AN ABSTRACT OF THE THESIS OF
Brett M. Fried for the degree of Master of Science in
Aqricultural and Resource Economics presented on December 16,
1992. Title: Usinq Valuation Functions to Estimate Chanqes in
the Quality of a Recreational Experience: Elk Huntinq in
Oreqon
Abs tract approved:
Richard M. Adams
Public
recreational
agencies
need
activities
wildlife species.
to
information
assist
on
the
in managing
value
of
fish and
Over the past two decades economists have
developed and applied techniques to measure the value of such
non-marketed commodities.
The contingent valuation method (CVM) is one technique
used by economists to measure net benefits associated with a
change in the quantity or quality of a non-marketed commodity.
Unlike other techniques such as the travel cost and hedonic
methods, which use market data to infer willingness to pay
(WTP), CVM directly elicits willingness to pay or willingness
to accept (WTA) information.
CVM is used in this thesis to estimate the use value of
changes in the quality of the elk hunting experience.
T h e
focus of the study is elk hunting on the Starkey Research
Forest in eastern Oregon.
Since 1988, the Oregon Department
of Fish and Wildlife (ODFW) and the U.S. Forest Service have
collected data on big game hunting on this area.
The
Starkey
data
sets
provide
unique
research
opportunities because of the enclosed nature of the Starkey
Research Forest (25,000 acres surrounded by 38 miles of fence)
and the duration of the survey effort (10 years).
Although
the surveys contain questions specific to an analysis by both
the contingent valuation and travel cost methods, only the
contingent valuation method is considered here.
Specific objectives of the thesis research include:
(1)
derivation of valuation functions for the two dichotomous
choice WTP and WTA elicitations,
(2) estimation of changes in
WTA and WTP for changes in significant explanatory variables,
and
(3)
suggestions
for
improvements
in
future
Starkey
surveys.
Valuation functions,
as used here, provide increased
flexibility over point estimates.
The explanatory variables
in a valuation function can be changed to obtain estimates of
the corresponding changes in WTP and WTA values. The valuation
functions can then be used to derive estimates of central
tendency for the contingent scenarios.
Results include an estimated median value of $113 per
hunter for access to hunting and an estimated mean value per
trip of $287 per hunter for increases in the elk herd (to
ensure an opportunity to shoot at an elk).
Additionally, the
flexibility of valuation functions is demonstrated by showing
how changes in the values of significant
(at the .05 level)
explanatory variables affect WTP and WTA values.
Four policy and three methodological implications are
gleaned from the results.
The four policy implications are
that: (1) the elk hunting recreational experience is providing
substantial benefits to Starkey hunters,
(2) willingness to
pay values are sensitive to the size of respondents' incomes,
(3) respondents are willing to pay more to increase the elk
herd to the point where they would be virtually certain of
having an opportunity to shoot at an elk and
(4)
Starkey
hunters object to the tradeoff between their right to hunt and
private goods.
The
three
methodological
implications
are
demonstration of the flexibility of WTP functions,
confirmation
of
the
importance
of
using
specific
unambiguous wording in the contingent scenarios, and
a
(1)
(2)
a
and
(3)
a
confirmation of the importance of pretests to determine the
initial ranges and number of bids in dichotomous choice
surveys.
Two problems with the current surveys are the loss of
efficiency due to the low number and narrow range of bids and
the lack of specificity and ambiguous wording used in some of
the contingent scenarios.
Suggestions include increases in
the number and range of the bids,
the inclusion of an
additional question and the inclusion of a Nnot for sale"
category.
Using Valuation Functions to Estimate Changes in the
Quality of a Recreational Experience: Elk Hunting in Oregon
by
Brett M. Fried
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Completed December 16, 1992
Commencement June 1993
APPROVED:
Professor of Agricultural and Resource Economics in charge of
major
Head of department of Agricultural and Resource Economics
Dean of Gradua
School
Date thesis presented
Typed by
..-
December 16, 1992
ACKNOWLEDGEMENT
The quality of the educational experience at OSU has
surpassed my highest expectations.
I
owe thanks to the
professors and colleagues who have mentored me and made my
experience at OSU so enjoyable and productive.
Particular
thanks must be extended to two individuals without whom the
completion of this thesis would have been impossible.
They
are:
Dr. Richard Adams for his insight, encouragement, editing
and contributions;
Bob Berreris for his support, advice, contributions and
friendship.
Additionally, I would like to thank,
Dr. Olvar Bergland for introducing me to the Starkey
Research Forest data sets and for starting me on the road
towards the preparation of my thesis;
Dr. David Ervin and Dr. Tremblay for being members of my
committee;
Lynn Starr, Dr. Chris Carter, Dr. Thomas Quigley and Mike
Wisdom for providing primary and secondary data;
and my wife, Alexis, without whom I would never have come to
graduate school.
TABLE OF CONTENTS
CHAPTER
PAGE
INTRODUCTION
1
Problem Statement
2
Objectives
5
Justification
5
THEORETICAL CONSIDERATIONS
7
Economic Efficiency
7
Consumer Surplus
8
The Consumer's Decision Problem
9
The Theoretical Model
14
Hypotheses
19
EMPIRICAL APPLICATION
22
The Starkey Surveys
22
Survey Design and Administration
25
Potential Sources of Bias
29
Data Analysis
30
RESULTS AND IMPLICATIONS
36
The WTP Models
36
The Logit Regressions
37
The Valuation Functions
42
The WTA Models
45
The Logit Regressions
46
The Valuation Functions
49
Implications
51
Implications From the WTP
Models
53
Implications From the WTA
Models
60
V.
Conclusion
63
REFERENCES
69
APPENDICES
Appendix 1:
Data From
the Starkey
Survey
74
Appendix 2:
The Starkey
Survey
87
LIST OF TABLES
TABLE
PAGE
1
Hicksian measures of welfare change
13
2
Summary information for the Starkey
24
3
Response rates for the Starkey surveys
26
4
BId levels by group for the DC Starkey
surveys
28
5
Percentage of yes responses by group
for the DC surveys
32
6
Missing responses to valuation
questions
35
7
Results from the logit model: increased 40
cost of hunting (ACCINC)
8
Results from the logit model: increased 41
opportunity to shoot at an elk (ELKP)
9
Results from the logit model: increased 47
willingness to accept payment to forgo
hunting (HUNTP)
10
Results from the logit model:
willingness to accept payment to forgo
hunting on the Starkey (HUNTPS)
48
11
Estimated median and mean WTP and WTA
values for different income levels
59
USING VALUATION FUNCTIONS TO ESTIMATE CHANGES IN THE
QUALITY OF A RECREATIONAL EXPERIENCE: ELK HUNTING IN OREGON
I. INTRODUCTION
Hunting for deer, elk and other big game species is an
important recreational activity for many Americans.
In
Oregon, approximately 110,000 hunters engaged in elk hunting
in 1990 (ODFW).
public lands.
The majority of big game hunting occurs on
Due to the lack of a competitive market for
hunting, recreation values can only be estimated through the
analysis
of
elicitation.
related
market
Direct
goods
elicitation
or
through
using
the
direct
contingent
valuation method has become increasingly popular in valuing
fishing and hunting experiences (see Cory and Martin, 1985;
Johnson and Adams, 1989; Loomis, 1988; Mitchell and Carson,
1989)
Public
recreational
agencies
need
activities
wildlife species.
to
information
assist
on
the
in managing
value
of
fish and
For the last four years, the USDA Forest
Service and the Oregon Department of Fish and Wildlife (ODFW)
have surveyed hunters who received deer or elk tags for
hunting on the Starkey Research Forest.
These hunts take
place within a 25, 000 acre research area that is enclosed with
38 miles of cattle, deer and elk proof fence.
surveys, scheduled to continue through 1998,
with multiple objectives in mind.
The Starkey
are developed
The main objective of the
2
surveys is to obtain data with which to estimate the values
and the economic impacts attributable to deer and elk hunting.
Given
the
characteristics
of
the
Starkey
hunting
experience, these data offer a unique opportunity to value
aspects of big game hunting in Oregon.
For purposes of this
thesis, only data concerning the economic valuations of elk
are considered.
Additionally, although the surveys contain
questions specific to an analysis by both the contingent
valuation and travel
cost methods,
only
the
contingent
valuation method is considered here.
PROBLEM STATEMENT
In
1870,
the Oregon legislature passed a
prohibiting the ukilling
law
and selling of deer and elk from
February 1 to June 1" (Harper et al., 1987, p. 1).
This law,
however, lacked the enforcement necessary to prevent the near
extinction of Oregon elk by the turn of the century.
that
time,
rigorously
enforced
legislation
and
Since
active
management have resulted in the return of a healthy population
of elk and deer in the State.
Management and regulation of wildlife species is not
without cost to Oregonians.
Financing for management and
enforcement comes primarily from hunting and license fees.
In addition,
there are opportunity costs associated with
wildlife management decisions.
A reduction (or increase) in
the number of deer and elk or other wildlife species in
3
multiple use management schemes may increase (decrease) the
benefits accruing to individuals deriving utility from other
uses.
The difficult tradeoffs involved in resource management
decisions have resulted in the search for tools to aid policy
makers in the decision making process.
most often used to
alternative
analysis.
sort
resource
out
The analytical tool
the economic tradeoffs of
allocations
has
been
benefit-cost
A problem, however, is obtaining estimates of the
benefits associated with increases in fish or wildlife species
populations.
One method for obtaining net benefit estimates for non-
marketed commodities such as elk hunting is the contingent
valuation method (CVM).
CVM involves the direct elicitation
of respondents' willingness to pay (WTP) or willingness to
accept (WTA) payment for changes in the quantity or quality
of a public good or service.
These direct elicitations are
done by surveys and elicit either open-ended or closed-ended
responses. In the open-ended elicitations, respondents simply
express the maximum they are willing to pay or accept.
In the
closed-ended elicitations, respondents vote yes or no to one
or more bid offers.
The hypothetical market in the Starkey surveys is the
market for elk and deer hunting.
are:
The contingent scenarios
(1) How much would you be willing to pay (accept) to
forego hunting? and (2) How much would you be willing to pay
4
for the virtual guarantee that you would have an opportunity
to shoot at an elk?
bias in CVM surveys.
Clearly, there are potential sources of
They include hypothetical, strategic,
compliance and starting point biases.
Further discussion of
potential biases and elicitation techniques
is
found in
Chapter 3.
The current focus among contingent valuation researchers
has moved away from tests of the method's reliability toward
refinements of method (see Cameron and James, 1988; Edwards
and Anderson, 1991;)
However, anomalies such as differences
between WTP and WTA elicitations and the number of protest
responses
Hanemann
still vex researchers.
(1991)
Current research by
indicates that these anomalies should be
preserved as an integral part of the results of CVM research.
One impetus for continued research on CVM has come from
the natural resource damage assessments of the Exxon oil spill
in Prince William Sound, Alaska. Researchers such as McFadden
(1992) have raised questions about the reliability of CVM,
particularly in regards to nonuse values.
The measuring of
nonuse values such as existence value (the value of knowing
that a certain quality or quantity of a resource exists) is
currently the most controversial area of CVM research.
This
study, however, is only concerned with measuring use values.
CVM researchers often develop point estimates specific
to a particular time and site (see Sorg and Loomis, 1984;
Brookshire, Randall and Stoll, 1980).
The disadvantage of
5
point estimates is their lack of flexibility.
That is, point
estimates are specific to the values used for the explanatory
variables in the original model.
An alternative approach is
to use a valuation function (Cameron, 1988),
in which the
values of the explanatory variables in a valuation function
can be changed to obtain estimates of the corresponding
changes in WTP and WTA values.
OBJECTIVES
The primary objective of this thesis is to estimate the
use value of changes in the quality of the elk hunting
experience.
Specific
objectives
include:
deriving
(1)
valuation functions for the two WTP and WTA elicitations,
(2)
deriving estimates of the changes in WTA and WTP for changes
in
significant
explanatory
variables
and
(3)
making
suggestions for improvements in future Starkey surveys.
JUSTIFICATION
There are three reasons why the Starkey data sets are
particularly
appropriate
objectives of this thesis.
for
addressing
the
identified
First, the enclosed nature of the
Starkey Experimental Forest and the extensive biological
monitoring allow for a high level of management of the hunted
species and the hunters.
This increases the ability of
researchers to control for differences between hunts.
6
Second, the long term nature of the study (ten years)
makes it possible to analyze changes over time and to provide
additional information on the reliability of the contingent
valuation method.
With the exception of 1988 (a survey for
deer hunting was not completed) and 1989 (bid levels were left
blank
on
one
of
the
surveys)
there have been
surveys
corresponding to every hunt on the Starkey Experimental
Forest.
With two elk hunts and one deer hunt per year, at the
conclusion of ten years, surveys will have been administered
to participants in 28 hunts.
Third, hunters realize that they will be required to
provide research data as a condition for hunting within the
area.
Researchers often encounter resistance when soliciting
hunters to complete surveys.
The hunters on the Starkey,
however, choose the Starkey with the understanding that they
will provide research information.
As noted later,
this
results in a high degree of compliance with the survey
procedures.
The remainder of
chapters.
this thesis
is
divided into
four
Chapter 2 describes the theoretical framework for
the model, Chapter 3 presents the application of the framework
to the valuation of elk hunting, chapter 4 describes the
results and implications from the application of the empirical
model and Chapter 5 provides conclusions gleaned from the
study and offers suggestions for further research.
7
THEORETICAL CONSIDERATIONS
II.
It is important to understand the theoretical foundation
of CVM because
preferences.
the
technique is
not based on revealed
With CVM, a contingent scenario is constructed
and then direct money measures for the changes in welfare are
elicited.
Consumer theory is used in the construction of the
contingent scenario and the subsequent analyses.
The context
for the development of the theoretical model is established
with a brief discussion of economic efficiency.
ECONOMIC EFFICIENCY
Economists operating within the framework of economic
efficiency can provide
insight
into potential
between alternate policy choices.
tradeoffs
Pareto optimality or
economic efficiency exists when an agent cannot be made better
of f without making another agent worse off.
Varian
(1992)
and
Nicholson
(1989)
Randall (1989),
provide
detailed
discussions of the necessary conditions to achieve efficient
solutions.
In Oregon, ODFW allocates access to big game hunting by
a quota system; a quota of hunters is set for each hunt area.
