AN ABSTRACT OF THE THESIS OF
Pei-Chien Lin for the degree of Master of Science in
Agricultural and Resource Economics presented on July 6,
1994. Title: Measuring Recreational Fishing Benefits in a
Multiple Site Framework: A Case Study of the Willamette
Spring Chinook Sports Fishery
Abstract approved:
Redacted for Privacy
Richard M. Adams
The management options chosen by decision makers
in
managing wildlife and fisheries have different effects for
diverse user groups. As a result, natural resource management
agencies often seek information to evaluate the effects of
alternative policies on the benefits provided to different
constituencies.
Over
the
past
decade,
economists
have
developed techniques to measure the benefits provided by such
nonmarket goods.
The random utility model (RUM), a variant of the travel
cost model
(TCM),
is one of the techniques developed by
economists to measure benefits associated with changes in the
quantity or quality of nonmarket goods. The advantages of
using RUN over other techniques are that the substitution
effects among different sites providing similar recreational
activities or services can be incorporated into the model to
avoid overestimating the benefits provided by a certain site.
RUM is used in this thesis to measure the welfare changes
caused by a reduction in fishing quality or closure of one of
the sites in a recreational fishing area. The focus of this
study is the spring chinook recreational fishery in the lower
Wjllaiuette River.
The 1988 Willamette Run Spring Chinook
Survey and the 1988 Willainette River Spring Chinook Salmon
Run
Report,
published by the Oregon
Department of
Fish and
Wildlife, provide the data set to do this research. Three
definitions of travel costs, TC1, TC2, and TC3 are derived
from the data set and used alternatively in the RUM framework.
Specific objectives of this thesis include: (1) estimate
how the attributes of a site (travel cost, congestion level
and fishing quality of the site) will affect the individual's
site choice; and (2) estimate the welfare changes arising from
the two hypothetical policies which change the quality of
fishing experience and which restrict the access of anglers to
a certain fishing site.
The results indicate that fishing sites on the Willamette
River are more attractive to anglers if the fishing quality is
increased, if more people visit, and if the site is relatively
inexpensive to reach. The results of the elasticities of
probabilities show that the travel cost has the largest effect
on individual site choice decision.
For different definitions of travel costs, the estimated
welfare losses caused by the first hypothetical policy (of a
reduction in fishing. success) for a representative angler in
the sample are $ 0.37,
$ 0.91, and $ 0.47 respectively, per
trip. For different definitions of travel costs, the aggregate
welfare losses associated with this hypothetical policy are $
82,309, $ 202,436, and $ 104,555.
For different definitions of travel costs, the estimated
welfare losses caused by the second hypothetical policy (a
closure of one site) for a representative angler in the sample
are $ 3.82,
$ 54.91, and $ 4.83 per trip respectively. The
aggregate welfare losses associated with this hypothetical
policy
are
$
849,786,
$
12,215,114,
and
$
1,074,467
respectively for TC1, TC2, and TC3.
Assuming that these two policies achieved the same
objectives, the policy implication of these results is that
the first policy is preferred because the welfare loss is much
smaller than the second one.
There is a methodological implication suggested by one of
the findings. A few of the individual results obtained from
the model with TC2 travel cost violate the assumption of
utility maximization. This implies that TC2 may over-value the
opportunity cost of time in the travel cost variable, and
points out the uncertain of the definition of travel cost used
in RUM analyses.
Measuring Recreational Fishing Benefits in a Multiple
Site Framework: A Case Study of the Willainette Spring
Chinook Sports Fishery
by
Pei-Chien Lin
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Completed July 6, 1994
Commencement June 1995
APPROVED:
Redacted for Privacy
Professor of Agricultural and Resource Economics in charge of
major
Redacted for Privacy
HeLf department of Agricultural and Resouce Economics
Redacted for Privacy
Dean of Gradu
School
Date thesis presented
Typed by researcher for
July 6. 1994
Pei-Chien Lin
ACKNOWLEDGEMENT
Ny two-year's graduate study at Oregon State University
is a wonderful experience in my life. I have been so fortunate
to meet many nice people who make up such a pleasant study
environment.
First of all, I would like to thank Dr. Richard N. Adams,
my major professor, who always encourages me and gives me
constructive advice. Without his inspirational guidance and
patient editing, this thesis would not be accomplished.
I
would also like to thank Dr. Bob Berrens who introduced this
interesting topic to me and helped me a lot in understanding
the framework of RUN.
I would like to express my gratitude to my committee
members: Dr. R. Bruce Retting, Dr. Carol H. Tremblay and Dr.
Keith
W.
Muckleston,
for providing
useful
comments
and
suggestions.
I also want to express my appreciation to all of my
friends. Especially, sincere thanks go to Shengli, Wen-Chyi
and Wen-Hwa. Their help and friendship have contributed a lot
to the completion of this thesis.
The final acknowledgement is reserved for my family, my
supportive parents and beloved younger sister and brother.
With their boundless love, I have all the time to concentrate
on my study.
TABLE OF CONTENTS
CHAPTER
1
2
PAGE
INTRODUCTION
1
1.1 PROBLEM STATEMENT
2
1.2 OBJECTIVES
6
1.3 STUDY AREA
6
THEORETICAL CONCEPTS
10
2.1 VALUATION OF RECREATIONAL DEMAND.
.
.
2.2 RANDOM UTILITY MODELS
3
10
13
2.2.1 Theoretical Issues
17
2.2.2 Welfare Considerations
20
EMPIRICAL APPLICATION
26
3.1 THE DATA
26
3.1.1 The 1988 Willamette Run Spring
Chinook Survey
26
3.1.2 Site Attribute Data-The 1988
Willainette River Spring Chinook
SaLmon Run Report
28
3.1.3 Survey Adninistration
33
3.1.4 Potential Sources of Bias
.
.
.
3.1.5 Data Analysis
3.2 DESCRIPTION OF EXPLANATORY VARIABLES.
33
34
40
3.2.1 Attributes of the Sites
40
3.2.2 Individual Angler
Characteristics
45
3.3 MODEL SPECIFICATION
46
CHAPTER
4
PAGE
RESULTS AND IMPLICATIONS
50
4.1 RESULTS OF THE CONDITIONAL LOGIT
MODEL
50
4.1.1 Comparison of Alternative
Models
50
4.1.2 Summary of Predicted
Probabilities
57
4.1.3 Average Probabilities and
Elasticities
59
4.1.4 Test of hA Assumption
62
4.2 WELFARE ANALYSIS
65
4.2.1 Estimated Welfare Losses from a
Reduction in Fishing Quality.
.
5
.
67
4.2.2 Estimated Welfare Losses for
Closure of Site 3
70
4.2.3 Substitution Effects
73
4.2.4 Summary of Results
75
CONCLUSIONS
77
REFERENCES
80
APPENDICES
85
Appendix 1: Data From The 1988 Willamette
Run Spring Chinook Survey
.
Appendix 2: The 1988 Willamette Run
Spring Chinook Survey
.
.
85
105
LIST OF TABLES
TABLE
3.1
3.2
PAGE
Weekly fishing quality indices, by site and
mode of fishing
30
Congestion level indices, by site and mode of
fishing
32
3.3
Trips realized and sample size at each site
3.4
Trips realized and the usable sample size at
each site
35
Average speed for individual angler to each
site
36
3.5
.
.
35
3.6
Average wage rate for each income group
3.7
Summary statistics of the site attribute
variables
39
Summary statistics of the individual
characteristic variables
40
3.8
3.9
Description of site attribute variables
3.10
Description of individual characteristic
variables
.
.
.
.
.
37
44
46
4.1
Conditional logit model estimates (TC1)
4.2
Conditional logit model estimates (TC2)
4.3
Conditional logit model estimates (TC3)
4.4
Fit of predicted probabilities for
model 1 (TC1)
58
Fit of predicted probabilities for
model 1 (TC2)
58
Fit of predicted probabilities for
model 1 (TC3)
59
Elasticities of probabilities with respect to
fishing quality index (TC3)
61
Elasticities of probabilities with respect to
congestion level index (TC3)
61
4.5
4.6
4.7
4.8
.
.
.
51
.
52
.
53
TABLE
4.9
PAGE
Elasticities of probabilities with respect to
travel cost (TC3)
61
4.10
The hA test for model 1 (TC1)
63
4.11
The hA test for model 1 (TC2)
63
4.12
The hA test for model 1 (TC3)
64
4.13
Estimated welfare losses for a reduction in
fishing quality by different methods (in 1988
dollars)
67
Aggregate welfare losses for a reduction in
fishing quality (in 1988 dollars)
70
4.14
4.15
Estimated welfare losses for closure of site
3 by different methods (in 1988 dollars).
.
4.16
Aggregate welfare losses for closure of site
3 (in 1988 dollars)
.
.
71
73
LIST OF FIGURES
FIGURE
1.
PAGE
Mainstream Columbia River Tribal set-net fishery
location
4
2
Willamette River study area
3.
Box plot for welfare losses due to a reduction
in fishing quality
68
Box plot for welfare losses due to closure of
site 3
72
4.
8
MEASURING RECREATIONAL FISHING BENEFITS IN A MULTIPLE
SITE FRAMEWORK: A CASE STUDY OF THE WILLAMETTE SPRING CHINOOK
SPORTS FISHERY
CHAPTER 1
INTRODUCTION
Salmon species occupy an important place in the history
and development of the Pacific Northwest. In addition to the
value of salmon to the Native American cultures
region,
salmon
are
important economically.
in this
They provide
recreational, commercial, existent and aesthetic benefits to
a diverse constituency. While the commercial value of salmon
has declined over time, the use value provided by recreational
salmon
fishing remains
important
aspect
of
the
Pacific
wildlife
and
fishery
Northwest lifestyle.
Decision
resources
for
makers,
in
managing
recreational
benefits,
must
choose
from
alternative management options, such as investment in habitat
or changes in regulations (e.g. harvest rates), regarding the
use of hunted or fished species. Some of these management
choices may affect the attributes of a site and thus affect
the quality of the recreation experience. Costs of management
changes,
such
as
investment
in
habitat
acquisition
and
improvement, can be estimated directly from input prices.
However, the benefits arising from management options are
generally not as easy to obtain.
2
Since there is no explicit market with which
to measure
the value of recreational experiences,
economists developed a
range of techniques to estimate the benefits provided
by these
noninarket
goods.
One
general
approach
information provided by related market
to
is
use
the
goods to estimate
indirectly the change in an individual's welfare. This
general
approach includes the Travel Cost Model (TCM), the Hedonic
Price Model (HPM), and the Random Utility
Model (RUM). The
other general approach is to elicit the
individual's benefits
directly, by asking their willingness to pay to consume more
of this good or their willingness to accept
compensation to
forgo the right to consume this good.
This approach is
represented by the Contingent Valuation Method (CVM).
Each general
advantages
and
influenced
by
approach
(or
weaknesses.
the
set
The
of methods)
choice
characteristics
of
of
the
has
technique
its
is
recreational
experience being examined. This study focuses on the issues of
substitution
effects
incorporation
of
among
quality
recreational demand choice.
different
factors
into
sites
the
and
the
individual's
Therefore, the random utility
model is the most appropriate technique.
Justification for
this choice is provided in chapter 2.
1.1 PROBLEM STATEMENT
Since 1950, construction of dams on the Santiam,
middle
Fork Wjllamette and Mckenzie rivers,
tributaries
of the
3
Willaiuette River have blocked over 400 stream miles that were
important spawning and rearing areas for native chinook salmon
and winter steelhead. Hatcheries, built to compensate for
these and other lost spawning areas in the Columbia Basin, now
contribute 70. percent of the upriver Columbia spring chinook
run, 50 percent of the upriver summer chinook run and nearly
all of the Willamnette River spring chinook run.
Spring chinook salmon bound for the Willamette River and
its tributaries annually begin entering the Columbia River
about the first of January. The run size in the lower
Willamette (below Willainette Falls) peaks in late March and
tappers of f into Nay as the fish move into the tributaries.
The allocation of this Willamette run of spring chinook has
traditionally been divided among two groups: commercial
gilinetters on the lower Columbia River and recreational
anglers on the Columbia River, Willamette River and its
tributaries. Since 1981, 24% of the run has been allocated to
commercial fishermen, with the remaining to sport anglers by
the Oregon Department of Fish and Wildlife (ODFW). Until 1994,
there was no Native American fishery for Willamette-bound
spring chinook.
Since the Columbia River Management Agreement went into
effect in 1977, Native American tribes have limited their
fishing to the Columbia River above Bonneville Dam (Figure 1).
However,
due to habitat degradation caused by dam
construction, and overfishing, these salmon stocks have
0
15
30
WASHINGTON
Miles
4
0
COLIA
00
ii
0
0
McNary
OREGON
Bonneville
WILLAJETTE
Darn
meDalleSç.
John Day
RIVRP
Portland
RIVER
I
I
< Treaty Indian Set-Net Fishery
I
I
I
I
I
Figure i. Mainstream Columbia River tribal
set-net fishery
location.
I
5
declined. In 1994, the upriver Columbia spring chinook run
"crashed", from a predicted 49,000 escapement over Bonneville
Dam to fewer than 20,000. Tribes were ordered by the state of
Oregon to cease fishing on the Columbia River before they
caught their allotment of salmon for traditional religious and
cultural ceremonies. The tribes then asserted a claim to the
Willamette River spring
chinook
run which has
remained
relatively healthy but has been the exclusive domain of non-
tribal sport fishermen.
Specifically, the tribes claimed a
treaty right to fish at Willamette Falls, one of the "usual
and accustomed place" protected under their 1855 treaties with
the United States. In response to their claims, ODFW allocated
2,500 fish to the tribes, to be taken at Willamette Falls.
The
involvement of
the Native American
fishery
in
Willamette-bound spring chinook increases competition for the
limited stock of chinook. Decisions to meet the legitimate
needs and rights of the tribes may change the attributes of
the sites (such as the catch rate) and thus affect
the fishing
experience of recreational anglers. As recreational anglers
are currently the major users of the Willamette spring chinook
run, their welfare changes caused by the changes in
allocation
are the focus of this thesis.
Specifically, in this thesis, two hypothetical policies,
which simulate changes in quality or loss of an entire fishing
site, are evaluated. The first involves an increase in Indian
catch to 5,000 fish on the lower Willamette.
The second
6
involves giving the Willamette Falls site exclusively to
Native Americans. Changes in recreational fishing benefits
caused by these hypothetical policies are estimated using the
RUN approach. The estimate results can then be used to compare
the net welfare effects of these policies.
1.2 OBJECTIVES
The overall objective of this thesis is to evaluate the
effects
of fishing quality and other attributes
on the
selection of fishing sites for recreational salmon fishing on
the lower Willamette River (including the Clackamas River).
The
analytical
framework used here also allows
for the
estimation of the welfare change associated with both changes
in access and in the quality of the fishing experience. The
specific objectives of this research include: (1) estimate how
the attributes of the site (travel cost, congestion level and
fishing quality of the site) will affect individual's site
choice; and (2) estimate the welfare changes arising from two
hypothetical management policies which (a) change the quality
of fishing experience and (b) restrict access of anglers to a
certain fishing site (Willamette Falls).
1.3 STUDY AREA
The spring chinook recreational fishery in the lower
Willainette River occurs between Oregon City and the confluence
7
of the Willamette and Columbia Rivers at St. Helens (Figure
Angling
2).
from
occurs throughout these 48 river miles, mostly
anchored
or
slow-moving
boats.
Angling
effort
is
substantial because this 48 mile stretch literally passes
through the Portland metropolitan area. During the fishing
season,
the recreational
fishery
in
this urban
area
is
characterized by the "hogline" phenomenon where boats are
congregated in a line, side by side,
at the most productive
sites.
Annual monitoring
and reporting on this recreational
fishery (below Willamette Falls) has been conducted by the
Oregon Department of Fish and Wildlife since 1964. Since 1974,
a sampling plan developed by the Survey Research Center of
Oregon State University has divided the Willamette River below
Willainette Falls into three sampling sections, with a fourth
section (the Clackainas River) added in 1979. These include (1)
the lower river fishery, including 4 miles of the
Willamette
River from the St. Johns Bridge to the mouth and 22 miles of
Multrioinah Channel from the head of the channel to St. Helens;
(2)
the middle river fishery extending 16 miles from the
Southern Pacific Railroad Bridge to the St. Johns Bridge;
(3)
the upper river fishery extending 6 miles from Willamette
Falls to the Southern Pacific Railroad Bridge at Lake Oswego;
and (4) the Clackanias River extending upstream 23 miles from
its confluence with the Willamette at Gladstone to River Mill
Dam. In this thesis, the definition of "sites" in the site
8
St. Helens
0
RIVER
St. Johns
Bridge
Portland
Southern Pacific
River Mill
Railroad Bridge
Dam
Willamette Falls
Gladstone
Figure 2. Willamette River study area.
9
choice set (site 1, site 2, site 3, and site 4) corresponds to
these sampling sections or reaches of the river. Thus, site,
as used subsequently refers to reaches or stretches of the
river, not to a specific site along the river. Also, this
thesis does not address potential fishing sites not included
in the survey, such as upriver near Eugene, Oregon. The effect
of this exclusion is discussed subsequently.
10
CHAPTER 2
THEORETICAL CONCEPTS
2.1 VALUATION OF RECREATIONAL DEMAND
Natural resource systems such as rivers,
lakes,
and
forests provide a range of services, including recreational
activities,
such
as
fishing,
boating,
swimming,
hiking,
skiing, hunting and camping. The value of these services from
resource systems
research
is
interest.
an issue of considerable policy and
From
an
economic
perspective,
these
services have two important features. First, their economic
values depend upon the characteristics of the natural resource
system which provides the service. Second, access to these
resource services is typically not allocated through markets.
Therefore, there is no price information to reflect the cost
of providing these services or of users' willingness to pay
(Freeman, 1993).
Economists have spent considerable effort developing
techniques to value noninarket goods and services. There are at
least three questions related to estimating the economic value
of
recreational
activities.
First,
how
is
the
flow
of
recreational services provided by a natural resource system to
be
defined
and measured?
Second,
how
is
the
value
of
introducing a new recreation site or of losing a recreation
site to be estimated ex ante. Third, how is the value of a
change in the quality of a recreation site or change in the
11
quality of the flow of recreation services from a natural
resource system to be estimated?
Two general approaches have been developed by economists
to measure the demand for nonmarket goods such as recreation
activities, and to address specifically the above questions.
The first category includes the indirect methods, which use
observed behavior and choices (revealed preferences) or market
data related to the pursuit of recreational activities, to
infer people's demand for recreation. These indirect methods
include travel cost models (Cesario and Knetsch,
and Nawas,
1970; Brown
1973; Bockstael et al., 1987), and new variants
such as random utility models (Bockstael et al.,
1989) and the
hedonic travel cost model (Brown and Mendelsohn,
1984).
The
other category, the direct method, seeks people's willingness
to pay for some posited change in the recreational service.
This
elicitation
procedure
valuation method (Loomis,
is
known
as
the
contingent
1988; Cameron and Huppert, 1991).
Travel cost models (TCM) using survey data collected from
observed site visits and related information have been widely
used in recreation analysis for at least three decades.
However,
it is well known that a travel cost model which
focuses only on the benefits provided by a given site in a
system of recreation sites will overestimate the benefits of
that site if substitution effects exist among sites. It is
also difficult to address the effect of quality changes at a
given site with the traditional TCM.
12
Contingent valuation methods
have been broadly
(CVM)
applied in the valuation of nonmarket goods and services in
the past decade. While more flexible than the traditional
travel cost model, CVM does not eliminate the difficult issue
of accounting for substitution across a system of sites. In
addition, CVM studies also carry some specific limitations and
problems,
including
the
nature
hypothetical
of
survey
questions.
When the analyst is concerned with valuing access to
recreational activities over a region or changes in quality at
one of the recreational sites in an area, the random utility
model
has
(RUM)
Specifically,
the
been
shown
RUM
is
to
be
helpful
useful
a
in
technique.
accounting
for
substitution effects across multiple sites which provide
similar amenities or services, thus avoiding the bias in the
estimate of benefits provided by each site. Though discrete
choice random-utility models are more complicated to estimate
than other models, such as the TCM, they are well suited to
explaining
individual
choice
among multiple
sites
as
a
function of the cost and other characteristics of the choice
set.
