**CONTRIBUTION OF THE EXISTING NECEDAH NATIONAL**

**WILDLIFE REFUGE TO THE REGIONAL ECONOMY CHAPTER 3**

The NNWR supports commercial, governmental and recreational activities
that contribute to the vitality of the local economy. In this chapter,
we consider the magnitude of this contribution. Specifically, we use
regional economic modeling techniques, also known as input/output
analysis, to characterize two categories of regional economic impacts:

-   For refuge administration and management activities, road
    maintenance, trapping and timber harvesting taking place on the
    NNWR, we determine how the input demand and output flow associated
    with these activities affect other industries in the region.

-   For key recreational activities such as wildlife viewing, fishing
    and hunting, we evaluate the linkages to supporting commercial
    industries such as sporting goods, restaurants and hotels.

We consider the impacts discussed above as they occur in three different
years: 1996, 2011 and 2026. These approximately correspond to the
present (or baseline condition), 15 years in the future and 30 years in
the future. This analysis enables a consideration of changes in the
regional economic contribution of the NNWR over time.

The chapter begins with a discussion of the concepts underlying regional
economic modeling. We then discuss the methodology used to develop the
regional models in this analysis, and present the results for the three
time horizons considered. The modeling results characterize the
magnitude of the economic relationship between the regional economy and
commercial, governmental and recreational activities taking place on the
NNWR.

# nnwr impact on the regional economy

The NNWR affects the economy through two media: the commercial and
governmental activities occurring on the Refuge, such as Refuge spending
and timber harvesting; and recreation expenditures by NNWR visitors.
Three of the four commercial and governmental activities taking place on
the Refuge (i.e., timber harvesting, trapping, and road maintenance)
would likely occur on that land regardless of whether it is managed by
the Refuge. Similarly, some local recreation spending would likely take
place regardless of the existence of the Refuge. In particular, the
number of outdoor recreation sites available in the area suggests that
local residents would take advantage of nearby recreational
opportunities in the absence of the Refuge. On the other hand, Refuge
spending by the U.S. Fish and Wildlife Service (FWS) represents a wholly
new injection of funds into the local economy that would not occur
otherwise. For this reason, only spending by the Refuge can be
considered a true stimulus to the local economy.

In addition, the estimates presented in this chapter reflect changes in
the output of the local economy, but not a change in overall national
output. Increases in output in the local economy reflect a
redistribution of spending from another part of the nation, not a net
increase in output for the nation as a whole. Similarly, decreases in
the output of the study area imply that business has moved elsewhere
within the U.S. to some other local economy. The appropriate measure of
net gains and losses in overall national economic activity is consumer
surplus, discussed in Chapter 5.

The estimates discussed below, therefore, should be considered in the
context of these issues. Although the gains and losses associated with
these changes in output are meaningful to the local economy, they are
not relevant for the nation as a whole.

# METHODOLOGY

## Overview of Regional Economic Modeling {#overview-of-regional-economic-modeling .unnumbered}

The concept of regional economic modeling seeks to characterize the
interdependence of industries in a geographic region. Industries both
purchase output from and supply input to other industries in a given
region. As a result, the contribution of a particular industry to the
regional economy is larger than the industry\'s output. For example, the
timber industry sells its output to furniture producers and other
processing industries, and, simultaneously, purchases trucks, saws, and
other inputs from other regional industries. The presence of these
linkages implies that employment and output in furniture and truck
production are dependent upon the existence of the timber enterprises.
An increase in timber output would spur increases in the output and
employment of these secondary industries. Alternatively, if output in
the timber industry were to decrease, the decline in total regional
employment and output would likely be larger than the

total employment and output losses in the timber production sector. The
goal of input/output modeling is to capture the extent to which
industries are dependent on each other in this manner, and how they
interconnect to form the regional economy.