Since the number of applicants typically exceeds the quota,
selection is by random drawing. All successful applicants pay
flat fees for tags and licenses. Additionally, the department
regulates bag limits and sets the length of hunting seasons.
Hunters
indicate
their
preferred
locations
in
their
8
applications, but the intensity of preference for different
hunting
locations
is
not
reflected
the
in
current
fee
structure.
In the absence of market prices, economists have devised
three methods for obtaining estimates of willingness to pay.
They are the hedonic price method, the travel cost method and
the contingent valuation method.
Whereas the travel cost and
the hedonic methods use market data to infer willingness to
pay, CVM directly elicits WTP or WTA information.
Each of
these methods can be used to estimate changes in the consumer
surplus or net benefits associated with a change in the
quantity or quality of a resource.
CONSUMER SURPLUS
Consumer surplus is a measure of the difference between
the amount a consumer actually pays for a good or service and
the maximum amount he is willing to pay.
When maximum
willingness
for a good,
to pay exceeds
the expenditure
consumer surplus can be measured by calculating the area
between the demand curve and the price line.
The
demand
consumer surplus
function most
commonly
associated with
is the Marshallian demand.
Marshallian
demands are generated by varying the price or quantity of a
good while holding money income constant.
For a normal good,
these changes in price or quantity result in an income and
substitution effect.
The substitution effect results in
9
movements along an individual's indifference curve, whereas
the
income
effect
results
indifference curve.
in
movement
to
different
a
Consequently, Marshallian measures of
consumer surplus only provide unique measures of welfare
change under the assumption of constant marginal utility of
income (Just et al., 1982).
Hicks'
(1943)
response
to
the
inadequacies
of
the
Marshallian measures of consumer surplus was to develop four
new welfare measures: equivalent surplus
(ES),
variation (Ev), compensating surplus
and compensating
variation.
(CS)
equivalent
The Hicksian measures hold utility constant and
"have been shown to be unique and ordinally related to utility
changes"
(Bergstrom,
1989:
217)
These measures can be
derived from the dual approach to consumer theory (Varian,
1992)
THE CONSUMER'S DECISION PROBLEM
The hunter's constrained optimization problem is to
maximize utility subject to a budget constraint.
His utility
function is assumed to be strictly increasing, continuous and
strictly quasi-concave (Varian, 1992).
It is also assumed
that the hunter's preferences are defined over the quantities
of G and
Q
consumed.
(1)
maxU=U(G,Q) s.t. mP*G
10
where,
U= utility
G= endogenously determined vector of market
goods
p= price vector of the market goods
Q= exogenously determined access to
environmental services
m= household income.
The
quantity of market
goods
is
considered to be
endogenously determined because the individual examines given
prices and then chooses the quantities of goods to consume.
The access to environmental services is, however, assumed to
be exogenously rationed.
The
solution
maximization
to
problem
hunter's
the
is
the
constrained
indirect
utility
utility
function
V(p,g,m). The indirect utility function describes the maximum
level of utility obtainable at price vector p, environmental
service vector q and income m.
Assuming prices are fixed, the
inverse of the utility function is the expenditure function,
(2)
ME(P,Q,U)
The expenditure function in equation two is the minimum level
of expenditure necessary to reach utility level U.
the dual of the maximization problem.
(3)
This is
Specifically,
E(P, Q, U) =min.P*G s. t. U(G, Q)
U'
If the prices of market goods are assumed to be constant and
utility is not allowed to change,
then a change in the
11
environmental variable must result in a change in the nominal
income.
This change in nominal income will be the difference
between two expenditure functions.
Of the non-market valuation techniques, CVM is the most
direct method for capturing this change in nominal income.
Specifically,
an individual could be asked how much he would
either pay or accept in order to avoid or be compensated for
a quantity or quality change in an environmental service.
The choice of which measure to use depends on the
presumed property rights.
If the respondents are assumed to
have the right to the pre-policy level of the environmental
service
(Q°),
then the appropriate welfare measure is the
Hicksian compensating measure (HC).
With compensation equal
to HC the hunter obtains utility level U° from the post-policy
level of environmental service Q1 (Randall and Hoehn, 1987).
(4)
HC(Q°,Q1,U°)=M°-E(P,Q',U°)
For an increment, the HC can be interpreted as
willingness to pay to obtain the increase in Q (WTPc).
For
a decrement, the HC can be interpreted as the willingness to
accept compensation for a reduction in access (WTAc-)
(See
Brookshire et al., 1987).
If the respondent has the right to the subsequent level
of
well-being,
the
appropriate measure
is
equivalent measure (HE), where
(5)
HE(Q°,Q',U1)=M-E(p,Q°,u1)
the Hicksian
12
With compensation equal to HE,
the hunter attains utility
level U1 at the pre-policy level of Q° (See Randall and Hoehn,
1987).
For an increment,
the HE can be interpreted as
willingness to accept compensation to
(WTAe+).
For a decrement,
forgo increased Q
the HE can be interpreted as
willingness to pay to avoid reductions in Q (WTPe-).
Table
1 further illustrates the differences between the two Hicksian
measures of welfare change.
The table shows which levels of
utility
constant,
are
being
held
and
the
level
of
environmental service that the consumer ends up with, for the
different money measures of welfare change.
13
TABLE 1. HICKS IAN MZASUP.ES OF WELFARE CHANGE
Hicksian
money
measure of
welfare
change
Reference
level of
utility
Reference
level of
environmental
services
Income
adjustment
for
increment
in services
(+)
Income
adjustment
for
decrement
in
services
(-)
compensa -
tion
measure
U0
WTPc (+)
WTAc (-)
equivalent
measure
U1
WTAe()
WTPe(-)
±errens,
14
Hicksian welfare measures are not only equivalent
or
compensating, but also surpluses or variations. With Hicksian
variation measures, it is possible for the respondent to make
optimizing adjustments. This is precluded in surplus measures
(see Brookshire et al., 1980).
THE THEORETICAL MODEL
The following function is used to explain the welfare
changes considered in this study.
Explanatory variables
include ability (ABIL), hours traveled
(DH),
(HRT),
days hunted
prior hunts on the Starkey (PRIOR), education (EDUC)
and age (AGE).
These variables capture differences in WTP due
to differences in strengths of preference between individuals.
The use of the hours travel variable in this analysis should
not be confused with its use in travel cost analyses.
Here
it is only included to control for differences in preferences
between individuals.
In travel cost models, hours traveled
data is often used to estimate the opportunity cost of time
associated with travel to
a
recreation site.
relationship between travel costs and visits
estimated.
A demand
can then be
The area under the calculated demand curve is the
estimated consumer surplus.
With CVM the consumer surplus is
gleaned directly from the respondents.
Consequently, in the
CVM context the hours travel variable is only included as a
measure of avidity.
15
Income (M) is included because for normal goods, income
is positively correlated with willingness to pay (WTP).
Q is
included because it represents the exogenous change being
valued.
(6)
WTP = f(M, ABIL, HRT, DH, PRIOR, EDUC, AGE, Q)
where,
M
ABIL
DH
HRT
PRIOR
EDUC
AGE
Q
=
=
=
=
=
=
=
=
household income
hunting ability
days spent hunting
hours traveled to the site
previous hunt on Starkey
level of education
age
quantity or quality change in
environmental service
This function can be translated into Hicksian measures
of welfare change (see Bergstrom, 1992).
First, to make the
discussion less cumbersome, six of the variables are combined
to form a vector of socioeconomic variables called S
where,
S = (EDUC, AGE, HRT, DH, ABIL, PRIOR)
Second, prices are assumed fixed and thus can be excluded from
the arguments.
The Hicksian welfare measure for willingness
to pay to avoid the loss of hunting is then
(7)
HE(Q°,Q',U')=M-E(Q°,s, V(Q',S))
where HE is the Hicksian equivalent measure, Q° is the initial
level of access to hunting, Q1 is no access to hunting for a
year and V is the indirect utility associated with S and Q.
16
The Hicksian welfare measure for willingness to pay for
an increased opportunity to harvest a deer or elk is
(8) HC(Q',Q°, U°) =M-E(Q's, V(Q°,S))
where HC is the Hicksian compensating measure,
Q°
is the
initial probability of harvesting an elk or deer and Q1 is the
increased probability of harvesting an elk or deer.
The individual's true willingness to pay is assumed to
be
an
unobservable random variable.
The
true model
corresponding to the individual hunter is
(9)
wTP1=f(x,u1)
where,
WTP = willingness to pay for the change in Q
X = vector of explanatory variables
= vector of coefficients on the
explanatory variables
u1= normally distributed error term.
Under a linear specification, the estimated model with
an open-ended elicitation technique would be WTP1 = Xt3, where
ordinary least squares could be used to regress willingness
to pay values on the explanatory variables.
However, with
dichotomous choice formats the WTP values must be inferred.
This inference can be accomplished by assuming a distribution
of the error terms and by translating the yes or no responses
into probabilities.
17
In this study, a logistic distribution of the error terms
is used for the following reasons.
First, linear probability
model can result in predictions less then 0 or greater than
1 which are outside of the range of possible probabilities.
Second,
because
the
logistic
function
is
a
close
approximation of the normal function, the normally distributed
error term assumption can be maintained (See Aldrich and
Nelson, 1987).
(10)
The estimated logistic model is
Pr(w=1) = P1 = [1 + exp(-Z(X,Tfl]1
where,
W
= 1 for a yes response and 0 for a no
P1
= probability of obtaining a yes
Z
T
X
response
= explanatory function
= bid variable
= explanatory variables.
Prior to Cameron's approach to utilizing referendum data,
researchers ran logistic regressions using "binary choice
formulations" (1988:356)
.
With this formulation, researchers
were only able to obtain measures
of central tendency.
However, because bids are varied across respondents, Cameron
demonstrates how to model this additional information into a
maximum likelihood procedure and derive WTP functions.
procedure
for
straightforward.
accomplishing
this
estimation
The
is
The parameters of the explanatory variables
from the censored logistic regression are divided by the
coefficient on the bid variable.
The logistic regression is
"censored" because the valuations are censored to be "greater
18
than or less than" a threshold value (Cameron, 1988: 359).
The bid variable and its coefficient are then excluded from
the resulting willingness to pay function.
willingness
The estimated
to pay function takes the form
(11)
WTPi=f(X)
where X is the vector of explanatory variables minus the bid
variable and
is the vector of modified coefficients.
19
HYPOTHESES
An advantage of deriving valuation functions (rather than
point estimates)
explanatory
is the ability to test hypotheses on the
variables.
Besides
formulating
hypotheses
concerning the explanatory variables, predictions are made
concerning the signs of the coefficients on the bid variables
in the logit regressions.
The following sixteen hypotheses
will be tested in this study.
The welfare changes are
measured as willingness to pay for a decrement in quality
(WTPe-) in the first five hypotheses, as willingness to pay
for an increment in quality (WTPc+) in the next six and as
willingness to accept a decrement (WTAc-) in the last four
hypotheses.
HYPOTHESES
SUPPOSITIONS
aWTPe-/aINcoME > 0
Assuming that access to elk
hunting is
a
normal good,
increased income will result in
increased willingness to pay.
awTPe-/aPRI0R > 0
If individuals have hunted on
the Starkey before,
hunting
there again indicates that they
are obtaining their preferred
choice.
This is reflected in
an increased willingness to pay
for access to hunting.
aPr(W=1)/aT
< 0
If access to hunting is a normal
good, the probability of a yes
response will
decline with
increases in price.
aWTPe-/aABIL
> 0
Hunting
ability reflects a
greater investment in developing
20
hunting skills.
Consequently,
hunting ability is positively
correlated with willingness to
pay for hunting.
awTPe-/aDH >
The more time spent hunting the
more likely a hunter is to
obtain an elk.
Consequently,
the number of days spent hunting
0
is positively correlated with
willingness to pay.
awTPc+,a INCOME >
0
Assuming
that
increased
harvesting opportunity is a
normal good, increased income
will
result
in
willingness to pay.
>
awTPc+/aPRIOR
0
increased
Because hunting on the Starkey
is
associated
with
higher
success rates, hunters' choice
to hunt on the Starkey reflects
their strength of preference for
of
higher
probabilities
harvesting a deer or elk.
apr(W=l)/ aT
<
0
If increased opportunity of
harvesting an elk is a normal
good,
the
probability
of
receiving yes responses will
decline with increases in price.
awTPc+/aHTR >
aWTPc/aDH
<
The number of hours traveled is
an indication of the intensity
of preferences for hunting.
Consequently, hours traveled is
positively
correlated
with
willingness to pay.
0
The more time spent hunting, the
likely a hunter is to
obtain an elk. Consequently, the
willingness to pay for increased
elk
numbers
is
negatively
correlated with days
spent
hunting.
0
more
awTPc/aABIL
<
0
The greater the hunting ability,
the more likely it is that a
hunter will be successful in
21
harvesting
an
elk.
Consequently, the less he is
willing to pay for increased elk
populations.
aWTAc-/aINCOME > 0
Although this relationship seems
clear for the WTP variables it
is much more ambiguous for the
willingness to accept variables.
However,
since the marginal
utility of income is highest for
those individuals with the least
income, they should also be
willing to sell for the least.
awTAc-,aPRIoR < 0
If individuals have hunted on
the Starkey before, hunting
there again indicates that they
are obtaining their preferred
choice.
This is reflected in
a positive correlation between
WTA and Prior.
aPr(w=1),aT
>
awTAc-IaABIL
The higher the bid, the greater
the probability that it will be
accepted.
0
>
0
Hunting
ability
reflects
a
greater investment in developing
hunting skills. The higher the
hunting ability the greater
amount an individual would have
to be paid to forgo hunting.
awTAc-/aDH > 0
The more time spent hunting, the
a hunter is to
more likely
obtain an elk.
Consequently,
the number of days spent hunting
is positively correlated with
willingness to accept.
22
III. EMPIRICAL APPLICATION
The preceding chapter developed the theoretical framework
for
the
empirical
model.
chapter
This
discusses
the
application of that framework to the valuation of elk hunting.
Specifically, a preview of potential problems and a context
for interpreting the results are provided through a discussion
of the survey instrument, the elicitation formats and data
characteristics.
THE STARKEY SJRVEYS
The Starkey Experimental Forest and Range, located 28
miles southwest of La Grande on the Wallowa-Jhjtman National
Forest, was established in 1940 as a Forest research area.