The application of random utility models to recreation
decisions has increased considerably in the past decade. Most
of these applications focused on measurement of the benefit of
improvements in water quality or fish catch in a multiple site
L3
framework (Bockstael et al.,,
1987; Bockstael et al., 1989;
Morey et al., 1991).
2.2 RANDOM UTILITY MODELS
The focus of the remainder of this chapter is on the
random utility model
discrete choice model,
(RUM or discrete choice model).
based on McFadden's
(1974)
The
random
utility framework, has been increasingly used to fit specific
features of recreation decision-making. For example, when an
individual has several alternatives in his recreational site
choice set, the discrete choice or random utility model, with
its emphasis on explaining choice among sites as a function of
the attributes of the available alternatives,
seems well
suited to replace the traditional travel cost model. The
advantage or gain from using the RUM framework (in terms of
its ability to describe substitution effects among sites)
comes at a cost; the inability to explain the total demand for
a recreation activity (Freeman, 1993).
The random utility model is actually a variant of the
general travel cost model
(Smith,
1989; Fletcher et al.,
1990). However, they differ in two important ways. First, the
travel cost model assumes that each individual decides the
total number of trips at the beginning of the season. In the
random utility model,
each trip is chosen independently.
Second, the RUM focuses explicitly on site choice by examining
different attributes among sites, whereas the travel cost
14
model generally minimizes the substitution effects among
sites.
The random utility approach to modeling recreational
demand imposes four important assumptions about how recreation
choices are made. They are:
The time horizon is altered from the season (as in the
TCM) to a single-trip occasion. When an individual selects one
recreation site for each trip occasion, visits to other sites
are excluded.
The decisions for each recreation choice are independent
across trip occasions. This assumption means that instead of
allocating her recreational activities at the begin of the
season, the individual makes a decision at the time of each
trip occasion.
Individuals compare the utility that could be realized
from all other related decisions, conditional on the selection
of a recreation site. In this framework, the indirect utility
function, V(*), is the maximum of a set of functions, Vk(*),
defined conditionally on the selection of each site, where k=
1, 2,.., J are the alternatives in the site choice set. This
indirect utility function set includes the
(represented
in
the
following
equation
implicit price
as
k')
of
the
selected site.
V(y,p11...,P) = Max (V1(y, pi),,V(y, Ps))
(2.2-1)
15
(4) The random utility model describes the probability that an
individual will
select any one
of the available sites.
Therefore, the individual's conditional utility function is
assumed to be stochastic.
In the framework of the random utility model, each person
(indexed by i), on each choice occasion, has available a set
of alternative destinations, call S1. If person ± visits site
j, she is assumed to obtain utility equal to
= U ( Qjj
is a vector of characteristics of site j
), where Q
Z
as
perceived by person i (e.g., travel cost from l's home to the
site and/or the quality of the recreation site), and Z
is a
vector of individual characteristics for i (e.g. age, fishing
experience, fishing skill etc.). The utility from a visit to
j by ± is composed of two parts: a portion which is
observable by the researcher (and common to all visitors), Vj
(Q3, Z), and a component that is not observable by the
researcher, e13. Therefore,
Uji =
N
(
Z) +
(2.2-2)
Estimation then proceeds by specifying a functional form for
the deterministic part of the utility (i.e.,V(*)) and assuming
a distribution for the unobservable component across the
population. One can use this specification to estimate the
probability that an individual with a given observed utility
level of V(*) will visit site j.
16
The estimation of the choice probabilities is based on a
maintained hypothesis of utility maximization. Thus, on any
given choice occasion, person i will visit site j if tJjj
> Ujk
for all other k in S1. That is, i visits j if the utility of
a visit to j is larger than the utility of visiting any other
sites in the alternative set.
Based on the researcher's
information, this means that the probability of i visiting j
is given by
Prob (site=j) = Prob (U
With
z)
=
> Ujk, f or all other k)
(2.2-3)
we have:
+
Prob (site=j) = Prob (V
+
>
= prob (eik < Vjk+
Vik +
eik)
V+ e)
(2.2-4)
The random component is additive and attributed to the
unmeasurable variation
variables.
distributed
(Weibull),
in tastes
If the e's are
with
a
type
then we have
well
as to omitted
independently and identically
I
a
as
extreme
value
multinomial
distribution
logit model.
The
multinomial logit model is the simplest model structure in the
random utility method and has been applied extensively in
research on individual choice among modes of transportation
(Hensher,1986),
allocation
of
commercial and recreational users
fishery
(Green,
stocks
between
1994),
and the
17
measurement of welfare change in bighorn sheep hunting (Coyne
and Adamowicz, 1992). However, the multinomial logit (NNL)
implicitly assumes independence of irrelevant alternatives
(hA);
i.e.,
the relative odds of choosing any pair of
alternatives remains constant no matter what happens in the
remainder of the choice set. Thus, this allows for no specific
pattern of correlation among the errors associated with the
alternatives.
practically
Sometimes,
appealing
behavior (Greene,
in some settings, hA is not
restriction
to
place
on
a
consumer
1993).
A more general nested logit model (McFadden,
1978),
specifically incorporating varying correlations among the
errors associated with the alternatives, can also be derived
from a stochastic utility maximization framework. However, the
cost of this advantage is that it complicates the estimation
procedure. Bockstael et al.
sportfishing
along
the
(1989) apply such a model to get
coast
of
Florida.
Some
subtle
variations on the RUN structure have also been developed for
specific applications to recreational issues (Kaoru et al.,
1994; Parson and Kealy,
1992; Morey et al., 1990).
2.2.1 Theoretical Issues
If there are J choices and Z
is the vector of individual
characteristics (e.g. age, sex, fishing experience etc.) for
individual i, then the probability that an individual with
characteristics Z
will choose the jth option is
18
exp
Z
'
Pu
(2.2-5)
Z exp(a'kZl)
k=1,2, . , J
where J is the number of choices facing each individual. With
some normalization, like a1 = 0, the number of parameters to
be
estimated
is
equal
to
the
number
individual
of
characteristics multiplied by J-1. This is the multinomial
logit
model
which
is
applied when
data
are
individual
specific.
The
discrete
terminology)
choice
model
(according
to
Greene's
is different from the inultinoinial logit (NNL)
model. The main difference between these models is that the
discrete choice model. considers the effects of choice
characteristics on the determinants of choice probabilities,
while the MNL model makes the choice probabilities dependent
on individual characteristics. If Qjj denotes the vector of
characteristics for choice j as perceived by individual i,
then the probability that individual i choose alternative j in
discrete choice model is
exp('Q)
Pu
=
exp( 'Quk)
k=l , 2
, . . ,
J
(2.2-6)
19
where J equals the number of possible alternatives in the
choice set. The number of parameters to be estimated is equal
to the number of attributes of the choice.
McFadden (1974) suggests an extension of the multinomial
logit model by combining the above two models.
model
(conditional logit model)
The McFadden
considers the effects of
choice attributes as well as individual characteristics on the
determinants of choice probabilities.
In RUM theory, individual i's utility from the recreation
experience provided by site j can be expressed as
( Q, Z) +
= Vj
where V1
(2.2-7)
is the indirect utility of individual i associated
with choosing site j, Qjj is the vector of site attributes and
Z
is the vector of individual characteristics.
The indirect utility function is assumed to be linear and
represented by
=
where Q and Z
represent
+
'X
,
=
'1
P'2j +
(2.2-8)
partitions of matrix X (X
variables
measuring
individual
characteristics,
partitions
of
vector
f3,
the
site
respectively.
are
the
=
[
,
zi ]),
attributes
1'
estimated
and
and
parameters
corresponding to the site attributes and the individual's
20
characteristics.
If
the
disturbances
are
assumed
to
be
distributed as type I extreme values, then the conditional
logit model expresses the choice probability as:
= Prob (site =
exp(P'X)
j) =
(2.2-9)
exp (Vik)
Eexp(I3'Xk)
Site-choice probabilities are used to derive a likelihood
function that is maximized to yield the parameter estimates.
The log likelihood function for the conditional logit model is
nJ
(2.2-10)
lnL = EL d.J in P.J
iJ
i=l,2,...,n for individual.
j=l,2,...,J for site choices.
where d
= 1 if alternative j is chosen by individual i, and
0 if not. Newton's method is used to find the solution.
2.2.2 Welfare Considerations
The classical tool
for measuring welfare change
is
consumer's surplus, which is simply the area to the left of
the Marshallian demand curve between prices p° and p1. In the
traditional travel cost model, a Narshallian demand curve is
21
derived and the welfare measure is the "consumer surplus"
associated with access to a recreational site.
However, the theoretically correct measure of consumer
surplus is not the Marshallian version but the Hicksian
version. Two concepts of consumers' surplus are recognized in
Hicksian consumer surplus theory: compensating variation and
equivalent variation. Compensating variation defines the value
of
change
a
in
quantity
or
quality
as
the
amount
of
compensation, paid or received, that would return consumers to
their
initial
welfare
position
after
the
change.
The
equivalent variation defines the value of a change in quantity
or quality, paid or received, that would bring consumers to
their subsequent welfare position if the change does not occur
(Randall, 1987). The presumed property right determines which
measure is appropriate to value the welfare change. If the
respondent is assumed to have the right to the initial level
of environmental service
(quantity or quality),
then the
Hicksian compensating measure (HC) is appropriate to measure
the welfare change. If the respondent is assumed to have the
right
to
the
subsequent
level
of
environmental
service
(quantity or quality), then the Hicksian equivalent measure
(HE) is theoretically correct measure of the welfare change.
If
the marginal utility
of money
is
constant then
the
compensating variation equals equivalent variation.
The initial research on welfare measures in discrete
choice model was carried out by Small and Rosen
(1981).
22
Theoretically, they included the quality variable q which is
considered exogenous by consumers into individual utility
functions. Thus
(2.2-li)
u= u(x,q)
By solving the problem of maximizing utility subject to
the budget constraint (px = in), the indirect utility function
and the expenditure function are well defined and satisfy
U = v [p, q, e(p, q,
U)]
(2.2-12)
Taking the quality derivative of equation (2.2-12) yields
By
1
-
(2.2-13)
)
(
A
where A= Bv/c3m is the marginal utility of income.
implication
of
equation
(2.2-13)
is
that
the
The
marginal
willingness-to-pay for a quality change is given by the
marginal utility of quality, converted to monetary units via
the marginal utility of income.
If individuals are assumed to have the property right to
the initial quality level of a site, then the measure of
change in economic welfare caused by the change in site
quality is the amount of money paid or received that would
23
leave the individual as well off as without the change
(compensating variation). In the random utility model, the
value of a change in a site attribute (or quality) is the
adjustment in the amount of travel cost to keep the individual
at the same utility level as before the change.
In the random utility model, once the parameters of V(*)
have been estimated, the monetary value of a change in
from
Q° to Q' can be calculated. For individual i, this value is
implicitly defined as
Q',Z)+e
V
where Y
= V(Y-C, Q° Z)+c
is the income of individual i,
for individual i to site j, Z
of individual i, and HC
(2.2-14)
is the travel cost
is a vector of characteristics
is the Hicksian compensating measure
of the welfare change associated with the change in Q.
However, two hurdles must be overcome before one can
proceed to calculate the compensating surplus (Freeman, 1993).
The first arises because for each individual, there is no
variation in income across the alternatives in the choice set,
so there is no independent estimate of the parameter on income
that gives the marginal utility of income. Fortunately, this
problem can be overcome by recognizing that the relevant
income measure is total cost less the cost or the price of the
recreational activity, C. Therefore the coefficient of
24
income is equal to the negative of the estimated coefficient
for the travel cost.
The second hurdle arises because of the unobserved
component of utility. As a result, it is impossible to know
whether each individual will visit the site in question or
not. If individual i does not visit the site, HC1
Bockstael et al.
is zero.
show that this uncertainty can be
(1991)
addressed by defining the welfare measure as the payment that
equates the researcher's expected value of realized utility
with and without the change in Q. Then HC
can be defined
implicitly as follows
Q', S)]=E[V*(Y_C, Q°1 S1))
(2.2-15)
where V*(.) is the maximum of
V(Y-C, Q1 S1),
for all j.
Given the assumption concerning the distribution of
(2.2-16)
and
assuming that the marginal utility of income is constant, then
the approximated compensating surplus can be obtained from
equation (2.2-17)
J
J
ln{Eexp[v(Qjl) 1}-ln{Eexp[v(QjO) ] }
j=1
HC
j=1
=
(2.2-17)
b
j= 1,2,.., J
25
where b represents the marginal utility of income (from the
estimated coefficient for the travel cost variable), and J is
the number of choices facing each individual.
Similar calculations can be used to obtain the loss from
deleting a site with a specified set of characteristics from
the individual's choice set. The expression is
J
J-1
ln{Eexp[v(QjO)]} -{ln[Zexp{v(QjO)]}
j=1
j=1
=
(2.2-18)
b
j= l,2,..,J-1, J
where J is the number of choices for each individual.
It
should
be
emphasized
again
that
these
welfare
measurements are based on a single choice occasion, i.e., one
trip.
26
CHAPTER 3
EMPIRICAL APPLICATION
The previous chapter contains a discussion of theoretical
concepts underlying the random utility model
(RUM).
This
chapter discusses the application of the RUM framework to
estimate the effect of changes in fishing quality and access
on the value of the fishing experience provided by the spring
chinook run in Willamette and Clackamas Rivers.
3.1 THE DATA
3.1.1 The 1988 Willamette Run Spring Chinook Survey
The Oregon Department of Fish and Wildlife funded a 1988
survey of recreational salmon anglers on the Willamette and
Clackamas Rivers. There were several sub-domains within the
class of anglers targeted for special analysis. These included
anglers fishing by boat and bank, anglers fishing during the
week or weekends,
and the
four geographic areas
(lower
section, middle section, upper section of Willamette River,
and lower Clackamas River) established for the ODFW creel and
count program. Personal interviews were collected at randomly
chosen sampling sites within each of the four areas.
A
clustered sampling approach was used. There are approximately
six sites where interviews could be conducted within each of
the four areas (Davis and Radtke, 1989). These detailed survey
results provided information that can be used to do analyses
27
of demand, preference, contingent valuation, and travel cost,
as well as information required to perform economic impact
analyses.
The information included in this survey can be classified
into the following categories:
(1) Demographic information: Including respondent's
gender, employment status, income, education level,
and age.
(2) Trip characteristics:
the primary purpose of this trip ( e.g. fishing or
some other activity).
days per trip.
(C) residence (Zip code)
round trip travel time.
round trip travel distance.
party size.
(3) Angling effort:
number of hours spent on fishing per trip.
number of fish landed.
(C) number of trips.
(d) equipment cost.
(4) Travel cost:
transportation.
camping/lodging.
food and drink purchased at stores.
guide fee.
28
boat gas/oil.
rental of boat and/or fishing equipment.
fishing tackle and bait.
supplies.
others.
(5) Hypothetical change in fishing quality:
a hypothetical change in run size.
a hypothetical change in congestion status.
(C) the willingness to pay for increasing run
size/congestion combinations.
From the information on zip code, an individual's round
trip distance to each site can be approximated and used to
derive travel cost variables.
3.1.2 Site Attribute Data -The 1988 Willamette River Spring
Chinook Salmon Run Report
The 1988 survey data do not include information on site
attributes needed for this study. The site attributes data set
is from the 1988 Willamette River Spring Chinook Salmon Run
report,
published by the Oregon Department of
Fish
and
Wildlife. Weekly records and estimates of spring chinook catch
and angler days in different sections of the Willamette and
Clackainas rivers can be used to construct fishing quality and
congestion level indices which represent the attributes of
each site.
29
The "expected catch"
one quality dimension of
a
fishing trip which might cause an individual to choose
a
is
particular site. Unfortunately, an individual's expected catch
rate is difficult to elicit. Instead, researchers usually use
the realized catch per trip, i.e. the ex post measure, as the
proxy of expected catch (Brown et al., 1965). Here, weekly
records of realized trips (angler days) and catch in each
section of the lower Willamette River and lower Clackamas
River are used to construct the fishing quality index. The
weekly fishing quality indices, which are different for each
site, are listed in table 3.1.
30
TABLE 3.1. WEEKLY FISHING QUALITY INDICES, BY SITE AND MODE OF
FISHING
Date
Jan.10
17
24
31
Feb. 7
14
21
28
Mar. 6
13
20
27
Apr. 3
10
17
24
May
1
8
15
22
29
June 5
12
Site 1
site 2
boat
boat
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.120
0.000
0.118
0.118
0.000
0.000
0.125
0.119
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.118
0.118
0.119
0.118
0.118
0.118
0.118
0.118
0.118
0.118
0.118
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.118
0.118
0.118
0.118
Site 3
boat
bank
Site 4
boat
bank
0.118
0.118
0.118
0.033
0.066
0.060
0.018
0.036
0.028
0.016
0.052
0.040
0.119
0.004
0.015
0.005
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.047
0.059
0.127
0.121
0.092
0.081
0.122
0.140
0.112
0.055
0.207
0.108
0.025
0.010
0.052
0.096
0.000
0.025
0.081
0.058
0.000
0.000
0.032
0.046
0.198
0.184
0.105
0.066
0.000
0.133
0.218
0.078
0.227
0.000
0.232
0.325
0.216
0.187
0.043
0.058
0.179
0.093
0.034
0.083
0.177
0.211
0.314
0.236
0.360
0.044
0.121
0.054
0.125
0.164
0.000
0.000
0.000
0.000
0.336
0.145
0.000
0.089
0.137
0.000
0.025
0.000
0.118
31
The
interaction
between
congestion
and
recreation
benefits has been investigated in a number of studies. Various
approaches have been developed to model this relationship
(McConnell, 1988; Wetzel, 1977). The implicit hypothesis is
that
congestion
will
affect
negatively
an
individual's
willingness to pay for a recreational experience. In early
research, congestion was treated as a quality attribute of the
recreational
experience
which
affects
negatively
an
individual's evaluation of recreational activities (Fisher and
Krutilla, 1972). Another alternative was to treat congestion
effects as a cost (Cesarlo,
1980). Deyak and Smith (1978)
point out that congestion may have an ex ante effect on
recreation. Specifically, "expected congestion" is considered
by recreationists as they make their site choice decision.
Weekly estimates of angler days taken from the 1988 Willamette
River Spring Chinook Report, are used to construct the "index
of congestion level" variable. The weekly congestion level
indices for different modes of fishing (i.e. bank or boat) at
each site are listed in table 3.2.
32
TABLE 3.2. CONGESTION LEVEL INDICES, BY SITE
ID MODE OF
FISHING
Date
Jan.10
17
24
31
Feb. 7
14
21
28
Mar. 6
13
20
27
Apr. 3
10
17
24
May
1
8
15
22
29
June 5
12
Site 1
site 2
boat
boat
boat
bank
boat
bank
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
3.22
0.00
3.53
4.84
0.00
0.00
2.08
3.74
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
4.84
6.05
6.74
6.64
3.74
4.84
6.52
6.14
5.83
5.69
6.52
6.23
4.44
5.13
5.54
5.13
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
6.45
8.36
9.05
8.87
5.83
7.02
7.69
7.17
4.84
7.12
8.23
7.85
3.74
7.05
7.40
7.14
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
7.43
9.56
9.59
9.65
7.82
8.57
8.64
8.28
8.11
8.68
8.86
8.85
7.48
7.81
7.28
7.07
0.00
6.77
6.44
6.59
0.00
5.79
5.86
6.62
9.10
8.98
8.50
7.51
0.00
8.31
7.82
7.18
6.60
0.00
8.86
8.92
8.74
8.29
6.91
7.24
6.69
7.79
6.08
5.32
6.54
6.76
7.17
7.30
7.15
6.11
5.75
7.35
5.87
6.10
0.00
0.00
0.00
0.00
4.90
5.43
6.01
6.31
6.93
0.00
7.31
0.00
Site 3
Site 4
33
3.1.3 survey Administration
The questionnaire in the 1988 survey was developed in
consultation with ODFW. This questionnaire had been pretested
using twenty field interviews of steelhead fishermen on the
Santiam River in March 1988. Some wording changes resulted
from the pretest, but the content remained the same.
Weekly schedules were developed based on the sampling
plan. Anglers were randomly chosen and interviewed at the
landing or bank fishing area. A random number between 1 and
100 was used to start a systematic choice of anglers either
removing boats or fishing from the bank. The questionnaire was
administered face-to-face with the option of the respondents
mailing
information
concerning
trip
expenditures.
