1996 Net Economic Values for Bass, Trout and Walleye Fishing, Deer, Elk and Moose Hunting, and Wildlife Watching Addendum to the 1996 National Survey of Fishing, Hunting and Wildlife-Associated Recreation

              1996 Net Economic Values
for Bass, Trout and
Walleye Fishing, Deer, Elk
and Moose Hunting, and
Wildlife Watching
Addendum to the 1996 National
Survey of Fishing, Hunting and
Wildlife-Associated Recreation
U.S. Fish & Wildlife Service
(Report 96-2)
1996 Net Economic Values
for Bass, Trout and
Walleye Fishing, Deer,
Elk and Moose Hunting,
and Wildlife Watching
Addendum to the 1996 National
Survey of Fishing, Hunting and
Wildlife-Associated Recreation
(Report 96-2)
August 1998
Kevin J. Boyle and Brian Roach
Department of Resource Economics and Policy
University of Maine
Orono, ME 04469-5782
and
David G. Waddington
Housing and Household Economic
Statistics Division
U.S. Bureau of Census
Washington, DC 20233
Division of Federal Aid
U.S. Fish and Wildlife Service
Washington, D.C. 20240
Director, Jamie Clark
Chief, Division of Federal Aid, Bob Lange
http://www.fws.gov/r9fedaid/
This report is intended to complement the National and State reports from the 1996 National
Survey of Fishing, Hunting, and Wildlife-Associated Recreation. The conclusions are the
authors and do not represent official positions of the U.S. Fish and Wildlife Service.
The authors acknowledge Martha Papp and Laura Teisl for their assistance with data
analyses. Thanks also to the people who reviewed earlier drafts of this report.
Front Cover—USFWS photo: Tom Stehn
U.S. Fish & Wildlife Service
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
II. Measures of Economic Value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
III. Estimating Net Economic Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
IV. Species Designations of States and Groupings of States for Data Analyses . . 10
V. Estimated Net Economic Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
VI. Using the Value Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
VII. Concluding Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
VIII. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Contingent-Valuation Sections from the 1996 National Survey of Fishing,
Hunting, and Wildlife-Associated Recreation for Fishing (Bass, Trout, and
Walleye), Hunting (Deer, Elk, and Moose), and Wildlife Watching
Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Estimation Procedures and Standard Error Calculation for
Net Economic Values
Appendix C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Probit Equation Results
Appendix D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Average Days of Participation
Appendix E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Censored Probit Marginal Coefficients
3
Contents
Estimates of the net economic value for
bass, trout and walleye fishing, deer, elk
and moose hunting, and primary non-residential
wildlife watching based on
contingent-valuation questions from the
1996 National Survey of Fishing,
Hunting, and Wildlife-Associated
Recreation are presented in this report
for selected groupings of states.
States were classified as having primarily
bass fishing, primarily trout fishing or
primarily walleye fishing. Based on these
classifications, anglers were asked to
answer a contingent-valuation question
for either their bass or their trout or
their walleye fishing during 1996. Bass
fishing refers to smallmouth and
largemouth bass and excludes white bass,
spotted bass, striped bass, striped bass
hybrids, and rock bass. Trout fishing
refers to all freshwater species commonly
known as trout.
Likewise, states were classified as
primarily deer hunting, primarily elk
hunting or primarily moose hunting.
Based on these classifications, hunters
were asked contingent-valuation
questions for their 1996 hunts.
People who took trips to watch wildlife at
least one mile from their residence were
asked a contingent-valuation question for
these activities during 1996.
Net economic values are developed for
current resource conditions, and
marginal net economic values are also
developed for changes in angler catch
rates and changes in hunter harvest
rates. The net economic values reported
here are appropriate measures of
economic value for use in cost-benefit
analyses, damage assessments, and
project evaluations.
4
Abstract
USFWS photo
5
I. Introduction
development of valuation estimates that
are specific to states where the activities
occurred rather than being specific to
residents of a state who may or may not
have participated within their state of
residency.
While these design advantages were
implemented to improve the usefulness
of the valuation data, reduced sample
sizes and survey implementation
procedures prevented us from developing
state specific valuation estimates as was
done in 1991. Rather, states had to be
grouped in order to develop statistically
significant estimates of value. These
groupings are explained in Section IV.
In the following section we discuss the
conceptual framework for net economic
values of wildlife-related recreation,
differentiating between net economic
values and economic impacts. A
discussion of the contingent-valuation
questions and the procedures used to
analyze the contingent-valuation data are
presented in the third section. The
groupings of states are presented in the
fourth section. Net economic value
estimates are reported in the fifth
section. The sixth section contains a
discussion of how to use the value data
presented in this report and concluding
comments are presented in the last
section.
The National Survey of Fishing,
Hunting, and Wildlife-Associated
Recreation (Survey hereafter) is the only
source of data on human use of wildlife
resources that is collected on a
consistent, state-by-state basis. The first
time net economic value data were
collected was in the 1980 Survey, and this
effort was repeated in the 1985, 1991 and
1996 Surveys. Estimates of net economic
value for bass, trout and walleye fishing,
deer, elk and moose hunting, and primary
nonresidential wildlife watching derived
from contingent-valuation questions in
the 1996 Survey are presented in this
report. Bass fishing refers to smallmouth
and largemouth bass and excludes white
bass, spotted bass, striped bass, striped
bass hybrids, and rock bass. Trout fishing
refers to all freshwater species commonly
known as trout. Primary nonresidential
wildlife watching refers to trips at least
one mile from home taken for the
primary purpose of observing,
photographing, or feeding wildlife
(wildlife watching hereafter).
In the 1991 Survey, states were assigned
fishing status as either primarily bass
fishing or primarily trout fishing. A
person who lived in a bass state was
asked a bass fishing valuation question
and was not asked a trout valuation
question, and vice versa for a person who
lived in a trout state. In 1996, selected
states in the upper Midwest were
designated as walleye states. In 1991, all
states were designated as deer hunting
and in 1996 selected states in the
northwest and northern Rocky
Mountains were designated as elk states
and Alaska was designated as a moose
state. State species designations for
fishing and hunting valuation questions
are identified in Section IV.
An additional change between the 1991
and 1996 contingent-valuation sections of
the Survey deals with respondents
assigned residency status. When a
person answered a valuation question in
the 1991 Survey, their valuation response
was assigned to their state of residence.
Thus, a person from Michigan who
hunted deer would have their deer
valuation response assigned to Michigan
even if they hunted deer in another state
(e.g., mule deer in Colorado). In the 1996
Survey, valuation responses were
assigned to the state where the activity
occurred. Thus, with the example above,
the valuation response by a person from
Michigan who hunted deer in Colorado
would be assigned to Colorado.
A third change between the 1991 and 1996
Surveys is the number of valuation
questions respondents answer. In 1991,
respondents could answer one question
each for fishing, hunting and wildlife
watching. In 1996, each respondent could
answer up to four fishing valuation
questions, four hunting valuation
questions, and two wildlife watching
valuation questions.
Using fishing as an example, if a
respondent fished for the designated
species in their state of residence, they
were asked a valuation question for that
species in their state of residence. A
person from Georgia (a designated bass
state), who fished for bass in Georgia,
would be asked a bass valuation question
for their bass fishing in Georgia. The
survey also identified individuals who
fished for bass, trout or walleye in
designated bass, trout and walleye states
other than their state of residence. If a
respondent fished for a designated
species they were asked a valuation
question for that species. If a person
fished for a species in two or more states
outside their state of residence that were
designated for the species, one of those
states was randomly chosen and
valuation questions were only asked for
that state. The same pattern was used for
the hunting and wildlife watching
questions.
These changes were implemented to
improve the usefulness of the valuation
data for states. Including walleye fishing
and elk and moose hunting valuation
questions allowed states to obtain
valuation data for the species they felt
was most relevant for their management
purposes. Clarifying the residence status
of participants allowed for the
In 1996 more than 35 million Americans
16 years of age and older took trips to
fish and spent more than $15 billion on
trip-related expenditures. Expenditures
are a useful indicator of the importance
of sport fishing activities to local,
regional, state and national economies,
but expenditures (economic impacts) do
not measure the economic benefit to
individual participants. Net economic
value, or consumer surplus, is the
appropriate economic measure of the
benefit to individuals from participation
in wildlife-related recreation (Bishop,
1984; Freeman, 1993; Loomis et al., 1984;
McCollum et al., 1992). Net economic
value is measured as participants’
“willingness to pay” above what they
actually spend to participate. The benefit
to society is the summation of willingness
to pay across all individuals.
There is a direct relationship between
expenditures and net economic value, as
shown in Figure 1. A demand curve for a
representative angler is shown in the
figure. The downward sloping demand
curve represents marginal willingness to
pay per trip and indicates that each
additional trip is valued less by the
angler than the preceding trip. All other
factors being equal, the lower the cost
per trip (vertical axis) the more trips the
angler will take (horizontal axis). The
cost of a fishing trip serves as an implicit
price for fishing since a market price
generally does not exist for this activity.
At $60 per trip, the angler would choose
not to fish, but if fishing were free, the
angler would take 20 fishing trips.
At a cost per trip of $25 the angler takes
10 trips, with a total willingness to pay of
$375 (area acde in Figure 1). Total
willingness to pay is the total value the
angler places on participation. The angler
will not take more than 10 trips because
the cost per trip ($25) exceeds what he
would pay for an additional trip. For each
trip between zero and 10, however, the
angler would actually have been willing
to pay more than $25 (the demand curve,
showing marginal willingness to pay, lies
above $25).
The difference between what the angler
is willing to pay and what is actually paid
is net economic value. In this simple
example, therefore, net economic value is
$125 (($50 – $25) 10 ÷ 2) (triangle bcd in
Figure 1) and angler expenditures are
$250 ($25 × 10) (rectangle abde in Figure
1). Thus, the angler’s total willingness to
pay is composed of net economic value
and total expenditures. Net economic
value is simply total willingness to pay
minus expenditures. The relationship
between net economic value and
expenditures is the basis for asserting
that net economic value is an appropriate
measure of the benefit an individual
derives from participation in an activity
and that expenditures are not the
appropriate benefit measure.
Expenditures are out-of-pocket expenses
on items an angler purchases in order to
fish. The remaining value, net willingness
to pay (net economic value), is the
economic measure of an individual’s
satisfaction after all costs of participation
have been paid.
Summing the net economic values of all
individuals who participate in an activity
derives the value to society. For our
example let us assume that there are 100
anglers who fish and all have demand
curves identical to that of our typical
angler presented in Figure 1. The total
value of this sport fishery to society is
$12,500 ($125 × 100).
6
II. Measures of Economic Value
Figure 1. Individual Angler’s Demand Curve for Fishing Trips
Expenditures
a e
b d
c
Net Economic Value
60
50
40
30
20
10
0
5 10 15 20 25
Cost per Trip
Trips per Year
Note that we have purposely excluded
angler expenditures from the
computation of societal benefits. Because
individuals spend all of their income, with
savings being a form of expenditure,
angler expenses are not counted as
benefits from a national accounting
perspective. Money that is not spent for
fishing at a particular site will be spent
for fishing at another site or might be
spent on an entirely different activity
(e.g., attending a baseball game). Thus,
any change in expenditures is simply a
transfer from one subgroup of society to
another subgroup.
