The contingent valuation method (CVM) - Oregon State University

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




aWTPc/aDH




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 u29,999

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 elk hunter preference study. Montana Dept. of Fish,

Wildlife and Parks, Helena. Aldrich, J. and C. R. Nelson. 1984. Linear Probability, Logit and Probit Models. Sage Publications, Beverly Hills.

Bergstrom, J. C. 1990. Concepts and measures of the economic

value of environmental quality: A review. Journal of Environmental Management 31: 215-228. Berrens, R. 1992. Using contingent valuation to estimate recreational impacts from proposed Columbia River system operational changes: Some experimental design and modelling considerations. (Draft Discussion Paper, April 5, 1992 AREC-OSTJ, Corvallis, OR.) Bishop, R. C., Heberlein, T. A., McCollom, D. W. andM. Welsh. 1988. A validation experiment for valuation

techniques. Dept. of Agricultural Economics, Univ. of Wisconsin, Madison. K. J. and R. C. Bishop. 1988. Welfare measurements using contingent valuation: A comparison of techniques. American Journal of Agricultural Economics 70(1): 20-28.

Boyle,

Brookshire, D. S., Randall, A. and J. R. Stoll. 1980. Valuing increments and decrements in natural resource service flows. American Journal of Agricultural Economics August: 478-488. Cameron, T. A. 1988. A new paradigm for valuing non-market goods using referendum data: Maximum likelihood estimation by censored logistic regression. Journal of Environmental Economics and Management 25: 355-379. Cameron, T. A. 1991. Interval estimates of non-market resource values from referendum contingent valuation surveys. Land Economics 67(4): 413-21. Cameron, T. A. and D.D. Huppert. 1991. Referendum contingent valuation estimates: sensitivity to the assignment of offered values. Journal of the American Statistical Association. 86. 910-918.

70

Cameron, T. A. and J. Quiggin. 1992. Estimation using contingent valuation data from a dichotomous choice with follow-up questionnaire. Unpublished manuscript.

Cocheba, D. J. and w. A. Langford. 1978. Wildlife valuation: The collective good aspect of hunting. Land Economics 54(4): 490-504. Cooper, J. and J. B. Loomis. 1992. Sensitivity of willingnessto- pay estimates to bid design in dichotomous choice contingent valuation models. Land Economics 68: 211-224.

Cory, D. C., Colby B. D. and E. H. Carpenter. 1988. Uncertain recreation quality and wildlife valuation: Are conventional benefit measures adequate? Western Journal of Agricultural Economics 13 (2): 153-162.

C. and W. E. Martin. 1985. Valuing wildlife for efficient multiple use: Elk versus cattle. Western

Cory, D.

Journal of Agricultural Economics 10(2): 282-293.

Creel, M.D. and J. B. Loomis. 1992. Modeling hunting demand in the presence of a bag limit, with tests of alternative specifications. Journal of Environmental Economics and Management 22: 99-113.

Duff ield, J. 1988. The net economic value of elk hunting in Montana. Montana Dept. of Fish, Wildlife and Parks, Helena. Edwards, S. F. and G. D. Anderson. 1987. Overlooked biases in contingent valuation surveys: Some considerations. Land Economics 63(2): 168-178.

Fried, B. F. 1991. The Starkey questionnaire: A preliminary data assessment. Report submitted to the Forest Service, USDA.

Hanemann, W. M. 1991. Willingness to pay and willingness to accept: how much can they differ? The American Economic Review 81(3): 635-647. Hanemann, W. M. 1984. Welfare evaluations in contingent valuation experiments with discrete responses. American Journal of Agri cultural Economics, August, 1984: 332-341.

Hoehn, J. P. and A. Randall. 1987. A satisfactory benefit cost indicator from contingent valuation. Journal of Environmental Economics and Management 14: 226-247. Johnson, N. S. 1988. A bioeconomic analysis of altering instream flows: Anadromous fish production and competing

71

demands for water in the John Day River Basin, Oregon. M.S. Thesis, Oregon State University.

Johnson, N. S. and R. M. Adams. 1988. Benefits of increased streamf low: The case of the John Day River steelhead fishery. 1846.

Water Resources Research

24 (11 November): 1839-

Johnson, N. S. and R. M. Adams. 1989. On the marginal value of a fish: Some evidence from a steelhead fishery. Marine Resource Economics 6: 43-55. Just, R. E. Hueth, D. L. and Schmitz, A. 1982. Applied Welfare Economics and Public Policy. Prentice Hall, Inc. New Jersey. Kahneman, D. and J.L. Knetsch. 1992. Valuing Public Goods: The Purchase of Moral Satisfaction. Journal of Environmental Economics and Management. 22: 57-70 Learner,

E.