No
respondents elected to use the mail option, although several
took copies of the survey form and promised they would return
the form if they could think of additional expenditure.
However, no forms were returned.
3.1.4 Potential Sources of Bias
This survey was conducted
format.
response
in an on-site,
intercept
Intercept field surveys generally have very high
rate
but
usually will
over
sample
the
"avid"
participants; i.e., it is reasonable to believe that on-site
intercept surveys are more likely to intercept individuals who
participate more
often.
However,
mail
surveys
may
also
34
oversainpie avid participants because such individuals may be
more likely to respond to the survey. This form of bias in
estimates is labeled as "sample-selectivity bias" (Morey et
al., 1991). The other potential source of bias is response
errors. The respondents may misunderstand the questions, or
cannot
recall
appropriately
clearly past
state
their
experiences,
response
on
or
the
they
cannot
hypothetical
problems (Davis and Radtke, 1989).
Small sample size is more likely to result in a bias
toward avid fishermen, requiring that the sample size and
sampling plan be carefully determined and designed. Other ways
to deal with "sample-selectivity bias" and the problem of
limited trip data have been discussed by Morey et al. (1991).
In the 1988 Willainette spring chinook survey, attempts to
minimize response errors included asking questions about
immediate and recent behavior; well-delineated hypothetical
situations; and clearly focused attitudes. Prognosis
type
questions, such as "how many times will you fish this year",
were
not
asked.
In
addition,
trained
and
experienced
interviewers were used in order to minimize response error
associated with interviewers' distortion (Davis and Radtke,
1989).
3.1.5 Data Analysis
A total of 302 interviews were conducted. Removing those
interviews which did not contain complete information about
35
day trip, income, and primary purpose of trip resulted in a
total of 266 usable observations. Since a clustered sampling
approach is used, the sample size for each site should be
proportional to the realized trips. Trips realized in the
season and the number of samples taken at each site
are
compared in Table 3.3 to examine the sampling scheme. Further,
the effect of removing some observations from the sample is
examined in table 3.4.
TABLE 3.3. TRIPS REALIZED AND SAMPLE SIZE AT EACH SITE.
SITE 1
Trips
Sample
size
Ratio
SITE
2
SITE 3
SITE 4
TOTAL
98,385
32,185
76,981
14,906
222,457
131
51
92
28
302
0.0012
0.0019
0.0013
0.0016
0.0014
TABLE 3.4. TRIPS REALIZED AND THE USABLE SAMPLE SIZE AT EACH
SITE.
SITE 1
Trips
Usable
Samples
Ratio
SITE
2
SITE 3
SITE 4
TOTAL
98,385
32,185
76,981
14,906
222,457
117
40
82
27
266
0.0018
0.0012
0.0012
0.0012
0.0011
36
In the 1988 survey, only information about the chosen
site for each individual is available. Other information, such
as round trip distance and travel time, to the other sites in
the choice set can not be obtained from this survey. However,
the information of zip codes is used to measure round trip
distance for each respondent to different sites. Information
on round trip distance for the observed choice and the zip
codes were also used to derive the round trip distance to
sites not chosen by each individual angler.
The average speed for individuals to each site is derived
by categorizing the sample into four groups according to the
site choices, and then taking the average speed for each
group. The average speed for each site is presented in table
3.5.
TABLE 3.5. AVERAGE SPEED FOR INDIVIDUAL MGLER TO EACH SITE
SITE 1
Average Speed
(miles per hour)
31.37
SITE 2
SITE 3
SITE 4
22.07
30.44
41.46
Measurements of round trip distance and average speed for
each respondent to different sites are used to calculate the
travel
time
and
the
opportunity
cost
of
that
time.
Respondents' incomes have been categorized into 9 groups. The
mean of each group is taken as the representative income for
respondents falling into a certain income group. The hourly
37
wage rate is estimated by dividing the mean income of each
group with 2080, assuming each person work 40 hours a week and
52 weeks in a year. The wage rate for each income group is
listed in table 3.6.
TABLE 3.6. AVERAGE WAGE RATE FOR EACH INCOME GROUP
Income
group
< $5,000
Mean
Mean wage rate
Mm
wage rate
00a
$
2,500
$
1.2
$
$ 5,000 $ 9,999
$
7,500
$
3.6
$
2.4
$ 10,000 $ 14,999
$ 12,500
$
6.0
$
4.8
$ 15,000 $ 19,999
$ 17,500
$
8.4
$
7.2
$ 20,000 $ 24,999
$ 22,500
$ 10.8
$
9.6
$ 25,000 $ 34,999
$ 30,000
$ 14.4
$ 12.0
$ 35,000 $ 49,999
$ 42,500
$ 20.4
$ 16.8
$ 50,000 $ 74,999
$ 62,500
$ 30.0
$ 24.0
$ 75,000 <
$
$ 43.3
$ 36.0
a
90000b
The minimum wage rate is derived by dividing the lower
bound of each income group by 2080. For example, the
minimum wage rate for the second income group is $ 2.4(= $
5,000 / 2080).
This number is arbitrarily picked.
There is a potential problem arising from the derivation
of travel time. Since travel time is obtained by dividing the
38
round trip distance by an assumed average speed to each
fishing reach, this "average speed" assumption is important.
For example, reach 2 is located in the heart of the Portland
metropolitan area. As a result, the average speed is assumed
to be 22 miles per hour, the lowest among the average speeds
to all reaches because of traffic congestion associated with
traveling through this area. However,
for those who live
outside Portland and hence travel longer distances to this
reach, the average speed (for the entire trip) is likely to be
higher than the estimated average speed of 22 miles per hour.
For such individuals,
this results in an overestimate of
travel time. The use of only day trips (from the survey) in
this research may partially mitigate this kind of measurement
error. That is, people who complete their trip in one day are
likely to live relatively close to Portland, thus the low
average
speed
assumption
should
be
valid
for
most
participants.
Travel costs include vehicle operating costs (variable
travel cost, measured as round trip mileage times $ 0.2875) as
well as the opportunity costs of travel time. Travel time was
estimated from the round-trip mileage to each site divided by
the average speeds listed in table 3.5. Two measures were used
for the opportunity cost of travel time.
In the first case,
for employed individuals (either full-time or part-time), the
mean per hour wage rate was used as the opportunity cost of
time. For students, unemployed,
retired or homemakers, the
39
minimum per hour wage rate was used as the opportunity cost of
time. In the second case, for both full-time and part-time
employed, one-third of the mean per hour wage rate was used as
the opportunity cost of time.
For students,
unemployed,
retired or homemaker, one-third of the minimum per hour wage
rate was used as the opportunity cost of time.
After removal of incomplete surveys,
a total of 266
respondents remained in the sample. Since each respondent
faces 4 site choices, there is a total of 1064 observations in
the data set. Summary statistics of the data are given in
tables
3.7
and
3.8.
TABLE 3.7. SUMMARY STATISTICS OF THE SITE ATTRIBUTE VARIABLES
Site 1
Site 2
Site 3
Site 4
Total
sample
a
b
Tcla
18.04
11.42
13.07
18.81
15.51
TC2a
49.56
38.97
37.73
45.45
42.93
TC3a
28.73
20.68
21.81
27.25
24.62
FIa
0.109
0.114
0.158
0.114
0.124
CGa
9.124
7.961
8.432
4.058
7.39
CHOICEb
116
41
82
27
266
Variables presented at their mean values.
Choice variable is the number of respondents observed at
each site in the sample.
40
TABLE 3.8. SUMMARY STATISTICS OF THE INDIVIDUAL CHARACTERISTIC
VARIABLES
Fya
AGa
Site
Site
Site
Site
1
2
3
4
Total
sainp le
46.10
49.80
43.23
35.15
44.68
CHOICEb
OBb
14.13
16.00
12.76
9.93
13.57
91
31
60
13
195
116
41
82
27
266
ab Variables presented at their nean values.
These variables are presented as the nuither observed at each
site in the sample.
3.2 DESCRIPTION OP EXPLANATORY VARIABLES
The explanatory variables are divided into two groups.
One group is used to describe the attributes or qualities of
sites, and the other is a set of characteristics for each
respondents. Previous research suggests that both sets of
variables affect individual site choice decisions.
3.2.1 Attributes of the Sites
The features of
a
recreation
site will affect the
recreation activities produced there and the site-choice
decision. In describing the effect of site attributes on an
individual decision, the physical attributes of a site should
be distinguished from its service attributes (Smith, 1989).
The physical attributes of a site may include water quality,
air quality, accessibility of the site, the stock of fish or
41
game, and numbers of camping sites and facilities. An example
of a service attribute is the level of congestion. Usually,
the term "quality" is used as a proxy for a mix or combination
of characteristics at a site. In fishing demand studies, the
catch rate or success rate are usually used as measures of
quality for each fishing site (Vaughan and Russell,
1982;
Bockstael et al., 1989).
Four variables describe the attributes for each site in
this study. They are:
Fl: an index of fishing quality. Fl is defined as the
ratio of the spring chinook catch to the number of angler days
at the site for that time period. The higher the ratio the
less time (angler days) that anglers must spend to catch a
fish. In other words, anglers will have a higher probability
of success
(catching a spring chinook) per trip.
Since a
spring chinook is the ultimate prize to many recreational
anglers, the "success rate" is an appropriate definition of
the fishing quality (Bockstael et al., 1989)..
CG:
an index of congestion.
It is defined, as the
natural log of lagged angler days, assuming people develop
their expectations based on the congestion level of each site
(total angler days) from the week prior to their site choice
decision.
Since variations in angler days
monotonic transformation of
a
natural
are large,
the
log function will
decrease the variation levels without changing the order of
levels.
42
(3) TC: travel costs. Travel costs are used as proxies for
the price of a recreational fishing trip. The inclusion of
opportunity cost of time and other costs, e.g., equipment cost
and fees, is still an open issue in the recreation literature.
Because of the uncertainty concerning the appropriate measure
of costs, three alternative travel cost variables are defined.
TC1, the variable cost of travel (Transit cost):
[$
0.2875 * the round trip distance from the angler's
residence to each fishing site].
TC2: the variable cost of travel plus the opportunity
cost of time. The measurements of opportunity cost of
time are differentiated into two groups as described
earlier, i.e., for the employed, the opportunity cost
of time is measured at their mean per hour wage rate.
Thus, travel cost is calculated as : (annual mean
income for each income group / 2080)
* (travel time)
+ {($ 0.2875) * (round trip distance)]. For students,
the unemployed, retired or homemakers, the
opportunity costs of time are measured at their
minimum per hour wage rate. That is, (annual minimum
income for each income group / 2080) * (travel time)
+ {($0.2875) * (round trip distance)].
TC3: the variable cost of travel plus the opportunity
cost of time. The same definition as TC2 except that
the opportunity cost of time is measured at one-third
of the wage rate for each group. The opportunity cost
43
of time for different occupational status may be
different. For people who must work, a higher value
on the opportunity cost of time (than for those who
do not have to work) is assumed.
44
TABLE 3.9. DESCRIPTION OF SITE ATTRIBUTE VARIABLES.
Variable
names
FI
Description
Fishing quality index.
Spring chinook catch at each site
Fl =
Total # of days fished for spring chinook
CGJt
Congestion index. If Nt denotes the total number of days
fished for spring chinook in a certain week, then:
CGJt = ln( N.i
TC1
Travel cost. Variable cost of travel, $0.2875/per mile
times the round trip distance (D) from the angler's
residence to each site.
TC]. = $ 0.2875 * D
TC2
Total travel cost. The estimated total travel cost for
each complete trip (variable cost of travel plus the
opportunity cost of travel time ,TTa).
1. for full time employed and part-time
employed
TC2 = TC1 + wage rateb * TT
2. for unemployed, student, retired, and
homemaker
TC2 = TCJ. + mm
TC3
wage rateC * TT
Total travel cost. The estimated total travel cost for
each complete trip (variable cost of travel plus the
opportunity cost of travel time, TT).
1. for full time employed and part-time
employed
TC3 = TC1 + 1/3 wage rate * TT
2. for unemployed, student, retired, and
homemaker
TC3 = TC1 + 1/3 mm
a
C
wage rate * TT
The travel time (TT) is estimated by dividing round trip distance by the
average speed for each respondent to each site.
Information on wage rate is not available in this survey. However,
information on individual's income level can be used to estimate the
wage rate. The estimation procedure is as follows: pick the mean value
of each income category and divide it by 2080, assuming each person
work 8 hours a week and 52 weeks in a year.
The minimum wage rate is calculated at the lower bound of each income
group.
45
3.2.2 Individual Angler Characteristics
The
characteristics
of
the
individual
angler
influence his or her tastes and preferences,
will
and further
affect his or her site-choosing decision. Variables used to
define the characteristics of the individual are:
FY: fishing years. This is the years of experience in
fishing for spring chinook on the Willamette or Clackamas
rivers. This information is acquired by asking the
respondent "How many years have you been fishing at least once
per year for spring chinook on the Willamette or Clackamas
Rivers ?". It is assumed that experience will affect personal
site choice.
AS: angling skill. This variable is measured by dividing
the total number of spring chinook landed with the trips taken
in this season for each angler. It is assumed that the fishing
skill of the angler will affect the site-choice decision.
AG: age. An age variable is often used in empirical work
to reflect individual's taste.
OB: boat ownership. A dummy variable for boat ownership;
OB = 1, if individual owns a boat, 0 otherwise.
46
Table
3.10.
VARIABLES.
DESCRIPTION
Variable
names
INDIVIDUAL
OF
CHARACTERISTIC
Description
FY
The successive years an angler has fished at
least once per year for spring chinook on the
Willaiuette and Clackamas rivers.
AS
Angling skill. For each respondent, N denotes
the number of spring chinook he or she landed
this season, and Nt denotes the trips he or she
took this season.
AS--
AG
The age of respondent
OB
Dummy variable for boat ownership. OB = 1 if
individual owns a boat, 0 otherwise.
3.3 MODEL SPECIFICATION
There are four site choices in the choice set; lower
section, middle section, and upper section of lower Willamette
river,
and
the
lower
Clackamas
river
(Figure
2).
The
probability of an individual i choosing a certain fishing site
or reach of the river based on site attributes and personal
characteristics, is
Prob (Y =
i) =
exp(P'X)
exP(P'1Q+I3'2Z)
(3.3-1)
4
k=i
4
'Xjk)
k=1
exp(I3'lQIk-4-p'2Z)
47
where j = 1, if lower section of Willainette River is chosen by
individual 1.
j = 2, if middle section of Willamette River is chosen
by individual 1.
j = 3, if upper section of Willamette River is chosen by
individual i.
j = 4, if lower Clackainas River is chosen by individual
1.
Q
:
the matrix of site attribute variables.
=
Q
{
Fl, CG, TC
the matrix of personal characteristics.
=
Z
Fl, AS, AG, OB I
It is useful to distinguish between the explanatory
variables of site attributes and personal characteristics. Let
Xj
=
[
Q
,
Z
J. Then
varies across the choices and
possibly across individuals as well. But Z
contains the
characteristics of the individual and is the same for all
choices.
The
terms
specific
to
individual
angler
characteristics which do not vary across alternatives fall out
of the probability expression.
If the model
is
to allow
individual specific effects, it must be modified. One method
is to create a set of dummy variables for the choices and
multiply each of them by the common Z. These coefficients are
then allowed to vary across the choices (Greene, 1993).
48
The three definitions of travel
cost variables will
result in different estimates of welfare change. Each of the
three types of travel cost variables, TC1, TC2, and TC3 are
included in the model, resulting in three distinct models.
TC1,
defined
conservative.
as
TC2,
the variable travel
cost,
is
the most
in addition to variable travel
cost,
includes the opportunity cost of travel time. TC3 is similar
to TC2, except that the opportunity cost of travel time is
calculated at one-third of the wage rate.
In the conditional logit model, the coefficients are not
a direct measure of marginal effects. However, the marginal
effects
for
continuous
variables
can
be
obtained
by
differentiating equation (3.3-1) with respect to x to get
-
(3.3-2)
-
(3.3-3)
api
aXk
where
is the probability of the
option being chosen, x
is the vector of explanatory variables with respect to
choice,
k
th
is the probability of the kth option being chosen,
and Xk is the vector of explanatory variables with respect to
the kth choice.
The vector
13
is the vector of estimated
coefficients. it is clear that through its presence in P
and
49
k' every attribute set x
affects all of the probabilities
(Greene, 1993).
One might prefer to report the elasticities
of the
probabilities, and these would be
am
am
- I3mXjm(1Pj)
(3.3-4)
-
(3.3-5)
Xjm
am
am
PmXkmPk
Xkm
where
is the probability of the
th
option being chosen,
'3m
is the estimated coefficient for the m' explanatory variable,
Xjm is the mth explanatory variable with respect to the
choice, and x
th
is the mti explanatory variable with respect
to the kth choice (Greene, 1993).
Since
there
is
no
ambiguity
about
the
scale
of
probability itself, whether one reports the derivatives or the
elasticities is a matter of personal choice. The elasticities
of probabilities will be reported in the empirical application
of this thesis.
50
CHAPTER 4
RESULTS AND IMPLICATIONS
In this chapter, the results of various models will be
compared. One of them will then be selected to estimate the
welfare variations associated with the site quality changes
arising from the hypothetical policies discussed earlier. The
Hausinan and McFadden test (1984) will be conducted to examine
the property of independent of irrelevant alternatives (hA).
Welfare changes caused by the change in fishing quality or the
closure of one site to all recreational anglers will be
estimated and compared. Finally, implications of these results
will be discussed.
4.1 RESULTS OP THE CONDITIONAL LOGIT MODEL
4.1.1 Comparison of Alternative Models
Three
sets
of
models
corresponding
to
different
definitions of travel costs are estimated and presented in
tables 4.1,
4.2,
and 4.3.
The coefficients are tested to
examine if they are significantly different from 0, using an
asymptotic t-statistic using the critical value from the
normal distribution. The models are tested for overall fit
using a likelihood ratio test that has a x2 distribution. A
likelihood ratio index is an informal goodness-of-fit index
used in a fashion similar to R2 in regression analysis.
51
TABLE 4.1. CONDITIONAL LOGIT MODEL ESTIMATES (TC1)
ModeL
1
2
3
4
7
6
5
TC1
-0.lOa
(0.0126)b
-0.101a
(0.013)
-0.092a
(0.012)
-0.082a
(0.011)
-0.101a
(0.013)
-0.093a
(0.012)
-0.093a
(0.012)
CG
0.11
(0.0837)
0.087
(0.083)
0.149a
(0.074)
0.318a
(0.084)
0.109
(0.085)
0.155a
(0.076)
0.161a
(0.078)
Fl
4.94a
(1.497)
5.039a
(1.511)
4.541a
(1.454)
3.653a
(1.362)
4.819a
(1.520)
4.361a
(1.485)
4.660a
(1.461)
AOl
0.029a
(0.009)
0.018
(0.011)
0.031a
(0.009)
AG2
-0.002
(0.008)
-0.004
(0.01)
0.0003
(0.008)
0.007
-0.0015
(0.01)
(0.008)
AG3
(0.008)
0.007
OB1
0.888
(0.513)
1.443a
(0.435)
1.478a
(0.439)
1.50a
(0.479)
082
0.266
(0.530)
-0.064
(0.403)
0.028
(0.405)
0.021
(0.471)
083
0.696
(0.491)
0.469
(0.384)
0.458
(0.385)
0.652
(0.438)
FYi
0.015
(0.018)
-0.007
(0.019)
FY2
-0.024
(0.017)
-0.008
(0.020)
FY3
-0.012
(0.017)
-0.016
(0.019)
AS1
-1.285
(0.862)
-1.188
(0.856)
AS2
-12352
-12.283
(187)
(190)
-0.152
(0.455)
-0.122
(0.450)
AS3
Log-LikeLihood
-274.64
-272.33
-27814
-289.9
-269.42
-273.17
-277.69
Restricted
Log-LikeLihood
-368.75
-368.75
-368.75
-368.75
-368.75
-368.75
-368.75
Nuilber of
266
266
266
266
266
266
266
cases
LikeLihood
ratio index
0.255
0.261
0.246
0.214
0.269
0.259
0.247
Adjusted
LikeLihood
ratio index
0.239
0.237
0.229
0.198
0.245
0.235
0.223
LikeLihood
ratio test
188.23c
192.84c
181.23c
157.72c
198.67c
191.18c
182.12c
a Asymptotically significant at 95 percent confidence level.
b
Asymptotic standard errors in parentheses.