In 1987 researchers enclosed 25,000 acres of the Starkey with
38 miles of cattle, deer and elk proof fence.
uto
test animal response to various
This was done
forest,
range,
and
recreational activities, and to changes in habitat caused by
those activities
N
(USFS - ODFW, 1989).
As part of this research effort, controlled hunts of elk
and deer are conducted within the enclosure.
The hunts are
used by researchers to change the size and composition of deer
and elk populations on the Starkey and to examine how deer and
elk respond to hunting pressure.
The hunters are thus an
integral part of the research effort.
A total of ten surveys from eleven hunts
(the two
antlerless 1989 elk hunts were combined and are considered as
23
one hunt) have been conducted by the Oregon Department of Fish
and Wildlife (ODFW) and the U.S. Forest Service (USFS).
of these ten hunts were elk hunts (see table 1).
Seven
Because of
the small sample sizes for the deer hunts, only elk hunts are
considered here.
24
TABLE 2. SU)*ARY INFORMATION FOR THE STARKEYa
HUNT
1988
1988
1988
1989
1989
1989
1989
1990
1990
1990
1991
1991
1991
a
b
DATE
Either-Sex
Bull Elk
Bull Elk
Bull Elkb
Either-Sex
Antlerless
Antlerless
Bull Elk
Buck Deer
Antlerless
Bull Elk
Buck Deer
Antlerless
Deerb
Deer
Elk
Elk
Elk
Elk
Oct
Oct
Nov
Sept
Sept
Dec
Dec
Aug
Sept
Dec
Aug
Sept
HUNTERS(No.) HARVEST(No.)
1-Oct 12
30
5-Nov 13
26 - Oct
2 - Sept 10
30 - Oct 11
9-Dec 15
30 - Jan
18 - Aug
29 - Oct
1-Dec
17 - Aug
28 - Oct
Nov 30-Dec
7
26
10
7
25
4
6
118
98
99
10
87
63
28
172
25
92
152
24
132
The above statistics were supplied by Mike Wisdom; U.S.
Forest Service - La Grande
No survey data were collected for this hunt.
82
50
49
3
61
33
9
51
8
61
45
9
64
25
SURVEY DESIGN AND ADMINISTPATION
All of the Starkey survey instruments were developed and
administered by the U.S.
Forest Service and the Oregon
Department of Fish and Wildlife Service and included WTPe,
WTPc and WTAc questions.
The surveys were mailed to all
individuals who obtained elk tags for hunting on the Starkey.
Surveys were also available at the entrance to the Starkey
Forest.
Individuals then deposited their completed surveys
at the entrance to the controlled hunt area.
Additional
survey forms were also available so that individuals who did
not bring their surveys with them could fill them out before
beginning their hunt. A total of ten hunts were surveyed over
a four year period.
Response rates to the ten hunts surveyed
are listed in Table 2.
26
TABLE 3. RESPONSE RATES FOR THE STARKEY StRVEYS
Survey
1
2
3
4
6
5
7
8
10
9
Elk Elk Elk Deer Elk Elk Deer Elk Elk Deer
1988 1988 1989 1989 1990 1990 1990 1991 1991 1991
(Oct) (Nov) (Dec)
Hunters
98
99
91
(Aug) (Dec)
87
172
92
2
(Aug)
(Dec)
152
132
24
(No.)
Observ.a 85
86
42
74
149
82
23
Response 87
Rate
87
46
85
87
89
92
144
95
109
20
83
83
(%)
Total
:
814/972 = 84%
a Returned surveys with a response to at least one question.
27
The two surveys administered in 1988 differ from each
other and from subsequent surveys. One difference between the
1988 and 1989 questionnaires was the replacement of the "openended't valuation questions with "iterative bidding" questions.
The only difference between subsequent surveys (post 1988) is
the level of the bid within each season.
reported in table 4.
The bids are
28
TABLE 4. BID LEVELS BY GROUP FOR THE DC STAREEY SURVEYSa
ype of Hunt
Group A
($)
Deer
Elk
25
50
Group B
($)
50
100
a DC refers to dichotomous choice.
Group
($)
100
250
C
Group D
($)
200
500
29
POTENTIAL SOT3RCES OF BIAS
As discussed in Chapter 1, there are several potential
sources of bias in contingent valuation.
With open-ended
questions, respondents may attempt to influence the outcome
of the survey,
valuation.
thus introducing strategic bias into the
In
addition,
respondents
frequently
have
difficulty in assigning values to commodities for which they
have limited points of reference, i.e., no previous experience
in valuing the commodity in question.
This can lead to
increased numbers of nonresponses and\or unreliable responses.
The dichotomous choice method used in the post-1989
surveys was designed to provide a more realistic contingent
market scenario and to avoid strategic bias. Respondents may,
however,
feel
compelled
to
"comply
with
the
presumed
expectation of the sponsor" (Mitchell and Carson, 1989: 236).
Another potential problem with the referendum method,
when compared with the iterated referendum method,
is its
inefficiency. The iterated referendum method has the potential
for reducing the size of the variance in the valuation
estimates (Mitchell and Carson,
1989:
103).
The iterated
responses are, however, still susceptible to compliance bias.
Additionally, there is the possibility of starting point bias
if an individual's response to the second bid is influenced
by his response to the first bid.
By their nature, contingent
valuation analyses also have the potential for hypothetical
bias.
30
One reason to expect less bias in the Starkey surveys is
that the hunters are informed prior to the hunt that they are
expected to participate in the Starkey research program.
Hunters surveyed without such prior information might be more
inclined towards dismissing the surveys or responding with
less care.
The high response rates on the Starkey surveys (average
of 84 percent) reduces the potential for sampling bias such
as the self-censoring of nonrespondents (see Edwards and
Anderson, 1987). Edwards and Anderson also discuss the
potential for bias due to the censoring of protest responses
(p. 168)
There is a potential for this type of bias in the
Starkey surveys.
Although the written responses of
individuals on the open-ended elicitations were recorded,
unusually high bids were censored, and none of the surveys
provide a means to record protest responses.
DATA ANALYSIS
Data from the 1989, 1990 and 1991 hunts are combined to
create one elk data set. Durmny variables corresponding to
each hunt were then utilized to control for differences
between hunts. Four of the seven elk hunts (1988 hunt 1, 1988
hunt 2, 1990 hunt 1, 1991 hunt 1) were for bull (antlered) elk
and the other three were for antlerless elk. Additionally,
dummy variables were used to test for differences in
willingness to pay associated with differences between years.
31
No significant differences were found.
Consequently these
variables were not included in the final model.
Because the open-ended elicitation format used in 1988
differed from the iterative bid format used in 1989, 1990 and
1991,
data from the 1988 surveys are not pooled.
The
iterative bid format used in the Starkey Survey is referred
to by Mitchell and Carson as "the take-it-or-leave-it with
follow up" approach (1989: 98).
Only the take-it-or-leave-it
portion of the survey is considered here.
Table 4 shows how
individuals responded to the dichotomous choice questions on
the elk surveys.
32
TABLE 5. PERCENTAGE OF YES RESPONSES BY GROUP FOR THE DC
SURVEYSa
Welfare
Measure
Group A
Group B
Group C
(%W=1 T=50) (W=1 T=100) (%W=1 T=200)
(1989-90-91) (1989-90-91) (1989-90-91)
Group D
(W=1 T=500)
(1989-90-91)
ACC INC
(WTPe-)
63 . 0
49.6
28.5
19.0
ELKP
(WTPc)
63.6
41.5
21.7
7.5
prjNTpb
0.0
1.8
11.3
17.3
5.1
18 . 0
30.2
36.1
(WTAc)
HUNTPS
(WTAc)
a
b
Percentage of yes responses from the total of yes and no
responses.
Missing responses are not included in the
total. DC refers to dichotomous choice.
Data for 1989 are missing for this variable.
Where:
ACC INC =
ELKP=
HtJNTP=
HIJNTPS =
Yes or no response on bid amount, 1=yes
Bid amount
Willingness to pay to avoid not hunting
Willingness
to
for
pay
increased
opportunity to harvest an elk.
Willingness to accept payment to give up
hunting for the year.
Willingness to accept payment to give tp
hunting at the Starkey for a year.
33
The correlations between the percentage of yes responses
to the WTP and WTA questions and the size of the bids is
consistent with economic
theory
respectfully) for normal goods.
(positive and negative,
In addition, the respondents
are differentiating between what they are willing to pay for
hunting Starkey and hunting in general.
The data in table 4 also indicate a problem with the bid
structure used for the WTA and WTP variables.
For the WTA
variables, the range of bids only captures a small percentage
of the potential bids.
This is particularly apparent for the
HUNTP variable where no positive responses were received for
the lowest bid and the highest bid results in only 17 percent
positive responses.
The reduction in the number of missing responses between
the 1988 and later surveys
(see table 5)
indicates that
changing the format had the desired effect.
however, two cautions that need to be considered.
valuation questions on
the
1988
There are,
First, the
open-ended surveys
are
different than the ones on the dichotomous choice survey.
Second, there was no place on the dichotomous choice survey
to indicate a protest response.
Although it is not possible
to isolate the protest responses from other responses on the
open-ended
surveys,
some
respondents
to
the
open-ended
questionnaire listed "not for sale" as their response. A "not
for sale" category is often included in the possible responses
to valuation questions on CVM surveys.
In future surveys,
34
including this category may provide useful information about
the extent and nature of the protest vote.
35
TABLE 6. MISSING RESPONSES TO VALUATION QUESTIONS
SURVEY
1
2
3
Elk
1988
Elk
1988
Elk
1989
(Oct)
(Nov)
(Dec)
4
Elk
1990
5
Elk
1990
6
Elk
199].
(Aug)
(Aug)
7
Elk
1991
(Dec)
VARIABLE
a
ACCINC
(%)
ELKP OR 47b
HUNTP
24.7b
(%)
HUNTPS
a
454b
70b
430b
360b
14.3
4.0
7.3
7.6
6.4
9.5
3.3
3.3
1.2
1.2
4.2
3.5
3.7
5.5
4.7
1.2
3.5
4.6
a
(%)
a This information was not collected for this
data set.
b
Outliers are included in the missing response
categories.
36
IV. RESULTS AND IMPLICATIONS
This chapter begins with a discussion of the results
from the logistic regressions and benefit functions,
and
concludes with some methodological and policy implications.
The results from the willingness to pay variables (ACCINC and
and the willingness to accept variables
ELKP)
HUNTPS) are discussed separately.
(HtJNTP and
This separation of the
discussion between the two elicitation formats (WTP and WTA)
facilitates the interpretation of the signs on the estimated
coefficients from the logit models.
THE WTP MODELS
The question associated with the ACCINC variable reads:
Would you choose not to hunt at all in 1990 if total
costs increased by
?
The results of the Starkey surveys are interpreted as if the
question had been,
Would you choose to hunt at all in 1990 if total costs
increased by
?
By making this change, the responses can be discussed in the
traditional willingness to pay fashion.
A yes response thus
corresponds to an individual's willingness to pay the bid
amount.
The question associated with the ELKP variable reads,
If the number of animals were sufficient to make it
virtually certain that you would have an opportunity to
shoot at an elk/deer, would you be willing to pay
additional to hunt?
37
The Logit Regressions
Tables 6 and 7 present model specifications, goodness of
fit measures, sample sizes and significance measures for the
two willingness to pay logit regressions.
The logarithmic
specification used for the ACCINC and ELKP models has the
advantage of allowing only positive willingness to pay values.
Theoretical
support
for
the
use
of
the
logarithmic
specifications is provided by Johansson, Kristoom and Maler
and Bowker and Stoll
(1989)
who argue that the
(1988)
logarithmic specification can be considered a first-order
approximation to a well-behaved indirect utility function
(Park et. al., 1991).
Following Learner (1983) different specifications can be
examined to test the robustness of the coefficients in the
models.
Learner advocates testing the robustness of a model
by seeing if the results change when different specifications
are attempted.
robust,
with
The ACCINC and ELKP model specifications are
the
only
sign
change
occurring
for
the
coefficient on the AD2 variable in the ELKP model.
The
robustness
of
both models
is
also
reflected
in
their
respective goodness of fit measures.
The McFadden R2 of .13
and the percentage of correct
predictions of 74 for the ACCINC model are similar to those
obtained in related studies.
Creel
For example, Park, Loomis and
(1991) obtained a Mcfadden R2
of
.13 and a percent
correct predictions of 71 with a similar logit model
(p. 68).
38
Both models use the Hicksian equivalent surplus welfare
measure, assume a logistic distribution of errors and use a
logarithmic model specification.
The goodness of fit statistics for the ELKP model are
better than those for the ACCINC model and related Park, Creel
and Loomis model.
A McFadden R2 of .12 and a percent of
correct predictions of 63 were obtained in the Park study,
whereas the ELKP model has a McFadden R2 of
percentage of correct predictions of 81.
.22
and a
The difference in
the goodness of fit statistics between the ACCINC and ELKP
models reflects the more appropriate bid structure used for
the ELKP question (see table 4).
Besides the measures of goodness of fit and robustness,
the sample sizes and likelihood ratio tests provide evidence
of the statistical properties of the two models.
The sample
size of 401 for the ACCINC model and of 413 for the ELKP model
fall within the range of sample sizes in related studies
(see
Whitehead and Blomquist, 1991; Park, Loomis and Creel, 1991).
Current research by Cameron and Huppert
(1991)
evidence that the results from censored
regressions are
provides
particularly sensitive to the size of the sample.
The likelihood ratio test (comparable to the F test for
OLS models) indicates that the coefficients of the explanatory
variables are different from zero at the .001 level of
39
significance for both models.
The coefficients on the bid
variables are also significant at the 99 percent confidence
level.