The development of a regional economic model involves substantial
sorting and organizing of economic data to characterize accurately the
workings of the regional economy. First, to reduce the number of factors
in the analysis, industries that affect the economy in a similar manner
are grouped into sectors. Creation of an *input/output matrix* enables
tracking of flows of goods and services between sectors. This matrix
describes how much of each sector\'s input needs are met by the outputs
of all other sectors in the area.[^1]

A regional economic model uses the input/output matrix to generate a set
of values known as multipliers, which further characterize the economic
links between a particular industry and the regional economy. The
multiplier quantifies the relationship between demand for a given
industry\'s output and the output required of the regional economy. For
example, an output multiplier of 1.26 associated with the timber
harvesting industry implies that demand for \$1.00 of timber requires
\$1.26 of output to be produced by the regional economy (i.e., the
timber industry and all other regional industries). As this example
suggests, industries with larger multipliers have a greater effect on
the regional economy. In addition to output multipliers, most
input/output models generate employment, value added and income
multipliers which share the same basic principles.

## Overview of the IMPLAN Model

Our regional economic models are developed using MicroIMPLAN (IMpact
Analysis for PLANning), designed by the U.S. Forest Service.[^2] This
particular model is used by many state and federal planning agencies to
evaluate the economic impact of policy choices. The IMPLAN input/output
matrix incorporates data from a number of federal and state entities,
including the Bureau of Economic Analysis and the Bureau of Labor
Statistics. To group the industries for purposes of developing the
input-output matrix and multipliers, IMPLAN uses the categories
developed in the U.S. Office of Management and Budget\'s Standard
Industrial Classification (SIC) code. We analyze the most recent data
available, which are from fiscal year 1994.

The results of IMPLAN\'s input-output analyses are presented as
estimates of several important economic indicators, including final
demand, total industry output, employee compensation income, property
income, value added and employment. Each of these indicators is
estimated for each industry group. The models and an overview of key
results are discussed below.

## Developing the NNWR Regional Economic Model

Because a large proportion of the commercial, governmental and
recreational activity of interest occurs in Adams, Juneau, Monroe and
Wood counties, our models are based upon data from these counties. Our
focus on these four counties is important in interpreting the model
results. Because the models incorporate data from these counties only,
the estimates of the regional economic contribution of NNWR-related
activities pertain solely to the joint economy of the four counties. The
models do not indicate the contribution of NNWR-related activities to
the economies of other counties or states, nor do they address impacts
on the economies of foreign nations.

+-----------------+------------------+--------------------------------+
| **Exhibit 3-1** |                  |                                |
|                 |                  |                                |
| **COMBINED      |                  |                                |
| BASELINE DATA   |                  |                                |
| FOR ALL         |                  |                                |
| INDUSTRIES IN** |                  |                                |
|                 |                  |                                |
| **ADAMS,        |                  |                                |
| JUNEAU, MONROE  |                  |                                |
| AND WOOD        |                  |                                |
| COUNTIES (1996  |                  |                                |
| \$)**           |                  |                                |
+-----------------+------------------+--------------------------------+
| **Variable**    | **Baseline       | **Description of Estimate**    |
|                 | Estimate**       |                                |
+-----------------+------------------+--------------------------------+
| Total Industry  | \$7,669,000,000  | Total output of all regional   |
| Output          |                  | industries.                    |
+-----------------+------------------+--------------------------------+
| Employment      | 94,576           | Total employment of all        |
|                 |                  | regional industries.           |
+-----------------+------------------+--------------------------------+
| Source: IMPLAN  |                  |                                |
| Data Files for  |                  |                                |
| Adams, Juneau,  |                  |                                |
| Monroe and Wood |                  |                                |
| counties.       |                  |                                |
+-----------------+------------------+--------------------------------+

The total output and employment for all industries in the four counties
in 1994 are presented in Exhibit 3-1. As noted above, we use these
baseline data to develop two sets of models: one that estimates the
economic contribution of NNWR commercial and governmental activity and
one that estimates the economic contribution of recreational activities
that take place at NNWR. To estimate the regional economic effects of a
particular policy scenario using IMPLAN, we enter into the model the
estimated change in output in each industry under consideration. The
model then calculates the change in the demand for inputs to that
industry, which causes a change in supplying industries\' output, a
change in demand for the inputs to those secondary industries, and so
on. As the initial change ripples through the economy, the model tracks
changes in the demand, output, employment and other economic parameters
associated with the industries in the region. These effects can be
classified as *direct*, *indirect* or *induced*, depending on the source
of the change:

-   *Direct effects* are the changes in production in industries
    producing items for which demand has changed, or which have suffered
    a supply shock. These are the changes specified initially by the
    modeler.