C
The coefficients are significant at 99 percent confidence level.
52
TABLE 4.2. CONDITIONAL LOGIT MODEL ESTIMATES (TC2)
ModeL
b
C
5
7
3
-0.043a
(0.005)b
-0.044a
(0.005)
-0.042a
(0.005)
-0.038a
(0.004)
-0043a
(0.005)
-0.041a
(0.005)
-0.042a
(0.005)
CG
0.115
(0.085)
0.091
(0.084)
0.157a
(0.0Th)
0.294a
(0.081)
0.114
(0.085)
0.162a
(0.074)
0.165a
(0.077)
Fl
5.354a
(1.509)
5.483a
(1.520)
4.848a
(1.464)
3.Th6a
(1.369)
5.277a
(1.524)
4.715a
(1.481)
4.992a
(1.4Th)
AG1
0.034a
(0.009)
0.023a
(0.010)
0.035a
(0.009)
AG2
0.004
(0.008)
0.002
(0.010)
0.005
(0.009)
AG3
0.010
(0.008)
0.002
(0.010)
0.010
(0.008)
1C2
a
4
2
1
6
031
0.870
(0.519)
1.664a
(0.427)
1.684a
(0.430)
1.631a
(0.475)
0B2
0.218
(0.530)
0.116
(0.409)
0.168
(0.411)
0.177
(0.475)
0B3
0.675
(0.490)
0.535
(0.391)
0.534
(0.391)
(0.441)
0.727
FYi
0.030
(0.019)
0.0004
(0.020)
FY2
-0.015
(0.019)
-0.006
(0.021)
FY3
-0.007
(0.018)
-0.015
(0.020)
AS1
-0.587
(0.755)
-0.665
(0.776)
AS2
-1.575
(1.122)
-1.446
(1.107)
AS3
-0.192
(0.470)
-0.147
(0.464)
Log-LikeLihood
-255.10
-252.82
-258.87
-270.01
-253.58
-257.45
-258.04
Restricted
Log-LikeLihood
-368.75
-368.75
-368.75
-368.75
-368.75
-368.75
-368.75
Nuiter of
cases
266
266
266
266
266
266
266
LikeLihood
ratio index
0.308
0.314
0.298
0.268
0.312
0.302
0.300
Adjusted
LikeLihood
ratio index
0.292
0.290
0.282
0.251
0.288
0.277
0.276
LikeLihood
ratio test
227.30c
231.87c
219.77c
197.47c
230.35c
222.61c
221.43c
Asymptotically significant at 95 percent confidence level.
Asymptotic standard errors in parentheses.
The coefficients are significant at 99 percent confidence level.
53
TABLE 4.3. CONDITIONAL LOGIT MODEL ESTIMATES (TC3)
ModeL
a
b
C
1
2
3
4
5
7
6
TC3
-0.075a
(0.009)b
-0.076a
(0.009)
-0.071a
(0.008)
-0.064a
(0.008)
-0.076a
(0.009)
-0.072a
(0.008)
-0.071a
(0.008)
CO
0.117
(0.085)
0.093
(0.084)
0.160a
(0.0Th)
0.328a
(0.085)
0.116
(0.086)
0.165a
(0.077)
0.169a
(0.080)
Fl
4.739a
(1.508)
4.864a
4.295a
(1.472)
3.252a
(1.378)
4.577a
(1.533)
4.lOOa
(1.525)
(1.502)
4.432a
(1.479)
AG1
0.031a
(0.009)
0.020
(0.011)
0.033a
(0.010)
AG2
0.0003
(0.008)
-0.001
(0.01)
0.003
(0.008)
AG3
0.008
(0.008)
-0.0006
(0.01)
0.009
(0.008)
081
0.933
(0.521)
1.568a
(0.440)
1.625a
(0.444)
1.595a
(0.484)
082
0.226
(0.534)
0.008
(0.404)
0.115
(0.407)
0.074
(0.475)
0B3
0.710
(0.490)
0.496
(0.385)
0.493
(0.386)
0.690
(0.440)
FYi
0.021
(0.018)
-0.004
(0.020)
FY2
-0.020
(0.017)
-0.007
(0.020)
FY3
-0.010
(0.017)
-0.016
(0.020)
AS1
-0.985
(0.738)
-1.020
(0.742)
AS2
-12.355
(180.8)
-12.300
(185.4)
AS3
-0.194
(0.460)
-0.185
(0.454)
Log-likelihood
-260.74
-258.10
-263.78
-275.48
-255.78
-258.99
-263.18
Restricted
Log-Likelihood
-368.75
-368.75
-368.75
-368.75
-368.75
-368.75
-368.75
Nunber of
cases
266
266
266
266
266
266
266
Likelihood
ratio index
0.293
0.300
0.285
0.253
0.306
0.298
0.286
Adjusted
Likelihood
ratio index
0.277
0.276
0.268
0.237
0.282
0.273
0.262
Likelihood
ratio test
216.03c
209.96c
186.55c
225.94c
219.53c
211.15c
221.31c
Asymptotically significant at 95 percent confidence level.
Asymptotic standard errors in parentheses.
The coefficients are significant at 99 percent confidence level.
54
Economic theory and previous research (Morey et
al.,,
1992; Bockstael et al., 1989) suggest that certain variables
are likely to influence recreation behavior. These variables
were discussed in chapters
2
and
3.
However,
for other
variables, in particular those describing individual angler
characteristics, the effect is uncertain or ambiguous. As a
result, their role in model specification is not clearly
established. Thus alternative model formulations, representing
hypothesis tests in these variables, is needed. In addition,
the alternative definitions of travel
cost presented
in
Chapter 3 give rise to other model specifications.
The likelihood ratio tests indicate that the estimated
coefficients of all of the models are significant at the 99
percent confidence level. Based on the likelihood ratio index,
the best-fitted model is model 5, which contains the fishing
quality index [Fl), congestion level index [CG], travel cost
[TC1, TC2, or TC3], age [AG1 i=l,2,3] and angling skill
[AS1
i=1,2,3], as explanatory variables. The first three variables
are attributes of the site,
while the last two reflect
individual characteristics. However, based on the asymptotic
t-statjstjcs, most of the estimated coefficients for personal
characteristics are not significant.
The estimated coefficients for travel costs and fishing
quality index are reasonably stable across the models and have
the expected signs. Though the significance level of the
congestion index variable varies across model specifications,
55
it retains a positive sign,
which indicates that spring
chinook salmon fishing in Willaniette and Clackamas rivers is
not a congestible good under current conditions. This result
is consistent with the findings of Berrens et al. (1993) on the
effect of congestion on the demand for spring chinook salmon
fishing in this area. Their research shows that there was no
evidence that spring chinook salmon fishing was a congestible
good over a range of postulated increases in user intensities.
Comparison of the estimated coefficients
among these
models suggests that there is not much difference among the
estimates. For the same estimation data set the likelihood
ratio index of a model will always increase or at least stay
the same when new variables are added to the utility function.
For this reason,
an adjusted likelihood ratio
index
is
calculated to eliminate the variable-adding effect. In terms
of efficiency,
model
1,
which contains only the fishing
quality index (Fl), congestion level index (CG), travel cost
(TC) and age (AG) as explanatory variables is most efficient
(based on the t-statistics test and the goodness-of-fit test).
Comparing model 1 and model 5, the addition of AS (angling
skill)
in model 5 improves the explanatory ability of the
model, but it is not significant. Therefore, model 1 is chosen
to determine the effects of changes in site attributes on
welfare.
In model 1, age (AG) is the only personal characteristic
used to explain the probability of individual site choice. As
56
mentioned in the previous chapter, if the probability of site
choice
is
believed
to
be
influenced
by
personal
characteristics, then the inclusion of variables presenting
personal characteristics must be handled as a set of dummy
variables for the choices, i.e., multiplying by a diagonal
matrix of the personal characteristic, such as age, with the
other
elements
as
zeros.
This
allows
coefficients
of
individual characteristics to vary across choices instead of
the characteristics. Since site 4 is used as the bench mark in
the model, three dummy variables for age are created. The
specification of model 1 is
Vij
where Q
= 13'x =
+ I3'2Z
={ Fl, CG, TC }, and Z
= { AG, i=1,2,3}
In this model, the estimated coefficients are not the
measures of the marginal effects. However, the signs of these
estimators indicate the direction of how these variables will
affect the probability of choosing a certain site or reach.
The results of model 1 suggests that a fishing site would be
more attractive to anglers if fishing quality is increased, if
more people visit, and if the site is inexpensive to reach.
The sign of the estimated coefficients for age indicates that
as age increases, site 1 is significantly more attractive than
site 4 (based on the significant asymptotic t-statistics of
AG1). There appears to be no difference in attractiveness of
57
site 2, site 3, and site 4 across different ages (based on the
insignificant asymptotic t-statistics of AG2 and AG3).
4.1.2 Summary of Predicted Probabilities
Another way to see how well the model performs is to
examine how the predicted probabilities
choices.
fit the realized
The sum of predicted probabilities
of choosing
alternatives in the choice set for each individual is 1. The
choice with the highest predicted probability in the choice
set is viewed as the predicted choice that an individual
angler, based on certain attributes of sites and her personal
characteristics, will make. Correct prediction refers to those
cases where the site or reach with the highest predicted
probability of selection corresponds
to the actual reach
visited. For example, if site 3 is the choice with the highest
predicted probability for the individual
angler,
and she
actually chose site 3, then this is classified as a correct
prediction. The summary and fit of the predicted probabilities
for model 1 are presented in tables 4.4, 4.5, and 4.6 with
respect to different travel costs.
58
TABLE 4.4. PIT OP PREDICTED PROBABILITIES FOR MODEL
Site 1
(TC1)
2.
Site 2
Site 3
Site 4
0.1541
0.3083
0.1015
0.420
0.186
0.306
0.088
Actual
visits
117
40
82
27
Predicted
visits
120
28
112
6
Correct
prediction
83
7
57
4
Sample
proportion
Average
predicted
probability
0.4361
TABLE 4.5. FIT OF PREDICTED PROBABILITIES FOR MODEL
Site 1
2.
(TC2)
Site 2
Site 3
Site 4
0.1541
0.3083
0.1015
0.415
0.185
0.310
0.090
Actual
visits
117
40
82
Predicted
visits
124
31
104
7
89
8
56
6
sample
proportion
Average
predicted
probability
Correct
prediction
0.4361
27
59
TABLE 4.6 FIT OF PREDICTED PROBABILITIES FOR MODEL 1 (TC3)
Site 1
Site 2
Site 3
Site 4
sample
proportion
0.4361
0.1541
0.3083
0.1015
Average
predicted
probability
0.418
0.185
0.307
0.090
27
Actual
visits
117
40
82
Predicted
visits
122
31
106
7
88
7
57
5
Correct
prediction
4.1.3 Average Probabilities and Elasticities
As discussed in Chapter 2, estimated coefficients are not
direct measures of marginal effects. In general, different
individuals facing the same set of choices will have different
utilities for each alternative, because the characteristics of
each alternative vary across people (e.g., travel costs) and
because individuals' characteristics (e.g., age) vary in the
population.
Individuals with different utilities for each
choice will have different choice probabilities. Since the
derivatives and elasticities depend on the choice probability,
different individuals will have different responses to changes
in factors which enter the utility function. With formulas
(3.3-4) and (3.3-5), the elasticities of the probabilities
(another measure of the marginal effect) can be calculated.
60
a researcher
Usually,
is
interested
in the average
probability or average response within the population. The
average probability for site choice j can be estimated as
pi = (
j
p)/n
= 1, 2,.., J for site choices.
i = 1, 2,.., n for individual.
Plugging the estimated average probability,
average
characteristics, and corresponding estimated coefficients into
formulas
(3.3-4)
probabilities
with
and
(3.3-5),
respect
to
the
fishing
elasticities
quality
of
index,
congestion level index, and travel cost can thus be estimated.
The model with TC3 is selected to estimate the elasticities.
The results are reported in tables 4.7, 4.8, 4.9 for fishing
quality
index,
congestion
level
index,
and
travel
cost
respectively. The results show that travel costs have the
biggest effects on the probability of site choice.
61
TABLE 4.7. ELASTICITIES OF PROBABILITIES WITH RESPECT TO
FISHING QUALITY INDEX (TC3)
Attribute Level of:
Site
Site 1
Site 2
Site 3
Site 4
Site 1
0.30
-0.22
-0.22
-0.22
Site 2
-0.10
0.44
-0.10
-0.10
Site 3
-0.23
-0.23
0.52
-0.23
Site 4
-0.05
-0.05
-0.05
0.49
TABLE 4.8. ELASTICITIES OF PROBABILITIES WITH RESPECT TO
CONGESTION LEVEL INDEX (TC3)
Attribute Level of:
Site
Site 1
Site 2
Site 3
Site 4
Site 1
0.62
-0.45
-0.45
-0.45
Site 2
-0.17
0.76
-0.17
-0. 17
Site 3
-0.30
-0.30
0.68
-0.30
Site 4
-0.04
-0. 04
-0. 04
0.43
TABLE 4.9. ELASTICITIES OF PROBABILITIES WITH RESPECT TO
TRAVEL COST (TC3)
Attribute Level of:
Site
Site 1
Site 1
Site 2
Site 3
Site 4
-1.25
0.90
0.90
0.90
Site 2
0.29
-1.26
0.29
0.29
Site 3
0.50
0.50
-1.13
0.50
Site 4
0.18
0.18
0.18
-1.86
62
4.1.4 Test of hA Assumption
The odds ratio in the conditional logit models
is
independent of the other alternatives. This property of the
logit model, whereby
j/k is independent of the remaining
choice probabilities is termed the
alternatives
estimation,
(hA).
This
is
a
independence of irrelevant
convenient
property
for
but not always an appropriate restriction on
consumer behavior.
When some of the choices are perfect
substitutes, the hA property will cause a serious bias in
estimating the probability of site choice.
Hausman and McFadden (1984) suggest that if a subset of
the choice set truly is irrelevant, omitting it from the model
will not change the parameter estimates in any systematic
fashion. That is, if the hA assumption holds for the full
choice set, then the logit model also applies to a choice from
any subset of alternatives. The test statistic is
- f3f)'[V
where
indicates
-
Vf1
- f)
the estimated coefficients
restricted set of alternatives,
coefficients based on the full
the
respective
-
estimates
of
13
choice
the
from
the
indicates the estimated
set,
and V
asymptotic
- Vf
are
covariance
matrices. The test statistic is asymptotically distributed as
Chi-squared with K degrees of freedom, where K is the number
63
of
elements
in
the
subvector
of
coefficients
that
is
identifiable from the restricted choice set model (i.e., the
dimension of
The results of the hA tests for eliminating site 1, site
2, site 3 alternatively from the choice set with respect to
different travel cost definitions are reported at tables 4.10,
4.11, and 4.12.
TABLE 4.10. THE hA TEST FOR MODEL 1 (TC1)
The site eliminated
from the choice set
a
The value of
test statistics
Critical valuesa
(d.f. = 4)
Site
1
4.900
<
9.488
Site
2
2.424
<
9.488
Site
3
1.359
<
9.488
The critical value for the x2 distribution is measured at
the 0.05 significance level.
TABLE 4.11. THE hA TEST FOR MODEL 1 (TC2)
The site eliminated
from the choice set
a
The value of
test statistics
Critical valuea
(d.f. = 4)
Site
1
0.006
<
9.488
Site
2
2.391
<
9.488
Site
3
0.625
<
9.488
The critical value for the x2 distribution is measured at
the 0.05 significance level.
64
TABLE 4.12. THE hA TEST FOR MODEL 1 (TC3)
The site eliminated
from the choice set
a
The values of
test statistics
Site
1
1.806
Site
2
2.787
Site
3
1.366
Critical valuea
(d.f. = 4)
<
9.488
9.488
<
9.488
The critical value for the x2 distribution is measured at
the 0.05 significance level.
Since all values of the test statistics are smaller than
the critical values at 0.05 significant level, model 1 passes
the hA test. The implications of passing the hA test are as
follows:
The property of hA is allowed and the conditional logit
model is appropriate (consistent with consumer theory) to be
used to predict the probability of individual site choice.
There is no significant evidence that these sites are
perfect substitutes for one another.
Passing the hA test means that the assumptions of the
logit model are appropriate for analysis of this data set. It
does not mean that there is no substitution effect among these
choices.
The practical importance of these hA test results for
this study is that the chosen logit model is justified for
welfare analysis of this recreational fishery. The following
section proceeds with the welfare analysis.
65
4 2 WELFARE ANALYSIS
Model 1 is used to determine the effects on recreational
benefits of changes in fishing quality or the exclusion of a
reach (reach 3) from the choice set for this population of
recreational anglers. Anglers are not limited to specific
sites; therefore,
as site conditions change,
anglers may
substitute one site for another, depending on the relative
attractiveness of the alternative site.
Two hypothetical policies, motivated in part by potential
need to meet Native Americans'
treaty rights to harvest
Willamette River spring chinook, are evaluated. They are:
(1)
Granting Indian tribes the right to catch 5,000 spring
chinook on the lower Willamette River from March 31 to midJune. This policy will affect the escapement of spring chinook
into the Willamette River and change the fishing quality
(success rate) for recreational anglers. According to the 1988
Willamette River Spring Chinook report,
the percentage of
spring chinook caught relative to the total run entering the
Willamette
is
approximately
26%.
Therefore,
for
this
hypothetical policy we assumed 5,000 spring chinook caught on
the lower Willamette River by the tribes implies that 1,300
(5,000 * 26%)
spring chinook salmon will not be taken by
recreational anglers on the lower Willamette and Clackamas
Rivers. This translates into a reduction in fishing quality
during the period from March 31 to mid-June. The reduction in
fishing quality leads to benefit losses to the recreational
66
anglers because of the maintained hypothesis that fishing
quality is positively related to individual utility.
(2)
Granting
Willamette
the
Willamette
(site
3)
Falls
exclusively
reach
to
the
lower
tribes
Indian
excluding recreational anglers from site
extreme policy,
of
3).
This
(and
is
an
which goes beyond the agreement reached
between ODFW and the tribes during the 1994 season. It thus
provides an upper bound welfare loss. The inaccessibility of
site 3 will cause some recreational anglers to go to other,
less preferred sites, and thus reduce their utility (a welfare
loss).
The three measurements or definitions of travel costs are
used in the conditional logit model; TC1, TC2, and TC3. These
three definitions of travel costs will result in different
estimates of benefit loss.
By using formulas 2.2-17 and 2.2-18, the benefit changes
associated with these hypothetical policies which reduce
fishing
quality
inaccessible
to
Specifically,
calculated
in
(lower
success
recreational
the
two
anglers
changes
of
ways.
The
can
consumer
first
make
or
rate)
be
3
calculated.
surplus
uses
site
the
can
be
average
characteristics of the users and of each sites to get an
average change in consumer surplus. The second method is to
evaluate the welfare change for each angler in the sample and
then take the average of these welfare changes or pick the
median of the welfare changes as the representative. These
67
estimated results will be reported and compared for the
hypothetical policies.
4.2.1 Estimated Welfare Losses from a Reduction in Fishing
Quality
The estimated per trip benefit losses caused by the
reduction in fishing quality for a representative angler are
reported in table 4.13. The estimated benefit losses from the
two different methods are fairly close. The model using TC2 as
the explanatory variable, as expected, yields the largest
estimated welfare loss, no matter which method is used.
TABLE 4.13. ESTIMATED WELFARE LOSSES FOR A REDUCTION IN
FISHING QUALITY BY DIFFERENT METHODS (IN 1988 DOLLARS)
Method 2
Method 1
Average
Median
Model with
TC1
$ 0.31
$ 0.38
$ 0.37
Model with
$ 0.79
$ 0.87
$ 0.91
$ 0.40
$ 0.46
$ 0.47
TC2
Model with
TC3
The advantage of using method 2 is that the distribution
of the estimated individual welfare changes can be obtained to
determine the most appropriate representative welfare loss in
the sample. Figure 3 presents the results of method 2 with a
Box plot for estimated benefit losses per trip for each
68
S
c'J-
T
-
U)
C',
V0
0)0
T
T
I
C
U)
a)
C,)
Cl)
oc'J.
a)
C',
S
a)
TC1
TC2
TC3
Alternative Travel Cost Definitions
Figure 3. Box plot for welfare losses due to a reduction in
fishing quality.