There are very limited conditions under
which expenditures might be counted as
benefits (McCollum et al., 1992). For
example, assume that 50 resident anglers
and 50 nonresident anglers fish a lake in
Colorado. If fishing was not allowed at
the lake, Colorado residents are likely to
fish elsewhere in Colorado. Their
expenditures are not lost from Colorado’s
economy; they are simply transferred to
another geographic area of Colorado. If
nonresidents, however, choose to fish in
another state, their expenditures would
be lost to Colorado’s economy. In this
case, nonresident expenditures constitute
new money in Colorado’s economy and
their removal would be counted as a
regional loss of $12,500 ($25 × 10 × 50).
Fishing, hunting and wildlife-watching
expenditures are recorded in the
National and State reports generated
from the 1996 Survey. Economic impacts
of fishing, hunting, and wildlife watching
are documented in separate reports.1 In
this report we present net economic
values, which are appropriate measures
of value for any benefit-cost evaluation of
a wildlife project. Net economic values
can enter these analyses as either
benefits gained for improvements or
benefits lost due to decrements.
Expenditures should only enter into
analyses to the extent that projects are
regional or local in nature, and
expenditures by participants would
clearly increase or decrease in the study
area as a consequence of the proposed
wildlife management decisions.
The example we developed for sport
fishing could have been developed in the
context of hunting or wildlife watching.
The basic concept of net economic value
is the same for all three activities.
7
1 The Economic Importance of Sport Fishing,
The Economic Importance of Hunting and the
1996 National and State Economic Impacts of
Wildlife Watching are available from the U.S. Fish
and Wildlife Service, Publication Unit, Route 1,
Box 166, Shepherd Grade Road, Shepherdstown,
WV 25443.
USFWS photo: Robert Shallenberger
8
Net economic values are estimated using
contingent valuation (Mitchell and
Carson, 1989). Contingent valuation is a
direct questioning approach by which
individuals are asked to reveal the value
they place on an item or activity within a
survey setting. The contingent-valuation
questions were asked using the
dichotomous-choice format (Bishop,
Heberlein, and Kealy, 1983; Cameron,
1988; Hanemann, 1984; McConnell, 1990).
Respondents were asked whether they
would pay a fixed dollar amount to
participate in an activity. The dollar
amounts and respondents’ “yes/no”
responses are used to infer the mean
values respondents place on each activity.
Respondents were asked to report their
total trip expenses to participate in an
activity during 1996, which is the
expenditure rectangle abde in Figure 1.
Respondents’ expenditures were used for
what is called the payment vehicle in the
contingent-valuation questions (Mitchell
and Carson, 1989). The payment vehicle
is the mechanism by which respondents
can express the net economic value they
place on the activity being evaluated.
Taking bass fishing as an example,
respondents were asked to recall their
total number of bass fishing trips, bass
caught, average length of bass caught,
and their total trip expenditures for
1996 before answering the contingent-valuation
question. The wording of the
valuation question was:
Fishing expenses change over time.
For example, gas prices rise and
fall. Would you have taken any trips
to fish PRIMARILY for largemouth
or smallmouth bass during 1996 in
[state of reference] if your total
costs were $________ more than
the amount you just reported?
Response categories were yes or no.
Note: respondents were only asked to
value fishing trips where bass fishing was
the “primary” activity. The trout and
walleye fishing questions were exactly
the same except “trout” or “walleye”
were substituted for “bass” in the
question. Similar valuation questions
were employed for deer, elk and moose
hunting, and wildlife watching. The
fishing, hunting and wildlife watching
valuation sections of the 1996 Survey are
replicated in Appendix A.
Dollar amounts in the valuation questions
were developed using estimated probit
equations from contingent-valuation
responses to the 1991 Survey
(Waddington, Boyle and Cooper, 1994,
Appendix D) and the procedure
developed by Copper (1993) for assigning
dollar amounts to dichotomous-choice
questions. Walleye fishing and elk and
moose hunting values were not estimated
in the 1991 surveys. In order to develop
bid amounts for states where these
activities were to be valued in 1996, the
estimation results for the 1991 activity
were used as a best approximation. For
example, deer hunting was valued in
Alaska in 1991 and moose were valued in
1996; the deer hunting valuation results
were used to develop bids for the moose
hunting valuation question. The same
procedure applies to states where
walleye fishing and elk hunting were
valued in 1996.
Responses to the contingent-valuation
questions are used to estimate probit
equations as formulated by Cameron and
James (1987). The estimation of these
equations used respondents’ ��yes/no”
responses as dependent variables, and
the dollar stimulus and other
independent variables. Explanatory
variables included in the final estimation
are presented in Table 1.
The fishing equations include the dollar
amount from the valuation question
(Bid), the number of fish the anglers
caught (# Bass, # Trout or # Walleye)
during 1996, and the average length of
the fish caught during 1996. When states
were grouped into regions for data
analysis, some regions included both bass
and trout states so a species variable
(Species) distinguishes between these
states. Walleye states were grouped as a
unique region so there is no overlap with
walleye states. The resident variable
indicates whether the valuation response
was for a resident (=1) or nonresident
(=0) of the state the valuation response
applies to.2
III. Estimating Net Economic Values
Table 1. Explanatory Variables in the Probit Equations
Fishing Hunting Wildlife Watching
Bid ($) Bid ($) Bid ($)
# Caught # Bagged Private (=1)
(Bass, Trout or Walleye) (Deer, Elk, Moose)
Inches Sex Public (=1)
(Avg. Length) (Buck or Bull =1)
Species (Trout=1) Other Big Game (=1) Photo (=1)
Resident (=1) Resident (=1) Fished (=1)
Resident (=1)
1 Due to the small numbers of nonresident
participants in many states, resident and
nonresident data were grouped for the analyses.
The hunting equations include the bid
variable, the number of deer a hunter
harvested (# Bagged) during 1996, a
dummy variable to indicate whether a
hunter bagged a buck or bull (Sex), a
dummy variable to indicate whether the
individual hunted other big game during
1996 (Other Big Game), and the resident
variable. Respondents were not asked if
they harvested more than one elk or
moose. # Bagged is a dummy variable
that equals one if an animal was
harvested in an elk or moose state.
Moose hunting was only valued in Alaska
and elk states constituted a unique region
so there was no multiple species regions
in the hunting data.
Similar variables are not appropriate for
wildlife watching because resources are
not being harvested and a single species
is not as likely to be targeted. In turn,
variables that characterize different
types of wildlife watching and activities
in which the individuals participated are
included to assess whether these
categorizations significantly affect
estimated net economic values. Dummy
variables are included to indicate
whether individuals watch wildlife on
private land (yes=1) or public land
(yes=1). The omitted category is
individuals who took trips to watch
wildlife on both private and public land.
Dummy variables indicating whether
individuals photographed wildlife while
on trips to watch wildlife (yes=1) and
whether they were an angler (yes=1) are
also included. The resident variable is
also included here.
The “Number Caught” and “Bagged”
variables are included in the fishing and
hunting equations to allow computation
of marginal values, the amount by which
net economic value increases or
decreases as the number of fish caught
(big game harvested) increases or
decreases. Similar interpretations apply
for the other explanatory variables used
in the equations. The purpose of
including these variables is to allow the
computation of marginal values for fish
and wildlife projects that either increase
harvest rates or protect resources to
prevent declines in harvest rates. In
many instances, all or nothing values, as
shown in Figure 1, are not appropriate.
Rather, a change in quality shifts the
demand curve, thereby resulting in a
change in net economic value (Figure 2).
In these instances, the change in net
economic value is the appropriate benefit
measure.
For example, assume a management
activity will increase catch rates for
anglers by 10 percent. This change in the
resource results in a shift of the demand
curve upward and to the right, as
presented in Figure 2. The benefit to the
angler of this increase in catch rate is the
area cfgd. Estimation of this area is
possible by including harvest rates as
explanatory variables in the estimated
probit equations.
Responses to the contingent-valuation
questions are analyzed by estimating
probit equations using weighted
maximum likelihood procedures
(Cameron, 1988; Greene, 1992). Maximum
likelihood estimation is used because the
dependent variable is discrete (0/1) and
the estimation is weighted because the
Survey is conducted with a probability
sample where observations have unequal
probabilities of being selected into the
sample. The estimated probit equations
are used to derive estimates of average
net economic value per year for each
activity. Ninety percent confidence
intervals are developed for these
averages (Cameron, 1991). A discussion of
the estimation procedures is presented in
Appendix B.
9
Figure 2. Shift in Angler Demand Curve for Fishing Trips Due to an
Increase in Catch Rate
Expenditures
a e
b d g
c
f
Change in Net Economic Value
60
50
40
30
20
10
0
5 10 15 20 25
Cost per Trip
Trips per Year
As noted above, valuation questions were
added for walleye fishing and elk and
moose hunting in the 1996 Survey.
In addition, selected states had their
bass or trout designations reversed;
Massachusetts and Rhode Island were
switched from being trout states to being
bass states and New Jersey was switched
from a bass state to a trout state. The
species designations for the fishing
valuation questions are presented in
Figure 3 and the hunting species
designations are presented in Figure 4.
While valuation estimates were reported
by state for the 1991 Survey, and the 1996
Survey was customized to allow more
species-specific valuation at the state
level, several issues prevented us from
reporting state-specific valuation
estimates using contingent-valuation
responses from the 1996 Survey.
The first issue is that the overall sample
size of the Survey was smaller in 1996
than 1991, with the consequent reduction
in state subsamples. This was done to
reduce the cost of the survey. Second,
the survey implementation procedure
required that we develop bids for all
potential respondents prior to the survey
implementation. In the application of the
survey, valuation questions and bids were
only applied to people who actually
qualified to answer the valuation
questions. Thus, the actual allocation of
bids to respondents (number of bids at
each bid amount) was different than the
original bid designs. Finally, the bid
design procedure developed by Cooper
(1993) tends to cluster bids near the
median. With small sample sizes, bid
allocations that do not represent the
initial designs, and bid amounts clustered
near the presumed median resulted in
relatively flat contingent-valuation
response functions to the bid amounts.
The consequence was coefficients on the
10
IV. Species Designations of States and
Groupings of States for Data Analyses
Figure 3. State Species Designations for Fishing Valuation Questions
Figure 4. State Species Designation for Hunting Valuation Questions
Bass Region
Trout Region
Walleye Region
Deer Region
Elk Region
Moose Region
bid variable that were insignificant for
most states for fishing, hunting and
wildlife watching. In turn, mean values
for these states were either negative,
included the origin in confidence
intervals, or otherwise did not conform
to standard theories. To address this
problem, states were grouped for
purposes of data analyses. Grouping
states increases sample sizes for
estimation and ameliorates the problems
noted above.
Estimating values by states makes sense
from an institutional perspective because
each state has its own unique licensing
and regulation structures for fishing and
hunting and differing management
strategies may affect wildlife viewing
opportunities. No institutional or
geographical guidance exists to suggest
how states should be grouped for
analysis purposes so we examined
several groupings of states.
The first groupings are U.S. Fish and
Wildlife Management Regions (Figure 5)
and U.S. Bureau of Census Regions
(Figure 6). These regions were used to
analyze the fishing, hunting and wildlife
watching data. Some of these groupings
included bass states and trout states,
which motivates the species variable in
the fishing equations. The walleye states
are always maintained as a distinct
region (Figure 3).
