurance, retirement, etc.),
proprietary income (all income received), other property type income
(payments from interest, rents, royalties, dividends, corporate profits,
etc.) and indirect business taxes (primarily excise and sales taxes). The
value added transactions associated with the default commercial fishing
and seafood processing sectors were removed and the appropriate values
for the 490 new sectors were added to the table.
Observed RPCs
The Observed RPCs table contains forced regional purchase coefficient
values for all states in the model. We removed the values associated
with the default commercial fishing and seafood processing sectors and
added the appropriate RPC values by state FIPS codes for the 490 new
sectors added to the model. We used the average RPC option in IMPLAN
Pro so the same RPC value was applied across states for each of the 490
new sectors. However, because of the way the model calculates impacts
across sectors, virtually all of the RPC values for the new sectors were
set to zero: the RPCs for the bait sectors were set to one. The
model-generated default RPC values were used for the other 507 sectors
in the model.
RPC Methods
This table contains information for creation of the regional purchase
coefficients. Similar to the modifications made for to many of
the other tables, we removed the information associated with the default
commercial fishing and seafood processing sectors and then added the
relevant information for the 490 new sectors to the table.
Deflator1
The Deflator1 table contains deflators that account for relative price
changes during different time periods. The IMPLAN Pro deflators
are derived from the Bureau of Labor Statistics Growth Model. The
2001 IMPLAN Pro data base contains deflators from 1977 to 2010 for each
commodity in the model. We eliminated the default commercial fishing
sector deflators and applied these same values to the 394 new harvesting
sectors in the table. We also removed the default seafood processing
sector and applied those deflators to the 23 new sub-regional seafood
processing sectors that we added to the table.
rptSAFinal Demands
This is a report table that is used by the IMPLAN Pro software to view
final demand purchases of industry outputs. Report tables are not used
by the IMPLAN Pro software for model construction or impact analysis;
they simply provide a means to view data from within the IMPLAN Pro system. Therefore,
modifications to this table are not absolutely necessary, but are required
in order to use the IMPLAN Pro reporting feature.
To keep the software fully functional we modified the final demand values
in this table. We removed the final demand values associated with
both the default commercial fishing and seafood processing sectors, and
added the appropriate data from the SAFinal Demands and SAForeign Exports
table. Note that modifications in ACCESS are not acknowledged by
the IMPLAN Pro software so the social accounts must be regenerated after
changes are made in order for the reporting features in IMPLAN Pro to
work properly.
rptSAIndustry Data
This is another report table that is used by the IMPLAN Pro software
to show industry output, employment, and value added information by sector. Changes
to this table will not effect model construction or impact analysis,
but need to be made in order to use the reporting feature in the IMPLAN
Pro software. We eliminated the industry data associated with the
default commercial fishing and processing sectors, and added the appropriate
data from the SAOutput table, SAEmployment table, and the SAValue Added
table.
6. IMPACT
ESTIMATION
The NERIOM provides analysts with a tool for assessing how the impact
of fishery management decisions may be distributed across different
sectors of regional and sub-regional economies. However, any
impact assessment must begin with an externally derived estimate of
how any such action would affect commercial fishing sales. The
economy-wide backward-linked impacts of proposed fishery management
actions are estimated by applying these exogenously determined gross
output changes to the appropriate NERIOM multipliers. As discussed
in Section 2, an IMPLAN Pro model can accept exogenous gross output
(i.e., sales) changes as entries if the RPCs of the directly impacted
sectors are set to zero. In addition to deriving exogenous gross
output changes for the harvesting sectors, however, the NERIOM also
requires exogenous estimates of how the sales will change for wholesale
seafood dealers, fish exchange/auctions, and seafood processors. Thus,
the RPCs for all of the fishing-related sectors in the NERIOM were
set to zero. By simultaneously multiplying the estimated exogenous
gross output changes for the harvesting sectors, wholesale seafood
dealer sectors, fish exchange/auction sectors, and seafood processing
sectors by their corresponding model-generated multipliers, the backward-linked
effects associated with changes in commercial fishing sales and the
additional backward-linked multiplier effects that may occur through
changes in local supply to wholesalers, auction houses, and seafood
processors can be measured. This modeling approach, however,
requires not only an external estimate of how management actions will
impact harvesting revenues, but also how the actions will impact the
revenues of auction houses, wholesalers, and seafood processors.