69
individual in the sample. Models with different definitions of
travel costs are compared. The horizontal straight line in
each Box plot is the median. The inter-quartile range defines
the upper and lower boundaries of the box. Outlying estimates
for consumer surplus per trip are presented as black dots.
The Box plot for the model with TC1 shows that the
distribution of the estimated consumer surplus for the sample
is symmetric, thus, the use of the arithmetic average of these
estimated consumer surpluses is appropriate. For the other two
models which use TC2 and TC3 as the travel cost variables, the
Box plots show that the distributions of these estimated
consumer surpluses for the sample is skewed. Thus, using the
median of the estimated welfare changes as representative is
more appropriate than the arithmetic average. Choosing the
median of the welfare changes in the sample is the safest way
to avoid bias caused by a skewed distribution. Therefore, the
median is picked to interpret the welfare loss caused by the
first
hypothetical
policy.
With
different
travel
cost
definitions, the policy (or a natural cause) which reduces by
5,000
the
number
of
spring
chinook
entering
the
lower
Willamette River will cause individual welfare losses of
$0.37, $0.91, and $0.47 per trip respectively. These values,
which are small in terms of expenditure data, suggest that
such a decrease of fish stock from the recreational fishing,
will have a negative effect on individual welfare.
70
Decision makers usually are more concerned about the
aggregate welfare changes caused by regulations or natural
phenomena.
The aggregate welfare
loss arising from this
hypothetical policy can be estimated by multiplying the
representative angler's loss with the total trips realized.
The total trips (angler days) to these four reaches of the
lower
Willainette
in
the
season
of
1988
were
222,457.
Therefore, the aggregate welfare losses associated with this
hypothetical policy are $
82,309, $ 202,436,
and $
104,555
respectively for TC1, TC2, and TC3.
TABLE 4.14. AGGREGATE WELFARE LOSSES FOR A REDUCTION IN
FISHING QUALITY (IN 1988 DOLLARS)
Loss of per
angler
TC1
$
0.37
$ 0.91
$ 0.47
TC2
TC3
Total angler
days
Aggregate
benefit loss
222,457
222,457
222,457
$
82,309
$ 202,436
$ 104,555
4.2.2 Estimated Welfare Losses for Closure of Site 3
The estimated per trip benefit losses resulting from the
hypothetical closure of site 3 (the reach from the Southern
Pacific
railroad
bridge
to
Willamette
representative angler are reported in table
Falls)
4.15.
to
a
As with the
change in quality, the estimated benefit losses from the two
methods are close, except for the model with TC2. With method
2 the estimated consumer surplus loss associated with
TC2 is
7]-
an order of magnitude larger than the others (a median loss of
$ 54.91, compared with $ 3.82 and $ 4.83 for TC1 and TC3,
respectively).
TABLE 4.15. ESTIMATED WELFARE LOSSES FOR CLOSURE OF SITE 3 BY
DIFFERENT METHODS (IN 1988 DOLLARS)
Method 2
Method 1
Median
Average
Model with
4.03
$ 3.75
$
Model with
TC2
$ 8.59
$ 46.96
Model with
$ 4.88
$
$
3.82
TC1
$ 54.91
5.68
$
4.83
TC3
Figure 4 presents the results of method 2 with a Box plot
for
estimated benefit
changes
(per
day
trip)
for
each
individual in the sample. Models with different definitions of
travel costs are compared. The Box plots show that the model
with TC2 has a much larger variation.
problem of using TC2
Another potential
is the appearance of outliers with
negative signs, which imply that the closure of site 3 will
increase the utility of a few individuals in the sample. This
indicates that TC2 may be over valuing the opportunity cost of
time in the travel cost variable which results in negative
utility for some individuals by taking the fishing trip. This
obviously violates the assumption of utility maximization,
72
0
0-
T
C0
C')
0
V
co
S
C)
C
.
Co
a)
S
00
Joa).,;Cl)
Co
S
.
-
a,
0
0.
cJ
S
TC1
TC2
TC3
Alternative Travel Cost Definitions
Figure 4. Box plot for welfare losses due to closure of site
3.
73
because these individual can get higher utility (0 with no
fishing) by deciding not to go fishing.
The median is picked to interpret the welfare loss caused
by the second hypothetical policy. With different travel cost
definitions, a policy which grants site or reach 3 exclusively
to Indian tribes (and thus excludes recreational anglers) will
cause individual welfare losses of $ 3.82, $ 54.91, and $ 4.83
per trip for TC1, TC2, and TC3, respectively.
The aggregate welfare loss caused by this hypothetical
policy can be obtained by multiplying the representative
angler's loss by the total trips. The aggregate welfare losses
caused
by
this
hypothetical
policy
are
$
849,786,
$
12,215,114, and $ 1,074,467 respectively for TC1, TC2, and
TC3.
TABLE 4.16. AGGREGATE WELFARE LOSSES FOR CLOSURE OF SITE 3 (IN
1988 DOLLARS)
Total angler days
Loss of per
angler
Aggregate benefit
loss
849,786
TC].
$
3.82
222,457
$
TC2
$ 54.91
222,457
$ 12,215,114
TC3
$
4.83
222,457
$
1,074,467
4.2.3 Substitution Effects
The substitution effects among the four recreational
fishing reaches or sites on the lower Willainette and Clackamas
74
Rivers are the focus of the thesis. Recreational fishing
reaches or sites on the upper Willamette River and its
tributaries (e.g. Santiam River) are potential substitutes for
the four fishing reaches considered here. However, survey
information
of
the
recreational
fishery
on
the
upper
Willainette River and its tributaries is not available and
hence this possible fifth site is not considered. To the
extent that some anglers may move upstream (approximately 70
miles to the Santiain site) if quality changes within the four
reaches considered here, there is a possible bias in the
welfare measurement. The possible bias would work in opposite
directions for each policy.
For the first hypothetical policy, the estimated welfare
losses may be underestimated due to exclusion of the fifth
site because the welfare losses from fewer fish for anglers
fishing on the upper Willamette River and its tributaries are
not
included.
estimated
For
welfare
the
second
losses
may
hypothetical
be
policy,
overestimated
the
because
recreational anglers can substitute for the closed fishing
reach with sites on the upper Willamette which are not
considered as choices in the site choices set defined in this
study. However, given distances to upper river sites, it is
expected that the substitution effects among the choices
defined
in
the
choice
set
are
much
larger
than
the
substitution effects between the four sites in the choice set
and the possible sites outside the choice set. Therefore, the
75
welfare losses caused by the second policy, though potentially
biased, are believed to be reasonable estimates.
4.2.4 Summary of Results
The estimated consumer surpluses for the selected model
with TC1 as the travel cost variable and the estimated
consumer surplus with TC2 as the travel cost variable bound
the estimated welfare changes caused by the two hypothetical
policies evaluated here.
allows
a
tribal
catch
For the hypothetical policy that
of
spring
chinook
on
the
lower
Willamette River (or a natural reduction in the run size of
5,000 fish), the estimated welfare loss of per day trip for a
representative angler ranges from $ 0.37 to $
hypothetical policy that
closes
site
3
to
0.97. For a
recreational
anglers, the estimated welfare loss per day trip ranges from
$ 3.82 to $ 54.91. The total trips (angler days) to Willamette
recreational fishing in 1988 are 222,457.
Therefore,
the
aggregate welfare losses caused by the first hypothetical
policy range from $ 82,309 to $ 202,436. The aggregate welfare
losses caused by the second policy range from $ 849,786 to $
12,215,114.
The results of this research show that policies that close
an entire site or reach, such as site 3, will cause much
larger welfare losses compared to the policy or actions that
reduce the run size. The losses associated with closure are
ten times larger than those associated with quality changes
76
when travel costs are measured as TC1 and TC3. When TC2 is
used, the difference between the policies is even larger.
However, caution should be exercised in interpreting and using
the estimates with TC2 because some of the observed responses
violate the assumption of utility maximization.
77
CHAPTER 5
CONCLUS IONS
This research focuses on both the role of substitution
effects across different recreation sites and the effect of
quality factors on recreational choice. The thesis uses random
utility theory to explain these relationships and to estimate
welfare changes caused by hypothetical policies regarding
Willamette River spring chinook in a multiple site framework.
The conditional logit model is used to estimate the
probability of an individual site choice. The results show
that fishing sites or reaches on the Willamette River are more
attractive to anglers if the fishing quality is increased, if
more people visit those sites, and if the site is relatively
inexpensive
to
reach.
The
inclusion
of
a
personal
characteristic, "age", as an explanatory variable in the model
shows that as age increases, site 1 is significantly more
attractive than site 4. However, the choices among sites 2, 3,
or 4 do not appear to be influenced by age.
The
property
of
the
logit
model,
independent of the remaining probability,
where
is
is termed the
independence of irrelevant alternatives (hA). This property
is not always an appropriate restriction on consumer behavior.
To test the appropriateness of hA in this setting,
the
Hausman-NcFadden test for the hA property was conducted. The
results indicate that the hA assumption as used in the
conditional logit model application to the Willamette spring
78
chinook recreational fishery is not inconsistent with consumer
theory.
Two hypothetical management policies are considered: (1)
observing treaty rights and granting Native Americans the
right to catch 5,000 spring chinook on the lower Willamette,
and (2) granting the Willainette Falls (site 3) exclusively to
tribal fishery. Both policies could also be described simply
as a reduction in run size and the loss of one site or reach.
The estimated welfare losses caused by the first policy range
from $ 0.37 to $0.91 per day trip for a representative angler.
The estimated welfare losses caused by the second policy range
from $ 3.82 to $ 54.91 per day trip for a representative
angler. By multiplying these individual welfare losses by the
total angler days in 1988, total estimated welfare losses are
between $
82,309 and $ 202,436 for the first policy and
between $ 8,49786 to $ 12,215,114 for the second policy.
Assuming both polices
achieved the
same
objective,
the
management implication of these results is that the first
policy is preferred because the welfare loss is much smaller
than the second one. Specifically, both the individual and
aggregate losses caused by the first policy are small ($ 0.37$0.97)
and the variation of these welfare losses is also
small.
There is a methodological implication suggested by one of
the findings.
Specifically,
the Box plot for the welfare
losses caused by the second policy indicates that some of the
79
estimates from the model with TC2 violate the assumption of
utility maximization. This implies that TC2, using the full
wage rate as the opportunity cost of time, may overestimate
travel cost, with such a high per trip cost, that for some
individuals it is irrational to take a fishing trip.
The estimate of round trip distance and travel time
introduces possible measurement
errors in the model.
The
results of this research could thus be improved by more
detailed information concerning individual trip information,
such as the round trip distance and the travel time for each
individual to each site. More information about the physical
attributes of the sites, such as the water clarity, the number
of ramps, and other amenities, may be helpful in explaining
the probability of an individual site choice decision. While
this research is limited by the nature of the existing data
set, the results demonstrate the potential and feasibility of
the random utility model as a tool in analyzing recreational
choice problems. The results also seem reasonably robust and
should
be
concerning
useful
this
to
policy
important
makers
resource.
in
future
Future
decision
recreational
surveys should consider including questions keyed specifically
to the application of random utility model to enhance the
applicability of such models to the recreational management
problems.
80
REFERENCES
1990. A
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APPENDICES
85
APPENDIX 1
DATA FROM THE 1988 WILLANETTE
RUN SPRING CHINOOK SURVEY
86
Definitions of variables:
************************************************************
OBS
Observations.
CHOS
Choices (site 1, site 2, site 3, or site 4).
SI
:
Duimuy variables. 1 for the site being chosen, 0 for
site not being chosen.
TC1 : Variable travel cost (= 0.2875 * round trip distance).
TC2
TC1 pluses the opportunity cost of time measured at
full wage rate.
TC3
TC1 pluses the opportunity cost of time measured at
one-third of the wage rate.
Fl
Fishing quality index.
FIA
Fishing quality index for the second hypothetical
policy.
A
Seini-dununy variables for age.
************************************************************
:
:
:
:
:
:
:
OBS
CHOS SI
1
2
2
3
1
4
0
1
2
0
0
0
1
1
2
3
0
0
0
4
4
1
2
6
7
9
1
4
0
0
0
1
1
2
3
4
0
0
0
3
5
1
4
3
3
0
0
1
1
2
0
3
4
0
0
1
1
2
3
0
0
4
0
1
1
2
3
4
0
0
0
1
1
2
3
0
0
0
4
TC1
TC2
20.09
7.13
4.08
6.91
4.49
42.25
49.84
51.98
10.09
59.46
67.29
67.34
15.89
TC3
17.28
5.83
3.28
6.14
1.69
26.20
33.56
37.64
5.28
31.93
39.37
42.76
5.87
18.17 104.22 46.85
25.42 112.68 54.50
30.48 107.30 56.08
1.15 11.37
4.56
18.17 76.67 37.67
25.42 84.75 45.19
30.48 82.71 47.89
11.50 41.55 21.52