For the hunting analysis the elk states
are also maintained as distinct regions
(Figure 4) and Alaska, the only state
where moose is valued, is maintained as a
distinct one-state region. These unique
groupings imply that it is not necessary
to have a variable designating walleye
fishing or elk and moose hunting when
analyzing the data using the U.S. Fish
and Wildlife Regions and the U.S.
Bureau of Census Regions.
In addition, representatives of the U.S.
Fish and Wildlife Service proposed
groupings of states for bass and trout
fishing, deer hunting and wildlife
watching that they thought might be
more useful than the U.S. Fish and
Wildlife Management Regions and U.S.
Bureau of Census Region. These regions
are denoted in Figures 7-10.
Figure 6. U.S. Bureau of Census Regions
Figure 7. Bass Regions
Figure 5. U.S. Fish and Wildlife Service Regions
Northern Region
Southern Region
Non-Bass States
Region 1
Region 2
Region 3
Region 4
Region 5
Region 6
Region 7
11
East North Central
East South Central
Middle Atlantic
Mountain
New England
Pacific
South Atlantic
West North Central
West South Central
12
Figure 9. Deer Regions
Figure 10. Wildlife Watching Regions
Figure 8. Trout Regions
Western Region
Mountain Region
Northeast Region
Non-Trout States
Pacific
West Southwest
East Southwest
Plains
Great Lakes
Middle Atlantic
New England
South East
Non-Deer States
West
Rocky Mountain
Plains
Great Lake
North Atlantic
South Central
South Atlantic
The U.S. Bureau of Census conducted
the 1996 Survey for the U.S. Fish and
Wildlife Service. The Bureau of Census
collected the data primarily by telephone;
respondents who could not be reached by
phone were interviewed in-person. Three
interviews were conducted at four-month
intervals to reduce recall bias associated
with asking respondents to report
participation in an activity for an entire
year. The response rate was 80 percent.
Contingent-valuation data were collected
in January 1997 for the 1996 calendar
year.
Estimated probit equations for each
activity by region are presented in
Appendix C. Annual net economic values
are computed from these equations. Net
economic values per day are computed by
dividing estimated net economic value
per year by the average number of days
individuals participated in the activity.
Days of participation were collected in
each of the three interviews and are
summed to arrive at annual days of
participation. Days of fishing (bass, trout
and walleye) used in this computation
represent days of fishing freshwater on
non-Great Lakes waters only. Great
Lakes fishing was dropped because there
is a possibility of double counting days.
Anglers could have fished in both non-
Great Lakes waters and Great Lakes
waters on the same day.3 Hunting (deer,
elk and moose) and wildlife watching
days represent all days taken to
participate in these activities. Wildlife
watching is defined as any trip at least
one mile from home taken for the
primary purpose of observing,
photographing, or feeding wildlife.
As noted above, the data were analyzed
by grouping states into various regions.
We focus the discussion in the text on
the U.S. Fish and Wildlife Service
Management Regions and report results
for other regions in the tables.
Fishing
Regional estimates of net economic value
per year with ninety percent confidence
intervals are shown in Table 2. Computed
net economic values per day are reported
in the last column of Table 2. Estimates
by region are reported for bass, trout,
and bass and trout combined. The species
for which value estimates apply are
denoted in the second column of the
table. Regions for which value estimates
are not reported had computed means
that were negative. The computed mean
for the walleye region was negative so we
do not report a walleye value.
We suspect this negative estimate is due
to the sampling issues discussed in the
previous section and do not reflect
negative or zero values for fishing.
Values for bass fishing range from $52
per year (Region 4) to $326 per year
(Region 5 bass states) across the U.S.
Fish and Wildlife Service Management
Regions (Table 2). The corresponding net
economic values per day are $3 and $19.
Trout fishing values range from $79 per
year (Region 5 trout states) to $375 per
year (Region 7, Alaska), with per day
13
V. Estimated Net Economic Values
2 This procedure may tend to underestimate days of
fishing resulting in overestimates of net economic
values per day. Net economic values per year are
not affected by this calculation.
USFWS photo
values of $6 and $38. Although Regions 2
and 6 include both bass and trout states,
only combined estimates are reported
because the coefficients on the species
variables in the probit equations for
these regions were not significantly
different from zero. The estimate for
Region 2 should be interpreted with
caution as the mean is very large, the bid
coefficient was insignificant in the probit
equation, and the confidence interval
contains the origin.
Net economic value per year, average
number of fish caught per angler per
year, and the marginal value of catching
an additional fish are presented in Table
3. The marginal values show the change
in net economic value per year that would
result from changing the average catch
rate by one fish per year.
The coefficient on “fish caught” was
significantly different from zero in the
probit equations for five of the seven
U.S. Fish and Wildlife Service Regions.
The marginal net economic value of
catching a fish ranges from $0.24
(Region 7) to $4.85 (Region 3).
14
Table 2. Net Economic Value for Fishing by Region
Net Economic Value Per Year
Region Species Mean Standard Ninety Net
Valued Error of Percent Economic
the Mean Confidence Value
Interval Per Day
U.S. Fish and Wildlife Service Regions
1 (CA, ID, NV, OR, WA) Trout 126 98 -35-287 12
2 (AZ, NM, OK, TX) Bass & Trout2 1154 1203 -825-31331 105
3 (IA, IL, IN, MO) Bass 222 56 129-314 15
4 (AL, AR, FL, GA, KY, LA,
MS, NC, SC, TN) Bass 52 199 -275-378 3
5 (CT, DE, MA, MD, ME,
NH, NJ, NY, PA, RI,
VA, VT, WV) Bass & Trout 150 41 83-217 10
(DE, MA, MD, RI, VA, WV) Bass 326 NA3 NA3 19
(CT, ME, NH, NJ, NY, PA, VT ) Trout 79 NA3 NA3 6
6 ( CO, KS, MT, NE, UT, WY) Bass & Trout2 289 20 256-323 25
7 (AK) Trout 375 11 357-394 38
U.S. Bureau of the Census Regions
Pacific (AK, CA, OR, WA) Trout 22 156 -234-279 2
Mountain (AZ, CO, ID, MT, NM, NV, UT, WY) Trout 268 41 201-336 27
West North Central (IA, KS, MO, NE) Bass 290 51 207-374 17
East North Central (IL, IN) Bass 381 54 292-471 24
Middle Atlantic (NJ, NY, PA) Trout NA4 NA4 NA4 NA4
New England (CT, MA, ME, NH, RI, VT) Bass & Trout2 156 106 -17-330 10
West South Central (AR, LA, OK, TX) Bass NA4 NA4 NA4 NA4
East South Central (AL, KY, MS, TN) Bass 312 244 -89-7131 19
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) Bass NA4 NA4 NA4 NA4
U.S. Fish and Wildlife Service Bass Regions
Northern (DE, IA, IL, KS, KY, MA, MD,
MO, NE, RI, VA, WV) Bass 262 35 205-320 16
Southern (AL, AR, FL, GA, LA, MS, NC,
OK, SC, TN, TX) Bass NA4 NA4 NA4 NA4
U.S. Fish and Wildlife Service Trout Regions
Western (CA, NV, OR, WA) Trout 3 162 -263-270 0
Mountain (AZ, CO, ID, MT, NM, UT, WY) Trout 269 45 195-342 27
Northeast (CT, ME, NH, NJ, NY, PA, VT) Trout 53 59 -43-150 4
U.S. Fish and Wildlife Service Walleye Region
Walleye Region (MI, MN, ND, OH, SD, WI) Walleye NA4 NA4 NA4 NA4
1 Estimated bid coefficient is not significantly different from zero (see Table C-1).
2 Separate bass and trout values are not reported because the variable on species was not significantly different from zero (see Table C-1).
3 Standard errors and confidence intervals not reported for separate species in regions with estimated mean values for bass and trout.