40
TABLE 7. RESULTS FROM THE LOGIT MODEL:
INCREASED COST OF HUNTING (ACCINC)
ESTIMATED COEFFICIENTSa
VARIABLES
Intercept
34062b (.9076)
Log of the Bid (LNT)
_.8651b (.1337)
Income (DY1)
.6925b (.2546)
Log of Hours Traveled
3245b (.1467)
(LNHTR)
Ability 1 (AD1)
.1549 (.3137)
Ability 2 (AD2)
.8432 (.4518)
Log of Days Hunt (LNDH)
-.1865 (.2861)
Prior Hunt (DPHS)
-.0971 (.2423)
Age (DAGE)
.1000 (.2536)
Education (DEDUC)
.0422 (.2484)
Hunt 2 1989 (HUNE89)
-.1012 (.4536)
Hunt 1 1990 (HtJNEA9O)
-.4555 (.3075)
Hunt 2 1990 (HUNE9O)
-.6016 (.3623)
Hunt 2 1991 (HtJNE91)
-.3759 (.3308)
401
Likelihood Ratio Teste
Percent Correct Pred.
McFadden R2
73
74
.13
a Asymptotic standard errors in parentheses.
Asymptotically significant at the 95 percent confidence
b
level.
C
The likelihood ratio test indicates that the
coefficients are significant at the 99 percent
confidence level.
41
TABLE 8 RESULTS FROM THE LOGIT MODEL:
INCREASED OPPORTUNITY TO SHOOT AT AN ELK (ELKP)
VARIABLES
ESTIMATED COEFFICIENTSa
Intercept
59998b (1.0270)
_13522b (.1593)
.7683b (.2771)
.2370 (.1593)
Log of the Bid (LNT)
Income (DY1)
Log of Hours Traveled
(LNHTR)
Ability 1 (AD1)
Ability 2
.2940 (.3430)
.9512 (.4848)
_6404b (.3086)
-.2452 (.2614)
.3273 (.2778)
.0776 (.2678)
-.5550 (.4949)
.0949 (.3296)
-.7267 (.3943)
-.5598 (.3646)
(AD2)
Log of Days Hunt (LNDH)
Prior Hunt (DPHS)
Age (DAGE)
Education (DEDUC)
Hunt 2 1989 (HUNE89)
Hunt 1 1990 (HUNEA9O)
Hunt 2 1990 (HUNE9O)
Hunt 2 1991 (HtJNE91)
413
Likelihood Ratio Testc 118
Percent Correct Pred. 81
McFadden R2
.22
a Asymptotic standard errors in parenthesis.
b
Asymptotically significant at the 95 percent confidence
level.
The likelihood ratio test indicates that the
coefficients are significant at the 99% confidence
level.
42
The negative signs on the coefficients of both bid
variables conforms to the expectation that the probability of
a yes response decreases
(increases)
as the price
(bid)
willingness
to pay
increases (decreases).
The Valuation Functions
The logit equations are rescaled into
functions by multiplying the constant term and all the slope
parameters (except for the coefficient on the bid variable)
1988),
by k (Cameron,
where:
The
k=-1/a and
a=the coefficient on the bid variable
willingness
to pay functions for the variables ACCINC and
ELKP are,
ACCINC:
(12) 1nWTP= 3.94 + .80(DY1) + .37(LNHTR) + .18(AD1) +
(.72)*
(.31)*
.97(AD2)
(.54)
.05(DEDUC)
(.29)
(.17)*
(.36)
-.21(LNDH) - .11(DPHS) + .12(DAGE)
(.28)
(.29)
(.33)
- .12(HUNE89) -.69(HUNE9O) (.52)
(.42)
.53(HTJNEA9O) -.43(HUNE91)
(.36)
(.38)
and,
ELKP:
(13) 1nWTP= 4.44 + .57(DY1) + 17(LNHTR) + .22(AD1) +
(.48)*
(.21)*
(.12)
(.25)
.70(AD2) - .47(LNDH) - .18(DPHS) + .24(DAGE) +
(.36)
(.23)
(.19)
(.20)
.06(DEDUC)
-.41(HtJNE89) + .07(HTJNEA9O) (.20)
(.37)
(.24)
.54(HtJNE9O) -.41(HUNE91)
(.29)
(.27)
43
where the standard errors are in parentheses and * denotes
significance at the .05 level.
The anti-logs of the fitted values are medians (Cameron,
1988).
The average of these medians is $113 for the ACCINC
model and $90 for the ELKP model.
Because
the
valuation
functions are geometric, measures such as the arithmetic mean
are inappropriate.
Arithmetic means are the expected values
of linear functions. Consequently, there is no reason to
presume that the expected value of any other functional form
will correspond to the arithmetic mean.
Both Cameron (1988)
and Hanemann (1984) discuss the use of scaling factors that
can be used to find the expected values of
functions with logistic error distributions.
log-linear
The equations
used by Cameron and Hanemann are,
(14)
where:
F(]. - k)r(1 + k)=(fl/a)/(sin(U/a))
k = -1/a
a = coefficient on the bid variable.
From the scaling factor equations in (14)
it is clear
that the absolute value of the coefficient on the bid variable
must be greater than one for the scaling factor to be defined.
Because the coefficient on the bid variable for the ACCINC
model is - .8651 and the coefficient on the bid variable for
the ELKP model is -1.3522, the scaling factor is only defined
for the ELKP model. The mean of the fitted values for the ELKP
model is then estimated by first multiplying the scaling
44
factor times the fitted values and then dividing the sum of
these values by the sample size. The resulting mean value of
$287 is more than 3 times the value of the calculated median
and is
clear evidence
of
a
skewed distribution of
the
willingness to pay values. The skewed distribution of the WTP
values is consistent with the choice of a log-logistic model.
The standard errors in equations
(12)
and
(13)
were
derived using the following Taylor series approximation,
var(P) =[yj/a2] var(a) +(-1/a] var(yj)
(15)
+ 2[y/a2]
a
where:
[-1/a] COV(U,yj)
= coefficient on the bid variable and
= coefficient on the explanatory variable
The variable which is most significant in the ACCINC and
ELKP models
is
the
income dummy variable
(DY1).
The
coefficients on the DY1 variable are significant at the 99
percent confidence level in both models.
The income question on the Starkey surveys includes six
categories.
These
categories,
varied by increments
of
$10,000, are u<10,000,N "10,000 - 19,999," "20,000 - 29,999,"
"30,000 -39,999," "40,000 - 49,999" and ">50,000."
Although
there are only 23 responses on the low end (<10,000), there
are 119
responses on the high end
(>50,000).
Clearly,
additional variation could have been captured by increasing
the number of categories on the high end.
The dummy variable,
45
DY1 represents income above $29,999.
The positive sign on the
coefficients of this variable indicates that willingness to
pay increases (decreases) with increases (decreases) in an
individual's income,
as expected.
The other significant
variable in the ACCINC valuation function is LNHTR (log of
hours traveled to the Starkey).
The positive correlation of
this variable with willingness to pay indicates a higher
preference
for elk hunting among those
traveled a greater distance.
individuals who
A low correlation coefficient
(.05) between the DY1 and LNHTR indicates that this result is
not due to collinearity.
LNDH
(log of the expected number of days that the
respondent will spend elk hunting) is also significant in the
ELKP valuation function.
The negative correlation of this
variable with WTP indicates a lower preference for increased
elk numbers among individuals who spend more days elk hunting.
Time spent hunting is a substitute for elk numbers (i.e. the
hunter with more time can "produce' his own elk).
THE WTA MODELS
There are two WTA questions on the survey. The first is:
If you could still hunt
somewhere else during the
general season, would you accept a payment of
from someone else and give up your hunting privilege on
Starkey this year?
The second WTA question is:
If someone would pay you to completely give up your
hunting privilege this year (Starkey AND elsewhere),
would you give it up for
?
46
The Logit Regress ions
Tables
9
and
10
summarize
the
results
from
the
willingness to accept logit regressions. Although willingness
to accept valuation questions are less common than willingness
to pay questions, Brookshire, Randall and Stoll (1980) use a
WTA format to estimate values associated with changes in the
quality of elk hunting experiences.
In comparing the observed
WTA values against WTA values derived from the WTP values,
they found that observed WTA values were
significantly
different from and up to an order of magnitude greater than,
derived WTAN (p. 487).
Such a divergence between WTA and WTP occurs in the
Starkey models.
However, because the bid amounts for the
Starkey WTP and WTA questions were the same, the bid levels
were too low to capture most of the variation (see table 4).
The lack of positive responses reduces the usefulness of
percent correct predictions as a measure of goodness of fit.
The
Mcfadden R2
(.23 and
.16) measurements compare
favorably with those found by Park, Creel and Loomis and
Whitehead and Blomquist (.12 and .13).
The coefficients on
both the HUNTP and HtJNTPS models are also significantly
different from zero at the 99 percent confidence level.
Additionally, all the coefficients are robust, with no sign
changes occurring for different specifications.
47
TABLE 9. RESTJTJTS FROM THE LOGIT MODEL:
WILLINGNESS TO ACCEPT PAYMENT TO FORGO HUNTING (HUNTP)
ESTIMATED COEFFICIENTSa
VARIABLES
_83282b (2.0713)
Intercept
15847b (.3564)
Log of the Bid (LNT)
.4956 (.4907)
Income (DY1)
Log of Hours Traveled
-.1257 (.2542)
(LNHTR)
Ability 1 (AD1)
-.6302 (.4752)
Ability 2 (AD2)
-.2236 (.8789)
_13g55b (.5181)
Log of Days Hunt (LNDH)
Prior Hunt (DPHS)
-.9248 (.4969)
Age (DAGE)
.6927 (.4982)
Education (DEDUC)
-.1138 (.4568)
Hunt 1 1990 (HUNEA9O)
-.4639 (.5230)
Hunt 2 1990 (HUNE9O)
-1.1596 (.6898)
Hunt 2 1991 (HUNE91)
-.7132 (.6446)
Likelihood Ratio Testc
Percent Correct Pred.
McFadden R2.
a
378
48
85
.23
Asymptotic standard errors in parenthesis.
b Asymptotically significant at the 95 percent confidence
level.
C
The likelihood ratio test indicates that the
coefficients are significant at the 99 percent
confidence level.
48
TABLE 10. RESULTS FROM THE LOGIT MODEL:
WILLINGNESS TO ACCEPT PAYMENT TO FORGO
HUNTING ON THE STARZEY (HtINTPS)
ESTIMATED COEFFICIENTS
VARIABLES
_37573b (1.0273)
9486b (.1639)
.4758 (.2985)
_3331b (.1636)
Intercept
Log of the Bid (LNT)
Income (DY1)
Log of Hours Traveled
(LNHTR)
Ability 1 (AD1)
Ability 2
-.3823 (.3278)
-.2100 (.5483)
_7847b (.3077)
-.3846 (.2874)
-.3737 (.2883)
.1449 (.2854)
(AD2)
Log of Days Hunt (LNDH)
Prior Hunt (DPHS)
Age (DAGE)
Education (DEDtJC)
Hunt 2 1989 (HtJNE89)
Hunt 1 1990 (HUNEA9O)
Hunt 2 1990 (HEJNE9O)
Hunt 2 1991 (HtJNE91)
Likelihood Ratio Testc
Percent Correct Pred.
McFadden R2
_1.4522b (.6898)
-.6784 (.3490)
-.6589 (.4053)
-.3623 (.3773)
413
70
77
.16
a Asymptotic standard errors in parenthesis.
ID Asymptotically significant at the 95 percent confidence
level.
The likelihood ratio test indicates that the
coefficients are significant at the 99 percent
confidence level.
49
The Valuation Functions
The
valuation
functions
for
the
HIJNTP
and HUNTPS
variables are,
HUNTP:
1nWTA= 5.25 - .31(DY1) + .08(LNHTR) + .40(AD1) +
(.63)*
(.31)
(.16)
(.31)
.14(AD2)
.88(LNDH) + .58(DPHS) - .44(DAGE) +
(.56)
(33)*
(.33)
+ .29(HUNEA9O)
.07(DEDtJC)
(.29)
.45(HtJNE91)
+
(.32)
.73(HtJNE9O)
(.44)
(.33)
+
(.42)
and,
HtJNTPS:
1nWTA= 3.96 - .50(DY1) + .35(LNHTR) + .40(AD1) +
(.69)*
(.32)
(.18)
(.35)
.22(AD2)
+ .83(LNDH) + .40(DPHS) +
(34)*
(.58)
.39 (DAGE)
(.31)
- .15 (DEDUC)
(.30)
+
(.31)
.72 (HUNEA9 0)
(.38)
+
1.53(HUNE89) + .69(HUNE9O) + .40(HtJNE91)
(.76)*
(.44)
(.40)
where the standard errors are in parentheses and * denotes
significance at the .05 level.
The median for HUNTP is approximately $1,634, and for
HUNTPS it is approximately $1,105.
The median for HUNTP is
more than fifteen times the median value for ACCINC. Although
this divergence appears high, Mitchell and Carson (1989)
report divergences of 1000 percent in some CVM studies.
The mean of the fitted values of the HUNTP model can be
determined because the bid coefficient of 1.6 is greater than
1
(Hanemann,
1984).
The HUNTP mean of
indicates that this distribution is skewed.
$3,529
clearly
50
Because the coefficient on the bid variable for the
HUNTPS model is less than 1, the mean value is undefined.
relative ranking of these WTA values is as expected.
words,
The
In other
the lower median value of $1,105 for HUNTPS model
conforms to the expectation that individuals would sell their
Starkey-specific recreational experience for less than they
would sell their right to hunt.
The coefficient on the variable LNDH (log of days hunted)
is significant at the 99 percent level.
The days hunted
variable reflects the number of days an individual plans to
hunt in a given year.
The coefficient on this variable
indicates that the more days an individual hunts, the more he
will have to be paid to forgo this experience.
the coefficient on the HTJNE89
Additionally,
(1989 antlerless elk hunt)
variable is significant at the 95 percent level in the HtJNTPS
valuation equation (17). The positive correlation between the
HUNTPS coefficient and WTA indicates that participants in this
hunt were less willing to sell their right to hunt on the
Starkey then participants in other hunts.
Interestingly, income, which is significant in both of
the WTP models,
is not significant in the WTA models.
Hunters are bound by their income constraints when responding
to the willingness to pay question but not when responding to
the willingness to accept question.
At the extreme, a hunter
with a $1 income could pay a maximum of $1 but could accept
any amount.
5]-
Hanemann (1991) has also shown that for valuations of
quantity
or
quality
changes
the
WTA values
substitution as well as an income effect.
reflect
a
Thus, Hanemann
explains large differences between the WTP and WTA values by
arguing that the "private-market goods available in their
choice set are collectively a rather imperfect substitute for
the public good under consideration" (p. 646).