8.63 49.47 22.24
15.87 70.36 34.03
12.65 44.54 23.28
0.58 7.79 2.98
13.57 44.41 23.85
20.82 55.11 32.25
25.87 57.18 36.31
14.37 21.58 16.78
9.20 16.16 11.52
23.00 35.61 27.20
28.75 40.33 32.61
52.90 91.30 65.70
39.33 98.84 59.17
32.09 67.28 43.82
37.15 67.06 47.12
15.87
5.18
2.88
5.75
0.29
18.17
25.42
30.48
2.88
18.17
25.42
30.48
0.86
CG
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
Fl
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
FIA
Al A2 A3 A4
0.044 61 0 0 0
0.087 0 61 0 0
0.106 0 0 61 0
0.000 0 0 0 61
0.044 35 0 0 0
0.087 0 35 0 0
0.106 0 0 35 0
0.000 0 0 0 35
0.044 48 0 0 0
0.087 0 48 0 0
0.106 0 0 48 0
0.000 0 0 0 48
0.044 46 0 0 0
0.087 0 46 0 0
0.106 0 0 46 0
0.000 0 0 0 46
0.044 41 0 0 0
0.087 0 41 0 0
0.106 0 0 41 0
0.000 0 0 0 41
0.044 32 0 0 0
0.087 0 32 0 0
0.106 0 0 32 0
0.000 0 0 0 32
0.044 42 0 0 0
0.087 0 42 0 0
0.106 0 0 42 0
0.000 0 0 0 42
0.044 28 0 0 0
0.087 0 28 0 0
0.106 0 0 28 0
0.000 0 0 0 28
0.044 80 0 0 0
0.087 0 80 0 0
0.106 0 0 80 0
0.000 0 0 0 80
87
OBS
10
CHOS SI
1
1
2
4
0
0
0
1
1
2
0
3
4
0
0
3
11
12
1
1
2
0
3
0
0
4
13
14
1
1
2
3
o
4
0
0
1
1
2
3
0
0
4
0
15
1
1
0
0
16
2
3
4
1
2
0
0
0
0
0
3
4
17
1
2
3
4
18
1
2
3
4
19
1
2
3
4
20
1
2
3
21
22
23
24
0
1
1
0
0
0
1
0
0
0
1
0
0
0
1
4
0
1
2
3
4
1
2
3
0
0
1
0
0
0
1
4
0
1
2
0
0
3
4
0
1
2
3
4
1
0
0
1
0
TC1
TC2
TC3
28.75 103.88 53.79
10.70 61.35 27.58
3.45 15.30
7.40
8.51 29.96 15.66
11.50 18.70 13.90
3.79
8.10
5.23
3.45
6.29
4.40
5.06
8.12
6.08
8.63 19.45 12.23
0.29
0.78 0.45
7.25 16.20 10.23
12.30 23.47 16.03
8.63 29.06 15.44
3.45 14.56 7.15
10.70 35.66 19.02
15.75 42.76 24.76
10.06
4.60
11.85
16.91
8.63
5.75
5.18
10.24
4.60
18.17
25.42
30.48
17.27
15.05
31.36
37.36
44.69
38.43
26.50
41.20
19.02
59.46
67.29
67.34
13.80
24.27
10.70
3.45
15.53
21.39
7.96
0.58
12.65
23.12
9.55
2.30
14.37
13.80
9.20
7.19
19.26
17.02
3.45
10.06
22.14
21.22
11.90
5.75
17.83
14.37
2.88
11.50
23.58
48.59
43.64
22.83
7.05
24.90
44.16
20.01
5.38
22.84
47.72
23.99
7.10
25.95
26.67
21.39
19.80
32.85
73.73
19.79
55.14
77.95
69.28
50.23
15.97
48.38
29.68
7.22
21.10
42.56
23.69 102.62
5.52 31.66
1.73 16.75
12.47
8.08
18.35
23.72
23.06
18.83
13.71
22.63
9.41
31.93
39.37
42.76
50.00
14.23
6.73
25.40
30.72
14.74
4.65
18.65
28.98
11.98
2.18
16.05
31.32
14.36
3.90
18.23
18.09
13.26
11.39
23.79
35.92
8.90
25.09
40.74
37.24
24.68
9.16
28.01
19.48
4.32
14.70
29.90
CG
Fl
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.14
0.00
8.87
7.17
7.14
0.00
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.025
0.000
0.047
0.092
0.025
0.000
FIA
Al A2 A3 A4
0.044 74 0 0 0
0.087 0 74 0 0
0.106 0 0 74 0
0.000 0 0 0 74
0.044 44 0 0 0
0.087 0 44 0 0
0.106 0 0 44 0
0.000 0 0 0 44
0.044 76 0 0 0
0.087 0 76 0 0
0.106 0 0 76 0
0.000 0 0 0 76
0.044 36 0 0 0
0.087 0 36 0 0
0.106 0 0 36 0
0.000 0 0 0 36
0.044 29 0 0 0
0.087 0 29 0 0
0.106 0 0 29 0
0.000 0 0 0 29
0.044 35 0 0 0
0.087 0 35 0 0
0.106 0 0 35 0
0.000 0 0 0 35
0.044 40 0 0 0
0.087 0 40 0 0
0.106 0 0 40 0
0.000 0 0 0 40
0.044 53 0 0 0
0.087 0 53 0 0
0.106 0 0 53 0
0.000 0 0 0 53
0.044 55 0 0 0
0.087 0 55 0 0
0.106 0 0 55 0
0.000 0 0 0 55
0.044 62 0 0 0
0.087 0 62 0 0
0.106 0 0 62 0
0.000 0 0 0 62
0.044 70 0 0 0
0.087 0 70 0 0
0.106 0 0 70 0
0.000 0 0 0 70
0.044 71 0 0 0
0.087 0 71 0 0
0.106 0 0 71 0
0.000 0 0 0 71
0.044 28 0 0 0
0.087 0 28 0 0
0.106 0 0 28 0
0.000 0 0 0 28
0.044 26 0 0 0
0.087 0 26 0 0
0.024 0 0 26 0
0.000 0 0 0 26
0.044 34 0 0 0
0.087 0 34 0 0
0.024 0 0 34 0
0.000 0 0 0 34
88
OBS
25
26
CHOS SI
1
0
2
0
3
4
1
1
2
27
3
4
1
1
0
0
1
0
0
0
1
0
0
0
2
3
28
4
1
2
3
29
4
1
2
3
4
30
31
1
2
3
4
1
2
3
4
32
34
35
0
0
0
0
1
1
0
0
0
4
1
1
2
0
0
3
1
4
0
0
0
1
2
3
1
3
4
0
1
0
0
0
1
0
0
0
1
0
0
0
1
1
2
0
0
0
1
2
3
1
3
4
1
2
39
1
1
2
3
2
38
0
0
0
3
4
37
1
4
4
36
0
0
0
0
1
2
33
0
0
0
3
4
TC1
TC2
7.19 18.68
8.63 28.23
9.78 24.20
5.75 12.71
15.09 65.38
2.16 12.37
1.15 16.18
13.23 46.57
24.84 107.60
11.27 64.64
4.03 19.05
16.10 56.69
21.62 108.06
8.05 53.80
0.72 18.75
12.79 51.50
21.62 47.56
8.05 21.78
0.72
6.13
12.79 24.41
1.6.10 52.57
5.18 21.84
15.81 56.67
27.89 75.69
17.25 74.73
3.45 19.79
7.56 33.52
8.63 23.65
17.37 45.13
3.79 12.42
1.73
4.57
1.73 8.94
13.57 22.60
0.81 1.57
7.25 12.21
8.05 14.05
17.25 56.33
2.01
8.49
5.75 15.97
17.83 48.38
29.44 43.86
15.87 51.94
8.63 22.84
20.70 45.74
1.73
8.94
5.35 17.50
12.59 33.34
17.65 39.01
2.01 8.01
13.57 39.23
20.82 49.36
25.87 51.92
8.63 23.05
6.90 22.58
13.05 34.56
18.11 40.02
0.58 6.58
14.52 41.98
21.76 51.61
25.97 52.12
TC3
CG
11.02
15.16
14.58
8.07
31.86
5.56
6.16
24.34
52.43
29.06
9.03
29.63
56.22
26.36
7.94
28.29
30.27
12.63
2.52
16.66
28.26
10.73
29.43
43.82
36.41
8.90
16.22
13.63
26.62
6.67
2.67
4.13
16.58
1.06
8.90
10.05
30.28
4.17
9.16
28.01
34.25
27.89
13.36
29.05
4.13
9.40
19.51
24.77
4.01
22.12
30.33
34.56
13.43
12.13
20.22
25.42
2.58
23.67
31.71
34.69
8.87
7.17
7.14
0.00
8.87
7.17
7.14
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
9.65
8.28
8.85
6.59
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.87
7.17
7.85
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
9.65
8.28
8.85
6.59
7.82
8.11
0.00
Fl
0.047
0.092
0.025
0.000
0.047
0.092
0.025
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.025
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.047
0.092
0.112
0.000
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
FIA
Al A2 A3
A4
0.044 37 0 0 0
0.087 0 37 0 0
0.024 0 0 37 0
0.000 0 0 0 37
0.044 64 0 0 0
0.087 0 64 0 0
0.024 0 0 64 0
0.000 0 0 0 64
0.044 41 0 0 0
0.087 0 41 0 0
0.106 0 0 41 0
0.000 0 0 0 41
0.044 38 0 0 0
0.087 0 38 0 0
0.106 0 0 38 0
0.000 0 0 0 38
0.044 47 0 0 0
0.087 0 47 0 0
0.106 0 0 47 0
0.000 0 0 0 47
0.044 31 0 0 0
0.087 0 3]. 0 0
0.106 0 0 31 0
0.000 0 0 0 31
0.044 42 0 0 0
0.087 0 42 0 0
0.106 0 0 42 0
0.000 0 0 0 42
0.044 23 0 0 0
0.087 0 23 0 0
0.024 0 0 23 0
0.000 0 0 0 23
0.044 18 0 0 0
0.087 0 18 0 0
0.106 0 0 18 0
0.000 0 0 0 18
0.044 38 0 0 0
0.087 0 38 0 0
0.106 0 0 38 0
0.000 0 0 0 38
0.044 30 0 0 0
0.087 0 30 0 0
0.106 0 0 30 0
0.000 0 0 0 30
0.056 42 0 0 0
0.077 0 42 0 0
0.052 0 0 42 0
0.024 0 0 0 42
0.056 41 0 0 0
0.077 0 41 0 0
0.052 0 0 41 0
0.024 0 0 0 41
0.056 65 0 0 0
0.077 0 65 0 0
0.052 0 0 65 0
0.024 0 0 0 65
0.056 58 0 0 0
0.077 0 58 0 0
0.052 0 0 58 0
0.024 0 0 0 58
89
OBS
CHOS SI
40
1
1
2
0
0
3
4
41
0
1
2
3
1
0
0
0
4
42
1
1
2
3
4
0
0
0
1
1
2
0
0
43
3
44
4
1
0
2
3
0
0
0
4
45
46
47
1
2
3
4
1
1
2
3
0
4
1
4
1
2
3
49
50
4
1
1
0
0
0
1
0
0
0
0
1
3
4
0
1
2
4
1
2
3
4
52
1
53
2
3
4
1
0
0
1
0
0
0
1
0
0
0
1
0
0
0
2
1
3
1
0
0
0
2
1
3
0
0
4
54
0
0
2
3
51
1
0
0
0
2
3
48
1
4
TC1
TC2
TC3
10.06
9.20
16.45
21.51
12.08
9.20
16.45
21.51
0.29
18.17
25.42
30.48
0.58
14.52
21.76
26.84
1.73
18.17
25.42
30.48
1.73
18.17
25.42
30.48
17.25
0.58
7.82
12.88
8.05
21.62
28.87
33.93
8.05
21.62
28.87
33.93
0.58
17.25
20.70
25.76
15.58
2.01
9.26
14.32
14.37
2.88
10.06
15.12
17.25
0.29
10.06
14.37
17.25
0.29
10.06
14.37
13.57
5.75
13.00
18.05
38.90
30.11
43.54
47.52
21.68
16.16
25.46
30.16
5.09
45.66
53.29
55.02
2.98
25.50
33.70
37.64
13.73
86.90
95.11
91.84
4.73
35.35
42.84
45.82
25.66
1.34
15.33
21.97
22.45
54.33
60.53
61.25
22.45
54.33
60.53
61.25
21.01
72.79
69.02
69.91
50.88
12.23
30.87
38.86
46.94
13.09
33.55
41.04
31.02
3.89
18.34
23.06
49.38
8.69
29.38
34.64
44.31
26.18
43.33
49.00
19.68
16.17
25.48
30.18
15.28
11.52
19.45
24.39
1.89
27.33
34.71
38.66
1.38
18.18
25.74
30.44
5.73
41.08
48.65
50.93
2.73
23.90
31.22
35.59
20.05
0.83
10.32
15.91
12.85
32.52
39.42
43.03
12.85
32.52
39.42
43.03
7.39
35.76
36.81
40.48
27.35
5.42
16.46
22.50
25.23
6.28
17.89
23.76
21.84
1.49
12.82
17.27
27.96
3.09
16.50
21.13
23.82
12.56
23.11
28.37
CG
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
Fl
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055,
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
FIA
Al A2 A3 A4
0.056 62 0 0 0
0.077 0 62 0 0
0.052 0 0 62 0
0.024 0 0 0 62
0.056 43 0 0 0
0.077 0 43 0 0
0.052 0 0 43 0
0.024 0 0 0 43
0.056 69 0 0 0
0.077 0 69 0 0
0.052 0 0 69 0
0.024 0 0 0 69
0.056 72 0 0 0
0.077 0 72 0 0
0.052 0 0 72 0
0.024 0 0 0 72
0.056 62 0 0 0
0.077 0 62 0 0
0.052 0 0 62 0
0.024 0 0 0 62
0.056 28 0 0 0
0.077 0 28 0 0
0.052 0 0 28 0
0.024 0 0 0 28
0.056 37 0 0 0
0.077 0 37 0 0
0.052 0 0 37 0
0.024 0 0 0 37
0.056 43 0 0 0
0.077 0 43 0 0
0.052 0 0 43 0
0.024 0 0 0 43
0.056 47 0 0 0
0.077 0 47 0 0
0.052 0 0 47 0
0.024 0 0 0 47
0.056 50 0 0 0
0.077 0 50 0 0
0.052 0 0 50 0
0.024 0 0 0 50
0.056 33 0 0 0
0.077 0 33 0 0
0.052 0 0 33 0
0.024 0 0 0 33
0.056 28 0 0 0
0.077 0 28 0 0
0.052 0 0 28 0
0.024 0 0 0 28
0.056 70 0 0 0
0.077 0 70 0 0
0.052 0 0 70 0
0.024 0 0 0 70
0.056 70 0 0 0
0.077 0 70 0 0
0.052 0 0 70 0
0.024 0 0 0 70
0.056 38 0 0 0
0.077 0 38 0 0
0.052 0 0 38 0
0.024 0 0 0 38
90
OBS
CHOS SI
55
1
0
].
56
2
3
4
1
0
0
0
1
0
0
2
3
4
57
1
2
3
58
4
1
2
3
4
59
1
2
3
60
61
4
1
66
1
2
0
0
0
1
3
1
4
0
1
0
0
3
1
4
0
1
0
2
3
0
4
1
0
0
0
1
0
0
0
4
1
2
3
69
1
2
1
3
68
0
0
0
0
0
2
67
1
0
0
0
0
0
1
2
65
1
0
0
0
4
3
4
64
0
0
1
4
63
0
2
3
2
3
62
0
1
1
1
4
0
1
0
2
3
0
4
1
2
3
4
0
0
0
0
1
1
TC1
TC2
TC3
33.70
20.13
12.88
24.96
21.62
8.05
5.75
10.81
13.57
0.29
7.25
12.30
17.37
1.44
3.45
8.51
17.25
1.73
6.61
11.67
17.37
0.58
3.45
8.51
15.09
1.44
3.45
8.51
17.37
0.58
3.45
8.51
27.95
14.37
5.18
17.25
20.13
5.75
5.75
14.26
20.82
7.25
2.88
14.95
27.95
14.37
5.75
17.25
14.75
1.18
2.88
14.95
20.82
7.25
1.73
14.95
13.57
1.35
2.24
1.73
96.46
53.73
37.61
60.13
27.37
10.45
7.33
12.99
58.78
15.31
32.12
43.33
31.23
5.04
6.29
13.65
74.73
16.75
29.32
41.10
31.23
4.18
6.29
13.65
49.28
11.65
11.50
23.10
45.13
7.79
9.13
18.81
61.47
38.89
16.00
32.91
52.30
18.82
20.17
31.51
45.79
19.60
8.29
28.52
91.25
60.66
26.18
46.82
42.22
4.30
11.28
36.02
90.17
41.56
16.75
52.64
58.78
7.75
9.94
16.75
54.62
31.33
21.12
36.68
23.54
8.85
6.28
11.54
28.64
5.30
15.54
22.65
21.99
2.64
4.40
10.22
36.41
6.73
14.18
21.48
21.99
1.78
4.40
10.22
26.49
4.84
6.13
13.37
26.62
2.98
5.34
11.94
39.12
22.55
8.78
22.47
30.85
10.11
10.56
20.01
29.14
11.36
4.68
19.47
49.05
29.80
12.56
27.11
23.91
2.22
5.68
21.97
43.93
18.68
6.73
27.51
28.64
3.48
4.81
6.73
CG
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
7.48
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
Fl
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.010
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
FIA
Al A2 A3 A4
0.056 68 0 0 0
0.077 0 68 0 0
0.052 0 0 68 0
0.024 0 0 0 68
0.056 74 0 0 0
0.077 0 74 0 0
0.052 0 0 74 0
0.024 0 0 0 74
0.056 33 0 0 0
0.077 0 33 0 0
0.052 0 0 33 0
0.024 0 0 0 33
0.056 72 0 0 0
0.077 0 72 0 0
0.052 0 0 72 0
0.024 0 0 0 72
0.056 39 0 0 0
0.077 0 39 0 0
0.052 0 0 39 0
0.024 0 0 0 39
0.056 66 0 0 0
0.077 0 66 0 0
0.052 0 0 66 0
0.024 0 0 0 66
0.056 36 0 0 0
0.077 0 36 0 0
0.052 0 0 36 0
0.024 0 0 0 36
0.056 36 0 0 0
0.077 0 36 0 0
0.052 0 0 36 0
0.024 0 0 0 36
0.056 46 0 0 0
0.077 0 46 0 0
0.009 0 0 46 0
0.024 0 0 0 46
0.056 40 0 0 0
0.077 0 40 0 0
0.052 0 0 40 0
0.024 0 0 0 40
0.056 39 0 0 0
0.077 0 39 0 0
0.052 0 0 39 0
0.024 0 0 0 39
0.056 39 0 0 0
0.077 0 39 0 0
0.052 0 0 39 0
0.024 0 0 0 39
0.056 62 0 0 0
0.077 0 62 0 0
0.052 0 0 62 0
0.024 0 0 0 62
0.056 51 0 0 0
0.077 0 51 0 0
0.052 0 0 51 0
0.024 0 0 0 51
0.056 32 0 0 0
0.077 0 32 0 0
0.052 0 0 32 0
0.024 0 0 0 32
91
OBS
70
CHOS SI
o
2
0
4
71
1
2
3
72
73
4
1
2
3
4
1
2
74
3
4
1
2
3
4
75
1
2
3
4
76
1
2
3
4
77
1
2
3
4
78
1
2
3
4
79
1
2
3
4
80
1
2
3
4
81
1
2
3
4
82
1
2
3
4
83
1
2
3
4
84
1
2
3
4
TC2
20.82 67.97
7.25 30.57
1
2.88 13.09
o 14.95 40.57
0 17.25 86.22
0
1.18
7.90
1
1.73 19.76
0
1.44
5.79
0 24.84 54.64
0 11.27 30.49
1
5.75 11.16
0 16.10 30.71
1
3
TC1
0
20.82 104.04
7.25
3.45
0 14.95
0 14.75
0
1.18
1
1.44
0 14.95
0 11.50
0
2.44
5.75
1
0 17.25
0 23.69
0 10.12
1
2.88
0 14.95
0 18.75
0
5.18
1
4.03
5.75
0
0 23.69
0 10.12
1
3.45
0 14.95
0 17.25
0
3.45
8.63
1
0 11.50
0 17.25
0
3.45
1
5.75
0 11.50
0 17.25
0
3.45
1
5.75
0 11.50
0 29.44
0 15.87
8.63
1
0 20.70
0 14.37
4.60
1
0
3.45
0
5.09
0 10.06
5.75
1
0 11.96
0
6.90
0
1
48.42
21.48
60.18
48.16
4.99
11.65
40.57
29.89
8.00
12.96
38.12
77.35
42.70
13.09
40.57
93.69
34.59
22.06
23.15
61.57
33.12
10.66
33.04
56.33
14.56
29.06
31.21
37.94
9.33
11.16
21.94
74.73
19.79
20.78
40.49
56.89
36.90
17.04
35.30
71.85
22.63
17.67
20.48
14.08
9.35
16.88
8.98
TC3
CG
36.53
15.02
6.28
23.49
44.86
3.87
8.94
3.18
34.77
17.68
7.55
20.97
54.13
23.73
10.67
33.05
25.89
2.45
4.84
23.49
17.63
4.29
8.15
24.21
41.58
20.98
6.28
23.49
48.74
16.95
11.24
12.71
36.32
17.79
5.85
20.98
30.28
7.15
15.44
18.07
24.15
5.41
7.55
14.98
36.41
8.90
10.76
21.16
38.59
22.88
11.43
25.57
37.38
11.82
9.14
11.25
11.40
6.95
13.60
7.59
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
7.48
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
Fl
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.010
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
FIA
Al A2 A3 A4
0.056 36 0 0 0
0.077 0 36 0 0
0.052 0 0 36 0
0.024 0 0 0 36
0.056 46 0 0 0
0.077 0 46 0 0
0.052 0 0 46 0
0.024 0 0 0 46
0.056 31 0 0 0
0.077 0 31 0 0
0.009 0 0 31 0
0.024 0 0 0 31
0.056 51 0 0 0
0.077 0 51 0 0
0.052 0 0 51 0
0.024 0 0 0 51
0.056 32 0 0 0
0.077 0 32 0 0
0.052 0 0 32 0
0.024 0 0 0 32
0.056 30 0 0 0
0.077 0 30 0 0
0.052 0 0 30 0
0.024 0 0 0 30
0.056 25 0 0 0
0.077 0 25 0 0
0.052 0 0 25 0
0.024 0 0 0 25
0.056 42 0 0 0
0.077 0 42 0 0
0.052 0 0 42 0
0.024 0 0 0 42
0.056 39 0 0 0
0.077 0 39 0 0
0.052 0 0 39 0
0.024 0 0 0 39
0.056 35 0 0 0
0.077 0 35 0 0
0.052 0 0 35 0
0.024 0 0 0 35
0.056 37 0 0 0
0.077 0 37 0 0
0.052 0 0 37 0
0.024 0 0 0 37
0.056 34 0 0 0
0.077 0 34 0 0
0.052 0 0 34 0
0.024 0 0 0 34
0.056 55 0 0 0
0.077 0 55 0 0
0.052 0 0 55 0
0.024 0 0 0 55
0.056 35 0 0 0
0.077 0 35 0 0
0.052 0 0 35 0
0.024 0 0 0 35
0.056 48 0 0 0
0.077 0 48 0 0
0.052 0 0 48 0
0.024 0 0 0 48
92
OBS
85
CHOS SI
1
2
o
3
1
0
0
0
2
1
3
0
4
2.
0
0
2
1
3
4
0
0
1
1
4
86
87
88
89
90
91
92
93
2
0
3
0
4
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1
2
3
4
0
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1
0
0
3
4
0
3
4
0
0
2.