-   *Indirect effects* are changes in production in industries linked
    with the directly affected industries. For example, a decrease in
    demand for the output of one of the directly affected industries
    will lead that industry to decrease demand for inputs, thereby
    affecting industries that supply those inputs.

-   *Induced effects* are changes in household consumption resulting
    from changes in employment brought about by the direct and indirect
    effects. For example, reductions in household consumption of medical
    and legal services may occur as a result of decreased regional
    employment.

The model then sums these effects across all industries, estimating the
change in regional output, employment and other indicators that would
result from the initial change in output.

It is important to recognize that the IMPLAN model estimates only the
effects stemming directly from the policy change and not complementary
effects that occur over time within the economy. For example, a
reduction in the output of the timber harvesting industry would likely
prompt local furniture producers to seek alternate supplies of lumber,
thereby mitigating output and employment losses in that sector.
Similarly, the IMPLAN model would not take into account the
re-employment in other industries of persons who lose their jobs as a
direct result of a decline in a particular industry. As a result, the
net output or employment change associated with a policy change may be
smaller than the effect estimated by the model. For purposes of our
analysis, this caveat implies that the long-run net employment and
output that these industries contribute to the regional economy may be
smaller than the model predicts.

In addition, as noted above, the latest year for which we have
input/output data is 1994. The analysis uses the 1994 regional economic
data as a proxy for the regional economy in 1996, 2011 and 2026.
Although using 1994 data as a proxy for the 1996 regional economy may be
reasonably accurate, the 1994 data likely would not reflect the regional
economy in 2011 and 2026 with significant accuracy. Thus, regional
economic impact estimates for 2011 and 2026 should be interpreted with
the understanding that the underlying input/output matrices may be
somewhat inconsistent with anticipated economic characteristics in 2011
and 2026.

# Estimating the Impact of Commercial and Governmental Activities in the NNWR

We first estimate the contribution of commercial and governmental
activities taking place on the NNWR to the regional economy, comprised
of Adams, Juneau, Monroe and Wood counties, for the three years under
study. We examine the regional economic impact of four activities:
Refuge spending, trapping, timber harvesting and maintenance of NNWR
roads by local towns. As noted above, to interpret these analyses, it is
important to recognize that Refuge spending represents an infusion of
funds into the local economy, whereas trapping, timber harvesting and
maintenance of roads may take place in some form on land occupied by the
Refuge whether or not the Refuge exists.

To apply the IMPLAN model, we posit the elimination of the four
activities in the four-county region. Although this hypothetical
construct is unrealistic, it is an effective modeling technique that
enables us to isolate the proportion of the region\'s output and
employment derived from the commercial and governmental enterprises
taking place on the NNWR. Because the model is linear, the decrease in
economic activity associated with the elimination of an industry is the
exact inverse of the contribution of that industry to the regional
economy. Therefore, the results obtained from this analysis illustrate
the industry\'s role in the regional economy.

Exhibit 3-2 presents baseline output estimates for NNWR-related
commercial industries and government enterprises for the years 1996,
2011 and 2026. The exhibit reports the same output in the years 1996,
2011 and 2026 for each of the activities because output in these
activities likely will remain static over the time period of the
analysis. The amount of timber and pelts harvested, the size of the NNWR
budget, and spending on road maintenance are expected to remain stable
over time. Therefore, the results of the relative impact of these
activities on the regional economy is also expected to remain static. As
shown, actual expenditures by the federal government on NNWR
administration constitute the largest proportion of NNWR-related output.
Road maintenance and timber harvesting follow, and trapping output is
the smallest of the four.