IMPLICATIONS
The
research
findings
reported
methodological and policy implications.
are
four
policy
implications
and
above
have
Specifically, there
three
methodological
implications that may be gleaned from this research.
methodological implications are:
Demonstrating the flexibility of WTP functions over
point estimates.
Confirming the importance of using unambiguous and
specific wording when specifying the attributes of
the commodity being valued.
Confirming the importance of using a pretest to
establish the initial ranges and number of bids in
dichotomous choice CVM survey.
both
The
52
The policy implications are:
The hunting recreational experience is providing
substantial
benefits
conservative estimate
to
hunters.
Starkey
(using
A
the median fitted
value from the ACCINC model) of the net benefits
per hunter is $113.
Starkey hunters are willing to pay additional costs
to increase the elk herd to the point where they
are virtually guaranteed a shot at an elk.
estimated value for this increment
The
in quality,
using the mean of the fitted values from the ELKP
model, is $287 per hunter.
The willingness to pay values are affected by the
size of the respondents'
incomes.
For example,
hunters who fall within the income category
29,999) have a mean fitted value
(0-
(for the ELKP
variable) of $179, and hunters with higher incomes
have a mean fitted value of $345.
Starkey hunters object to the tradeoff between their
right to hunt and private goods.
The apparent objection
to this tradeoff could result in opposition to any
efforts to privatize this resource.
53
The following discussion attempts to integrate both policy and
methodological implications. Additionally, because the policy
implications from the WTP and WTA models differ, the original
separation
between
these
two
elicitation
formats
is
maintained.
Implications From the WTP Models
The importance of using specific language in describing
the
contingent
scenario
is
illustrated by the problems
encountered in the interpretation and modeling of the ACCINC
data.
Because the ACCINC question
includes
the
general
category "hunting" instead of the more specific "elk hunting,"
it is difficult to identify the attributes of the commodity
being valued.
For example, some hunters may have answered the
ACCINC question as if it referred only to elk hunting, while
others may have thought it referred to their entire bundle of
hunting experiences.
Placing a value on the entire bundle of
hunting experiences is a much more complicated exercise than
valuing a specific hunting experience.
In contrast, the ELKP
question specifically mentions elk hunting.
The difficulty experienced by hunters in formulating a
response to the ACCINC question is reflected in the percent
of missing values (see table 5).
The proportion of missing
values for ACCINC is 11 percent, whereas for the ELKP variable
it is only 4 percent.
The goodness of fit statistics for the
ACCINC model are also the lowest of all the models.
54
Another methodological implication of this study is the
importance of carefully choosing the number and range of bids
Ideally, the range and
on dichotomous choice CVM studies.
nunther of the bids is structured so that it captures most of
the variation in valuation estimates.
Specifically, the more
variation that is captured between the lowest and highest bid
levels, the greater the explanatory power of the model.
In this study, only four dichotomous bids are used and
One result of this choice of
the range is from $50 to $500.
bids is that 19 percent of the respondents voted for the
highest bid level. Boyle and Bishop
experienced a
(1988)
similar problem in their CVM analysis of boaters in Wisconsin.
They concluded that the initial selection of bid ranges Hcan
affect final value estimates
(p. 25).
pretesting
avoid
as
a
way
to
inappropriateness of bid ranges.
They go on to suggest
problems
with
the
With the ELKP variable,
only 7 percent of the respondents voted for the $500 bid
level.
Problems with the appropriateness of the bid ranges and
ambiguous wording in the ACCINC question are reflected in the
lack of a defined mean for the fitted values.
Hanemann (1984)
suggests that the median is an appropriate measure of net
willingness to pay because of its robustness and lack of
sensitivity to outliers. McFadden (1992), however, points out
that the median is a biased estimator of central tendency when
the distribution of WTP estimates is not symmetric.
McFadden
55
further argues that most CVM studies result in nonsymmetric
estimates of WTP and consequently have median values that are
downwardly biased estimates of the true mean.
In this context
the median may be viewed as a conservative estimate of the
current net economic use value of elk hunting.
However,
serious problems with the bid structure and a
lack of
specificity in the contingent scenario reduce the usefulness
of the $113 figure.
McFadden's argument that the medians are downwardly
biased estimates of the true mean is supported by the CVM
studies where the means are approximately 100 percent larger
then the medians (see Boyle and Bishop, 1988; Kahneman and
Knetsch, 1991).
A large difference between the mean and the
median is also found in a study by Loomis, Cooper and Allen
(1988).
The Loomis, Cooper and Allen study is similar to
this study in that it assumes a logistic distribution of the
error terms, uses a logaritbmic functional model specification
and elicits values for elk hunting.
The estimated mean for
the Loomis, Cooper and Allen "ELKP-type model"
larger than the median.
difference
This
compares
used
closely to the
(3.2 times larger) between the mean and median
fitted values obtained for the ELKP model.
is
is 3.1 times
only
to
illustrate
the
This comparison
similarity
in
skewed
distributions of WTP estimates, not to compare the valuation
estimates.
56
A comparison between
the valuations
studies
in
is
complicated by differences in the wording of the questions,
the choice of welfare measure, the method used to calculate
net willingness to pay estimates, and the sample population.
For this study, the sample population is hunters who received
elk tags
specific
a controlled hunt
to
at
the Starkey
The 38 miles of elk proof
Experimental Forest.
fence
surrounding the area and an average success rate of 43 percent
(1988-1990) illustrate the unique nature of this hunt.
The
average success rate for elk hunting in Oregon is 16 percent
(1988-1990)
Although both WTP
were
questions
not
specifically
targeted at the Starkey, hunters who choose the Starkey on
their applications may not be statistically representative of
hunters
who
choose
other
areas.
Additionally,
the
respondents' high expectations for their hunting experience
on
Starkey could bias
the
their valuation estimates.
Consequently, it is preferable to obtain estimates specific
to a particular area.
The
transferability
contingent
method
Cameron
of
the
valuations.
does,
results
Unlike
however,
from
increase
dichotomous
studies
where
the
choice
numerical
integration techniques are used to derive point estimates of
net willingness to pay, the Cameron method can be used to
derive
functions
from the
logistic
regressions.
These
functions can then be transferred to other areas where new
57
location-specific values
explanatory variables.
can be used as values
for the
Using a function instead of point
estimates makes it possible to control for differences in
hunter attributes such as income.
Benefit transfer studies,
such as the one by Sorg and Loomis (1984), typically do not
adjust estimated values for differences in hunter incomes
across samples.
Although the $287 mean of the fitted values for the ELKP
variable is not as transferable as the function, this point
estimate
does
have
policy
implications.
Clearly,
the
willingness (at least among Starkey hunters) to pay additional
money for the increased opportunity to shoot at an elk
indicates a preference for increased elk numbers.
The $287
figure thus gives managers and decision makers an idea of the
magnitude of the preferences for increases in the elk herd
size.
Another policy implication of this research is that
equity concerns should be considered when pursuing revenue
enhancing policies such as increases in elk tag fees.
policy
implication
follows
from the
sensitivity of
This
net
willingness to pay values to income levels.
To estimate the importance of the income explanatory
variable in explaining net willingness to pay, the data set
was split into two subsets.
The first subset includes only
those observations where respondents have incomes lower than
$29,999; the second subset includes only those individuals
58
with incomes above $29,999.
The valuation functions are then
used to derive the average medians and means of the fitted
values for the subsets.
Table 10 shows that the median and
mean of the fitted values for the ELKP model almost double
between income categories.
Additionally, the median for the
ACCINC variable more than doubles between income categories.
The differences in willingness to pay for different
income levels indicates that the population of Starkey hunters
is heterogenous in regard to income. The heterogeneity of the
population of Starkey hunters is also demonstrated by the
significance of the hours travel variable in the ACCINC model
and days hunt variable in the ELKP model.
The coefficient of
.37 on the LNHTR variable in the ACCINC equation (12), and the
coefficient of .47 percent on the LNDH variable in the ELKP
equation (13), can be interpreted as elasticities. Thus, both
LNHTR and LNDH are inelastic with a 1 percent change in LNHTR
(LNDH), resulting in an estimated .37 percent (.47 percent)
change in the respondents' willingness to pay.
59
TABLE 11. ESTIMITED MEDIAN AND MEAN WTP AND WTA
VALUES FOR DIFFERENT INCOME LEVELS
INCOME
LEVELS
AVERAGE
OF FITTED
MEDIAN
VALUES
ACCINC
AVERAGE
OF FITTED
MEDIAN
VALUES
ELKP
($)
($)
AVERAGE
OF FITTED
MEAN
VALUES
ELKP
($)
Income
All
113
90
287
Income
58
56
179
141
108
345
(0-29, 999)
Income
(>29,999
60
Implications From the WTA Models
CVM researchers have long struggled with the size of
values generated from WTA models (see Mitchell and Carson,
1989; Brookshire, Randall and Stoll, 1980; Hanemann, 1991;
Randall and Hoehn, 1987).
This study is no exception, with
median fitted values of $1,634 and $1,105 for the HUNTP and
HUNTPS variables and $3,529 for the mean on the ELKP fitted
values.
Prior to Hanemann's theoretical research (1991) the
tendency of CVM practitioners was to dismiss the WTA formats
as inappropriate for the particular commodity being valued
(see Brookshire, Randall and Stoll, 1980).
Randall and Stoll
(1980)
show how WTA values can be
calculated from WTP values (for quantity changes) by modifying
Willig's
(1976)
calculations
(for price changes).
They
demonstrate that a necessary component for translating WTP
values into WTA values is the "price flexibility of income."
Hanemann then takes the Randall and Stoll theoretical
formulation
a
step
further
by
showing
how
the
price
flexibility can be further decomposed into an income and
substitution elasticity.
The size of the difference between
the WTP and WTA values is then inversely related to the size
of the elasticity of substitution between the public commodity
and private goods.
In this thesis,
it is not possible to differentiate
between the substitution and income effects.
Estimating the
income effects is complicated by the discrete nature of both
61
the income variable and the change being valued (hunting to
not hunting).
Additionally, problems associated with the bid
structure for the ACCINC variable would cloud the results of
any attempt to obtain income effects.
The supposition that the elasticity of substitution
between elk hunting and private goods (for Starkey hunters)
is less than one is thus an intuitive one.
Mitchell and
Carson (1989) argue that the Randall and Stoll (1980) bounds
suggest that WTP and WTA measures should be "within 5% of each
for most
other
Consequently,
it
contingent
appears
valuation
income
studies"
effects
alone
(p.
are
32).
not
sufficient to explain the divergences between these measures.
The WTA models also demonstrates the importance of
including variables for days hunted and hours traveled in
hunting valuation analyses.
In the HTJNTP and HUNTPS models,
a 1 percent increase in the number of days spent hunting elk
causes an estimated .88 percent increase in the amount hunters
are willing to accept as payment for hunting and a .83 percent
increase in the amount hunters are willingness to accept as
payment for hunting on the Starkey.
The derived valuation functions from the WTP and WTA
elicitations indicate that the population of hunters applying
for elk tags to hunt on the Starkey are heterogeneous in
income,
days hunt and\or hours traveled.
Additionally,
including both WTA and WTP elicitations on surveys can provide
valuable information on how the contingent scenario is viewed
62
by the respondents.
If enough CVM surveys include both types
of elicitation formats (WTA and WTP), then a comparison of
differences between the estimated values across questions
could provide information on the degree to which various
public good commodities are substitutable with private goods.
63
V. CONCLUSION
The focus of this study is on the derivation of valuation
functions and estimates of central tendency for four different
contingent scenarios.
Using three years of data on elk hunts
on the Starkey, values are obtained for the per trip WTP to
avoid the loss of hunting privileges and per trip WTP to
ensure an opportunity for a shot at an elk.
Additionally,
values are obtained for the per trip WTA for giving up the
right to hunt and per trip WTA for giving up the right to hunt
on the Starkey.
Although the results are promising, caution should be
exercised in the application of these results in a policy
context.
The ambiguous nature of some of the contingent
scenarios and the low number and small range of the bids
reduce the applicability of the results.
Additionally,
respondents who choose the Starkey may not be statistically
representative
of
hunters
who
choose
other
areas,
and
Respondents' expectations for their Starkey hunting experience
may bias their valuation estimates.
The results include an estimated median value of $113 per
hunter for access to elk hunting and an estimated mean value
of $287 per hunter per trip for increases in the elk herd to
ensure an opportunity to shoot at an elk.
Additionally, the
flexibility of valuation functions is demonstrated by showing
how changes in the values of significant (at the .05 level)
explanatory variables affect WTP and WTA values.
Another
64
advantage of
the use of the valuation function is that
explanatory variables can be changed to reflect attributes
specific to other hunting areas.
The four policy implications are that:
hunting recreational experience
(1)
the elk
is providing substantial
benefits to Starkey hunters, (2) willingness to pay values are
sensitive to the size of respondents' incomes,
(3) Starkey
hunters are willing to pay more to increase the elk herd to
the point where they would be virtually certain of having an
opportunity to shoot at an elk, and (4) Starkey hunters object
to the tradeoff between their right to hunt and private goods.
The three methodological implications are (1)
a demonstration
of the flexibility of WTP functions over point estimates,
a
confirmation of
(2)
the importance of using specific and
unambiguous wording in the contingent scenarios, and (3) a
confirmation of the importance of pretests to determine the
initial ranges and number of bids in dichotomous choice
surveys.
In the process of completing this research, some problems
were
encountered.
Chief
among these problems was
the
reduction in efficiency caused by the low number and narrow
range of bids.
Increases in efficiency could have been gained
by utilizing the additional information from the responses to
the iterated bids.
There are, however, two reasons why the information from
the iterated bids was not used in this analysis.
First,
65
although techniques are available to utilize the additional
information associated with iterated bids, recent critiques
in the literature by Cameron (1992) and McFadden and Leonard
(1992) demonstrate that there are
problems associated with
the endogeneity of the second bid.
Second, the dichotomous
bid elicitation format is currently the method of choice for
the majority of CVM researchers. Consequently, by using the
dichotomous bids the results from this study are comparable
with the results from other studies, such as the one by Park,
Loomis and Creel (1991).
Another problem encountered in this study is the lack of
specificity and ambiguous wording of some of the contingent
scenarios.
Because the Starkey surveys are to continue for
the next five years, there is the potential for ameliorating
some of the existing problems and for obtaining further
information.