In previous applications of this approach, we began with a mathematical
programming model to estimate how ex-vessel revenues for each of the
commercial harvesting gear sectors in the NERIOM might change upon
implementation of the proposed regulations. Mathematical programming
models are but one method that may be used to estimate potential changes
in harvesting revenues. Other methods may be appropriate depending
upon the management changes that may be under consideration.
Once the exogenous ex-vessel revenue changes are determined, adjustments
can be made to account for the additional revenue changes that may
occur to the auction houses, seafood wholesalers, and seafood processors. To
estimate these changes, we first constructed a dealer transaction matrix
from 2001 Northeast dealer weigh-out data. Northeast weigh-out
slips record all transactions between federally permitted harvesters
and wholesale dealers. Thus, it was possible to create a transaction
matrix that showed the value of seafood sold by each of the 394 commercial
fishing gear sectors in the NERIOM to each of the 23 wholesale dealers
and 4 fish exchange/auction houses in the model. Fish/exchange
auction houses are also required to hold a federal dealer permit. The
transaction matrix could then be used to determine how changes in a
particular harvesting sector’s ex-vessel revenues would affect
the value of purchases associated with the 23 wholesale dealers and
the 4 fish exchange/auction houses. Wholesale seafood dealer
gross revenue changes were then calculated by assuming the mark-up
on these purchases was 40% - the average Fulton Market margin in 2001. The
values of the mark-ups were then entered as the change in direct sales
for each wholesale dealer sector in the NERIOM since these sectors
are treated as margin sectors by the model. The 4 fish exchange/auction
houses are also treated as margin sectors in the model. The values
of these mark-ups were calculated by dividing the average exchange
fee per pound (11 cents) by the average price per pound of seafood
sold at each exchange. The estimated gross revenue changes associated
with the 4 fish exchange/auction houses were then multiplied by the
mark-up values (approximately 9.7%) and entered as the change in direct
sales for each fish exchange/auction house. Gross revenue changes
for seafood processors are estimated following the steps described
below.
Step 1 -- Calculate the estimated value of seafood exported
by wholesale seafood dealers. Multiply the estimated gross
revenue changes for each wholesaler by the average proportion of total
seafood sales that is exported out of the U.S. by each wholesaler (derivation
of these proportions were explained in Section 5).
Step 2 -- Calculate the value of seafood left in the
Northeast that is available to be purchased by Northeast seafood processors. This
step simply requires removing the estimated value of seafood exported
by wholesale dealers. Thus, these values are obtained by subtracting
the export estimates calculated in Step 1 for each wholesale dealer
from the estimated wholesaler’s gross revenue change.
Step 3 -- Calculate the estimated value of wholesale
seafood that will actually be purchased by seafood processors in the
Northeast. As
noted earlier IMPLAN Pro data for the Northeast shows seafood processors
purchasing approximately 48% of the sales produced by the commercial
fishing sector in the Northeast in 2001. This value can be determined
by constructing a default Northeast IMPLAN Pro model and then viewing
the commodity balance sheet for the commercial fishing sector. As
we have said, most of the seafood purchased by seafood processors generally
flows through wholesalers before reaching the processing level and
all federally permitted commercial harvesters are required to sell
to establishments that hold federal dealer permits. Thus, in
the NERIOM it is assumed that seafood processors will purchase product
from wholesalers and not directly from commercial harvesters. As
such, 48% of Northeast wholesale dealer sales – and not commercial
harvester sales - are assumed to be purchased by seafood processors
in the region. The value of the purchases are then calculated
by multiplying the result of Step 2 by 0.48 for each wholesale dealer
in the model. The other 52% of wholesale seafood sales are purchased
by industries such as eating and drinking establishments, hospitals,
hotels, etc. and by final demand sectors other than exports (i.e.,
households and government entities; see Figure 2). These purchases
are assumed to remain constant in the NERIOM, however.