4 Value not reported because estimated mean is negative.
15
Table 3. Marginal Values for Catching an Additional Fish by Region
Region Species Net Average Marginal
Valued Economic Number Value
Value of Fish Per Fish
Per Year Caught
U.S. Fish and Wildlife Service Regions
1 (CA, ID, NV, OR, WA) Trout 126 42 0.71
2 (AZ, NM, OK, TX) Bass & Trout 11541 35 NA2,4
3 (IA, IL, IN, MO) Bass 222 33 4.85
4 (AL, AR, FL, GA, KY, LA,
MS, NC, SC, TN) Bass 52 59 3.81
5 (CT, DE, MA, MD, ME,
NH, NJ, NY, PA, RI,
VA, VT, WV) Bass & Trout 150 42 2.96
(DE, MA, MD, RI, VA, WV) Bass 326 62 2.96
(CT, ME, NH, NJ, NY, PA, VT) Trout 79 37 2.96
6 (CO, KS, MT, NE, UT, WY) Bass & Trout 289 47 2.652
7 (AK) Trout 375 39 0.242
U.S. Bureau of the Census Regions
Pacific (AK, CA, OR, WA) Trout 22 44 0.71
Mountain (AZ, CO, ID, MT, NM, NV, UT, WY) Trout 268 35 2.75
West North Central (IA, KS, MO, NE) Bass 290 49 6.05
East North Central (IL, IN) Bass 381 34 1.44
Middle Atlantic (NJ, NY, PA) Trout NA3 NA3 NA3
New England (CT, MA, ME, NH, RI, VT) Bass & Trout 1561 41 3.86
West South Central (AR, LA, OK, TX) Bass NA3 NA3 NA3
East South Central (AL, KY, MS, TN) Bass 3121 69 4.61
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) Bass NA3 NA3 NA3
U.S. Fish and Wildlife Service Bass Regions
Northern (DE, IA, IL, KS, KY, MA, MD,
MO, NE, RI, VA, WV) Bass 262 49 3.60
Southern (AL, AR, FL, GA, LA, MS, NC,
OK, SC, TN, TX) Bass NA3 NA3 NA3
U.S. Fish and Wildlife Service Trout Regions
Western (CA, NV, OR, WA) Trout 3 43 0.78
Mountain (AZ, CO, ID, MT, NM, UT, WY) Trout 269 36 2.81
Northeast (CT, ME, NH, NJ, NY, PA, VT) Trout 53 32 3.38
U.S. Fish and Wildlife Service Walleye Region
Walleye Region3 (MI, MN, ND, OH, SD, WI) Walleye NA3 NA3 NA3
1 Estimated bid coefficient is not significantly different from zero (see Table C-1).
2 Value not reported because coefficient on catch is not significantly different from zero.
3 Value not reported because estimated mean value per year is negative.
4 Value not reported because marginal value per fish is negative.
16
Table 4. Net Economic Value for Hunting by Region
Net Economic Value Per Year
Region Mean Standard Ninety Net
Error of Percent Economic
the Mean Confidence Value
Interval Per Day
U.S. Fish and Wildlife Service Regions
1 (CA, NV, WA) NA2 NA2 NA1,2 NA2
2 (AZ, NM, OK, TX) 42 1005 –1610-16961 5
3 (IA, IL, IN, MI, MN, MO, OH, WI) 216 68 105-328 21
4 (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN) 104 735 �����1105-13131 7
5 (CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VA, VT, WV) 102 179 –192-395 9
6 (KS, ND, NE, SD, UT) 285 18 255-314 39
U.S. Bureau of the Census Regions
Pacific (CA, WA) NA2 NA2 NA1,2 NA2
Mountain (AZ, NM, NV, UT) 301 86 160-442 58
West North Central (IA, KS, MN, MO, NE, NE, SD) 205 45 132-278 26
East North Central (IN, IL, MI, OH, WI) 283 68 172-394 24
Middle Atlantic (NJ, NY, PA) NA3 NA3 NA1,3 NA3
New England (CT, MA, ME, NH, RI, VT) 375 51 290-459 29
West South Central (AR, LA, OK, TX) 923 526 58-1787 69
East South Central (AL, KY, MS, TN) 334 196 11-657 23
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) NA3 NA3 NA1,3 NA3
U.S. Fish and Wildlife Service Deer Regions
Pacific (WA, CA, NV) NA2 NA2 NA1,2 NA2
West Southwest (AZ, NM, UT) 299 94 144-453 59
East Southwest (OK, TX) 238 889 –1223-17001 20
Plains (IA, KS, MO, ND, NE, SD) 237 36 178-296 29
Great Lakes (IN, IL, MN, MI, OH, WI) 216 91 67-366 20
Middle Atlantic (DE, MD, NJ, NY, PA) NA2 NA2 NA1,2 NA2
New England (CT, MA, ME, NH, RI, VT) 374 51 290-459 29
Southeast (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN, VA, WV) NA2 NA2 NA1,2 NA2
Other Big Game Species
Elk Region (CO, ID, MT, OR, WY) 410 42 342-478 59
Moose Region (AK) 624 51 541-7081 61
1 Estimated bid coefficient is not significantly different from zero (see Table C-2).
2 Value not reported because estimated mean is negative.
3 Value not reported because estimated mean was implausibly large (>$1,500)
Deer Hunting
Regional estimates of net economic value
per year with ninety percent confidence
intervals for hunting are presented in
Table 4. Computed net economic values
per day are reported in the last column of
Table 4.
Net economic values for five of the U.S.
Fish and Wildlife Service Management
Regions are reported. Net economic
values range from $42 per year (Region
2) to $285 per year (Region 6). The
corresponding values per day are $5 and
$39. A net economic value for deer
hunting is not reported for Region 1
because the mean economic value per
year is negative. The interpretation of
this negative mean is the same as
discussed for the fishing results above.
17
Table 5. Marginal Value for Bagging an Additional Deer by Region
Net
Region Economic Average Marginal
Value Number Value Per
Per Year Bagged Animal
U.S. Fish and Wildlife Service Regions
1 (CA, NV, WA) NA1,2 NA2 NA2
2 (AZ, NM, OK, TX) 42 0.78 417
3 (IA, IL, IN, MI, MN, MO, OH, WI) 216 0.65 2043
4 (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN) 104 1.05 168
5 (CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VA, VT, WV) 102 0.63 372
6 (CO, KS, MT, ND, NE, SD, UT, WY) 285 0.63 39
U.S. Bureau of the Census Regions
Pacific (CA, WA) NA1,2 NA2 NA2
Mountain (AZ, NM, NV, UT) 301 0.30 347
West North Central (IA, KS, MN, MO, NE, NE, SD) 205 0.61 107
East North Central (IN, IL, MI, OH, WI) 283 0.68 188
Middle Atlantic (NJ, NY, PA) NA1,4 NA4 NA4
New England (CT, MA, ME, NH, RI, VT) 375 0.28 NA5,6
West South Central (AR, LA, OK, TX) 923 0.85 NA6
East South Central (AL, KY, MS, TN) 334 1.08 148
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) NA1,4 NA4 NA4
U.S. Fish and Wildlife Service Deer Regions
Pacific (CA, NV, WA) NA1,2 NA2 NA2
West Southwest (AZ, NM, UT) 299 0.27 796
East Southwest (OK, TX) 238 0.86 266
Plains (IA, KS, MO, ND, NE, SD) 237 0.69 138
Great Lakes (IN, IL, MI, MN, OH, WI) 216 0.64 200
Middle Atlantic (DE, MD, NJ, NY, PA) NA1,2 NA2 NA2
New England (CT, MA, ME, NH, RI, VT) 374 0.28 NA6
Southeast (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN, VA, WV) NA1,2 NA2 NA2
Other Big Game Species
Elk Region (CO, ID, MT, OR, WY) 410 0.68 NA5,6
Moose Region (AK) 6241 0.42 149
1 Estimated bid coefficient is not significantly different from zero (see Table C-2).
2 Value not reported because estimated mean is negative.
3 Coefficient on number bagged is not significantly different from zero.
4 Value not reported because estimated mean was implausibly large (>$1,500)
5 Estimated marginal value is not significantly different from zero.
6 Value not reported because estimated marginal value is negative.
The annual net economic value of elk
hunting is $410 and the per day value is
$59. The comparable numbers for moose
hunting in Alaska (Region 7) are $624 per
year and $61 per day. The marginal value
of bagging an additional deer is highest in
Region 2 ($417) and lowest in Region 6
($39); the opposite of the annual net
economic value estimates. A marginal
value for bagging an elk is not reported
because the coefficient on this variable
was not significantly different from zero
in the probit equation. The marginal value
of bagging amoose in Alaska is $149.
18
Table 6. Net Economic Value for Wildlife Watching
Net Economic Value Per Year
Region Mean Standard Ninety Net
Error of Percent Economic
the Mean Confidence Value
Interval Per Day
U.S. Fish and Wildlife Service Regions
1 (CA, HI, ID, NV, OR, WA) 234 134 14-4541 20
2 (AZ, NM, OK, TX) 251 135 29-4731 19
3 ( IA, IN, IL, MI, MN, MO, OH, WI) NA2 NA2 NA2 NA2
4 (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN) 115 110 –65-296 10
5 (CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VA, VT, WV) 92 63 –11-196 9
6 (CO, KS, MT, ND, NE, SD, UT, WY) 290 17 262-317 28
7 (AK) 696 63 593-799 34
U.S. Bureau of the Census Regions
Pacific (AK, CA, HI, OR, WA) 263 122 63-464 19
Mountain (AZ, CO, IA, MT, NM, NV, UT, WY) 312 31 260-3641 31
West North Central (IA, KS, MN, MO, ND, NE, SD) 184 18 154-213 17
East North Central (IL, IN, MI, OH, WI) NA2 NA2 NA2 NA2
Middle Atlantic (NJ, NY, PA) NA2 NA2 NA1,2 NA2
New England (CT, MA, ME, NH, RI, VT) 191 37 131-251 16
West South Central (AR, LA, OK, TX) 315 40 249-382 24
East South Central (AL, KY, MS, TN) 112 106 –62-286 9
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) 99 124 –105-304 10
U.S. Fish and Wildlife Service Suggested Groupings
West (AK, CA, HI, NV, OR, WA) 259 119 64-4551 19
Rocky Mountain (AZ, CO, ID, MT, NM, UT, WY) 313 31 263-364 30
Plains (IA, KS, MO, ND, NE, SD) 199 15 175-224 17
Great Lake (IN, IL, MI, MN, OH, WI) NA2 NA2 NA2 NA2
North Atlantic (CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VT) 18 121 –181-217 2
South Central (AR, LA, OK, TX) 315 40 249-382 24
South Atlantic (AL, FL, GA, KY, MS, NC, SC, TN, VA, WV) 100 121 –100-299 10
1 Estimated bid coefficient is not significantly different from zero (see Table C-3).
2 Value not reported because estimated mean is negative.
Wildlife Observation
Regional estimates of net economic value
per year with ninety percent confidence
intervals for wildlife watching are
presented in Table 6. The last column of
Table 6 contains computed net economic
values per day for wildlife watching.
With respect to Fish and Wildlife Service
Regions, estimates of net economic
value per year range from $696 in
Alaska (Region 7) to $92 in Region 5.
The respective values per day are
$34 and $9.
Three types of values have been
reported, mean net economic values per
year per participant, net economic values
per day of participation, and marginal net
economic values based on harvesting an
additional fish or big game animal. Each
of these values has a slightly different
use and interpretation in conducting
benefit and cost calculations of wildlife
management and policy decisions.
Mean net economic values per year per
participant can be thought of as “all or
nothing values.” Take trout fishing in
Region 5 as an example, with a mean
value of $79 (Table 2). The $79 represents
the mean value to a trout angler in
Region 5 given the current resource
condition and trout fishing regulations.
This is an estimate of the net economic
value portrayed in Figure 1. If the
Region chose for some reason to prohibit
trout fishing, $79 is an estimate of the
average loss to an angler who fishes for
trout. Thus, while mean net economic
values per year per participant are
interesting in terms of characterizing the
current value of the resource and in
calculating losses for a catastrophic
change in the resource, they are not
applicable for most management and
public policy decisions faced by resource
managers.
Management and policy decisions
(actions) generally increase or decrease
participation rates, or increase or
decrease harvest rates, resulting in
marginal changes in resource availability.
Let us continue with the Region 5
example. Assume an environmental
pollution accident results in the closure of
a lake to fishing for a whole season. If a
fishery manager knows the number of
days of fishing that occur on the lake over
the whole season, 1,200 for example, it is
possible to develop a rough estimate of
the fishery losses from the accident. This
estimate is accomplished by multiplying
the net economic value per day (Table 2)
by the days of participation, resulting in
$7,200 ($6 × 1200). As previously noted,
net economic value per day is computed
by dividing mean net economic value
  If an action changes participation, it is
necessary to consider the extent to which
participants substitute to another site to
fish or hunt. Failure to consider
substitution will result in overestimation
of resource losses; and
  Using per participant value estimates
to compute losses or benefits requires
additional information, particularly on
resource conditions and participation
rates.
Thus, the value estimates reported here
must be used with caution in order to
avoid misuse of this information, which
would result in incorrect estimates of
aggregate costs or aggregate benefits.
19
per year by the number of days of
participation (Appendix D). Two caveats
apply to this estimate of losses. If anglers
shift their fishing effort to another lake
and contingent valuation responses do
not account for this substitution, then
$7,200 is an overestimate of the losses.
The second caveat relates to whether the
accident diminishes fishing quality after
the lake has been reopened to fishing,
perhaps due to a reduction in the biomass
of the fish stock. In this case the $7,200 is
an underestimate of the loss and it is
necessary to estimate the reduction in
value due to the change in the quality of
the fishery. This is an application where
the marginal values can play a role.
Let us assume that trout fishing on the
lake is closed for one year, substitution is
reflected in the contingent valuation
responses and the catch rate is reduced
by 10 percent next year when the fishery
is reopened. The fishery returns to
normal in the third year. The loss in the
first year is the $7,200. Assume 300
anglers fish the lake in the second year.
The loss in the second year is $3,286
(0.10 × 37 × 2.96 × 300). Referring to
Figure 2, the 10 percent reduction would
shift the demand curve to the left,
portraying a loss in the net economic
value. In this example the loss per angler
is $10.95 (0.10 × 37 × 2.96). This loss is
computed by multiplying the 10 percent
reduction in catch rates by the average
catch rate (37 trout per year per angler)
by the marginal value of a trout ($2.96
per fish per angler) (Table 3). The total
loss is $10,486 ($7,200 + $3,286).