Exhibit 3-2 also shows the results of the IMPLAN analysis, i.e., the
estimated contribution of each industry to the regional economy. The
second, third and fourth columns in Exhibit 3-2 present estimates of the
total regional output, employment and employee compensation associated
with each activity. These estimates reflect not only output and
employment in the NNWR commercial or governmental activity under study
but also output and employment in secondary industries that are
dependent upon that activity.

The conclusions drawn from the NNWR-dependent commercial and
governmental activity model are as follows:

-   Refuge spending has the greatest effect on the regional economy,
    accounting for just under \$1 million and approximately 16 jobs, and
    contributing \$315,000 to employee salaries.

-   NNWR timber harvesting and road maintenance contribute similarly to
    employment in the region, accounting for 1.3 and 1.7 jobs,
    respectively, and contributing approximately \$30,000 each to
    regional salaries. However, timber harvesting accounts for
    approximately \$190,000 of the regional economy, whereas road
    maintenance contributes only slightly more than half that amount
    (\$100,000).

-   Commercial trapping plays a minor role in the overall regional
    economy, accounting for only approximately \$9,000 of regional
    output and less than one job.

+---------+---------+----------------+---------------+----------------+
| **      |         |                |               |                |
| Exhibit |         |                |               |                |
| 3-2**   |         |                |               |                |
|         |         |                |               |                |
| **R     |         |                |               |                |
| EGIONAL |         |                |               |                |
| E       |         |                |               |                |
| CONOMIC |         |                |               |                |
| CONTR   |         |                |               |                |
| IBUTION |         |                |               |                |
| OF NNWR |         |                |               |                |
| COM     |         |                |               |                |
| MERCIAL |         |                |               |                |
| AND     |         |                |               |                |
| GOVER   |         |                |               |                |
| NMENTAL |         |                |               |                |
| ACT     |         |                |               |                |
| IVITIES |         |                |               |                |
| IN      |         |                |               |                |
| 1996,   |         |                |               |                |
| 2011    |         |                |               |                |
| AND     |         |                |               |                |
| 2026**  |         |                |               |                |
+---------+---------+----------------+---------------+----------------+
| **\     | **O     | **Contribution | *             | **Contribution |
| Act     | utput** | to Regional    | *Contribution | to Employee    |
| ivity** |         | Output**       | to Regional   | Compensation   |
|         | **(1996 |                | Employment    | (1996 \$)**    |
|         | \$)**   | **(1996 \$)**  | (persons)**   |                |
+---------+---------+----------------+---------------+----------------+
| NNWR    | \$      | \$890,625      | 15.7          | \$313,453      |
| Refuge  | 624,200 |                |               |                |
| S       |         |                |               |                |
| pending |         |                |               |                |
+---------+---------+----------------+---------------+----------------+
| Timber  | \$      | \$186,543      | 1.3           | \$33,817       |
| Har     | 153,758 |                |               |                |
| vesting |         |                |               |                |
+---------+---------+----------------+---------------+----------------+
| T       | \       | \$102,072      | 1.7           | \$31,919       |
| ownship | $96,000 |                |               |                |
| S       |         |                |               |                |
| pending |         |                |               |                |
| on NNWR |         |                |               |                |
| Road    |         |                |               |                |
| Main    |         |                |               |                |
| tenance |         |                |               |                |
+---------+---------+----------------+---------------+----------------+
| T       | \$6,858 | \$9,334        | \<1           | \$1,371        |
| rapping |         |                |               |                |
+---------+---------+----------------+---------------+----------------+
| Source: |         |                |               |                |
| IEc     |         |                |               |                |
| IMPLAN  |         |                |               |                |
| an      |         |                |               |                |
| alysis. |         |                |               |                |
+---------+---------+----------------+---------------+----------------+

# Estimating the Impact of Recreational Activity in the NNWR

We have also developed an IMPLAN model to characterize the role of
recreational activities that take place at the NNWR for each of the
three years under study. The recreational activities examined in this
model include recreational fishing, recreational hunting and wildlife
viewing. The initial premise of this model is identical to that of the
industry models, in that we assume the elimination of recreational
opportunities in order to isolate the contribution of these activities
to the regional economy. However, recreation itself has little direct
effect on the economy. Rather, the purchases of recreation-related goods
and services are the medium through which recreation affects the
regional economy. For example, people who decide to fish may purchase
boats, rods and other equipment from sporting goods stores.