Step 4 -- Determine the value of purchases by seafood
processors that will be derived from each wholesaler. In Step
3, the value of wholesale seafood dealer output that is purchased by
seafood processors was calculated. In this step, we have to allocate
the purchases to specific seafood processors. In states with
more than one sub-region, seafood processors are assumed to purchase
from all in-state wholesale dealers according to seafood processor
output proportions contained in the NERIOM.
Step 5 -- Convert the impacts on purchases to gross revenue
changes. To
calculate estimated gross revenue changes, margins are applied to the
values estimated in Step 4. The margins were derived from each
sub-regional seafood processing sector’s production function
and represent the value of output less the cost of the seafood purchased. The
margins varied from 62% to 67% across sub-regions, and since seafood
processors are considered producing sectors in the NERIOM, the estimates
calculated in Step 4 were marked-up according to the appropriate sub-regional
margin values. These producer values are then entered as the
change in direct sales for each seafood processing sector.
The estimated direct changes in gross revenues for harvesters, fish
exchanges/auctions, wholesale seafood dealers, and seafood processors
are then compiled into an impact vector and entered into a Microsoft
Excel template that can be imported into IMPLAN Pro (see Appendix I
in Olson and Lindall, 1999 for details about the template). The
impact vector can then be applied to the multipliers in the NERIOM
to arrive at the economy-wide impacts of the proposed regulation in
the Northeast.6
Allocation of Indirect and Induced Impacts to Sub-regions
The NERIOM explicitly addresses sub-regional impacts for the seafood
producing sectors that are estimated to be directly affected: the commercial
harvesting gear sectors, the fish exchange/auction sectors, the wholesale
seafood dealer sectors, and the seafood processing sectors. The
sum of the sub-regional impact estimates for these sectors in the NERIOM
equals the regional impact. The NERIOM, however, calculates only
Northeast region-level impacts for the sectors that are estimated to
be indirectly affected, therefore a method had to be developed that
could apportion the estimated region-wide effects on indirectly affected
sectors to the sub-regions contained in the NERIOM.
The allocation method we developed is based on the relative
importance of each sub-region’s economy (determined to be directly
affected) to the total Northeast region’s economy. We assumed
that the indirect impacts would be distributed according to output,
personal income, and employment shares in each of the sub-regions that
were directly impacted. The shares were first determined by constructing
separate default IMPLAN Pro models for each of the 24 sub-regions.
Then the default output, personal income, and employment estimates
contained in each sub-regional model were divided by the regional totals. Using
output estimates as an example, if Tir is equal
to the total default output for each industry sector (i) in
each sub-region (r), then the shares
can be represented as . These
shares are then adjusted three times before they are applied to the
IMPLAN-generated region-wide estimates of indirect impacts.
The first adjusted share calculation maintains the unadjusted
shares (Sirs) for industries
located in the directly impacted sub-regions and the non-maritime sub-region
and sets the remaining shares to zero. If Dr indicates
the sub-regions that are directly impacted (i.e., revenue changes will
occur for commercial harvesters, seafood dealers, fish exchange/auction
houses, or seafood processors in that sub-region) and equals
the first adjusted share then for all Dr > 0 then = Sir. If Dr=
0 then =
0. Additionally,
if the subscript r equals the
non-maritime sub-region then let r = NM and = = SiNM .