Although unrealistic in its simplicity, the
example does aid in the understanding of
how to use the value estimates. Similar
examples could be developed for actions
that affect bass fishing, and can be to
applied to deer hunting and wildlife
watching. We do not report marginal
values for wildlife watching. The key
issues that must be understood are:
  Each of the different values estimates
has slightly different interpretations and
uses;
VI. Using the Value Estimates
USFWS photo: Ralph Town
Net economic values represent the values
above and beyond what participants
actually spend to participate in an
activity. This value information can be
used to assess the current value of
participation in these activities. Marginal
values can be used to compute benefits or
costs of increasing or decreasing the
availability of selected wildlife resources.
Marginal values provide a starting point
for resource managers evaluating
changes in resource availability, whether
it is a planned improvement or an
unforeseen change.
Given the groupings of data reported
here, we suggest wildlife managers use
estimates from groups that include their
states. There is no clear guidance as to
which groupings of states best represent
value estimates. We leave this decision to
wildlife managers to choose the grouping
they feel best represents conditions in
their state.
20
VII. Concluding Comments
USFWS photo: Luther Goldman
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and Cindy S. Sorg. 1984. “A Field Guide
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Peterson, and Cindy S. Swanson. 1992. “A
Managers Guide to the Valuation of
Nonmarket Resources: What Do You
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Peterson, C.S. Swanson, D.W. McCollum,
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21
References
22
Appendix A
Contingent-Valuation Sections from the 1996 National Survey of Fishing,
Hunting, and Wildlife-Associated Recreation for Fishing (Bass, Trout,
and Walleye), Hunting (Deer, Elk, and Moose), and Wildlife Watching
Fishing Economic Evaluation
In the next few questions, I will ask you about ALL your trips taken during the ENTIRE calendar year of 1996 to PRIMARILY
fish for [trout/bass/walleye] in [state].
Sometimes you may take a [trout/bass/walleye] fishing trip where you are away from your home for one night or several nights.
Other times, you may take a [trout/bass/walleye] fishing trip where you leave from and return to your home on the same day. In
total, how many trips did you take to fish PRIMARILY for [trout/bass/walleye] during 1996 in [state]?
_______Trips taken (Allow 3 digits)
How many [trout/bass/walleye] did you catch during 1996 in [state]? We are asking for how trout/bass/walleye] you CAUGHT and
we ARE NOT asking for how many [trout/bass/walleye] you KEPT.
_______ (Allow 4 digits)
What was the average length in inches of the [trout/bass/walleye] you caught during 1996 in [state]?
_______ Inches (Allow 2 digits)
Some [trout/bass/walleye] fishing trips cost more than others. For example, on a long trip you may spend money for food, travel,
and lodging. On a short trip, where you may only fish for a few hours, you may only spend money for gas. How much did [your
trip/an average trip] cost you during 1996 where you fished PRIMARILY for [trout/bass/walleye] in [state]?
$______ Cost per trip (Allow 6 digits)
Since you took [fill trips from above] [trout/bass/walleye] fishing trips and the average trip cost was $[fill average cost from above],
this means that you spent about $[number trips • average cost per trip] in total for ALL of your trips during 1996 to fish
PRIMARILY for [trout/bass/walleye] [state]. Would you say that this total cost is about right?
(1) ____Yes
(2) ____No
If No — How much would you say is the total cost of your [number of trips] trips to fish PRIMARILY for [trout/bass/walleye]
during 1996 in [state]?
$______ Total Cost (Allow 8 digits)
Fishing expenses change over time. For example, gas prices rise and fall. Would you have taken any trips to fish PRIMARILY for
[trout/bass/walleye] during 1996 in [state] if your total costs were $[bid value] more than the amount you just reported?
(1) ____Yes
(2) ____No
23
Hunting Economic Evaluation
In the next few questions, I will ask you about ALL your trips taken during the ENTIRE calendar year of 1996 PRIMARILY to
hunt [deer/elk/moose] in [state].
Sometimes you may take [a/an] [deer/elk/moose] hunting trip where you are away from your home for one night or several nights.
Other times, you may take [a/an] [deer/elk/moose] hunting trip where you leave from and return to your home on the same day. In
total, how many trips did you take PRIMARILY to hunt [deer/elk/moose] during 1996 in [state]?
_______ Trips (Allow 3 digits)
Did you bag [buck deer/bull elk/bull moose] in 1996 in [state]?
(1) ____Yes
(2) ____No
(designated deer states only)
Some states allow hunters to bag more than one DEER. How many DEER did you bag during 1996 in [state]?
_______ Deer (Allow 2 digits)
Did you bag a [buck/bull elk/bull moose] in 1996 in [state]?
(1) ____Yes
(2) ____No
Some [deer/elk/moose] hunting trips cost more than others. For example, on a long trip you may spend money for food, travel,
and lodging. On a short trip, where you may only hunt for a few hours, you may only spend money for gas. How much did
[your trip/an average trip] cost you during 1996 when you went PRIMARILY to hunt [deer/elk/moose] in [state]?
$______ per trip (Allow 6 digits)
Since you took [fill trips from above] [deer/elk/moose] hunting trips and the average trip cost was $[fill average cost from above],
this means that you spent about $ [number trips * average cost per trip] in total for ALL of your trips during 1996 PRIMARILY
to hunt [deer/elk/moose] in [state]. Would you say that this total cost is about right?
(1) ____Yes
(2) ____No
If No — How much would you say is the total cost of your [fill trips from above] trips taken during 1996 PRIMARILY to hunt
[deer/elk/moose] in [state]?
$______ Total Cost (Allow 8 digits)
Hunting expenses change over time. For example, gas prices rise and fall. Would you have taken any trips PRIMARILY to hunt
[deer/elk/moose] during 1996 in [state] if your total [deer/elk/moose] hunting costs were $ [bid value] more than the amount you
just reported?
(1) ____Yes
(2) ____No
Wildlife Watching
In the next few questions, I will ask you about ALL your trips taken for the PRIMARY PURPOSE of observing, photographing,
or feeding wildlife during the ENTIRE calendar year of 1996 in [state].
In your [current and previous interview] you reported taking [fill trips] [trip/trips] of at least one mile for the PRIMARY
PURPOSE of observing, photographing, or feeding wildlife in [state]. Is that correct?
(1) ____Yes
(2) ____No
If No — How many trips of at least one mile did you take for the PRIMARY PURPOSE of observing, photographing, or feeding
wildlife in [state] during 1996?
_______ (Allow 3 digits)
In your [current and previous interview], you reported that you spent $[fill trip expenditures] in total for [your trip/all of your
trips] during 1996 where your PRIMARY PURPOSE was to observe, photograph, or feed wildlife in [state]. Would you say that
this total cost is about right?
(1) ____Yes
(2) ____No
If No — How much would you say is the total cost of your [fill trips] [trip/trips] during 1996 where your PRIMARY PURPOSE
was to observe, photograph, or feed wildlife in [state]?
$______ Total Cost (Allow 8 digits)
Wildlife watching expenses change over time. For example, gas prices rise and fall. Would you have taken any trips during 1996 for
the PRIMARY PURPOSE of observing, photographing, or feeding wildlife in [state] if your total costs were $ [bid value] more
than the amount you just reported?
(1) ____Yes
(2) ____No
24
25
The procedures used to estimate the net economic values and standard errors that are presented in the main body of this report
are described in this appendix. This discussion is divided into three sections: 1) estimation of net economic values (willingness to
pay); 2) estimation of the probit coefficients; and 3) confidence interval estimation.
Estimation of the Net Economic Values
The net economic values presented in this report are derived using the censored normal probit approach (Cameron, 1988; 1991).
Estimates of net economic values using this approach are identical to those derived using Hanemann’s linear random utility model
(Hanemann, 1984; 1989). A comparison of these two approaches is made in McConnell (1990).
The censored normal regression model assumes that WTP can be represented as
1)
where WTPi is the respondent’s true unobserved value and the disturbance term ui is identically and independently disturbed
normally with mean 0 and dispersion parameter β. Because the censored normal approach allows WTP to be modeled as a linear
function of the explanatory variables, βi can be interpreted as the change in WTP for a unit change in xi, which is an interpretation
that cannot be made of conventional probit coefficients. Cameron (1991) details how the β coefficient vector (which excludes the bid
coefficient) can be calculated from a conventional logit regression (which includes the bid vector as an explanatory variable).
Taking the expected value of WTP in Equation (1) yields
2)
For this report, Gauss programs developed by Cooper were used to estimate the β’s.
Estimation of the Probit Coefficients
The coefficients described in section III of this report are estimated using a maximum likelihood estimation routine.
The log-likelihood function is
3)
where yi, i=1,…,N, is the dependent dummy variable that is equal to 1 for a yes response and 0 for a no response, γ* is the
conventional probit coefficient vector, and the normal cumulative density function is the probability that yi=1 (Judge et al., 1985).
The data were adjusted by sampling weights to account for the fact that the survey sampled some regions at higher rates than
others. Not doing so could lead to biased coefficient estimates. Multiplying the data by the weights gives greater weight to the
observations from the regions with the lower probability of being selected and decreases the weight to the observations from the
regions with higher probability of being selected. For estimation, the weights are multiplied by the sample size and divided by the
sum of the weights so that the sum of the weights across the observations is the sample size (Greene, 1992). Performing weighted
estimation without scaling the weight variable in this manner can result in very low standard errors, and thus, very high
t-statistics for the estimated coefficients (Greene, 1992).
Confidence Interval Estimation
To tell us if the benefit measures are statistically different from zero as well as to allow statistical comparisons between the
estimated benefit measures, it is necessary to construct confidence intervals around the benefit measures. In this paper, confidence
intervals around the welfare benefit estimate are constructed using an analytic method (Cameron, 1991). Since WTP Equation (2)
is linear, it is a simple matter to construct an interval estimate for E(WTP) (e.g., see Johnston, p. 1996). Cameron’s (1991)
procedure is used to transform the conventional probit coefficient vector ˆγ* into the β vector (essentially by dividing the
explanatory variable coefficients by the negative of the bid coefficient) and the covariance matrix for ˆγ* into the covariance matrix
Σg. Given Σg and Equation (1) and appealing to the central limit theorem, a (1-θ) × 100 percent confidence interval around
E(WTP|x0) is
4)
Other methods for constructing confidence intervals are described in Cooper (1994).