The challenge is to strike a balance between maintaining
consistency and improving the accuracy and reliability of the
results.
The following are suggestions for improving the
surveys:
(1)
For the WTP elicitations, add dichotomous bids of $30,
$150,
$200,
$350
and
$650.
Consistency
can
be
maintained between the data from future surveys and data
from existing surveys because an individual responding
with a "yes" to a bid of $150 would also say yes to a
66
$100 bid.
Additionally, bids lower then the current
minimum bid of $50 could be excluded from the analyses.
The iterative bids for the WTP elicitations can remain
the same.
For the WTA elicitations, add dichotomous
bids of $750, $1,000, $1,250, $1,500, $1,750 and $2,000.
Additionally,
add iterative bids of
$1,500, $1,750, $2,000 and $2,500.
$1,000,
$1,250,
The increase in the
number and range of dichotomous bids should result in
increased efficiency.
(2)
Although the wording of the contingent scenarios
should remain unchanged to maintain consistency, an
additional WTP question should be added.
This
question could read:
Would you choose to hunt elk on the Starkey if total
costs increased by
By
including
this
?
question
in
future
Starkey
surveys, the use value of the Starkey elk hunting
experience could be estimated.
Additionally, the
responses to this question might provide insight
into the perceived difference in the use value of
hunting elk on the Starkey versus the use value of
hunting in general.
67
(3)
A "not for sale" category should be included in the WTA
elicitations.
The purpose of this category
provide a screen for protest responses.
is
to
Respondents
could then be grouped according to their response to
this category.
In
five years,
completed,
when the Starkey surveys have been
the resulting data sets should provide a rich
source of opportunities for CVM researchers.
Economists,
however, need not wait until 1997 to begin taking advantage
of the research opportunities provided by the existing bank
of data.
Because of the potential for controlling for differences
between hunts and the large number of data sets collected over
time, the Starkey project provides a unique opportunity for
conducting future research on benefits transfer and stability
of preferences.
The valuation functions and logistic models
developed in this thesis provide the necessary structure for
conducting further research on benefits transfer. The impetus
for conducting benefit transfer research comes from the
prohibitive costs associated with conducting a new CVM study
for each separate site.
As discussed in the previous section, four policy and
three methodological implications result from the derivation
of the valuation functions and examination of how changes in
68
the values of the explanatory variables affect the WTA and WTP
estimates. Additionally, this research provides the necessary
base for exploiting future research opportunities afforded by
the Starkey data sets.
69
REFERENCES
Adams, R.M., Bergland, 0., Musser, W.N., Johnson, S.L., and
L. M. Musser. 1989. User fees and equity issues in public
hunting expenditures: the case of ring-necked pheasants
in Oregon. Land Economics 65: 376-385.
Allen, S. 1988.
Montana bioeconomics study: Results of the
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APPENDICES
74
APPENDIX 1
DATA FROM THE STARKEY SURVEYS
75
The data sets are in order with HUNTE89 first with 42
observations, HUNE9O with 82, HIJNEA9O with 149, HUNE91 with
109 and HEAtJG91 with 144.
= Refers to missing value
For HUNTE89 the bid levels for the
inadvertantly left blank.
hp
variable were
Variables = definition [corresponding question number on the
survey)
= group
GR
ACC
= wtp to avoid loss of hunting [13]
= wtp for gaurenteed elk or deer [8]
= wta payment for hunting starkey [19)
EP
HPS
= wta payment for hunting [20]
= income [211
DH
= days spent hunting [6)
HTR = hours spent traveling to starkey [3)
ABIL = hunting ability [5)
PRHS = prior hunt starkey [10]
AGE = age of respondent [21)
HP
INC
= wtp to avoid loss of hunting [13)
= wtp for gaurenteed elk or deer [8)
ACC
EP
EDtJC = level of education [21)
OBS GR ACC EP
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
1
3
3
4
1
4
1
3
2
1
1
2
2
1
1
4
1
3
3
3
4
3
2
2
1
.
2
.
2
1
1
2
2
2
2
2
2
2
2
1
1
1
1
1
HPS HP INC DH
HTR ABIL PRHS AGE EDUC
2
.
4
7
5.0 4
1
3
2
2
.
5
6
11.0 3
2
4
3
1
2
.
4
2
5
8.0 4
3
1
2
2
.
2
4
1
2
6.0
3
2
2
2
4
1
.
6
7
6.0
4
5
2
2
.
3
6
7.0 4
1
6
1
2
2
.
2
11.0 4
2
5
1
6
2
2
6
7
7.0 3
2
4
2
1
2
2
7
5.0 3
2
6
2
1
2
4
5
.
3
2
3
2
2
2
6
7
10.0 3
2
3
2
2
2
10.0 4
5
7
2
4
4
1
2
5
7
0.5 4
2
4
2
1
2
7
6.0
4
1
4
6
3
1
2
4
4
7.0 4
2
1
3
2
2
7
5
2
4
5.5 4
3
1
2
4
7
5.0 4
2
5
5
2
2
5
7
4.0 5
2
4
3
2
.
2
9
.
3
1
.
1
2
2
.
6
1.0 3
1
6
2
2
5
2
2.0 4
2
4
2
2
2
4
7
8.0 4
2
5
2
2
2
76
OBS GR ACC EP
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
1
3
3
4
1
2
4
2
4
3
2
4
2
3
2
3
4
1
3
3
1
3
1
3
2
1
1
1
1
1
2
1
.
.
1
1
2
2
2
2
2
2
.
1
1
2
1
1
1
2
2
2
1
2
2
.
1
1
2
2
2
2
1
1
1
1
2
2
2
2
2
2
1
1
1
1
1
1
2
2
2
2
3
3
2
2
1
2
1
.
2
1
2
2
2
2
4
4
1
1
2
1
2
2
2
.
.
4
1
4
4
1
2
2
3
4
1
1
4
2
3
4
4
1
.
1
1
1
1
2
2
3
3
1
2
1
2
2
2
2
1
2
.
4
5
.
4
6
7
7
5
4
5
2
2
HTR
5
3
6
2
2
6
2
5
9
.
.
2
6
.
5
3
7
5
.
.
1
.
2
.
1
2
2
1
.
2
1
HPS HP INC DH
2
2
2
1
1
2
2
1
2
2
2
2
1
2
2
2
2
2
1
2
1
2
2
2
2
2
2
2
2
1
.
2
2
2
.
.
.
.
2
2
2
2
2
2
2
2
2
1.
2
2
2
2
2
2
2
4
4
6
4
4
4
3
3
3
3
6
4
3
6
5
5
5
5
3
3
5
5
6
2
2
2
1
I
2
2
2
2
2
2
2
2
4
7
4
4
4
4
5
7
4
4
4
710.0
8
.
3
5
3
4
4
4
3
3
.
3
3
4
3
1
4
3
3
3
6.0
2.0
2.0
1.0
10.0
5.0
5.0
5.0
6.0
9.0
9.0
8.0
3.0
4.5
4.0
5.0
7
2
3
4
7
7
7
6
7
4
6
1
2
5
5
4
4
3
3
2
2
2
2
4
4
4
5
610.0
2
7
9
7
7
9
2
.
5
8.0
6.0
8.0
1.0
6.0
5.0
4.0
7.0
6.0
6.0
3.5
6.0
2
2
7
7
5
7
5
4
5
7.0
6.0
1.5
.
7.0
7.0
7.0
2.0
11.5
10.0
5.5
10.0
5.0
5.5
5.0
5.0
4.5
9.0
6.0
ABIL PRHS AGE EDUC
7
7
7
4
5
2
1
3
4
5
4
1
1
1
1
2
2
2
2
2
1
2
1
1
2
2
2
2
2
1
1
1
1
2
1
2
2
1
1
1
2
2
2
5
3
4
1
1
1
4
1
1
1
1
2
5
3
3
4
1
3
4
4
3
3
1
4
3
3
2
2
2
1
2
1
1
2
2
2
5
5
5
1
2
4
4
2
2
6
2
3
3
4
2
4
4
4
4
4
5
2
3
3
3
2
4
4
4
4
2
5
2
2
3
3
6
2
4
4
4
4
6
3
4
4
4
5
2
6
4
4
4
4
6
4
4
3
4
3
5
3
5
6
4
5
1
2
3
1
4
4
7
5
2
3
6
3
3
2
3
2
2
3
5
2
2
2
4
3
2
2
2
3
2
2
2
77
OBS GR ACC EP
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
3
1
4
1
.
3
3
4
2
1
4
3
2
4
3
4
1
2
4
1
1
1
1
4
4
3
2
2
3
2
3
1
3
4
2
3
2
4
4
1
1
2
3
2
1
1
1
.
1
1
2
1
1
1
.
1
1
2
1
1
1
1
1
1
2
2
2
1
1
2
2
1
1
1
1
2
2
2
2
2
2
2
1
2
2
2
1
1
1
2
2
1
2
1
2
2
2
3
2
3
1
1
3
2
2
2
2
1
2
1
1
2
2
2
1
1
1
2
4
3
1
2
2
2
2
2
2
1
2
1
1
2
2
1
2
2
2
2
2
1
2
HPS
2
2
2
1
1
1
2
2
2
2
2
2
1
2
2
2
1
2
2
2
2
INC
HP
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
1
2
2
2
1
1
2
2
2
1
2
1
2
2
2
2
2
1
1
2
1
2
2
2
2
2
1
2
2
2
2
2
2
2
1
2
2
2
2
2
2
HTR ABIL PRRS AGE EDIJC
DH
4
7
6
6
5
5
6
7
6.0
7.0
5.0
3
.
5
4
6
5
6
3
.
4
5
6
6
4
6
6
.
1
6
4
4
5
3
7
9
7
7
5
7
7
7
7
7
4
5
7
7
6
7
2
7
7
5
4
1
6
2
3
4
4
4
7
7
4
3
3
6
2
5
5
5
5
3
4
4
3
5
3
.
.
6.0
.
6.0
7.0
6.0
510.0
7
6.0
7
7.0
9
8.0
7
7.0
3
6
6
3
6
5
.
6
3
3
5.0
1.0
4.0
10.0
3.0
9
7
3
2
7
6
6
5
6
2
.
5.0
7.0
6.0
9.5
9.0
4.0
1.5
7.0
1.5
6.0
1.5
1.0
4.0
1.0
1.0
0.5
1.0
1.0
1.0
1.5
0.5
4.0
5.0
4.0
6.0
5.5
5.5
1.5
3
2
5
3
4
5
4
2
2
3
3
3
3
4
3
3
4
3
3
5
5
5
3
5
4
3
3
3
3
4
3
4
5
3
3
2
4
2
4
5
4
4
3
4
3
3
5
5
4
5
1
4
1
2
2
1
5
4
4
4
2
1
4
2
2
2
2
1
2
2
1
1
2
1
1
1
1
1
2
1
2
1
.
2
2
1
2
2
2
2
2
2
2
2
1
1
1
1
1
2
1
2
1
2
2
2
.
3
5
5
2
2
5
7
2
5
3
4
7
5
6
4
5
5
4
4
4
4
2
6
4
4
5
5
6
4
4
4
4
4
4
4
4
5
2
4
5
3
4
6
4
4
1
4
1
3
5
4
4
3
5
6
6
5
6
2
5
2
1
2
2
5
2
3
5
5
4
3
4
5
4
2
2
4
5
2
5
4
2
2
2
1
6
1
7
2
2
2
2
78
OBS GR ACC
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
2
2
4
2
4
1
3
3
1
2
3
3
1
1
3
2
2
1
1
1
2
1
.
.
2
1
1
2
1
2
2
1
1
.
1
2
4
1
1
1
2
2
1
1
1
1
1
2
3
2
4
4
1
2
2
3
2
1
2
2
4
2
1
2
1
4
4
4
1
1
1
1
1
2
2
4
.
2
3
3
3
3
2
4
1
1
1
4
4
1
EP
2
1
1
1
1
1
1
1
1
1
2
1
2
2
2
1
2
1
2
1
1
2
1
2
1
.
2
2
1
2
1
1
2
HPS HP INC DH
2
2
1
2
2
2
2
2
2
1
2
2
1
2
2
2
2
1
2
2
2
2
2
2
2
.
.
2
2
2
1
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
1
2
1
1
1
2
2
2
2
2
2
1
2
2
2
2
1
1.
2
2
2
2
2
2
2
2
2
1
1
1
2
2
2
1
1
2
2
2
1
2
1
2
.
2
2
2
2
2
1
1
2
.
2
2
2
2
1
2
2
2
2
2
2
2
4
5
6
6
6
6
3
5
3
5
6
3
3
4
2
6
5
5
4
5
4
3
3
6
6
2
4
6
4
4
4
4
4
3
.
3
.
3
3
3
1
4
1
3
.
3
.
HTR ABIL PRHS AGE EDUC
3
3
3.0
0.5
512.0
2
3
9
3
8
4
4
4
5
3
3
6
5
8
6
5
5
9
8
2
3
5
6
9
5
4
7
9
5
3
2
9
4
4
1
4
6
4
2
9
8
8
4
4
1.
4
8
2
2
3
4
4
6
4.0
6.0
7.0
1.0
5.5
0.5
.
1.0
2.0
1.5
2.0
2.0
2.0
1.0
1.0
1.0
1.5
.
0.5
1.0
1.0
1.0
2.0
1.0
1.0
0.5
9.0
.
2.0
1.0
1.0
1.5
1.0
1.0
1.0
4.0
1.0
4.0
1.0
1.0
5.0
5.0
1.0
1.0
1.0
3.0
4.5
4
4
1
1
4
2
4
3
3
3
4
4
3
3
4
4
4
3
2
3
3
3
3
4
4
1
.
3
3
3
3
3
2
4
5
1
2
4
4
3
3
1
4
4
1
4
2
3
2
1
2
1
2
2
1
2
2
2
2
2
2
2
1
2
2
2
2
2
2
1
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
1
2
2
1
2
2
2
2
2
2
2
2
2
3
5
4
6
5
2
2
4
2
4
4
4
4
3
6
4
3
4
4
3
1
2
2
3
3
4
2
3
7
5
5
2
2
5
6
4
4
4
4
3
4
2
3
2
2
2
6
1
2
4
3
4
4
3
5
4
4
3
5
4
3
1
3
2
3
2
6
2
3
6
5
2
2
2
1
2
5
6
4
6
4
6
4
2
4
1
5
1
1
5
2
3
2
2
2
3
1
3
1
1
79
OBS
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
GR ACC
2
3
4
1
2
1
1
1
3
3
2
2
2
4
1
1.