The shares are then adjusted a second time to capture
the relative importance of industry output in each of the directly
affected sub-regions to total industry output across all of the directly
affected sub-regions. If Bir is set equal to and equals
the second adjusted share then for , otherwise
if then =
0. Finally, the non-maritime Northeast region shares for
each industry i are then subdivided
into non-maritime New England shares and non-maritime Mid-Atlantic
shares. The adjusted shares are calculated based on the proportion
of direct impacts that occur in each of the two aggregate sub-regions
to the total direct impacts in the Northeast region. Mathematically,
if equals
the third adjusted share and the subscripts NE and MA represent
New England and Mid-Atlantic sub-regions, respectively, then for r
= NM, and . For
all , .
The region-wide IMPLAN-generated impact estimates for each of the
indirectly affected sectors can then be apportioned across the directly
impacted sub-regions by multiplying the estimates by the third adjusted
shares. The shares sum to one which ensures that the sum of the
resulting sub-regional impacts will equal the model-generated region-wide
impacts.
Hypothetical Impact Assessment
Hypothetical reductions in medium bottom trawl ex-vessel revenues
are used to illustrate the outputs produced by the NERIOM. If
it is assumed that a reduction in landings is required to meet an annual
rebuilding target, and the reduction in landings is predicted to cause
a $500,000 decline in total ex-vessel revenues for medium bottom trawl
vessels in each of the coastal sub-regions from Maine through New York,
the NERIOM can be employed to assess the multiplier effects (sales,
personal income, and employment) associated with the estimated decline
in revenues. Tables 10 through 15 show the results of
this hypothetical impact assessment.
The total exogenously determined direct loss in ex-vessel
revenues for medium bottom trawl vessels across all the sub-regions
in the Northeast sums to $5.5 million ($5.0 million across the sub-regions
in New England and $500 thousand in the Mid-Atlantic sub-regions; Table 10 and Table 11, respectively).7 Exogenously determined losses
in wholesale dealer sales of seafood are estimated to approach $1.16
million in New England and $230 thousand in the Mid-Atlantic. Additionally,
exogenously determined losses in sales by fish exchange/auction houses
in New England are estimated to be $196 thousand. Finally, exogenously
estimated losses in seafood processing revenues are estimated to be
$5.56 million in New England and $430 thousand in the Mid-Atlantic. As
previously mentioned, wholesale dealers and fish exchange/auction houses
are treated as margin sectors so their sales estimates in the NERIOM
reflect only the value added to the seafood being sold (i.e., total
gross sales less the cost of the purchased seafood ). The
NERIOM then uses all of the exogenously determined estimates of revenue
changes to the commercial harvesters, wholesale dealers, fish exchange/auction
houses, and seafood processors to calculate the indirect and induced
multiplier effects of those changes. At the greatest level of
detail, indirect and induced effects can be estimated for up to 507
sectors in each of the 24 sub-regions. Thus, for presentation
purposes, the results have been aggregated into 15 summary categories
for each sub-region. Of the total sales impact in New England,
68% is associated with direct gross revenue losses to medium bottom
trawlers, seafood dealers, fish exchange/auction houses, and seafood
processors. The remaining 32% of the losses in New England are
the indirect and induced effects that occur among industries that provide
goods and services in the production of seafood. The largest
indirect revenue losses in New England are estimated for establishments
classified as service industries ($1.83 million). Across the
Mid-Atlantic, however, the indirect and induced effects comprise nearly
84% of the total sales impact. This seeming disproportionate
effect is mainly a function of the substantial industry-level infrastructure
that exists in the Mid-Atlantic sub-regions. Mid-Atlantic based
businesses produce a greater percentage of the goods and services that
are used in the production of seafood in the Northeast region. The
largest indirect and induced revenue losses in the Mid-Atlantic are
predicted to occur in service industries ($1.83 million), finance,
insurance, and real estate ($947.36 thousand), and wholesale trade
($812.35 thousand).