Appendix B
Estimation Procedures and Standard Error
Calculation for Net Economic Values
26
Table C-1. Probit Equation Results for Fishing
Explanatory Variables
Region Constant Bid Catch Inch Resident Species n Chi-squared %
(# Fish) (Length) Correct
Predictions
U.S. Fish and Wildlife Service Official Regions
Appendix C
Probit Equation Results
26
-0.1399
(0.2067)
-0.0010
(0.0002)
0.0007
(0.0003)
0.0546
(0.0113)
-0.3505
(0.1829)
1 (CA, ID, NV, OR, 665 762.97 63
WA)
-0.7913
(0.3811)
-0.0004
(0.0006)
0.0010
(0.0008)
0.0750
(0.0143)
-0.5988
(0.2153)
0.2833
(0.1736)
2 (AZ, NM, OK, 356 436.20 63
TX)
-0.1572
(0.3330)
-0.0021
(0.0005)
0.0102
(0.0020)
0.0102
(0.0150)
0.2016
(0.2325)
3 (IA, IL, IN, MO) 374 418.33 72
-0.4888
(0.2260)
-0.0008
(0.0003)
0.0029
(0.0005)
0.0481
(0.0091)
-0.2342
(0.1235)
4 (AL, AR, FL, 845 1015.48 67
GA, KY, LA, MS,
NC, SC, TN)
0.4032
(0.2136)
-0.0021
(0.0003)
0.0061
(0.0008)
0.0235
(0.0085)
-0.4225
(0.1139)
-0.3431
(0.1017)
5 (CT, DE, MA, 1118 1228.33 70
MD, ME, MY,
NH, NJ, PA, RI,
VA, VT, WV)
0.4048
(0.2674)
-0.0023
(0.0007)
0.0060
(0.0009)
0.0123
(0.0092)
-0.3777
(0.1038)
0.1488
(0.1274)
6 (CO, KS, MT, 899 1140.54 65
NE, UT, WY)
3.6076
(2.1062)
-0.0120
(0.0055)
0.0028
(0.0023)
0.0920
(0.0249)
-0.4183
(0.2978)
7 (AK) 104 115.36 65
U.S. Bureau of the Census Regions
-0.3482
(0.2713)
-0.0009
(0.0002)
0.0006
(0.0004)
0.0592
(0.0131)
-0.2750
(0.2443)
Pacific (AK, CA, OR, 499 552.91 68
WA)
0.2398
(0.1991)
-0.0011
(0.0005)
0.0030
(0.0005)
0.0257
(0.0076)
-0.4312
(0.0834)
Mountain (AZ, CO, 1244 1626.55 61
ID, MT, NM, NV,
UT, WY)
0.0580
(0.3118)
-0.0017
(0.0005)
0.0101
(0.0021)
0.0172
(0.0154)
-0.2986
(0.2215)
West North Central 322 386.15 66
(IA, KS, MO,
NE)
0.9510
(1.1877)
-0.0051
(0.0021)
0.0074
(0.0024)
0.0009
(0.0230)
0.8202
(0.4861)
East North Central 194 202.81 74
(IN, IL)
-0.2095
(0.3738)
-0.0019
(0.0006)
0.0071
(0.0021)
0.0152
(0.0178)
-0.2123
(0.2739)
Middle Atlantic (NJ, 245 251.15 72
NY, PA)
0.0274
(0.3719)
-0.0015
(0.0008)
0.0059
(0.0012)
0.0292
(0.0113)
-0.4920
(0.1345)
0.0897
(0.1708)
New England (CT, 612 721.97 68
MA, ME, NH,
RI, VT)
-1.1346
(0.4599)
-0.0006
(0.0006)
0.0012
(0.0007)
0.0887
(0.0166)
0.1281
(0.2534)
West South Central 262 307.54 68
(AR, LA, OK,
TX)
0.0461
(0.6094)
-0.0009
(0.0010)
0.0044
(0.0010)
0.0315
(0.0146)
-0.4870
(0.1858)
East South Central 378 466.56 64
(AL, KY, MS,
TN)
-0.4282
(0.2512)
-0.0008
(0.0003)
0.0044
(0.0007)
0.0410
(0.0114)
-0.3610
(0.1586)
South Atlantic (DE, 605 697.39 69
FL, GA, MD,
NC, SC, VA, WV)
27
0.0467
(0.2016)
-0.0020
(0.0004)
0.0068
(0.0013)
0.0177
(0.0104)
-0.3836
(0.1454)
New England (CT, 657 715.67 71
ME, NH, NJ,
NY, PA, VT)
-0.3197
(0.2787)
-0.0009
(0.0002)
0.0009
(0.0004)
0.0519
(0.0131)
-0.2401
(0.2607)
Western Trout (AR, 500 558.42 66
CA, NV, OR, WA)
0.1965
(0.2084)
-0.0010
(0.0005)
0.0029
(0.0006)
0.0275
(0.0081)
-0.4243
(0.0865)
Mountain Trout (AZ, 1139 1491.58 62
CO, ID, MT, NM,
UT, WY)
0.0365
(0.2038)
-0.0018
(0.0003)
0.0066
(0.0008)
0.0306
(0.0090)
-0.2433
(0.1250)
Northern Bass (DE, 1088 1256.03 69
IA, IL, IN, KS,
KY, MA, MD, MO,
NE, RI, VA, WV)
-0.7107
(0.2321)
-0.0006
(0.0003)
0.0022
(0.0005)
0.0569
(0.0093)
-0.1221
(0.1346)
Southern Bass (AL, AR, 838 1016.00 66
FL, GA, LA, MS, NC,
OK, SC, TN, TX)
0.1575
(0.0780)
-0.0003
(0.0002)
-0.0014
(0.0004)
-0.0006
(0.0010)
-0.0005
(0.0021)
Walleye (MI, MN, 516 665.41 72
ND, OH, SD, WI)
Table C-2. Probit Equation Results for Hunting
Explanatory Variables
Region Constant Bid # of Sex of Hunt Resident n Chi- %
Animals Animal Other squared Correct
Bagged1 Bagged Big Game Prediction
U.S. Fish and Wildlife Service Official Regions (Deer Hunting)
2 (AZ, NM, OK, TX)
3 (IA, IL, IN, MI,
MN, MO, OH, WI)
4 (AL, AR, FL, GA,
KY, LA, MS, NC,
SC, TN)
5 (CT, DE, MA, MD,
ME, NH, NJ, NY,
PA, RI, VA, VT,
WV)
6 (KS, ND, NE, SD,
UT, WY)
U.S. Bureau of the Census Regions (Deer Hunting)
Pacific (CA, WA)
Mountain (AZ, NM, NV,
UT)
West North Central
(IA, KS, MN, MO,
NE, NE, SD)
Table C1. Probit Equation Results for Fishing (continued)
Explanatory Variables
Region Constant Bid Catch Inch Resident n Chi-squared %
(# Fish) (Length) Correct
Predictions
1 (CA, NV, WA) -1.3252
(1.6510)
-0.0003
(0.0008)
0.4180
(0.4863)
-0.4925
(0.5755)
0.3326
(0.3214)
0.6408
(1.5570)
109 109.43 73
0.0281
(0.7567)
-0.0006
(0.0011)
0.2400
(0.0975)
-0.2528
(0.2583)
0.4715
(0.2083)
-0.2392
(0.4371)
222 283.21 65
0.1164
(0.2586)
-0.0014
(0.0004)
0.2781
(0.0717)
-0.1149
(0.1330)
0.3026
(0.1414)
-0.0079
(0.2030)
736 948.92 64
-0.4147
(0.3885)
-0.0004
(0.0006)
0.0694
(0.0350)
0.3464
(0.1247)
0.2548
(0.1089)
0.2212
(0.1477)
738 961.71 63
-0.1319
(0.1776)
-0.0007
(0.0003)
0.2600
(0.0589)
0.0682
(0.1173)
0.2966
(0.0794)
-0.1212
(0.1090)
1147 1461.04 63
2.0845
(0.4083)
-0.0040
(0.0008)
0.1582
(0.1365)
-0.0996
(0.1835)
0.3451
(0.2044)
-1.1252
(0.2953)
390 492.38 60
-0.3769
(2.4449)
-0.0004
(0.0010)
0.5061
(0.6398)
-0.7945
(0.7467)
0.4266
(0.3801)
-0.3033
(2.4121)
79 76.22 80
0.9854
(0.3586)
-0.0012
(0.0005)
0.4064
(0.5962)
-0.4432
(0.6074)
-0.0674
(0.2691)
-0.7066
(0.2924)
209 277.07 58
0.0844
(0.3226)
-0.0021
(0.0005)
0.2254
(0.0838)
-0.0651
(0.1440)
0.4560
(0.1463)
0.1627
(0.2650)
591 750.93 62
28
East North Central
(IN, IL, MI, OH,
WI)
Middle Atlantic (NJ,
NY, PA)
New England (CT, MA,
ME, NH, RI, VT)
West South Central
(AR, LA, OK, TX)
East South Central
(AL, KY, MS, TN)
South Atlantic (DE, FL,
GA, MD, NC, SC,
VA, WV)
U.S. Fish and Wildlife Service Deer Regions
Pacific (CA, NV, WA)
West Southwest (AZ,
NM, UT)
East Southwest (OK,
TX)
Plains (IA, KS, MO,
ND, NE, SD)
Great Lakes (IN, IL,
MI, MN, OH, WI)
Middle Atlantic (DE,
MD, NJ, NY, PA)
Table C2. Probit Equation Results for Hunting (continued)
Explanatory Variables
Region Constant Bid # of Sex of Hunt Resident n Chi- %
Animals Animal Other squared Correct
Bagged1 Bagged Big Game Prediction
-1.3255
(1.6513)
-0.0003
(0.0008)
0.4180
(0.4862)
-0.4926
(0.5755)
0.3326
(0.3214)
0.6410
(1.5574)
109 109.42 73
1.4486
(0.4264)
-0.0011
(0.0007)
0.8973
(0.7388)
-0.8954
(0.7549)
-0.2050
(0.2972)
-1.2314
(0.3725)
179 229.16 60
0.2233
(1.4276)
-0.0009
(0.0024)
0.2330
(0.1275)
-0.2353
(0.3493)
0.5679
(0.2822)
-0.2916
(0.5878)
121 152.86 66
0.3818
(0.3222)
-0.0023
(0.0005)
0.3212
(0.0928)
-0.2260
(0.1512)
0.5163
(0.1401)
-0.0918
(0.2666)
496 615.39 63
0.1218
(0.3095)
-0.0013
(0.0005)
0.2563
(0.0827)
-0.0806
(0.1559)
0.2172
(0.1836)
-0.0089
(0.2361)
554 720.68 65
-1.1185
(0.3911)
-0.0000
(0.0006)
0.4417
(0.1318)
0.0022
(0.2233)
0.4205
(0.1366)
0.3221
(0.2287)
406 480.32 68
0.5148
(0.2078)
-0.0012
(0.0003)
-0.1078
(0.1664)
0.2574
(0.2484)
0.5354
(0.1452)
-0.2292
(0.1513)
467 611.02 60
-0.3270
(0.2330)
-0.0003
(0.0004)
0.0836
(0.0315)
0.2761
(0.1055)
0.2200
(0.0896)
0.0434
(0.1175)
1013 1330.17 63
1.4414
(0.3910)
-0.0027
(0.0008)
-0.2220
(0.1461)
0.6356
(0.1831)
0.5716
(0.1620)
-0.6592
(0.2573)
421 515.75 60
3.7033
(3.7240)
-0.0066
(0.0054)
0.9864
(0.4582)
-0.4053
(0.5117)
0.2666
(0.3191) 75 86.53 65
0.3336
(0.3352)
-0.0016
(0.0005)
0.2938
(0.0906)
-0.1648
(0.1688)
0.2059
(0.1934)
-0.0702
(0.2497)
457 590.38 65
-1.3150
(0.5457)
0.0001
(0.0010)
0.5447
(0.1617)
-0.1577
(0.2726)
0.4584
(0.1582)
0.4617
(0.2873)
301 350.62 70
0.5148
(0.2078)
-0.0012
(0.0003)
-0.1078
(0.1664)
0.2574
(0.2484)
0.5354
(0.1452)
-0.2291
(0.1513)
467 611.02 60
-1.0260
(0.7767)
0.0008
(0.0012)
0.1914
(0.0892)
0.0024
(0.2383)
0.4684
(0.2007)
0.0736
(0.3746)
244 313.43 61
-0.0367
(0.5009)
-0.0012
(0.0007)
0.1716
(0.0555)
0.2269
(0.1817)
0.2047
(0.1612)
0.1307
(0.1866)
375 454.20 67
-0.2535
(0.2548)
0.0001
(0.0004)
0.0311
(0.0401)
0.2727
(0.1331)
0.1768
(0.1090)
-0.1735
(0.1512)
619 834.23 61
New England (CT, MA,
ME, NH, RI, VT)
South East (AL, AR,
FL, GA, KY, LA,
MS, NC, SC, TN,
VA, WV)
Other Big Game Species
Elk Region (CO, ID,
MT, OR, WY)
Moose Region (AK)
1 Respondents were asked if they bagged a moose or elk but not the number bagged so for the elk and moose regions this variable equals 0 or 1, where 1 means
they bagged an animal.