1
2
3
4
o
0
0
1
0
0
0
1
1
2
0
0
0
4
1
3
96
4
1
2
3
99
1
4
1
1
2
3
0
0
0
1
2
3
4
98
1
0
0
0
4
97
0
1
2
3
95
0
0
2
2
94
1
1
0
0
0
1
1
2
3
4
1
0
0
0
2
0
3
0
0
4
1
TC1
TC2
TC3
CG
Fl
19.32
5.75
13.00
18.05
17.02
3.45
10.70
15.75
22.20
14.37
4.37
9.43
17.25
22.43
29.67
34.73
14.37
1.34
2.24
1.01
14.37
8.63
2.42
1.73
0.29
17.25
20.70
25.76
5.75
9.20
50.21
20.17
34.41
39.90
73.73
18.48
47.42
55.47
96.15
44.43
19.38
33.20
37.68
94.63
98.93
94.26
26.38
3.88
5.32
2.02
26.38
24.94
5.73
3.46
10.50
72.79
69.02
69.91
35.80
52.77
29.62
10.56
20.13
25.34
35.92
8.46
22.94
28.99
46.85
24.39
9.37
17.35
24.06
46.49
52.76
54.57
18.38
2.19
3.27
1.34
18.38
14.06
3.52
2.30
3.69
35.76
36.81
40.48
15.77
23.72
49.33
52.91
5.13
37.67
45.19
47.89
5.13
37.67
45.19
47.89
8.28
24.35
31.50
36.20
34.20
2.22
4.34
2.11
46.12
0.94
26.66
31.78
15.50
6.37
14.66
20.20
17.10
17.32
37.72
42.26
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
8.95
7.82
8.11
0.00
0.059
0.081
0.010
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.010
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
0.059
0.081
0.055
0.025
23.00 101.98
28.75 101.23
1.73 11.94
18.17 76.67
25.42 84.75
30.48 82.71
1.73 11.94
18.17 76.67
25.42 84.75
30.48 82.71
7.48 9.88
21.62 29.80
28.87 36.78
33.93 40.76
23.00 56.60
1.18 4.30
2.65 7.72
1.44
3.46
17.25 89.37
0.29
1.92
10.06 51.52
14.37 57.86
11.50 23.50
3.91 11.30
10.06 23.86
15.12 30.35
11.50 28.30
9.20 33.56
23.00 67.15
28.75 69.27
FIA
Al A2 A3 A4
0.056 29 0 0 0
0.077 0 29 0 0
0.009 0 0 29 0
0.024 0 0 0 29
0.056 54 0 0 0
0.077 0 54 0 0
0.052 0 0 54 0
0.024 0 0 0 54
0.056 55 0 0 0
0.077 0 55 0 0
0.052 0 0 55 0
0.024 0 0 0 55
0.056 45 0 0 0
0.077 0 45 0 0
0.052 0 0 45 0
0.024 0 0 0 45
0.056 18 0 0 0
0.077 0 18 0 0
0.052 0 0 18 0
0.024 0 0 0 18
0.056 18 0 0 0
0.077 0 18 0 0
0.052 0 0 18 0
0.024 0 0 0 18
0.056 38 0 0 0
0.077 0 38 0 0
0.052 0 0 38 0
0.024 0 0 0 38
0.056 31 0 0 0
0.077 0 31 0 0
0.052 0 0 31 0
0.024 0 0 0 31
0.056 63 0 0 0
0.077 0 63 0 0
0.009 0 0 63 0
0.024 0 0 0 63
0.056 45 0 0 0
0.077 0 45 0 0
0.052 0 0 45 0
0.024 0 0 0 45
0.056 74 0 0 0
0.077 0 74 0 0
0.052 0 0 74 0
0.024 0 0 0 74
0.056 68 0 0 0
0.077 0 68 0 0
0.052 0 0 68 0
0.024 0 0 0 68
0.056 31 0 0 0
0.077 0 31 0 0
0.052 0 0 31 0
0.024 0 0 0 31
0.056 29 0 0 0
0.077 0 29 0 0
0.052 0 0 29 0
0.024 0 0 0 29
0.056 48 0 0 0
0.077 0 48 0 0
0.052 0 0 48 0
0.024 0 0 0 48
£6
550
001
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CHOS SI
1
2
0
3
4
1
0
2
117
3
4
1
1
1
0
0
0
1
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1
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122
123
124
125
126
127
128
1
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0
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3
1
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0
0
TC1
18.40
8.63
2.43
6.79
14.37
1.35
2.88
1.01
11.50
2.44
4.31
17.25
12.65
5.18
10.06
15.12
11.50
8.63
2.43
6.79
11.50
2.44
5.75
17.25
20.82
7.25
0.86
12.94
24.84
11.27
8.63
16.10
11.50
3.91
10.06
15.12
TC2
40.47
19.45
5.43
12.94
46.94
5.70
13.09
2.73
29.89
8.00
11.52
38.12
24.45
12.03
18.47
25.79
41.55
49.47
10.76
23.89
25.92
8.00
15.22
38.12
48.51
20.95
6.86
25.96
81.11
47.56
18.84
43.69
17.62
6.87
12.46
21.21
31.05 101.39
11.50 48.53
5.75 15.97
10.35 28.09
14.37 46.94
1.35
5.70
2.88 13.09
2.73
1.01
4.60 14.82
17.25 72.79
20.70 69.02
25.76 69.91
4.60 7.00
17.25 30.30
20.70 32.05
25.76 36.13
17.25 38.88
3.45 11.29
7.56 20.02
11.50 25.41
17.25 29.25
3.45 6.71
7.56 12.75
11.50 17.29
TC3
CG
25.76
12.23
3.43
8.84
25.23
2.80
6.28
1.58
17.63
4.29
6.72
24.21
16.58
7.46
12.87
18.68
21.52
22.24
5.20
12.49
16.31
4.29
8.91
24.21
30.05
11.81
2.86
17.28
43.60
23.37
12.03
25.30
13.54
4.90
10.86
17.15
54.50
23.84
9.16
16.26
25.23
2.80
6.28
1.58
8.01
35.76
36.81
40.48
5.40
21.60
24.48
29.22
24.46
6.06
11.71
16.14
21.25
4.54
9.29
13.43
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
Fl
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.052
0.081
0.127
0.122
0.052
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
FIA
Al A2 A3 A4
0.121 38 0 0 0
0.116 0 38 0 0
0.196 0 0 38 0
0.077 0 0 0 38
0.121 38 0 0 0
0.116 0 38 0 0
0.196 0 0 38 0
0.077 0 0 0 38
0.121 31 0 0 0
0.116 0 31 0 0
0.196 0 0 31 0
0.077 0 0 0 31
0.121 33 0 0 0
0.116 0 33 0 0
0.196 0 0 33 0
0.077 0 0 0 33
0.121 43 0 0 0
0.116 0 43 0 0
0.196 0 0 43 0
0.077 0 0 0 43
0.121 59 0 0 0
0.116 0 59 0 0
0.196 0 0 59 0
0.077 0 0 0 59
0.121 65 0 0 0
0.116 0 65 0 0
0.196 0 0 65 0
0.077 0 0 0 65
0.121 39 0 0 0
0.116 0 39 0 0
0.196 0 0 39 0
0.077 0 0 0 39
0.121 74 0 0 0
0.116 0 74 0 0
0.196 0 0 74 0
0.077 0 0 0 74
0.121 47 0 0 0
0.116 0 47 0 0
0.196 0 0 47 0
0.077 0 0 0 47
0.121 39 0 0 0
0.116 0 39 0 0
0.196 0 0 39 0
0.077 0 0 0 39
0.121 32 0 0 0
0.116 0 32 0 0
0.196 0 0 32 0
0.077 0 0 0 32
0.121 24 0 0 0
0.116 0 24 0 0
0.196 0 0 24 0
0.077 0 0 0 24
0.121 27 0 0 0
0.116 0 27 0 0
0.196 0 0 27 0
0.077 0 0 0 27
0.121 27 0 0 0
0.116 0 27 0 0
0.196 0 0 27 0
0.077 0 0 0 27
95
OBS
130
131
CHOS SI
1
2
3
4
1
2
132
3
4
1
2
3
133
136
1
3
4
0
0
0
1
0
0
0
1
1
2
3
0
4
1
4
0
0
1
1
2
0
0
0
1
2
3
4
140
142
143
144
0
0
0
1
2
3
0
0
0
1
1
2
3
0
4
0
0
1
1
2
3
0
4
141
1
1
4
139
0
2
4
138
1
1
3
137
0
0
0
0
0
2
135
1
4
3
134
1
0
0
0
0
0
1
1
2
3
0
4
0
0
1
1
2
0
3
0
4
0
1
1
2
0
3
4
0
0
1
1
2
3
0
0
4
0
TC1
TC2
38.89
9.33
16.91
21.94
22.65
0.90
11.04
16.77
77.35
19.79
33.46
40.49
77.35
26.39
11.73
27.13
10.50
76.67
84.75
82.71
0.86 12.86
18.17 86.90
25.42 95.11
30.48 91.84
0.86 15.89
18.17 104.22
17.25
3.45
7.56
11.50
17.25
0.58
7.82
12.88
17.25
3.45
7.55
11.50
17.25
4.60
2.65
7.71
0.29
18.17
25.42
30.48
25.42 112.68
30.48 107.30
37.38 73.43
14.95
22.20
27.25
17.25
0.58
6.15
11.21
5.75
17.25
20.70
25.76
5.75
17.25
20.70
25.76
17.25
48.93
58.77
60.23
38.88
1.88
16.29
24.78
15.35
43.35
43.41
46.51
16.57
46.67
46.29
49.14
62.33
13.46
20.13
4.03
4.60
9.66
14.37
14.37
21.62
26.68
29.73
65.20
23.09
20.40
34.01
28.80
47.04
57.24
58.96
30.82 176.78
37.95 168.26
43.13 151.84
18.69 40.32
1.15
3.76
8.40 22.23
TC3
CG
Fl
FIA
Al A2 A3 A4
2446 9.56 0.127 0.121 33
5.41
10.68
14.98
19.05
0.68
8.89
14.18
37.28
8.90
16.18
21.16
37.28
11.86
5.67
14.18
3.69
37.67
45.19
47.89
4.86
41.08
48.65
50.93
5.87
46.85
54.50
56.08
49.39
26.28
34.39
38.25
24.46
1.01
9.53
15.73
8.95
25.95
28.27
32.68
9.36
27.06
29.23
33.55
32.28
79.47
81.39
79.36
25.90
2.02
13.01
18.88
35.15
10.38
9.87
17.78
19.18
25.26
33.49
37.44
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
9.56
8.57
8.68
6.77
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0
0
0
0.116 0 33 0 0
0.196 0 0 33 0
0.077 0 0 0 33
0.121 30 0 0 0
0.116 0 30 0 0
0.196 0 0 30 0
0.077 0 0 0 30
0.121 48 0 0 0
0.116 0 48 0 0
0.196 0 0 48 0
0.077 0 0 0 48
0.121 51 0 0 0
0.116 0 51 0 0
0.196 0 0 51 0
0.077 0 0 0 51
0.121 31 0 0 0
0.116 0 31 0 0
0.196 0 0 31 0
0.077 0 0 0 31
0.121 17 0 0 0
0.116 0 17 0 0
0.196 0 0 17 0
0.077 0 0 0 17
0.121 46 0 0 0
0.116 0 46 0 0
0.196 0 0 46 0
0.077 0 0 0 46
0.121 28 0 0 0
0.116 0 28 0 0
0.196 0 0 28 0
0.077 0 0 0 28
0.121 29 0 0 0
0.116 0 29 0 0
0.196 0 0 29 0
0.077 0 0 0 29
0.121 35 0 0 0
0.116 0 35 0 0
0.196 0 0 35 0
0.077 0 0 0 35
0.121 40 0 0 0
0.116 0 40 0 0
0.196 0 0 40 0
0.077 0 0 0 40
0.121 37 0 0 0
0.116 0 37 0 0
0.196 0 0 37 0
0.077 0 0 0 37
0.121 38 0 0 0
0.116 0 38 0 0
0.196 0 0 38 0
0.077 0 0 0 38
0.121 38 0 0 0
0.116 0 38 0 0
0.196 0 0 38 0
0.077 0 0 0 38
0.121 49 0 0 0
0.116 0 49 0 0
0.196 0 0 49 0
0.077 0 0 0 49
96
OBS
145
146
CHOS SI
1
1
2
3
o
0
0
4
1
2
.3
4
147
1
2
3
4
148
1
2
3
4
149
1
2
2
1
3
2
0
0
0
1
0
0
0
0
3
1
4
1
0
0
2
1
3
4
1
2
3
0
0
4
0
0
0
1
2
4
4
152
1
2
3
4
153
154
155
156
1
1
2
157
1
1
4
0
0
0
1
3
1
4
1
0
0
0
2
159
0
0
3
2
158
0
0
0
1
1
4
3
151
1
0
0
0
1
0
o
0
3
150
1
0
0
0
0
1
0
0
0
3
1
4
0
0
1
2
3
4
o
1
0
TC1
TC2
TC3
CG
17.18 9.56
20.73 8.57
28.55 8.68
32.95 6.77
42.37 9.56
7.15 8.57
31.90 8.68
36.14 6.77
23.37 9.59
30.84 8.64
3.61 8.86
1.82 6.44
94.01 169.07 119.03 9.59
80.50 123.70 94.90 8.64
78.20 142.54 99.65 8.86
83.26 133.55 100.02 6.44
17.25 44.83 26.44 9.59
2.98 8.64
0.58 7.79
2.65
7.00
4.10 8.86
2.02 6.44
1.44 3.18
17.25 31.02 21.84 9.59
2.88 6.48 4.08 8.64
4.82
3.37 8.86
2.65
1.44 2.31
1.73 6.44
20.41 58.44 33.09 9.59
5.75 22.55 11.35 8.64
11.90 34.75 19.52 8.86
16.96 40.87 24.93 6.44
14.37 29.68 19.48 9.59
6.90 21.30 11.70 8.64
8.34 17.48 11.39 8.86
5.75 10.38
7.29 6.44
35.19 72.65 47.68 9.59
21.62 54.33 32.52 8.64
14.37 28.78 19.18 8.86
19.43 35.09 24.65 6.44
14.37 46.94 25.23 9.65
11.50 31.93 18.31 8.28
8.34 27.80 14.83 8.85
9.04 6.59
5.75 15.61
11.90 27.74 17.18 9.65
5.35 15.46 8.72 8.28
11.50 23.50 15.50 8.85
23.58 47.31 31.49 6.59
35.42 129.68 66.84 9.65
21.85 104.50 49.40 8.28
21.28 45.28 29.28 8.85
26.34 79.36 44.01 6.59
17.25 28.73 21.08 9.65
2.01 3.92
2.65 8.28
7.48 13.48 9.48 7.07
19.55 29.39 22.83 6.59
13.66 24.56 17.29 9.65
4.60 9.82
6.34 8.28
10.06 17.26 12.46 7.07
15.12 24.26 18.17 6.59
12.08 28.14 17.43 9.65
9.20 26.60 15.00 8.28
23.00 47.00 31.00 8.85
28.75 57.69 38.40 6.59
14.37
14.37
21.62
26.68
28.75
3.45
17.94
23.00
17.25
24.44
2.65
1.44
22.79
33.43
42.40
45.50
69.61
14.56
59.82
62.42
35.61
43.64
5.55
2.60
Fl
0.127
0.122
0.207
0.081
0.127
0.122
0.207
0.081
0.121
0.140
0.108
0.058
0.121
0.140
0.108
0.058
0.121
0.140
0.108
0.058
0.121
0.140
0.108
0.058
0.121
0.140
0.108
0.058
0.121
0.140
0.108
0.058
0.121
0.140
0.108
0.058
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.058
0.177
0.198
0.133
0.058
0.177
0.198
0.133
0.232
0.177
FIA
Al A2 A3 A4
0.121 26 0 0 0
0.116 0 26 0 0
0.196 0 0 26 0
0.077 0 0 0 26
0.121 43 0 0 0
0.116 0 43 0 0
0.196 0 0 43 0
0.077 0 0 0 43
0.114 73 0 0 0
0.133 0 73 0 0
0.102 0 0 73 0
0.055 0 0 0 73
0.114 62 0 0 0
0.133 0 62 0 0
0.102 0 0 62 0
0.055 0 0 0 62
0.114 29 0 0 0
0.133 0 29 0 0
0.102 0 0 29 0
0.055 0 0 0 29
0.114 75 0 0 0
0.133 0 75 0 0
0.102 0 0 75 0
0.055 0 0 0 75
0.114 64 0 0 0
0.133 0 64 0 0
0.102 0 0 64 0
0.055 0 0 0 64
0.114 67 0 0 0
0.133 0 67 0 0
0.102 0 0 67 0
0.055 0 0 0 67
0.114 70 0 0 0
0.133 0 70 0 0
0.102 0 0 70 0
0.055 0 0 0 70
0.187 41 0 0 0
0.126 0 41 0 0
0.220 0 0 41 0
0.168 0 0 0 41
0.187 66 0 0 0
0.126 0 66 0 0
0.220 0 0 66 0
0.168 0 0 0 66
0.187 52 0 0 0
0.126 0 52 0 0
0.220 0 0 52 0
0.168 0 0 0 52
0.187 40 0 0 0
0.126 0 40 0 0
0.220 0 0 40 0
0.168 0 0 0 40
0.187 71 0 0 0
0.126 0 71 0 0
0.054 0 0 71 0
0.168 0 0 0 71
0.187 48 0 0 0
0.126 0 48 0 0
0.220 0 0 48 0
0.168 0 0 0 48
97
OBS
160
CHOS
162
163
1
2
3
4
1
4
1
4
1
4
1
4
1
4
1
4
1
2
3
1
2
3
4
1
2
3
4
1
2
3
173
4
1
2
3
174
22.95
14.83
21.58
27.83
28.00
23.84
26.58
31.44
1.95
22.75
30.06
34.57
16.78
14.40
17.68
22.70
32.28
0.74
21.58
26.43
22.05
5.19
24.50
29.17
5.73
41.08
48.65
50.93
4.29
41.08
48.65
50.93
18.85
14.40
27.20
32.61
0
0
4
4
172
42.99
32.98
44.61
53.25
55.24
48.53
49.85
54.31
3.55
31.92
39.35
42.75
21.58
20.20
23.15
28.07
62.33
1.65
44.61
50.55
31.65
8.67
37.62
41.52
13.73
86.90
95.11
91.84
12.29
86.90
95.11
91.84
22.05
20.20
35.61
40.33
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
1
2
3
171
12.94
5.75
10.06
0 15.12
1
14.37
0 11.50
0 14.95
0 20.01
1
1.15
0 18.17
0 25.42
0 30.48
1
14.37
0 11.50
0 14.95
0 20.01
1
17.25
0
0.29
0 10.06
0 14.37
1 17.25
0
3.45
0 17.94
0 23.00
1
1.73
0 18.17
0 25.42
0 30.48
1
0.29
0 18.17
0 25.42
0 30.48
1 17.25
0 11.50
0 23.00
0 28.75
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
3
3
170
24.15
1.85
3.74
3.24
23.30
4.24
3.68
1.48
27.13
5.44
4.29
1.68
33.25
6.50
19.26
25.27
36.71
16.31
4.13
9.52
0
2
169
37.94
3.20
5.92
6.85
41.15
9.84
6.55
2.42
52.63
13.44
8.39
3.03
65.25
13.75
37.66
45.57
76.78
36.28
8.54
18.22
2
2
3
168
17.25
1.18
2.65
1.44
14.37
1.44
2.24
1.01
14.37
1.44
2.24
1.01
17.25
2.88
10.06
15.12
16.68
6.33
1.93
5.18
1
2
3
167
Fl
1
2
3
166
CG
3
3
165
TC3
4
2
164
TC2
0
0
0
1
2
161
TC1
SI
4
1
2
3
4
0
1
0
0
1
0
0
0
1
0
0
0
1 115.00 211.00 147.00
0 109.25 274.54 164.35
0 116.50 244.28 159.09
0 121.56 219.45 154.19
1
0
0
FIA
Al A2 A3 A4
0.187 29 0 0 0
0.126 0 29 0 0
0.220 0 0 29 0
0.168 0 0 0 29
0.187 63 0 0 0
0.126 0 63 0 0
0.220 0 0 63 0
0.168 0 0 0 63
0.187 75 0 0 0
0.126 0 75 0 0
0.220 0 0 75 0
0.168 0 0 0 75
0.187 71 0 0 0
0.126 0 71 0 0
0.220 0 0 71 0
0.168 0 0 0 71
0.187 38 0 0 0
0.126 0 38 0 0
0.220 0 0 38 0
0.168 0 0 0 38
0.187 67 0 0 0
0.126 0 67 0 0
0.220 0 0 67 0
0.168 0 0 0 67
0.187 53 0 0 0
0.126 0 53 0 0
0.220 0 0 53 0
0.168 0 0 0 53
0.187 46 0 0 0
0.126 0 46 0 0
0.220 0 0 46 0
0.168 0 0 0 46
0.187 64 0 0 0
0.126 0 64 0 0
0.220 0 0 64 0
0.168 0 0 0 64
0.187 68 0 0 0
0.126 0 68 0 0
0.220 0 0 68 0
0.168 0 0 0 68
0.187 58 0 0 0
0.126 0 58 0 0
0.220 0 0 58 0
0.168 0 0 0 58
0.187 62 0 0 0
0.126 0 62 0 0
0.220 0 0 62 0
0.168 0 0 0 62
0.187 65 0 0 0
0.126 0 65 0 0
0.220 0 0 65 0
0.168 0 0 0 65
0.187 52 0 0 0
0.126 0 52 0 0
0.220 0 0 52 0
0.168 0 0 0 52
0.187 73 0 0 0
0.126 0 73 0 0
0.220 0 0 73 0
0.168 0 0 0 73
98
OBS
175
176
CHOS SI
1
2
3
4
0
0
0
1
1
2
4
0
0
0
3
177
178
1
1
2
3
4
0
0
0
1
1
2
3
0
0
0
4
179
180
181
182
183
1
2
3
4
1
1
1
2
0
0
0