Important differences in impacts across the 23 sub-regions are also
predicted to occur. Revenue losses are estimated to range from
a high of $4.35 million in the New York Seacoast sub-region to no estimated
losses in the 9 sub-regions south of New Jersey (excluding the non-maritime
Mid-Atlantic sub-region). The comparatively large losses shown
for the New York Seacoast sub-region can generally be traced to a greater
reliance on goods and services produced from within the New York Seacoast
sub-region than from any of the other sub-regions in the NERIOM. In
the 9 sub-regions south of New Jersey, there are no predicted direct
revenue losses for any of the seafood producing sectors (i.e., medium
bottom trawlers, seafood dealers, fish exchange/auctions, or seafood
processors). It is assumed, therefore, that there will also be
no indirect or induced effects in these sub-regions. In the Downeast
Maine sub-region and in the New Jersey North and New Jersey South sub-regions
there are no predicted direct revenue losses associated with medium
bottom trawlers, but there are direct losses associated with one or
more of the other seafood producing sectors. Indirect and induced
effects are, therefore, predicted to occur as supporting businesses
supply fewer goods and services to seafood dealers, fish exchanges/auctions,
and seafood processors. It is also worth noting that supporting
businesses in the non-maritime sub-regions in New England and the Mid-Atlantic
are predicted to incur substantial revenue losses even though there
are no direct revenue losses associated with any of the seafood producing
sectors in these sub-regions. These losses result because as
the revenues of businesses located in the coastal sub-regions decline,
they, in turn, purchase smaller quantities of goods and services from
non-maritime businesses (i.e., indirect effects). In addition,
as employee earnings decline, household spending falls, initiating
further rounds of revenue repercussions in the non-maritime sub-regions
(i.e., induced effects).
Although losses in sales represent a loss in output,
losses in personal income and jobs are a more telling indicator of
economic impact. The
total loss in personal income is estimated to range up to $1.67 million
in the New York Seacoast sub-region (Table 12 and Table 13). In
terms of jobs, the are no estimated impacts in the 9 sub-regions south
of New Jersey while the New York Seacoast sub-region is predicted to
incur the largest job impact (35; Table 14 and Table 15). As
was the case for changes in sales, a much larger proportion of the
total income and employment impacts fall on the seafood producing sectors
in New England than in the Mid-Atlantic. Nearly 70% of the total
income impact and total employment impact in New England can be traced
to medium bottom trawlers, seafood dealers, fish exchange/auctions,
and seafood processors. In the Mid-Atlantic, however, only 18%
of the total estimated income changes and 23% of the total employment
changes can be traced to the seafood producing businesses. As
previously indicated, the disparity is generally due to the substantial
industry-level infrastructure that supplies seafood establishments
in the Mid-Atlantic region.
7. DISCUSSION
The NERIOM has been designed to calculate the backward linked multiplier
effects induced by exogenous changes in gross revenues for commercial
harvesters, fish exchange/auction houses, seafood dealers, and seafood
processors. Assessments such as this are essential to fishery
management agencies that want to know how management actions will impact
regional economies.
One of the more useful features of the modeling approach presented
here is that it is based on traditional Leontief input-output relationships,
so it can be incorporated into ready-made input-output systems such
as IMPLAN Pro. Ready-made models reduce the cost and complexity
of model formulation and the time required to generate impact estimates. This
is important, because the primary reason for building the NERIOM is
for use in relative policy appraisals of alternative fishery management
actions. The ability to be able to predict the multiplier effects
of policy-induced changes in a timely manner, will allow regional decision
makers in the Northeast to compare and contrast the outcomes of alternative
management strategies prior to choosing the final measures.