29
Table C3. Probit Equation Results for Wildlife Watching
Explanatory Variables
State or Region Constant Bid Private Public Photo Fish Resident n Chi- %
squared Correct
Prediction
Fish and Wildlife Service Official Regions
0.6029
(0.3373)
-0.0010
(0.0008)
-0.5449
(0.4070)
-0.5671
(0.1166)
0.3726
(0.1203)
-0.1685
(0.1223)
-0.3064
(0.1383)
1 (CA, HI, ID, NV, 525 664.96 63
OR, WA)
0.6371
(0.4276)
-0.0011
(0.0009)
0.7802
(0.2059)
0.1455
(0.1539)
-0.1032
(0.1388)
0.1867
(0.1434)
-0.7507
(0.1696)
2 (AZ, NM, OK, TX) 384 481.81 59
-0.0998
(0.1940)
-0.0013
(0.0004)
-0.0919
(0.1698)
-0.2689
(0.1164)
0.5794
(0.1067)
0.0280
(0.1078)
-0.0749
(0.1367)
3 (IA, IL, IN, MI, 661 780.68 68
MN, MO, OH, WI)
0.4154
(0.2113)
-0.0012
(0.0005)
-0.3268
(0.1461)
-0.3363
(0.0865)
0.2827
(0.0830)
-0.1319
(0.0836)
-0.2531
(0.0883)
4 (AL, AR, FL, GA, 1009 1299.01 66
KY, LA, MS, NC,
SC, TN)
0.3737
(0.1653)
-0.0017
(0.0004)
-0.1645
(0.1578)
-0.5617
(0.0827)
0.3525
(0.0794)
0.0413
(0.0834)
-0.1186
(0.0920)
5 (CT, DE, MA, MD, 1113 1341.13 63
ME, NH, NJ, NY,
PA, RI, VA, VT, WV)
0.8407
(0.3285)
-0.0034
(0.0011)
-0.1168
(0.2669)
-0.2290
(0.0990)
0.7831
(0.1030)
0.0436
(0.1001)
-0.4358
(0.1015)
6 (CO, KS, MT, ND, 740 896.26 64
NE, SD, UT, WY)
-1.9932
(1.176)
-0.0024
(.0013)
1.5095
(1.3290)
-0.4675
(.2808)
0.9690
(.7155)
-0.3196
(.2796)
-0.5567
(.3261)
7 (AK) 99 122.03 72
U.S. Fish and Wildlife Service Suggested Groupings
0.6282
(0.2941)
-0.0009
(0.0006)
-0.4863
(0.3866)
-0.5694
(0.1126)
0.3934
(0.1200)
-0.1871
(0.1185)
-0.3736
(0.1283)
West (AK, CA, HI, NV, 562 699.71 63
OR, WA)
0.5542
(0.2012)
-0.0016
(0.0005)
0.4448
(0.3818)
-0.1599
(0.9869)
0.4115
(0.1032)
0.1546
(0.1022)
-0.5180
(0.9915)
Rocky Mountain (AZ, 724 912.12 61
CO, ID, MT, NM,
UT, WY)
0.7780
(0.2694)
-0.0048
(0.0008)
-0.6316
(0.2560)
-0.1819
(0.1372)
1.0914
(0.1316)
-0.0399
(0.1299)
-0.1864
(0.1416)
Plains (IA, KS, MO, ND, 478 525.85 67
NE, SD)
-0.3452
(0.2428)
-0.0008
(0.0004)
0.0201
(0.1962)
-0.2624
(0.1385)
0.4896
(0.1266)
-0.0140
(0.1292)
-0.0126
(0.1733)
Great Lake (IN, IL, MI, 468 538.66 68
MN, OH, WI)
0.2961
(0.1900)
-0.0013
(0.0005)
-0.2194
(0.1742)
-0.6480
(0.0913)
0.4218
(0.0875)
0.1067
(0.0921)
-0.2012
(0.1022)
North Atlantic (CT, DE, 930 1097.83 63
MA, MD, ME, NH,
NJ, NY, PA, RI, VT)
1.0617
(0.4491)
-0.0025
(0.0009)
0.5734
(0.1989)
0.0612
(0.1772)
-0.0458
(0.1519)
0.1795
(0.1506)
-0.6087
(0.2027)
South Central (AR, LA, 317 397.08 60
OK, TX)
0.3444
(0.2052)
-0.0011
(0.0005)
-0.2939
(0.1516)
-0.3049
(0.0837)
0.2563
(0.0807)
-0.1824
(0.0824)
-0.1633
(0.0863)
South Atlantic (AL, FL, 1052 1364.65 65
GA, KY, MS, NC,
SC, TN, VA, WV)
30
U.S. Bureau of the Census Regions
0.6469
(0.3115)
-0.0009
(0.0006)
-0.4869
(0.4028)
-0.5757
(0.1190)
0.3824
(0.1270)
-0.1731
(0.1251)
-0.3887
(0.1352)
Pacific (AK, CA, HI, 505 629.00 63
OR, WA)
0.5191
(0.1877)
-0.0015
(0.0004)
0.4340
(0.3738)
-0.1671
(0.0949)
0.4248
(0.0990)
0.1292
(0.0983)
-0.4896
(0.0955)
Mountain (AZ, CO, IA, 781 985.76 61
MT, NM, NV, UT,
WY)
0.5383
(0.2472)
-0.0043
(0.0007)
-0.1466
(0.2161)
-0.1744
(0.1252)
0.9200
(0.1193)
-0.1467
(0.1163)
0.0213
(0.1290)
West North Central 564 647.28 66
(IA, KS, MN, MO,
ND, NE, SD)
-0.1311
(0.2700)
-0.0009
(0.0004)
-0.1047
(0.2160)
-0.2675
(0.1530)
0.4957
(0.1397)
0.0731
(0.1450)
-0.1886
(0.1973)
East North Central (IL, 382 438.74 69
IN, MI, OH, WI)
0.8203
(0.8015)
-0.0033
(0.0022)
-0.0712
(0.3739)
-0.4750
(0.2281)
0.5061
(0.2180)
0.1602
(0.2258)
-0.3606
(0.2978)
Middle Atlantic (NJ, 166 183.48 64
NY, PA)
0.9103
(0.2688)
-0.0029
(0.0008)
-0.1950
(0.2572)
-0.7464
(0.1183)
0.4186
(0.1166)
-0.2305
(0.1331)
-0.2301
(0.1191)
New England (CT, MA, 547 649.26 64
ME, NH, RI, VT)
1.0617
(0.4491)
-0.0025
(0.0009)
0.5734
(0.1989)
0.0612
(0.1772)
-0.0458
(0.1519)
0.1795
(0.1506)
-0.6087
(0.2027)
West South Central 317 397.08 60
(AR, LA, OK, TX)
0.2586
(0.3445)
-0.0018
(0.0011)
-0.1777
(0.2269)
-0.2231
(0.1523)
0.5262
(0.1433)
-0.2377
(0.1442)
-0.0990
(0.1571)
East South Central 349 423.85 64
(AL, KY, MS, TN)
0.3755
(0.1989)
-0.0010
(0.0005)
-0.2253
(0.1815)
-0.3930
(0.0916)
0.1900
(0.0891)
-0.1166
(0.0913)
-0.1593
(0.0944)
South Atlantic (DE, FL, 854 1113.84 65
GA, MD, NC, SC,
VA, WV)
Table C3. Probit Equation Results for Wildlife Watching (continued)
Explanatory Variables
State or Region Constant Bid Private Public Photo Fish Resident n Chi- %
squared Correct
Prediction
USFWS photo
31
Table D1. Average Days per Year for Fishing
Region Species Valued Average Days/Year
U.S. Fish and Wildlife Service Regions
1 (CA, ID, NV, OR, WA) Trout 10.7
2 (AZ, NM, OK, TX) Bass & Trout 11.0
(OH, TX) Bass 11.4
(AZ, NM) Trout 10.8
3 (IA, IL, IN, MO) Bass 14.4
4 (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN) Bass 16.4
5 (CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VA, VT, WV) Bass & Trout 15.0
(DE, MA, MD, RI, VA, WV) Bass 16.8
(CT, ME, NH, NJ, NY, PA, VT) Trout 13.7
6 (CO, KS, MT, NE, UT, WY) Bass & Trout 19.3
(KS, NE) Bass 19.3
(CO, MT, UT, WY) Trout 10.1
7 (AK) Trout 10.0
U.S. Bureau of the Census Regions
Pacific (AK, CA, OR, WA) Trout 11.3
Mountain (AZ, CO, ID, MT, NM, NV, UT, WY) Trout 10.1
West North Central (IA, KS, MO, NE) Bass 17.4
East North Central (IL, IN) Bass 15.6
Middle Atlantic (NJ, NY, PA) Trout 13.0
New England (CT, MA, ME, NH, RI, VT) Bass & Trout 15.6
(MA, RI) Bass 18.4
(CT, ME, NH, VT) Trout 14.2
West South Central (AR, LA, OK, TX) Bass 13.8
East South Central (AL, KY, MS, TN) Bass 16.3
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) Bass 16.1
U.S. Fish and Wildlife Service Bass Regions
Northern (DE, IA, IL, IN, KS, KY, MA, MD, MO, NE, RI, VA, WV) Bass 16.2
Southern (AL, AR, FL, GA, LA, MS, NC, OK, SC, TN, TX) Bass 15.7
U. S. Fish and Wildlife Service Trout Regions
Western (AR, CA, NV, OR, WA) Trout 11.3
Mountain (AZ, CO, ID, MT, NM, UT, WY) Trout 10.1
Northeast (CT, ME, NH, NJ, NY, PA, VT) Trout 13.7
U.S. Fish and Wildlife Service Walleye Regions
Walleye Region (MI, MN, ND, OH, SD, WI) Walleye 16.3
Appendix D
Average Days of Participation
32
Table D2. Average Days per Year for Hunting
Region Average Days/Year
U.S. Fish and Wildlife Service Regions
1 (CA, NV, WA) 7.9
2 (AZ, NM, OK, TX) 8.9
3 (IA, IL, IN, MI, MN, MO, OH, WI) 10.3
4 (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN) 15.8
5 (CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VA, VT, WV) 11.8
6 (CO, KS, MT, ND, ND, SD, UT, WY) 7.4
U.S. Bureau of the Census Regions
Pacific (CA, WA) 8.8
Mountain (AZ, NM, NV, UT) 5.2
West North Central (IA, KS, MN, MO, NE, NE, SD) 7.8
East North Central (IL, IN, MI, OH, WI) 11.8
Middle Atlantic (NJ, NY, PA) 10.0
New England (CT, MA, ME, NH, RI, VT) 12.9
West South Central (AR, LA, OK, TX) 13.4
East South Central (AL, KY, MS, TN) 14.7
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) 14.2
U.S. Fish and Wildlife Service Deer Regions
Pacific (CA, NV, WA) 7.9
West Southwest (AZ, NM, UT) 5.1
East Southwest (OK, TX) 12.2
Plains (IA, KS, MO, ND, NE, SD) 8.3
Great Lakes (IN, IL, MI, MN, OH, WI) 10.7
Middle Atlantic (DE, MD, NJ, NY, PA) 10.8
New England (CT, MA, ME, NH, RI, VT) 12.9