Because of this indirect link, modeling the contribution of recreational
activities to the regional economy involves an additional step in which
the modeler must estimate expenditures per recreation day on different
recreation-related goods and services. The primary IMPLAN sectors
affected by recreational activity are:

-   hotels and lodging;

-   grocery stores and restaurants;

-   sporting goods stores; and

-   transportation.

Once these per-day expenditures have been estimated, the annual volume
of recreation expenditures can be calculated by multiplying the
expenditures per day with our estimate of the annual number of NNWR
visitor days for each activity. This annual estimate of visitor days
reflects the extent of the link between recreation and economic
activity.

To model the elimination of recreational opportunities in the NNWR, we
assume that the number of recreation visitor days spent in the NNWR
falls to zero from estimated activity levels in each of the three years
under study. Thus, annual expenditures to support recreational
activities would also be zero. To create this scenario within the model,
we specify a reduction in visitor days equal to the recorded NNWR
visitation in 1996 and estimated visitation for 2011 and 2026. We also
supply the model with estimates of expenditures per visitor day. The
model multiplies these factors together to estimate the reduction in
output, final demand, employment and other indicators in affected
regional businesses resulting from the reduction in the number of
recreation days.

Three issues and caveats pertaining to the recreational activity model
are noteworthy. First, the model includes the visitors to the Refuge who
reside in the local area. In the absence of the Refuge, local residents
may take advantage of similar recreational opportunities nearby (e.g.,
Sandhill Wildlife Area, Wood County Wildlife Area, and Meadow Valley
Wildlife Area). As a result, the expenditures made by these visitors
represent spending that likely would have taken place regardless of the
existence of the Refuge, and thus do not constitute an infusion of funds
into the local economy. In this case, the model may overestimate
slightly the contribution of the Refuge to the local economy. However,
the recreation areas nearby provide slightly different amenities than
the Refuge. To the extent that residents would seek a recreational
experience very

similar to that provided by the Refuge, they may travel outside the
study area to find it. In this case, the model would correctly treat
expenditures associated with the Refuge as an infusion of funds because,
in the absence of the Refuge, residents would go elsewhere.

In addition, the model accounts for the fact that some purchases
associated with NNWR recreation may occur outside the region. For
example, many hunters live outside of the four-county region included in
the model and may purchase items such as ammunition near their homes. By
adjusting for extra-regional recreational purchases, the model avoids
overestimating the regional economic effect of recreation.

Finally, the estimates of per-day expenditures we use to develop the
regional economic analysis are not directly comparable to the estimates
of consumer surplus we use to estimate the surplus value of NNWR
recreation in Chapter 5. Whereas surplus value is a measure of a
consumer\'s willingness to pay for an activity over and above current
expenditures on that activity, the estimates used in these models
reflect the actual expenditures made by recreators. We use these
different estimates because the two analyses serve different purposes.
Whereas the analysis of the surplus value reflects the net societal
value of the recreational experience itself, the regional economic model
estimates the contribution of recreation to the regional economy.

Exhibit 3-3 shows the inputs underlying the recreational activity model
and the model\'s estimate of the total contribution to regional output
and employment of NNWR recreational opportunities. The \"Annual Number
of Recreation Days\" column shows the total number of recreation days
per year for each activity. The visitor day estimate for 1996, reported
in Chapter 2, is derived from RMIS data for that year. Based on
historical trends in the number of state-wide hunting, fishing and
viewing days derived from the *1991 National Survey of Fishing, Hunting,
and Wildlife-Associated Recreation*, we estimate that annual hunting
days will decline by approximately one-third between 1996 and 2011, and
by another third between 2012 and 2026. In addition, annual wildlife
viewing days are estimated to increase by 18 percent between 1996 and
2011, and by another 18 percent between 2012 and 2026. Finally, fishing
days are expected to remain constant throughout the time period under
study.