Ready-made regional input-output models also offer considerable industry
detail to trace backward linkages and to generate disaggregated estimates
of indirect and induced multiplier effects. In contrast, the
multiplier effects generated from other types of input-output models
(e.g., mixed exogenous/endogenous variables models and spreadsheet-type
models based on limited input-output multipliers) are usually derived
from aggregated or condensed versions of these same ready-made models. As
a consequence, the models lack the sectoral detail contained in the
NERIOM and generally do not provide the multiplier effects necessary
for fully informed decision making.
The multiplier effects generated by the NERIOM, however, are static
and should be viewed as the immediate/short-term impacts of the change
being analyzed. There are several technological assumptions built
into the model, such as fixity of prices and zero-substitution elasticities
in consumption and production that make it difficult to assess how
the seafood producing sectors will adjust over time. For example,
vessels may be able to offset initial ex-vessel revenue losses due
to a cutback in a landings quota by switching to other fisheries. In
addition, direct revenue losses to seafood dealers, auction houses,
and seafood processors may also decline over time as seafood flows
from other fisheries increase. The reduction in local supply
may also cause prices and/or imports to increase lowering the estimated
direct revenue losses even further. Unfortunately, these types
of longer run adjustments are generally not captured in input-output
models. As harvesters, seafood dealers, and seafood processors
adapt to policy-induced reductions in supply over time, it is likely
that at least some of the losses estimated with the NERIOM will be
avoided through shifts to other fisheries, price increases, and additional
imports.
The impacts estimated by the NERIOM also exclude the retail level. Although
it is possible that restaurants and food service establishments in
the Northeast could experience a reduction in local supply because
of a restrictive fishery management action, we have assumed that consumers
would simply choose from among the many other close substitutes (e.g.,
other fish species, chicken, turkey, etc.) such that retail level gross
revenues would remain unchanged.
At present, the NERIOM can accept input data for the years 2001 through
2010. Although the data contained in IMPLAN Pro are based on
economic relationships in 2001, the impacts of management actions in
succeeding years are determined by converting the estimated changes
in gross revenues to year 2001 dollars before the impacts are estimated. The
NERIOM then automatically converts the impact estimates back to the
year of the input data (through 2010). This process accounts
for the effects of inflation on the impact estimates. However,
the economic relationships that existed in 2001 are only approximations
of the ones that may exist in subsequent years. Technological
change and price variability may alter an industry’s production
process over time, and hence the businesses that are impacted by changes
in fishery management actions. Technology and prices tend to
change rather slowly, however, so the mismatch between the economic
relationships in 2001 and near subsequent years is likely to be minimal. Nonetheless,
since the NERIOM assumes these economic relationships remain constant,
an element of uncertainty is introduced into the model’s estimates.
Product flow assumptions are another source of possible uncertainty. The
federal Northeast dealer data base tracks the flow of seafood from
harvesters to seafood dealers and most fish exchange/auction houses
(e.g., the flow of seafood to Fulton Market in New York is unknown),
but documentation of successive levels of sales among seafood industry
establishments in the Northeast region are not available. In
particular, virtually no data is available to measure transactions
between and among fish exchange/auction houses, seafood dealers, and
seafood processors. Thus, the NERIOM assumes a linear flow of
product sales from harvesters to fish exchange/auction houses and wholesale
seafood dealers, and then finally onto seafood processors. To
the extent that these simplifying assumptions underestimate the number
of transactions between the seafood producing establishments in the
Northeast region, the value added by each successive level of sale
will be underestimated. If this is the case, the impacts generated
by the NERIOM are also underestimated.
The allocation method used to translate region-wide indirect impacts
into sub-regional impacts ensures that that the sum of the sub-regional
impacts will equal the model-generated region-wide impacts, but the
method is based on weighted ratios that may only approximate true economic
relationships in the Northeast region. For example, the total
Northeast region ice manufacturing sales impact in the hypothetical
impact assessment shown above was $40.69 thousand (Table 10 and Table 11). This model-generated indirect impact was then distributed
across sub-regions that were predicted to be directly impacted based
on the relative importance of ice manufacturing across each of the
directly impacted subregions and the non-maritime sub-regions. Since
ice manufacturing output (i.e., sales) in the Boston Massachusetts
sub-region comprises 6.48% of the regional total, 6.48% ($6,283) of
model-generated regional total is allocated to the Boston Massachusetts sub-region. While the proportional allocation
method is computationally efficient and concise, it only distributes
the model-generated region-wide indirect and induced impacts to sub-regions
where direct impacts are predicted to occur. If businesses located
in sub-regions that are not predicted to be directly impacted are affected
the weighting method may misrepresent the distribution of impacts.