3
3
4
1
4
3
3
3
1
4
2
4
1
3
4
3
1
1
2
1
1
1
2
1
1
1
2
1
2
2
2
2
2.
1
2
1
1
1
1
2
.
1
1
1
2
1
1
1
2
2
3
2
2
1
4
1
1
1
2
1
2
2
1
1
1
1
4
2
1
2
2
2
2
2
2
1
2
2
1
2
2
2
1
2
EP
1.
2
2
1
2
1
.
2
1
2
1.
2
1
2.
1
1
2
2
1
2
2
2
1
2.
2
2
2.
2
1
2
2
2
1
2
2
2
2
2.
1
2
1
2
1
1
2
1
.
1
2
1
HPS
INC DH
HP
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
1
2
1
2
2
.
2
2
2
2
2
1
2
2
.
.
2
2
2
2
1
2
2
1.
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
HTR
3
4
1
4
6
4
4
8
5
4
5
4
.
9
2
4
3
3
2
8
1
8
8
9
3
4
4
5
9
3
5
4
6
6
5
4
1
4
6
1
6
3
2.
9
4
1
3
3
9
4
5
9
3
5
9
2
2
1
2
4
4
6
6
4
2
6
5
5
5
5
1
6
3
2
2
2
2
2
4
8
2
3
4
3
9
6
2
2
2
2
2
2
2
2
2
2
2
6
6
6
4
.
6
3
6
.
3
3
2
6
2
5
9
3.0
2.0
2.0
3.5
2.0
7.0
1.0
2.0
2.5
2.0
4.0
3.0
1.5
5.5
6.0
0.5
0.5
5.0
.
6.0
6.0
.
5.5
5.0
8.0
5.0
6.0
6.0
6.0
6.0
6.0
.
6.0
5.0
5.5
.
ABIL PRHS AGE EDtJC
2
2
1
3
3
4
3
3
1
3
4
3
2
3
4
3
5
2
3
3
3
1
3
4
4
4
4
4
3
4
3
1
4
3
4
3
1
4
6.0
1.0
5.0
1.0
6.0
4
712.0
3
9
9
9
7
5
7.0
9.0
8.0
3.0
8.0
812.0
9
8
9.0
12.0
2
2
4
3
4
1
4
3
2
1
4
4
.
1
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2.
2
2
2
2
2
2.
1
2
1
1
2
2
2
2
2
2
2
2
1
2
2
1
2
1
2
2
2
2
2
2
2
2
4
3
1
3
3
6
5
1
3
3
5
5
5
2
4
4
4
4
4
4
3
3
3
6
4
2
5
2
2
2
1
3
4
4
2
2.
2
2
2
3
2
3
5
4
2
6
3
6
2
2
2
3
6
5
2
5
4
4
4
9
3
5
2
4
4
5
4
2
1
2
5
3
5
2
2
2
1.
4
4
4
5
3
5
6
6
6
3
3
5
4
7
2
5
3
2
2
2
80
OBS GR ACC EP
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
1
2
3
1
3
1
1
3
2
4
4
1
1
2
2
1
2
2
1
2
2
1
2
1
1
2
3
3
3
1
3
1
3
4
2
1
2
2
1
1
4
3
4
4
3
2
4
4
1
1
.
3
1
2
1
1
1
1
1
2
1
1
2
1
1
1
1
2
1
2
1
1
1
1
1
2
1
1
2
2
4
2
2
1
3
2
4
1
2
2
2
1
1
1
3
3
4
1
2
1
HPS
INC DH
HP
2
1
.
.
1
.
2
2
2
2
2
1
.
1
2
1
2
2
2
2
2
2
2
2
2
1
2
1
2
1
2
2
2
1
1
2
2
1
2
1
2
2
2
2
2
2
2
2
1
1
1
1
1
2
1
2
1
2
2
2
2
2
1
1
2
2
2
2
1
2
2
1
2
1
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
.
5
2
5
5
5
4
1
4
3
1
2
6
6
5
.
4
6
.
3
3
4
6
2
5
5
5
6
6
9
9
9
4
4
6
3
9
4
9
1.0
1.0
2.0
6.5
6.5
6.5
6.0
1.0
.
2
3
10.0
1.5
1.0
1.0
8.0
5.0
0.5
0.5
5.0
8.0
10.0
7
7
9
5
6.0
5.5
5.5
15.0
2
7
5
5
7
914.0
5
6
6
7
7
4
4
9
9
1
1
6
6
6
4
7
1.0
5.0
5.0
3.0
3.0
.
1.0
1.0
914.0
8
0.5
1.5
1.5
5.0
8.0
8.0
6.0
4
6
9
3
7
7
7
5
910.0
.
5
.
.
2
2
2
1
2
2
2
1
2
2
3
2
6
7
5
210.0
.
1
HTR
3
3
4
4
6
9
9
1.0
9.0
810.0
8
5
5
5
10.0
1.5
5.0
5.0
ABIL PRHS AGE EDUC
5
4
4
4
2
4
3
4
4
3
5
1
3
2
5
5
3
5
3
3
3
2
3
1
5
4
3
1
1
5
4
1
3
1
3
3
3
3
4
3
3
2
3
4
2
4
4
2
2
2
1
1
1
3
2
2
2
2
2
2
2
2
3
3
4
6
1
4
2
2
4
1
4
4
5
3
4
5
2
1
5
2
2
2
2
2
1
1
1
1
2
4
1
3
3
6
4
3
.
2
2
2
1
1
2
2
2
2
2
2
2
1
2
1
1
2
2
3
4
5
2
2
1
4
3
2
2
1
2
2
2
2
2
2
2
1
1
2
2
2
4
2
2
2
5
4
5
6
6
5
1
4
1
2
4
4
4
3
1
4
4
6
5
6
6
5
4
4
4
4
2
2
2
3
2
2
5
2
1
3
4
1
2
1
2
5
3
2
2
1
6
5
2
3
2
6
3
5
2
2
81
OBS GR ACC EP
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
1
1
4
1
1
2
2
2
1
1
1
2
3
1
1
1
4
.
2
2
3
1
4
2
4
3
3
4
2
3
1
4
3
1
3
2
3
3
3
1
3
2
2
4
1
1
2
2
1
1
1
1
.
1
1
1
1
1
2
2
2
1
2
2
1
2
2
2
1
2
1
2
2
2
2
2
2
2
2
2
.
.
2
2
1
2
1
2
2
2
1
1
2
2
1
2
1
1
1
2
1
1
4
2
2
2
2
4
1
1
1
2
2
.
1
2
2
2
2
2
3
2
2
3
1
3
2
2
2
1
1
2
2
2
1
2
2
1
1
1
2
2
1
.
2
2
1
HP
2
2
2
2
2
2
2
2
1
2
2
2
1
2
2
2
2
.
2
1
2
2
.
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
4
2
2
3
3
6
4
5
6
2
4
6
6
6
2
5
4
6
4
4
4
2
1
1
2
2
2
1
2
.
2
2
2
2
2
2
2
.
2
2
2
2
2
2
2
2
2
2
2
6
3
5
1
3
4
6
5
5
6
2
2
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
HTR ABIL PRHS AGE EDUC
DH
INC
2
2
1
2
.
2
1
HPS
.
6
6
.
5
4
3
5
4
4
3
4
6
6
4
3
5
7
6
7
5
4
5.0
1
1
.
4
4
4
2
1
1
1
7.0
2.5
8.0
411.0
7
9
6
5
.
5
3
5
5
.
.
5
4
7
6
7
3
5
2
2
6
3
4
7
4
3
5
2
3
6
7
7
5
6
9
4
4
7
7
3
4
7
7
5
6.0
6.0
8.0
5.0
10.0
.
3.0
10.1
1.0
10.0
3
2
4
4
2
4
3
3
4
4
3
3
4
.
12.2
9.5
2.5
1.5
8.0
6.0
7.0
7.0
6.0
2.0
2.0
8.1
8.0
6.0
6.0
1.5
8.0
6.0
5.5
7.0
1.5
5.5
.
3.0
3.1
.
6.0
8.0
6.0
5.0
8.0
6.0
2
.
2
3
2
2
5
2
4
3
3
4
.
3
4
5
4
3
3
4
3
4
3
3
2
.
4
4
2
4
5
5
4
2
1
2
1
2
1
2
2
1
1
2
1
1
1
2
2
2
2
1
1
5
3
4
4
1
5
6
5
3
3
4
2
4
4
4
5
5
4
1
5
3
5
4
2
2
1
2
2
2
1
1
1
6
3
3
2
1
1
2
2
5
2
2
1
1
2
2
2
2
1
1
2
1
2
2
3
4
4
4
4
4
4
3
3
6
4
2
2
3
4
1
4
6
5
1
2
3
5
3
1
4
2
2
5
2
2
2
7
5
2
2
5
6
1
1
2
6
5
1
4
5
3
4
6
4
2
2
1
2
2
2
2
3
3
2
2
5
4
3
5
2
1
2
82
OBS
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
GR ACC EP
4
1
2
3
1
1
1
1
3
2
2
2
3
.
1
2
4
2
2
1
3
3
4
2
4
3
1
1
4
1
1
2
2
2
2
2
1
2
2
2
2
2
1
2
1
1
2
1
1
1
2
2
2
1
3
2
.
2
1
1
1
2
1
4
1
1
2
2
2
1
.
3
1
1
2
1
2
1
2
1
1
1
4
1
1
2
1
3
2
3
1
2
4
4
1
3
1
2
4
4
2
1
2
2
2
2
2
2
2
2
2
1
2
1
1
2
2
2
2
2
2
1
2
2
1
1
1
HPS
.
2
1
2
2
1
2
1
1
2
1
1
2
1
1
2
2
.
1
1
1
2
1
1
2
2
1
1
2
2
2
2
2
1
2
2
2
2
1
2
2
2
2
1
1
2
INC DH
HP
2
2
1
2
2
4
HTR
7
ABIL PRHS AGE EDUC
8.0
1
1
710.0
710.0
9
4.5
3
3
4
2
6
3
7
7
3
5
4
.
2
6
4
4
5
2
.
2
2
6
2
2
4
5
4
2
2
2
1
2
6
2
610.0
7
3
6
3
7
7
5
1
6
4
4
4
3
2
6
7
2
6
5
2
4
4
2
6
5
2
2
.
.
2
1
2
2
2
2
2
2
2
2
2
2
1
2
2
2
.
2
2
2
1
2
2
2
2
2
2
3
2
5.0
5.0
6.0
5.5
5.0
3
5
9
9
4
6
4
6
5
3
6
6
5
5
3
7
8
3
.
1
5
1
9
7
5
1
7
0.5
2.0
1.5
2.5
5.0
6.0
10.0
1.0
1.0
4.0
5.0
.
0.5
0.5
6.0
5.0
8.0
5.0
8.0
8.0
.
610.0
.
3
3
4
4
3
4
4
4
5
3
4
3
2
2
3
1
5
4
2
2
4
3
4
2
4
4
3
4
5
4
4
2
910.0
4
2
5
2
3
4
7
4
3
3
9
9
5
5
2
6
6
4
1
2
1
2
2
2
2
2
2
2
3
5
5
5
4
1
2
4
7
2
2
2
2
2
2
2
2
2
2
1
9
5
.
8.0
6.0
8.0
2
2
2
2
2
4
4
6
2
2
1
4
4
4
4
2
2
1
1
3
4
4
2
2
2
2
2
4
3
1
6
1
2
2
2
1
1
2
2
2
2
5
1
4
2
6
1
4
2
1
1
1
1
2
1
1
1
1
1
1
1
2
2
.
3
3.5
2
2
1
2
2
1
1
1
1
2
4
2
7.0
4.0
4.0
3.5
7.0
7.0
7.0
7.0
5
4
4
4
4
5
1
3
3
3
5
4
3
3
4
6
6
4
6
5
5
4
2
2
5
7
1
5
2
2
2
1
2
5
5
2
4
1
2
2
3
4
4
4
6
2
6
1
2
2
5
1
2
4
2
1
1
1
2
4
1
2
1
2
2
3
1
3
2
4
4
4
4
2
2
2
2
3
1
3
4
2
4
83
OBS GR ACC EP
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
3
1
1
1
1
4
2
1
4
4
1
1
2
2
2
1
1
2
2
1
1
2
HPS HP INC
2
2
2
2
2
2
3
2
2
2
2
2
1
2
4
5
2
6
2
2
1
1
2
2
2
2
2
.
.
.
.
.
.
3
2
1
3
3
1
.
.
1
2
2
1
2
2
2
2
1
2
1
2
1
1
1
1
2
2
1
2
2
2
1
4
3
1
1
2
3
1
4
2
1
1
1
3
.
4
1
1
3
4
4
4
2
3
2
4
3
1
4
4
4
4
1
3
1
2
1
.
1
2
1
2
2
2
2
2
1
1
1
1
2
2
2
2
1
2
2
1
1
1
2
1
2
1
1
1
1
2
2
2
2
2
2
2
3
1
1
2
1
1
.
1
1
1
2
1
2
2
1
2
2
2
1
2
1
2
2
2
4
6
2
2
2
2
1
2
6
2
2
2
2
2
2
5
1
6
1
2
3
2
2
1
1
1
2
.
2
1
5
.
2
3
4
1
.
.
4
1
2
2
2
2
2
1
2
2
2
2
2
1
2
2
2
1
1
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
.
3
3
4
2
3
2
4
6
5
3
2
2
2
2
2
2
1
2
2
2
4
2
2
2
6
1
5
3
2
HTR
DH
3
5
5
4
4
2
.
6
3
9
7
9
9
9
4
7
5
7
6
9
3
4
3
3
3
6
5
7
7
5
8
3
7
7
4
9
4
5
3
4
4
6
4
9
9
9
9
9
9
4
5
5
3
7
4
.
9
2
4
ABIL PRHS AGE EDUC
3.5
2.0
2
6.0
6.0
4
4
4
4
.
6.0
9.0
1.0
.
8.0
0.0
4.0
6.0
1.5
0.5
2.0
1.0
2.0
5.5
4.0
1.0
1.0
5.5
8.0
9.0
11.0
7.5
7.0
5.5
.