+---------+------------+--------------+---------------+----------------+
| **      |            |              |               |                |
| Exhibit |            |              |               |                |
| 3-3**   |            |              |               |                |
|         |            |              |               |                |
| **R     |            |              |               |                |
| EGIONAL |            |              |               |                |
| E       |            |              |               |                |
| CONOMIC |            |              |               |                |
| CONTR   |            |              |               |                |
| IBUTION |            |              |               |                |
| OF NNWR |            |              |               |                |
| RECRE   |            |              |               |                |
| ATIONAL |            |              |               |                |
| O       |            |              |               |                |
| PPORTUN |            |              |               |                |
| ITIES** |            |              |               |                |
|         |            |              |               |                |
| **IN    |            |              |               |                |
| 1996,   |            |              |               |                |
| 2011    |            |              |               |                |
| AND     |            |              |               |                |
| 2026**  |            |              |               |                |
+---------+------------+--------------+---------------+----------------+
| **\     | **Annual   | **           | *             | **Contribution |
| Act     | Number of  | Contribution | *Contribution | to Employee    |
| ivity** | Recreation | to Regional  | to Regional   | Compensation   |
|         | Days**     | Output**     | Employment    | (1996 \$)**    |
|         |            |              | (persons)**   |                |
|         |            | **(1996      |               |                |
|         |            | \$)**        |               |                |
+---------+------------+--------------+---------------+----------------+
| ***Year |            |              |               |                |
| 1996    |            |              |               |                |
| (Basel  |            |              |               |                |
| ine)*** |            |              |               |                |
+---------+------------+--------------+---------------+----------------+
| Fishing | 7,325      | \$243,276    | 5.9           | \$65,338       |
+---------+------------+--------------+---------------+----------------+
| Hunting | 9,230      | \$249,237    | 7.2           | \$72,526       |
+---------+------------+--------------+---------------+----------------+
| W       | 106,835    | \$2,054,201  | 55.3          | \$530,949      |
| ildlife |            |              |               |                |
| Viewing |            |              |               |                |
+---------+------------+--------------+---------------+----------------+
| ***Year |            |              |               |                |
| 2011*** |            |              |               |                |
+---------+------------+--------------+---------------+----------------+
| Fishing | 7,325      | \$243,276    | 5.9           | \$65,338       |
+---------+------------+--------------+---------------+----------------+
| Hunting | 6,350      | \$171,478    | 5.0           | \$49,900       |
+---------+------------+--------------+---------------+----------------+
| W       | 125,933    | \$2,421,412  | 65.2          | \$625,862      |
| ildlife |            |              |               |                |
| Viewing |            |              |               |                |
+---------+------------+--------------+---------------+----------------+
| ***Year |            |              |               |                |
| 2026*** |            |              |               |                |
+---------+------------+--------------+---------------+----------------+
| Fishing | 7,325      | \$243,276    | 5.9           | \$65,338       |
+---------+------------+--------------+---------------+----------------+
| Hunting | 4,369      | \$117,657    | 3.4           | \$34,238       |
+---------+------------+--------------+---------------+----------------+
| W       | 149,192    | \$2,868,631  | 77.2          | \$741,455      |
| ildlife |            |              |               |                |
| Viewing |            |              |               |                |
+---------+------------+--------------+---------------+----------------+
| Source: |            |              |               |                |
| IEc     |            |              |               |                |
| IMPLAN  |            |              |               |                |
| an      |            |              |               |                |
| alysis. |            |              |               |                |
+---------+------------+--------------+---------------+----------------+

We applied these estimates of annual visitor days to the average
expenditures per day for each activity, shown in Exhibit 2-8. These
estimates characterize the link between recreation participation and
economic impact. Importantly, expenditures per day are not projected to
increase in real terms during the three time periods studied, so these
estimates were used for each of the models for 1996, 2011 and 2026. The
final three columns of Exhibit 3-3 show the contribution of each
recreational activity to total regional output, employment and employee
compensation for each of the years examined. The model indicates the
following:

