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 nonresidential 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 contingentvaluation 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 dg

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 nonGreat 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 a moose 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
American Sport Fishing Association.

1998. “The Economic Importance of

Sport Fishing.” 1033 North Fairfax St.,

Alexandria, VA.

Bishop, Richard C. 1984. “Economic

Values Defined.” In Valuing Wildlife:

Economic and Social Perspectives, D.F.

Decker and G.R. Goff (eds), Westview

Press, Boulder, CO.

Bishop, Richard C., Thomas A.

Heberlein, and Mary Jo Kealy. 1983.

“Contingent Valuation of Environmental

Assets: Comparisons With a Simulated

Market.” Natural Resources Journal

23:619-633.

Boyle, Kevin J. 1990. “Design of

Contingent-Valuation Questions for the

1991 National Survey of Fishing,

Hunting, and Wildlife-Associated

Recreation: Final Report.” Report to

U.S. Fish and Wildlife Service.

Boyle, Kevin J., and Richard C. Bishop.

1988. “Welfare Measurements Using

Contingent Valuation: A Comparison of

Techniques.” American Journal of

Agricultural Economics

70(February):20-28.

Cameron, Trudy Ann, and Michell D.

James. 1987. “Efficient Estimation

Methods for Use with Closed-Ended

Contingent Valuation Survey Data.”

Review of Economics and Statistics

69(May):269-276.

Cameron, Trudy Ann. 1988. “A New

Paradigm for Valuing Non-Market Goods

Using Referendum Data: Maximum

Likelihood Estimation by Censored

Logistic Regression.” Journal of

Environmental Economics and

Management 15(September):355-379.

Cameron, Trudy Ann. 1991. “Interval

Estimates of Non-Market Resource

Values From Referendum Contingent

Valuation Surveys.” Land Economics

67(November):413-421.

Cooper, Joseph C. 1993. “Optimal Bid

Selection for Dichotomous Choice

Contingent Valuation Surveys.” Journal

of Environmental Economics and

Management 24(January):25-40.

Cooper, Joseph. 1994. “A Comparison of

Approaches to Calculating Confidence

Intervals for Benefit Measures from

Dichotomous Choice Contingent

Valuations Surveys.” Land Economics

70(February):111-122.

Duffield, John W., and David A.

Patterson. 1991. “Inference and Optimal

Design for a Welfare Measure in

Dichotomous Choice Contingent

Valuation.” Land Economics

67(May):225-239.

Freeman, A. Myrick. 1993. The

Measurement of Environmental and

Resource Values: Theory and 

Methods. Resources for the Future,

Washington, DC.

Greene, William, H. 1992. Limdep

Version 6.0: User’s Manual and

Reference Guide. Bellport, New York.

Econometric Software, Inc.

Hanemann, W. Michael. 1984. “Welfare

Evaluations in Contingent Valuation

Experiments with Discrete Response

Data.” American Journal of Agriculture

Economics 66(August):332-341.

Hanemann, W. Michael. 1989. “Welfare

Evaluations in Contingent Valuation

Experiments with Discrete Response

Data: Reply.” American Journal of

Agriculture Economics

71(November):1056-1061.

Johnston, J. 1984. Econometric Methods.

Third Edition, McGraw-Hill, Inc.

Judge, G., W. Griffiths, R. Hill, H.

Lutkepohl, and T. Lee. 1985. The Theory

and Practice of Econometrics. Second

Edition. John Wiley and Sons, Inc.

Loomis, John B., George L. Peterson,

and Cindy S. Sorg. 1984. “A Field Guide

to Wildlife Economic Analysis.”

Transactions of the Fourty-ninth North

American and Natural Resources

Conference: 315-324.

McCollum, Daniel W., George L.

Peterson, and Cindy S. Swanson. 1992. “A

Managers Guide to the Valuation of

Nonmarket Resources: What Do You

Really Want to Know?” In Valuing

Wildlife Resources in Alaska, G.L.

Peterson, C.S. Swanson, D.W. McCollum,

and M.H. Thomas, (eds), Westview Press,

Boulder, CO.

McConnell, K.E. 1990. “Models for

Referendum Data: The Structure of

Discrete Choice Models for Contingent

Valuation.” Journal of Environmental

Economic and Management

18(January):19-34.

Mitchell, Robert C., and Richard J.

Carson. 1989. Using Surveys to Value

Public Goods: The Contingent Valuation

Method. Washington, D.C. Resources for

the Future.

Park, Timothy, John B. Loomis, and

Michael Creel. 1991. “Confidence

Intervals for Evaluating Benefits

Estimates from Dichotomous Choice

Contingent Valuation Studies.” Land

Economics 67(February):64-73.

Southwick Associates for the

International Association of Fish and

Wildlife Agencies. “The Economic

Importance of Hunting.” 1033 North

Fairfax St., Alexandria, VA.

U.S. Fish and Wildlife Service. “1996

National and State Economic Impacts of

Wildlife Watching.” Report 96-1.

Washington, DC.

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