South East (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN, VA, WV) 14.6
Other Game Species
Elk Region (CO, ID, MT, OR, WY) 6.9
Moose (AK) 10.2
33
Table D3. Average Days per Year for Wildlife Watching
Region Average Days/Year
U.S. Fish and Wildlife Service Regions
1 (CA, ID, NV, WA, OR) 11.5
2 (AZ, NM, OK, TX) 12.9
3 (IA, IL, IN, MI, MN, MO, OH, WI) 11.1
4 (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN) 11.2
5 (CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VA, VT, WV) 10.8
6 (CO, KS, MT, ND, NE, SD, UT, WY) 10.5
7 (AK) 20.2
U.S. Bureau of the Census Regions
Pacific (AK, CA, HI, OR, WA) 13.9
Mountain (AZ, CO, IA, MT, NM, NV, UT, WY) 10.2
West North Central (IA, KS, MN, MO, ND, NE, SD) 11.1
East North Central (IL, IN, MI, OH, WI) 12.6
Middle Atlantic ( NJ, NY, PA) 8.5
New England (CT, MA, ME, NH, RI, VT) 11.6
West South Central (AR, LA, OK, TX) 13.4
East South Central (AL, KY, MS, TN) 12.2
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) 10.0
U.S. Fish and Wildlife Service Suggested Regions
West (WA, CA, OR, NV, HI, AK) 13.3
Rocky Mountain (ID, MT, WY, UT, CO, AZ, NM) 10.3
Plains (ND, SD, NE, KS, IA, MO) 11.4
Great Lake (MN, WI, IL, IN, MI, OH) 12.1
North Atlantic (MD, DE, PA, NJ, NY, CT, RI, MA, VT, NH, ME) 11.0
South Central (AR, LA, OK, TX) 13.4
South Atlantic (AL, FL, GA, KY, MS, NC, SC, TN, VA, WV) 10.4
34
Table E1. Censored Probit Marginal Coefficients for Fishing
Region Catch Inch Resident Species
(# Fish) (Length)
U.S. Fish and Wildlife Service Regions
1 (CA, ID, NV, OR, WA) 0.71 55.28 –354.72
2 (AZ, NM, OK, TX) –2.58 –199.22 1590.90 –752.72
3 (IA, IL, IN, MO) 4.84 4.85 96.14
4 (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN) 3.81 64.04 –311.53
5 (CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VA, VT, WV) 2.96 11.39 –204.84 –166.38
(DE, MA, MD, RI, VA, WV) 2.65 5.40 –165.65
(CT, ME, NH, NJ, NY, PA, VT) 0.24 7.66 –34.81
U.S. Bureau of the Census Regions
Pacific (AK, CA, OR, WA) 0.71 66.09 –306.92
Mountain (AZ, CO, ID, MT, NM, NV, UT, WY) 2.75 23.11 –388.10
West North Central (IA, KS, MO, NE) 6.05 10.29 –178.53
East North Central (IL, IN) 1.44 0.17 160.69
Middle Atlantic (NJ, NY, PA) 3.69 7.96 –110.95
New England (CT, MA, ME, NH, RI, VT) 3.86 18.99 –319.62 58.26
West South Central (AR, LA, OK, TX) 4.61 33.39 –516.53
East South Central (AL, KY, MS, TN) 1.66 135.81 196.09
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) 5.86 54.97 –483.59
U.S. Fish and Wildlife Service Fishing Regions
New England (CT, ME, NH, NJ, NY, PA, VT) 3.38 8.84 –192.02
Western (AR, CA, NV, OR, WA) 0.78 59.15 –273.62
Mountain (AZ, CO, ID, MT, NM, UT, WY) 2.81 26.50 –409.57
All Bass States 3.32 45.19 –166.75
Northern (DE, IA, IL, IN, KS, KY, MA, MD, MO, NE, RI, VA, WV) 3.60 16.55 –131.68
Southern (AL, AR, FL, GA, LA, MS, NC, OK, SC, TN, TX) 3.53 92.32 ���198.09
Walleye (MI, MN, ND, OH, SD, WI) –4.67 –2.16 –1.78
Appendix E
Censored Probit Marginal Coefficients
35
Table E2. Censored Probit Marginal Coefficients for Hunting
Region # Sex of Hunt Resident
Animals Animal Other Big
Bagged1 Bagged Game
U.S. Fish and Wildlife Service Regions
1 (CA, NV, WA,) 1274.69 –1501.83 1014.19 1953.82
2 (AZ, NM, OK, TX) 417.07 –439.38 819.35 –415.59
3 (IA, IL, IN, MI, MN, MO, OH, WI) 203.91 –84.23 221.88 –5.82
4 (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN) 168.19 840.21 617.84 536.53
5 (CT, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VA, VT, WV) 372.00 97.53 424.46 –173.43
6 (CO, KS, MT, ND, NE, SD, UT, WY) 39.14 –24.64 85.38 –278.39
U.S. Bureau of the Census Regions
Pacific (CA, WA) 1211.85 –1902.42 1021.36 –726.12
Mountain (AZ, NM, NV, UT) 346.91 –378.32 –57.55 –603.14
West North Central (IA, KS, MN, MO, NE, NE, SD) 107.11 –30.91 216.67 77.31
East North Central ( IL, IN, MI, OH, WI) 188.00 –105.42 131.70 –44.94
Middle Atlantic (NJ, NY, PA) –5967.36 1727.83 –5022.36 –5058.04
New England (CT, MA, ME, NH, RI, VT) –86.37 206.13 428.77 –183.48
West South Central (AR, LA, OK, TX) –256.32 –3.26 –627.20 –98.58
East South Central (AL, KY, MS, TN) 147.83 195.46 176.35 112.60
South Atlantic (DE, FL, GA, MD, NC, SC, VA, WV) –251.16 –2204.97 –1429.17 1402.94
U.S. Fish and Wildlife Service Suggested Deer Regions
Pacific (CA, NV, WA) 1274.50 –1501.90 1014.14 1954.54
West Southwest (AZ, NM, UT) 795.51 –793.82 –181.74 –1091.74
East Southwest (OK, TX) 265.90 –268.54 648.13 –332.75
Plains (IA, KS, MO, ND, NE, SD) 138.22 –97.27 222.21 –39.51
Great Lakes (IN, IL, MI, MN, OH, WI) 199.95 –62.85 169.38 –6.91
Middle Atlantic (DE, MD, NJ, NY, PA)
New England (CT, MA, ME, NH, RI, VT) –86.36 206.16 428.80 –183.55
South East (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN, VA, WV) 300.16 991.70 790.24 156.01
Other Game Species
Elk Region (CO, ID, MT, OR, WY) –82.59 236.51 212.68 –245.26
Moose (AK) 148.66 –61.08 40.18
1 Respondents were asked if they bagged a moose or elk but not the number bagged so for the elk and moose regions this variable equals 0 or 1, where 1 means
they bagged an animal.
36
Table E3. Censored Probit Marginal Coefficients for Wildlife Watching
Region Private Public Photo Fish Resident
U.S. Fish and Wildlife Service Regions
1 (CA, ID, HI, NV, WA, OR) –539.66 –561.69 368.99 –166.91 –303.51
2 (AZ, NM, OK, TX) 705.53 131.58 –93.33 168.80 –678.88
3 (IA, IL, IN, MI, MN, MO, OH, WI) –70.38 –205.87 443.63 21.47 –57.32
4 (AL, AR, FL, GA, KY, LA, MS, NC, SC, TN) –282.73 –290.94 244.60 –114.10 –218.98
5 (CT, DE, MA, MD, ME, NH, NJ, NY, PA,
RI, VA, VT, WV) –96.89 –330.76 207.57 24.30 –69.87
6 (CO, KS, MT, ND, NE, SD, UT, WY) –34.46 –67.56 231.05 12.85 –128.57
7 (AK) –626.24 193.93 –401.99 132.60 230.94
U.S. Bureau of the Census Regions
Pacific (AK, CA, HI, OR, WA) –533.15 –630.43 418.79 –189.52 –425.66
Mountain (AZ, CO, IA, MT, NM, NV, UT, WY) 293.63 –113.06 287.43 87.42 –331.29
West North Central (IA, KS, MN, MO, ND,
NE, SD) –33.98 –40.44 213.29 –34.02 4.95
East North Central (IL, IN, MI, OH, WI) –116.52 –297.62 551.55 81.29 –209.89
Middle Atlantic (NJ, NY, PA) –2614.27 –4191.33 4179.42 2008.81 –1954.76
New England (CT, MA, ME, NH, RI, VT) –68.24 –261.14 146.45 –80.64 –80.49
West South Central (AR, LA, OK, TX) 233.36 24.90 –18.66 73.06 –247.74
East South Central (AL, KY, MS, TN) –98.58 –123.76 291.91 –131.85 –54.89
South Atlantic (DE, FL, GA, MD, NC, SC,
VA, WV) –220.78 –385.24 186.18 –114.25 –156.12
U.S. Fish and Wildlife Service Suggested Regions
West (AK, CA, HI, NV, OR, WA) –534.73 –626.12 432.63 –205.79 –410.88
Rocky Mountain (AZ, CO, ID, MT, NM, UT, WY) 283.54 –101.93 262.31 98.55 –330.17
Plains (IA, KS, MO, ND, NE, SD) –132.24 –38.08 228.51 –8.36 –39.04
Great Lake (IN, IL, MI, MN, OH, WI) 26.57 –347.36 648.23 –18.55 –16.64
North Atlantic (CT, DE, MA, MD, ME, NH,
NJ, NY, PA, RI, VT) –167.61 –495.00 322.17 81.50 –153.69
South Central (AR, LA, OK, TX) 233.36 24.90 –18.66 73.06 –247.74
South Atlantic (AL, FL, GA, KY, MS, NC,
SC, TN, VA, WV) –267.22 –277.20 233.01 –165.79 –148.49
U.S. Department of the Interior
U.S. Fish & Wildlife Service
Division of Economics
Arlington, Virginia
http://www.fws.gov