-   Wildlife viewing has the greatest effect on the combined economies
    of the four counties during all three time periods. This activity
    accounts for between \$2.1 million and \$2.9 million of regional
    output and between 55 and 77 jobs, contributing \$531,000 and
    \$741,000, respectively, to employee salaries. Although
    recreationalists engaged in nonconsumptive activities spend fewer
    dollars than do anglers and hunters, the significantly higher number
    of recreation days spent on wildlife viewing compared to hunting and
    fishing make it the greatest contributor to the regional economy of
    the three. In addition, the number of wildlife viewing days per year
    is projected to increase over time, unlike annual fishing and
    hunting days, thereby increasing the relative impact of this
    activity over time. As noted in Chapter 2, the estimate of the
    annual number of recreation days is associated with significant
    uncertainty. As a result, the estimates of the regional economic
    impact of wildlife viewing on the NNWR should be interpreted with
    this uncertainty in mind.

-   Currently, recreational hunting has the second greatest effect on
    the regional economy, accounting for \$249,000 and 7.2 jobs,
    contributing \$65,000 to employee salaries. This category also has
    the highest per-day expenditures of the three activities. However,
    annual hunting days are expected to decline over time, whereas
    fishing days are expected to remain constant. Therefore, during 2011
    and 2026, hunting becomes the recreational activity contributing the
    smallest proportion of regional output, declining to a contribution
    of \$118,000 and 3.4 jobs, contributing \$32,000 to employee
    salaries. As noted in Chapter 2, the estimates of the annual number
    of hunting days are associated with a high degree of certainty;
    thus, the estimates of the regional economic impact of NNWR hunting
    are fairly accurate.

-   Currently, fishing produces the third greatest regional economic
    effect, accounting for \$243,000 of regional output and 5.9 jobs,
    contributing \$65,000 employee salaries. Because annual fishing days
    are expected to remain constant, fishing surpasses hunting in
    economic contribution in 2011 and 2026 despite the fact that its
    regional economic contribution remains constant. Fishing is the
    second most expensive activity per day. As noted in Chapter 2, the
    estimate of the annual number of fishing days is somewhat imprecise;
    therefore, the estimates of the regional economic impact of NNWR
    fishing should be interpreted with this uncertainty in mind.

# SUMMARY

This chapter provides an analysis of the regional economic contribution
of key NNWR commercial, governmental and recreational activities. We
conducted the analysis using IMPLAN, a widely-used regional economic
impact model. Our methodology involved positing the hypothetical
elimination of key commercial, governmental and recreational activities
to isolate their influence on the regional economy. The IMPLAN models
suggest that commercial

and governmental activities associated with the NNWR, combined with
recreational activity on the Refuge, make a valuable contribution to the
regional economy by supporting secondary enterprises and jobs. Important
results include the following:

-   NNWR spending is the most important activity in the regional economy
    of the four activities studied, contributing approximately \$900,000
    in each of the years under study. The regional economic impacts of
    NNWR timber harvesting and road maintenance activities are
    approximately an order of magnitude smaller, contributing \$190,000
    and \$100,000 to the economy, respectively. Trapping on the NNWR
    produces a very small regional economic effect, contributing
    approximately \$7,000 to the regional economy.

-   Expenditures by individuals participating in wildlife viewing at the
    NNWR play a role in the regional economy; projected increases in the
    number of visitor days to the NNWR for this activity imply that the
    contribution of this activity will increase significantly from 1996
    to 2026. Similarly, individuals engaged in hunting and fishing also
    provide business to a number of local industries engaged in catering
    to recreational activities and tourism. The gradual decline in
    annual hunting days projected to occur from 1996 to 2026 implies
    that the contribution of this activity to the local economy also
    will decline.

[^1]: ^?^ It is important to emphasize that market prices, not consumer
    surplus, provide the basis for input/output analysis. The estimates
    provided are based on the dollar values of flows of actual goods and
    services, which do not reflect consumers\' total willingness to pay
    for these items. In this sense, input/output analysis differs
    significantly from the recreational consumer surplus analysis
    provided in Chapter 5.

[^2]: ^?^ The IMPLAN model is owned and maintained by the Minnesota
    IMPLAN Group, Inc. (MIG).