1983. Let's take the con out of econometrics.

American Economic Review. 73. Loomis, J. B. 1990. Comparative reliability of the dichotomous

choice and open-ended contingent valuation techniques. Journal of Environmental Economics and Management 18: 7885.

Loomis, J. B. 1988. Contingent valuation using dichotomous choice models. Journal of Leisure Research 20(1): 46-56. Loomis, J. B. 1989. Test-retest reliability of the contingent valuation method: A comparison of general population and visitor responses. American Journal of Agricultural Economics February 1989: 76-84.

Loomis, J. B., Cooper, J. and S. Allen. 1988. The Montana elk hunting experience: A contingent valuation assessment of

economic benefits to hunters. Montana Dept. of Fish, Wildlife and Parks, Helena.

Loomis, J. B., Creel, M. and J. Cooper. 1989. The economic benefits of deer in California: Hunting and viewing values. (Institute of Ecology Report 32) University of California, Davis, Davis, CA. Loomis, J. B. and R. G. Walsh. 1986. Assessing wildlife and environmental values in cost-benefit analysis: State of the art. Journal

of Environmental

Management 22: 125-131.

72

McConnell,

K.

E.

Models for referendum data: the discrete choice models for contingent

1990.

structure of

valuation. Journal of Management 18: 19-34.

Environmental

Economics

and

McFadden, D. and G. Leonard. 1992. On the contingent valuation

method for measuring the social value from passive use of environmental resources. Unpublished manuscript. Mitchell, R. C. and R. T. Carson. 1989. Using Surveys to Value Public Goods: The Contingent Valuation Method. Resources for the Future, Washington, D.C.

Nicholson, W. 1989. and Extensions.

Microeconomic Theozy: Basic Principles Dryden Press, Chicago.

Park, T. A. and J.B. Loomis. 1991. Joint estimation of contingent valuation survey responses. In: Papers of the 1991 Annual Meeting, Western Agricultural Economics Association: 102-108. Park,

T. A., Loomis, J. B. and M. Creel. 1991. Confidence for evaluating benefits estimates from dichotomous choice contingent valuation studies. Land intervals

Economics 67 (1 February): 64-73.

Pindyck, R. S. and D. L. Rubinfeld. 1991. Econometric Models and Economic Forecasts. McGraw-Hill, New York.

Quigley, T. M. 1992. Forest health in the Blue Mountains: Social and economic perspectives. (Gen. Tech. Report PNWGTR-296) Pacific Northwest Research Station, Forest Service, USDA, Portland, OR.

Randall, A. 1987. Resource Economics: An Economic Approach to Natural Resource and Environmental Policy. John Wiley, New York.

Randall, A. and J. R. Stoll. Consumer's surplus in commodity space. The American Economic Review 70(3): 449-455.

Seller, C., Stoll, J. R. and J. Chavas. 1985. Validation of empirical measures of welfare change: A comparison of nonmarket techniques. Land Economics 61(2): 156-175.

Sorg, C. F. and J. B. Loomis. 1984. Empirical estimates of amenity forest values: A comparative review. (General Technical Report RM-107) USDA Forest Service, Rocky

Mountain Forest and Range Experiment Collins, CO.

Station,

Fort

73

Sorg, C. F. and L. J. Nelson. 1986. Net economic value of elk hunting in Idaho. (Resource Bulletin RN-12) USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO. Varian, H. R. 1992. Microeconomic Analysis. Norton, New York.

Walsh, R. G., Johnson, D. M., and J. R. McKean. 1988. Review of

outdoor recreation economic demand

studies with

nomnarket benefit estimates, 1968-1988. Colorado Water Resources Research Institute, Fort Collins, CO.

Whitehead J. C and G. C. Blomquist. 1991. Measuring Contingent Values for Wetlands: Effects of Information about Related Environmental Goods. Water Resources Research.

27: 2523-2531.

Willig, R. D. 1976. Consumer's surplus without apology. The American Economic Review 66(4): 589-597. Wisdom, M. 1992. The Starkey Project: New technologies chase

old questions about deer and elk management. Western Wildlands 18(1): 32-38.

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 6

4 1 2 2

87

APPENDIX 2 STARKEY RESEARCH FOREST SURVEY

88

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)

91

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'

92

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

93

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): 15.00 Hours of PAID vacation per month:

NO