Consideration was given to allocating indirect and induced impacts
across all 24 sub-regions according to the default fixed output, income,
and employment shares in each sub-region. This method would result
in estimates of indirect and induced impacts in all 24 sub-regions,
but it is insensitive to the location of the direct impacts. The
allocation method we followed places more weight on indirectly affected
businesses located in the sub-regions where the direct effects take
place. Construction of a full multi-regional model that explicitly
measures the linkages between indirectly affected sectors in each of
the sub-regions would likely provide more accurate sub-regional estimates
of indirect impacts. However, a model of this kind in the Northeast
region is computationally unworkable since it would require over 12
thousand sectors to measure the economic relationships that exist between
the indirectly affected sectors in each of the 24 sub-regions (i.e.,
507 possible indirectly affected sectors * 24 sub-regions = 12,168
sectors).
ENDNOTES
1. This is known as closing the model with respect to households. In
a closed input-output model, the household sector is moved from the
final-demand column and placed inside the A matrix, making it one of
the endogenous sectors.
2. Employment and personal income multipliers can also be derived
from input-output models by multiplying the model-generated output
changes by an industry’s employment to output ratio, and an industry’s
personal income (employee compensation plus proprietor’s income)
to output ratio, respectively.
3. An RPC measures the portion of the total regional demand
for a particular industry sector’s output that is supplied by
local producers.
4. For a derivation of the equivalence between the total aggregate
impact estimates generated from a mixed exogenous/endogenous variables
model and the approach described in Tanjuakio, Hastings, and Tytus
(1996) see Steinback 2004.
5. The addition of fish exchanges/auctions could not be extended
to the New York sub-region where the largest wholesale market in the
Northeast operates – the Fulton Market, because specific data
on the flow of seafood through this market, though formerly available
is not now so.
6. Although the effects on the economy of direct changes in
gross revenues for harvesters, fish exchanges/auctions, wholesale seafood
dealers, and seafood processors are estimated simultaneously in the
NERIOM, the model avoids double counting because the RPC’s for
these sectors have been set to zero. Setting the RPCs of the
outputs produced by the directly impacted sectors to zero prior to
constructing the direct input coefficient matrix (A matrix) effectively
prevents other local industries from buying these outputs and, thus,
removes the possibility of double counting impacts. In other
words, the impacts associated with changes in seafood dealer revenues
and fish exchange revenues exclude the impacts associated with changes
in commercial harvesting revenues, and revenue changes associated with
seafood processors exclude those changes attributable to seafood dealers,
fish exchanges, and commercial harvesters. Thus, the impacts
associated with revenue changes to these sectors can be summed to obtain
the total effect on sales, income, and employment without double counting
impacts.
7. In 2001, there were no reported landings for medium bottom
trawl vessels in the Downeast Maine sub-region. Thus, there are
no losses in ex-vessel revenues reported for the medium bottom trawl
sector in this sub-region in Table 10.
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Acronyms |
CGEM |
= Computable General Equilibrium Model |
NAICS |
= North American Industry Classification System |
NEPA |
= National Environmental Policy Act |
NERIOM |
= Northeast Region Input-Output Model |
NMFS |
= National Marine Fisheries Service |
SIA |
= Social Impact Assessment |
URI |
= University of Rhode Island |
VTR |
= NMFS’ Vessel Trip Reports |
|