7.0
5.0
2.0
8.0
6.0
8.0
5.5
8.0
.
6.0
1.5
6.0
8.5
1.0
7.0
8.0
5.0
1.5
6.0
2.0
1
4
3
2
4
2
1
.
.
3
4
4
4
4
4
1
2
1
1
1
.
.
1
3
1
3
5
1
4
4
5
4
1
3
3
3
2
3
3
3
4
2
3
4
5
3
5
3
4
4
2
5
2
1
5
3
3
4
3
4
1
4
2
2
2
2
2
1
1
2
1
1
2
2
2
2
1
1
1
2
1
2
1
1
2
1
2
2
2
2
1
2
1
1
2
1
2
2
2
2
5
3
4
1
4
1
4
5
5
3
4
6
4
4
1
4
4
4
1
4
4
3
4
2
6
3
4
6
4
3
3
3
1
4
2
5
6
3
4
4
4
2
1
4
2
4
4
2
2
3
5
5
1
2
1
3
4
6
5
2
3
5
6
2
3
2
3
1
5
4
5
5
2
2
5
2
5
4
2
2
3
1
1
1
5
2
6
2
7
2
1
5
84
OBS GR ACC
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
2
4
3
4
4
2
1
3
2
1
1
1
2
2
1
4
3
3
2
1
1
2
1
1
3
3
1
3
2
3
4
2
3
3
1
2
4
4
3
3
2
4
1
1
3
2
3
2
4
2
2
1
1
1
1
1
1
2
1
2
2
1
1
1
2
1
1
1
1
.
2
2
1
2
2
2
2
.
2
1
1
1
1
1
2
2
2
1
2
1
1
2
2
2
1
1
1
1
1
EP
1
2
2
2
2
2
2
1
2
2
1
1
2
2
1
2
1
2
2
.
1
1
1
1
2
2
1
2
1
2
2
2
1
2
1
1
1
HPS HP
2
1
2
2
1
2
2
2
2
2
2
2
.
1
2
2
2
2
2
.
2
1
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
.
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
.
.
.
2
1
2
1
2
2
1
2
2
2
2
2
2
2
1
1
2
1
2
1
HTR
DH
INC
ABIL PRHS AGE EDUC
6
3
5
8
810.0
4
4
3
.
9
3
3
6
3
6
1
3
5
3
3
3
5
4
5
5
6
6
9
6
8
8
5
9
5
3
7
5
3
4
.
6
.
4
2
3
3
5
6
6
5
4
2
.
4
3
3
3
6
6
2
6
6
2
6
4
1
5
4
5
3
3
5
3
5
5
5
6
5
9
.
10.0
9.0
1.5
6.0
0.5
4.0
4.0
12.0
4.5
5.0
10.0
1.0
3.0
8.0
1.0
4.0
1.0
5.0
5.0
5.0
.
7.0
7.0
6.0
9
5.0
8
6.0
8
6.0
7
1.0
5
5.0
8
.
4
2.0
7
.
5.0
9
910.0
9
9.0
1.0
9
4
5.0
9
1.5
9
.
8
6.0
7.0
3
8
6.0
3
1.5
7
.
3
4.0
9
2.0
9
2.0
9
2.0
3
3
1
5
4
3
3
1
3
1
3
3
4
4
1
2
4
3
4
2
4
4
4
5
3
4
3
4
2
4
5
4
4
2
4
4
1
2
3
3
3
4
4
3
3
4
2
2
2
2
2
4
4
1
2
2
1
5
1
4
2
2
2
5
3
1
4
1
1
2
2
2
2
1
1
1
2
2
2
2
2
1
1
2
4
3
4
4
3
1
3
4
2
5
5
1
1
1
2
2
2
1
4
4
2
2
1
1
1
1
1
4
1
5
5
5
4
4
6
2
4
2
2
2
2
2
2
1
1
2
1
2
6
4
4
5
1
3
3
3
3
4
4
6
4
4
5
2
1
1
5
2
1
7
2
2
2
2
2
1
2
3
6
4
1
3
7
2
2
4
2
7
2
2
2
1
1
2
1
2
2
2
5
2
5
3
1
2
1
2
2
2
4
2
2
2
85
OBS GR ACC EP
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
3
4
2
1
4
2
1
3
2
3
3
3
4
4
4
4
4
4
4
1
4
3
1
2
4
1
2
2
2
2
2
1
1
2
2
1
1
1
2
2
2
2
2
1
2
1
1
1
1
1
1
1
2
1
1
2
2
1
2
3
2
1
2
2
2
2
2
3
1
2
2
2
1
4
1
1
2
2
2
1
2
2
4
1
2
2
2
1
4
1
3
1
4
2
4
2
4
3
2
1
1
1
2
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
1
1
2
2
2
1
2
2
1
2
2
2
2
2
1
1
HPS HP INC DH
1
2
2
2
1
2
2
2
2
2
2
2
2
2
2
1
2
2
1
2
1
2
2
1
2
6
3
1
6
8
5
2
2
2
2
2
2
2
2
2
4
2
2
3
2
2
3
4
8
9
5
5
7
6
8
9
8
9
9
9
7
7
7
8
8
6
9
3
5
2
8
6
2
2
2
2
2
2
2
1
1
2
2
2
1
2
2
1
2
2
2
2
2
2
2
1
2
2
1
2
2
2
1
2
1
2
2
2
2
2
2
2
2
2
2
1
1
2
1
2
1
2
2
1
2
2
HTR ABIL PPJIS AGE EDUC
2
2
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
6
6
6
6
.
2
4
3
4
4
4
4
6
6
3
4
4
5
4
6
6
6
5
5
6
5
5
.
6
4
3
4
6
6
4
4
1
6
.
5
3
5
8
5
9
4
8
9
7
9
4
8
3
9
8
7
9
5
8
5
7
7
8
9
1.0
5.0
5.0
1.5
7.0
12.0
7.0
10.0
2.0
6.0
5.0
.
5.0
.
2.5
9.0
6.0
6.0
6.0
1.0
1.5
.
10.0
6.0
6.0
4.5
8.0
6.0
.
3.5
5.0
3.5
6.0
5.0
1.5
5.0
5.0
.
3.0
5.5
.
7.0
6.0
6.0
6.0
7.0
2.0
1.0
6.0
5.0
2
1
5
3
2
4
3
4
1
4
4
3
3
1
1
3
1
4
4
4
2
3
5
4
3
2
4
3
4
3
4
1
3
3
3
3
4
4
1
2
2
4
1
4
2
2
2
2
2
2
1
1
1
2
2
2
1
2
3
2
2
2
1
1
1
2
1
2
1
1
1
1
1
2
1
1
2
1
2
2
4
4
4
4
4
4
2
2
2
1
1
1
2
2
2
2
2
2
1
5
1
4
3
4
2
4
4
5
2
4
3
4
1
2
3
2
4
2
5
1
2
2
4
5
4
4
4
4
4
2
2
2
2
5
3
5
4
5
6
4
3
5
2
3
4
3
3
4
4
3
5
5
5
4
4
4
3
4
5
3
3
3
4
4
4
4
5
3
2
5
4
2
2
2
2
6
5
5
3
2
7
2
5
2
2
5
6
4
2
1
1
3
2
4
5
1
2
2
2
86
OBS GR ACC EP
523
524
525
526
3
1
2
2
.
1
2
2
.
2
2
HPS HP INC DH
1
2
1
2
2
2
2
2
1
2
5
2
HTR ABIL PRHS AGE EDUC
9
1.5
9
.
9
9
6.5
10.0
4
1
5
3
1
2
1
1
2
6
2
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APPENDIX 2
STARKEY RESEARCH FOREST SURVEY
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Dear Starkey Hunter,
As you are aware, there are many studies being conducted on the Starkey
Experimental Forest. One of our goals is to gather information to
assist National Forest Managers as they plan management of resources
for multiple-use. We believe we have reliable estimates of costs and
benefits associated with timber harvest and livestock grazing, but we
currently lack good measures of values associated with recreation and
wildlife. We are asking your help as forest recreation users in
obtaining recreation values. The attached questionnaire is designed to
gain an understanding of wildlife and recreation values as perceived by
you, the users.
Please take the time to complete this questionnaire prior to
riving
at Starkey.
It will take you about 20 minutes to complete.
We will
ask for them at the gate as you check in for the hunt, and if they are
already filled in it will shorten your check in time.- We realize you
cannot be completely accurate in some of your estimates of costs, time,
etc., but we are interested in your expectations. Therefore they need
to be completed prior to the hunt. Thank you for taking the time to
complete this now.
Chris Carter
Oregon Departaent of Fish and Wildlife
Tom Quig]ey
Forest S.rvice
89
Date
Hunter ID
Interviewer
Starkey Hunting Valuation
Study Questionnaire
Please provide the following information. All information you provide
will be held in the strictest of confidence. No reports will refer to
you specifically nor will the personal information be given to any
other individual or group. The information will be used to help
determine the values associated with hunting on the Starkey
Experimental Forest.
Was the primary purpose of the trip to hunt?
NO
How many days do you plan to hunt the Starkey?
How many hours do you plan to hunt the Starkey in total?
(actual daylight hours of hunting)
How many hours did it take for you to travel to Starkey?
How did you travel to get to Starkey? Mark all the modes of
travel you used to get to Starkey:
Car or Pick-up
Motor Home
Other (Specify)
$.
Was the trip planned to coincide with:
_Visiting relatives
_Visiting friends
_Vacaçion to do other things in NE Oregon
Only the hunt
_Other (specify)
90
5.
How do
novice
2
1
6.
r.ite 2/our hunting ab[litjes (1-5):
intermediate
expert
4
5
How many DAYS have you or will you do the following during 1990:
(NUMBER of DAYS)
Hunt deer
_____Hunt upland game
Hunt elk
Hunt other species
_____Hunt bear
Hunt waterfow
7.
3
Ocean fishing
Freshwater fishing
If you had not been selected to hunt the Starkey, what do you
think you would have done instead?
Approximately how many big-game have you personally killed?
Elk
Deer
Bear
Antelope
Moose
How many non-hunters traveled with you to the Starkey?
Have you hunted the Starkey area before? _____YES
_____NO
What was the primary reason you requested the Starkey hunt?
(mark ONE only)
_____Have hunted this area before
Noie].ty of hunting in an enclosure
Novelty of hunting in a research project
More likely to be successful in tagging deer/elk
Other (Specify)
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12.
What is the
ount of expenditures you have and/or will have
associated with the hunt? (NE Oregon is assumed to be Wal.lova.
Umatilla, Morrow, Grant, Baker, and Union counties.)
Outside NE
Inside NE
Oregon
Oregon
Transportation
$
$
Lodging
Food from Stores
Food in Restaurants
Supplies
License & Fees
We are attempting to determine the value of wildlife in the Starkey
area.
No plans are being made to charge extra fees for access to
Starkey.
The following hypothetical questions are being asked so that
comparisons can be made with wildlife values and other resources
values.
13.
Would you choose not to hunt at all in 1990 if total costs
increased by
7
YES. I would not hunt.
____NO, I would still hunt.
13a. IF YES, would you choose
13b. IF NO to question 13
not to hunt
total costs
above, would you choose
increased by
7
not to hunt if toc1.
YES
NO
costs increased by
7
YES
NO
if
14. Please consider how important you view the following activities
in your own life.
Please circle whether you consider these to be
highly important (H), of medium importance (N), low importance (L),
or of no importance (N).
-
HNLN
HMLN
H )fL N
HMLN
HMLN
HMLN
HNLN
H MLN
HMLN
HMLN
HMLN
HMLN
golf, tennis, bowling
fishing
hunting
swimming, hiking, biking, skiing
camping, backpacking, horseback riding
rafting. canoeing, kayaking. water-skiing
watching sports on TV
watching sports in person
livestock grazing in the National Forests
timber harvesting in the National Forests
wilderness ar.u; in th Natinaj F.rt
wildlife vjewji'
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1.5. What form of recreation would you consider most equal in 'value
with the hunting experience you will 'e having at Starkey?
16. We are interested in your general attitude about hunting deer
Please circle whether you consider these reasons highly
and elk.
low importance (L), or
important (H). of medium importance
of no importance (N).
O.
H M L N
H M 1.. N
H )( L N
H H L N
H H L N
H H L N
I enjoy being out of doors and experiencing the
natural environment.
I enjoy socializing with friends and/or relatives
in the outdoor setting.
I believe the meat viii satisfy a real need for
my family.
I have hunted for years and the tradition will.
continue.
I consider hunting as the best use of my spare and/or
vacation time.
I really don't have any alternatives I consider
of equal importance.
1.7. How likely do you think it viii be that you will have an
opportunity to shoot at an elk/deer? ______% chance
18. If the number of animals were sufficient to make it
virtually certain that you would have an opportunity
o shoot at an elk/deer, would you be willing to pay
additional to hunt?
_____YES
_____NO
18b. IF NO to q.18 above,
l8a. IF YES would you be willing
to pay
additional?
voul4 you be willing to
additional?
pay
_____YES
_____NO
NO
YES
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If you could still hunt somewhere .lse during the general season,
would you accept a payment of ..
from someone else and give up
your hunting privilege on Starkey this year?
______YES
NO
]9a. IF YES, you11 you accept a
payment of'.
for your
hunting privilege on Starkey?
______YES
______NO
19b. IF NO to q.19 above.
would you accept a payment
of
for your hunting
privilege on Starkey?
'
YES
NO
If someone would pay you to completely give up your hunting
privilege thu year (Starkey AND elsewhere), would you give
itupfor
YES
NO
20*. I? YES,
20a. IF NO to q.20 above, voulr1
you accept a payment of.
to give up your hunting of
elk/deer this season?
would you accept a
payment of_____ to give up
your hunting of elk/deer for
the season?
YES
YES
NO
Personal information:
Age: _____< 16
16-24
25-34
Male
35-50
51-64
> 65
Female
Mumber in your household including yourself.
Axe you retired?
Yes
No
Average hours you work for pay:
< 10
31-40
10-20
41-50
21-30
> 50
Your hourly wage rate (S/hour):
<$3.00
59.00-12.00
3.00-6.00
12.00-15.00
6.00-9.00
> 15.00
Hours of PAID vacation per month:
NO