1
2
3
4
0
0
0
1
2
0
3
4
0
0
1
1
0
0
0
1
2
3
4
1
3
4
1
2
3
4
187
1
2
3
188
4
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
1
2
0
0
0
3
189
1
2
2
186
0
1
4
185
0
0
3
4
3
184
1
4
1
2
3
4
1
0
0
0
TC1
TC2
1.73
17.25
20.70
25.76
17.25
6.90
14.15
19.21
2.88
17.25
20.70
25.76
2.59
17.25
20.70
25.76
17.25
5.12
7.19
12.25
2.30
17.25
20.70
25.76
11.50
9.20
23.00
28.75
1.73
17.25
20.70
25.76
1.73
17.25
20.70
25.76
1.15
17.25
20.70
25.76
17.25
3.45
7.56
11.50
17.25
3.45
7.56
11.50
17.25
2.88
10.06
16.75
98.94
91.78
90.70
62.33
39.58
62.71
67.62
8.29
46.67
46.29
49.14
9.80
56.45
54.81
56.92
24.45
10.92
13.10
19.65
7.10
43.35
43.41
46.51
35.50
44.00
86.07
86.64
4.13
30.30
32.05
36.13
16.75
98.94
91.78
90.70
7.15
49.87
49.08
51.69
47.90
14.56
25.21
31.21
71.25
23.02
38.67
46.23
71.25
19.19
51.46
TC3
CG
6.73 9.65
44.48 8.28
44.39 8.85
47.41 6.59
32.28 9.65
17.79. 8.28
30.33
35.34
4.68
27.06
29.23
33.55
4.99
30.32
32.07
36.15
19.65
7.05
9.16
14.71
3.90
25.95
28.27
32.68
19.50
20.80
44.02
48.05
2.53
21.60
24.48
29.22
6.73
44.48
44.39
47.41
3.15
28.12
30.16
34.40
27.47
7.15
13.45
18.07
35.25
9.97
17.93
23.08
35.25
8.31
23.86
15.12 475.66 30.35
0.58 3.58 1.58
5.35 10.40 7.03
12.59 21.23 15.47
17.65 26.54 20.61
8.63 39.27 18.84
8.63 36.40 17.88
15.87 52.92 28.22
12.65 34.33 19.88
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
Fl
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
FIA
Al A2 A3 A4
0.187 52 0 0 0
0.126 0 52 0 0
0.220 0 0 52 0
0.168 0 0 0 52
0.187 54 0 0 0
0.126 0 54 0 0
0.220 0 0 54 0
0.168 0 0 0 54
0.187 54 0 0 0
0.126 0 54 0 0
0.220 0 0 54 0
0.168 0 0 0 54
0.187 40 0 0 0
0.126 0 40 0 0
0.220 0 0 40 0
0.168 0 0 0 40
0.187 65 0 0 0
0.126 0 65 0 0
0.220 0 0 65 0
0.168 0 0 0 65
0.187 67 0 0 0
0.126 0 67 0 0
0.220 0 0 67 0
0.168 0 0 0 67
0.187 50 0 0 0
0.126 0 50 0 0
0.220 0 0 50 0
0.168 0 0 0 50
0.187 78 0 0 0
0.126 0 78 0 0
0.220 0 0 78 0
0.168 0 0 0 78
0.187 27 0 0 0
0.126 0 27 0 0
0.220 0 0 27 0
0.168 0 0 0 27
0.187 59 0 0 0
0.126 0 59 0 0
0.220 0 0 59 0
0.168 0 0 0 59
0.187 67 0 0 0
0.126 0 67 0 0
0.220 0 0 67 0
0.168 0 0 0 67
0.187 70 0 0 0
0.126 0 70 0 0
0.220 0 0 70 0
0.168 0 0 0 70
0.187 84 0 0 0
0.126 0 84 0 0
0.220 0 0 84 0
0.168 0 0 0 84
0.187 32 0 0 0
0.126 0 32 0 0
0.220 0 0 32 0
0.168 0 0 0 32
0.187 30 0 0 0
0.126 0 30 0 0
0.220 0 0 30 0
0.168 0 0 0 30
99
OBS
190
CHOS SI
1
0
2
1
3
1
o
o
0
2
1
3
1
o
0
0
2
1
3
0
0
4
191
4
192
4
193
1
2
3
195
196
1
2
1
3
4
1
0
0
0
2
1
3
1
0
0
0
2
1
3
0
0
4
4
197
3
0
0
0
4
1
1
2
0
0
3
1
4
0
0
0
1
2
198
199
1
2
3
200
201
203
0
1
0
2
3
0
4
o
0
0
1
1
1
4
0
1
1
2
3
0
0
4
0
0
0
1
2
3
204
1
4
2
3
202
1
0
0
0
4
194
0
1
1
2
0
0
0
3
1
4
0
4
TC1
11.90
11.50
18.75
23.80
10.06
8.63
15.87
20.93
15.30
1.73
8.97
14.03
17.25
3.45
7.55
11.50
14.37
14.37
21.62
26.68
21.85
5.75
4.60
11.50
16.39
2.88
4.69
5.75
16.68
6.33
1.93
2.88
18.40
8.63
0.58
5.64
21.85
5.75
5.75
11.50
14.37
1.35
1.44
TC2
TC3
30.93 18.25
25.92 16.31
49.63 29.04
52.60 33.40
23.45 14.53
20.63 12.63
37.63 23.12
42.00 27.95
88.73 39.77
8.94
23.38
53.35 23.76
65.00 31.02
44.83 26.44
10.66
5.85
19.98 11.69
25.41 16.14
46.94 25.23
34.81 21.19
72.09 38.44
72.41 41.92
79.99 41.23
29.75 13.75
8.BQ
17.21
34.65 19.22
53.51 28.76
13.09
6.28
8.35
15.65
15.61
9.04
72.23 35.19
36.28 16.31
8.54 4.13
17.90
7.88
91.97 47.85
57.64 28.24
18.61
7.79
22.68 12.46
71.35 38.35
24.26 11.92
15.97
9.16
31.21 18.07
46.94 25.23
5.70
2.80
11.65
4.84
i.oi 2.73 1.58
20.13 65.71 35.32
8.34
4.03 16.98
6.86
3.45 13.67
8.51 23.10 13.37
10.06 34.06 18.06
4.03 19.25 9.10
11.27 42.18 21.57
16.33 49.21 27.29
21.85 109.21 56.82
5.75 38.43 18.83
3.45 21.48 10.67
11.50 46.29 25.43
15.01 75.01 39.02
1.44
9.61
4.71
10.06 46.12 24.50
15.12 60.87 33.43
CG
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.65
8.28
8.85
6.59
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
7.24
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
Fl
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.198
0.133
0.232
0.177
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.179
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
FIA
Al A2 A3 A4
0.187 37 0 0 0
0.126 0 37 0 0
0.220 0 0 37 0
0.168 0 0 0 37
0.187 63 0 0 0
0.126 0 63 0 0
0.220 0 0 63 0
0.168 0 0 0 63
0.187 33 0 0 0
0.126 0 33 0 0
0.220 0 0 33 0
0.168 0 0 0 33
0.187 33 0 0 0
0.126 0 33 0 0
0.220 0 0 33 0
0.168 0 0 0 33
0.187 48 0 0 0
0.126 0 48 0 0
0.220 0 0 48 0
0.168 0 0 0 48
0.187 61 0 0 0
0.126 0 61 0 0
0.220 0 0 61 0
0.168 0 0 0 61
0.187 32 0 0 0
0.126 0 32 0 0
0.220 0 0 32 0
0.168 0 0 0 32
0.174 42 0 0 0
0.206 0 42 0 0
0.308 0 0 42 0
0.199 0 0 0 42
0.174 44 0 0 0
0.206 0 44 0 0
0.308 0 0 44 0
0.199 0 0 0 44
0.174 25 0 0 0
0.206 0 25 0 0
0.169 0 0 25 0
0.199 0 0 0 25
0.174 21 0 0 0
0.206 0 21 0 0
0.308 0 0 21 0
0.199 0 0 0 21
0.174 38 0 0 0
0.206 0 38 0 0
0.308 0 0 38 0
0.199 0 0 0 38
0.174 59 0 0 0
0.206 0 59 0 0
0.308 0 0 59 0
0.199 0 0 0 59
0.174 47 0 0 0
0.206 0 47 0 0
0.308 0 0 47 0
0.199 0 0 0 47
0.174 42 0 0 0
0.206 0 42 0 0
0.308 0 0 42 0
0.199 0 0 0 42
100
OBS
205
206
CHOS SI
1
0
2
3
1
4
0
1
0
2
0
3
207
4
1
2
3
208
4
1
2
3
209
4
1
2
210
211
213
214
215
216
217
0
0
1
0
0
0
1
0
0
0
1
0
1
2
3
0
4
0
0
1
2
1
2
3
0
1
0
1
0
0
0
1
4
0
1
2
3
0
1
4
0
0
1
1
2
3
0
0
4
0
1
2
3
4
1
0
0
0
1
1
2
0
0
0
1
0
0
3
4
1
3
4
1
2
3
219
0
4
2
218
1
3
3
4
212
0
4
0
1
0
0
0
1
2
3
1
4
0
0
0
TC1
15.09
2.88
1.15
6.21
TC2
TC3
49.28 26.49
13.09
6.28
3.83
2.04
16.85
9.76
78.31 255.72 137.45
64.75 273.21 134.23
57.50 129.01 81.34
62.56 169.79 98.30
16.68 61.05 31.47
6.33 30.25 14.30
0.86 12.86 4.86
5.18 15.59 8.65
18.40 60.08 32.29
8.63 36.40 17.88
0.58 10.79 3.98
1.73
4.68 2.71
14.37
1.35
2.01
1.01
12.22
10.58
14.37
19.46
20.82
7.25
0.58
12.65
18.40
8.63
2.88
7.94
14.37
10.58
14.37
19.43
21.85
5.75
4.60
11.50
23.00
8.63
2.42
7.48
14.37
5.75
10.06
15.12
2.30
13.57
20.82
25.87
1.44
14.52
21.76
26.82
8.63
5.12
7.19
12.25
46.94
5.70
12.23
2.73
39.90
44.65
45.02
52.82
54.10
23.71
7.79
27.95
42.88
24.94
8.88
15.92
68.38
70.61
73.51
78.13
66.93
32.98
20.40
40.49
44.63
28.23
6.39
16.52
36.01
18.82
26.64
33.42
12.52
57.26
69.41
70.22
9.84
52.96
63.54
64.63
44.69
34.20
36.80
49.30
25.23
2.80
5.42
1.58
21.44
21.94
24.59
30.58
31.91
12. 73
2.98
17.75
26.56
14.06
4.88
10.60
32.38
30.59
34.09
39.00
36.88
14.83
9.87
21.16
30.21
15.16
3.74
10.49
21.59
10.11
15.59
21.22
5.71
28.13
37.01
40.66
4.24
27.33
35.69
39.43
23.06
16.76
19.04
27.08
CG
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
7.24
6.54
Fl
FIA
Al A2 A3 A4
0 00
0.184 0.174 42
0.218 0.206 0 42 00
0.325 0.308 00 42 0
0.211 0.199 00 0 42
0.184 0.174 37
0.218 0.206 0 37 00
0.325 0.308 00 37 0
0.211 0.199 00 0 37
0 00
0. 184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.179
0.211
9 . 10 0.184
8.31 0.218
7.24 0.325
6.54 0.211
9.10 0.184
8.31 0.218
8.86 0.325
6.54 0.211
9.10 0.184
8.31 0.218
8.86 0.325
6.54 0.211
9.10 0.184
8.31 0.218
8.86 0.325
6.54 0.211
9.10 0.184
8.31 0 . 218
8.86 0.325
6.54 0.211
9.10 0.184
8.31 0.218
8.86 0.325
6.54 0.211
9.10 0.184
8.31 0.218
8.86 0.325
6.54 0.211
9.10 0.184
8.31 0.218
8.86 0.325
6.54 0.211
0.174 16
0.206 0
0 00
16 00
0.308 00 16
0
0.199 00 0 16
0.174 41
0.206 0
0 00
41 00
0.308 00 41
0
0.199 00 0 41
0.174 37
0.206 0
0 00
37 00
0.308 00 37
0
0.199 00 0 37
0.174 43
0.206 0
0 00
43
00
0.308 00 43
0
0.199 00 0 43
0 00
0.174 63
0.206 0
63 00
0.174 39
0.206 0
39 00
0.308 00 63 0
0.199 00 0 63
0 00
0.308 00 39
0
0.199 00 0 39
0.174 27
0.206 0
0 00
27
00
0.308 00 27
0
0.199 00 0 27
0.174 40
0.206 0
0 00
40 00
0.308 00 40
0
0.199 00 0 40
0.174 54
0.206 0
0 00
54 00
0.308 00 54
0
0.199 00 0 54
0.174 53
0.206 0
0 00
53 00
0.308 00 53
0
0.199 00 0 53
0.174 40
0.206 0
0 00
40
00
0.308 00 40
0
0.199 00 0 40
0.174 44
0.206 0
0 00
44 00
0.308 00 44
0
0.199 00 0 44
0.174 46
0.206 0
0 00
46 00
0.308 00 46
0
0.199 00 0 46
101
CBS
220
221
CHOS SI
1
2
3
0
0
4
0
0
0
1
2
3
222
224
0
1
0
0
4
1
2
3
4
1
2
3
4
225
226
227
228
229
230
231
232
233
234
1
4
2
3
223
1
1
0
0
0
1
0
0
0
1
4
0
0
0
1
0
1
2
0
0
3
1
4
0
0
0
1
2
3
1
2
3
4
1
1
2
3
0
0
0
4
1
1
2
0
0
3
0
0
4
1
1
0
2
3
0
0
4
1
1
2
3
0
0
0
4
1
1
2
0
0
0
3
4
1
1
2
3
0
0
0
4
1
1
2
0
0
3
1
4
0
TC1
TC2
29.44 96.13
15.87 66.97
8.63 18.84
20.70 56.18
11.50 37.55
5.18 21.84
7.19 17.40
5.75 15.61
17.25 26.43
1.18
2.08
4.31 6.71
1.44
2.02
39.22 128.05
25.65 108.22
18.40 38.83
7.48 20.29
30.02 78.01
16.45 53.82
9.20 23.62
14.26 31.51
16.68 47.74
6.33 23.07
2.88 11.28
5.18 12.47
16.68 61.05
6.33 30.25
2.88 14.88
5.18 15.59
16.68 23.33
6.33
9.91
2.88 4.68
5.18
6.74
14.37 46.94
8.43 35.58
8.34 27.80
5.75 15.97
28.87 75.02
15.30 50.05
8.05 21.31
17.25 31.67
21.85 94.65
5.75 32.98
4.60 20.40
11.50 41.55
46.12 119.85
32.55 106.51
25.30 66.99
37.38 66.22
17.25 37.94
3.45
9.33
7.55 16.88
14.37 25.20
14.37 71.75
1.34
8.96
2.24 11.47
0.29 18.29
11.50 29.89
2.44 8.00
7.19 14.40
17.25 38.12
TC3
CC
51.67
32.90
12.03
32.53
20.18
10.73
10.59
9.04
20.31
1.48
5.11
1.63
68.83
53.17
25.21
11.75
46.01
28.90
14.01
20.01
27.03
11.91
5.68
7.61
31.47
14.30
6.88
8.65
18.89
7.52
3.48
5.70
25.23
17.48
14.83
9.16
44.25
26.88
12.47
22.06
46.12
14.83
9.87
21.52
70.69
57.20
39.20
46.99
24.15
5.41
10.66
17.98
33.50
3.88
5.32
6.29
17.63
4.29
9.59
24.21
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
9.10
8.31
8.86
6.54
8.98
7.82
8.92
6.76
8.98
7.82
8.92
6.76
8.98
7.82
8.92
6.76
8.98
7.82
8.92
6.76
8.98
Fl
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.184
0.218
0.325
0.211
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
7.82 0.078
8.92 0.216
6.76 0.314
8.98 0.105
7.82 0.078
8.92 0.216
5.75 0.054
8.98 0.105
7.82 0.078
8.92 0.216
5.75 0.054
8.98 0.105
7.82 0.078
8.92 0.216
6.76 0.314
FIA
Al A2 A3 A4
0.174 38 0 0 0
0.206 0 38 0 0
0.308 0 0 38 0
0.199 0 0 0 38
0.174 42 0 0 0
0.206 0 42 0 0
0.308 0 0 42 0
0.199 0 0 0 42
0.174 51 0 0 0
0.206 0 51 0 0
0.308 0 0 51 0
0.199 0 0 0 51
0.174 31 0 0 0
0.206 0 31 0 0
0.308 0 0 31 0
0.199 0 0 0 31
0.174 47 0 0 0
0.206 0 47 0 0
0.308 0 0 47 0
0.199 0 0 0 47
0.174 19 0 0 0
0.206 0 19 0 0
0.308 0 0 19 0
0.199 0 0 0 19
0.174 23 0 0 0
0.206 0 23 0 0
0.308 0 0 23 0
0.199 0 0 0 23
0.099 28 0 0 0
0.074 0 28 0 0
0.204 0 0 28 0
0.297 0 0 0 28
0.099 27 0 0 0
0.074 0 27 0 0
0.204 0 0 27 0
0.297 0 0 0 27
0.099 39 0 0 0
0.074 0 39 0 0
0.204 0 0 39 0
0.297 0 0 0 39
0.099 39 0 0 0
0.074 0 39 0 0
0.204 0 0 39 0
0.297 0 0 0 39
0.099 26 0 0 0
0.074 0 26 0 0
0.204 0 0 26 0
0.297 0 0 0 26
0.099 26 0 0 0
0.074 0 26 0 0
0.204 0 0 26 0
0.051 0 0 0 26
0.099 41 0 0 0
0.074 0 41 0 0
0.204 0 0 41 0
0.051 0 0 0 41
0.099 35 0 0 0
0.074 0 35 0 0
0.204 0 0 35 0
0.297 0 0 0 35
102
OBS
235
CHOS SI
1
2
3
236
4
1
2
3
4
237
238
1
2
3
4
1
2
3
4
239
1
2
3
4
240
1
2
3
4
241
1
2
3
4
242
1
2
3
4
243
1
2
3
4
244
1
2
3
245
4
1
2
3
246
247
4
1
2
3
4
1
2
3
248
4
1
2
3
249
4
1
2
3
4
TC].
16.68
6.33
1
2.01
0
5.18
0 14.37
0
2.88
1
8.63
0 15.12
0 14.37
0
8.43
0
8.34
1
5.75
0 18.40
0
8.63
0
2.43
1
1.73
0 14.43
0
0.86
0
7.25
1
8.05
0 17.25
0
1.18
1
2.88
0
1.44
0 14.37
0
8.43
1
6.90
0
5.75
0 18.40
0
8.63
1
4.31
0
9.37
0
0
TC2
TC3
CG
Fl
72.23
36.28
17.04
18.22
41.15
10.49
25.43
36.44
37.36
27.60
22.08
12.96
47.82
28.23
6.42
8.94
37.51
2.82
19.18
15.26
56.33
4.99
13.09
3.90
62.27
48.37
21.93
20.25
79.71
49.47
19.34
33.00
35.19
16.31
7.02
9.52
23.30
5.41
14.23
22.23
22.04
14.82
12.92
8.15
28.21
15.16
3.76
4.13
22.12
1.52
11.22
10.45
30.28
2.45
6.28
2.26
30.34
21.74
11.91
10.58
38.84
22.24
9.32
17.25
8.98
7.82
8.92
6.76
8.98
0.105
0.078
0.216
0.314
0.105
0.078
0.093
0.314
0.105
0.078
0.216
0.054
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
0.105
0.078
0.216
0.314
1 172.50 316.70 220.57
0 158.93 520.12 279.33
0 151.69 401.62 235.00
0 156.75 346.37 219.95
1 143.75 259.11 182.20
0 130.24 426.22 228.90
0 123.25 326.33 190.95
0 128.31 283.54 180.05
7.82
6.69
6.76
8.98
7.82
8.92
5.75
8.98
7.82
8.92
6.76
8.98
7.82
8.92
6.76
8.98
7.82
8.92
6.76
8.98
7.82
8.92
6.76
8.98
7.82
8.92
6.76
8.98
7.82
8.92
6.76
8.98
7.82
8.92
6.76
1
25.87 42.11 31.29 8.98
0 33.18 89.75 52.04 7.82
0 27.03 60.44 38.16 8.92
0 32.09 61.21 41.79 6.76
1
17.25 24.45 19.65 8.98
0
4.60 9.82 6.34 7.82
0
7.88 14.36 10.04 8.92
0 12.94 20.75 15.54 6.76
1
8.63 13.43 10.23 8.98
0
7.48 13.13
9.36 7.82
0 14.72 22.79 17.41 8.92
0 19.78 27.75 22.44 6.76
1 43.13 50.33 45.53 8.98
0 32.03 44.14 36.07 7.82
0 24.78 31.58 27.05 8.92
0 29.84 35.85 31.85 6.76
1
71.88 153.60 99.12 8.98
0 60.78 256.47 126.01 7.82
0 53.53 178.50 95.19 8.92
0 58.59 159.02 92.07 6.76
FIA
Al A2 A3 A4
0.099 39 0 0 0
0.074 0 39 0 0
0.204 0 0 39 0
0.297 0 0 0 39
0.099 75 0 0 0
0.074 0 75 0 0
0.088 0 0 75 0
0.297 0 0 0 75
0.099 42 0 0 0
0.074 0 42 0 0
0.204 0 0 42 0
0.088 0 0 0 42
0.099 30 0 0 0
0.074 0 30 0 0
0.204 0 0 30 0
0.297 0 0 0 30
0.099 41 0 0 0
0.074 0 41 0 0
0.204 0 0 41 0
0.297 0 0 0 41
0.099 31 0 0 0
0.074 0 31 0 0
0.204 0 0 31 0
0.297 0 0 0 31
0.099 41 0 0 0
0.074 0 41 0 0
0.204 0 0 41 0
0.297 0 0 0 41
0.099 43 0 0 0
0.074 0 43 0 0
0.204 0 0 43 0
0.297 0 0 0 43
0.099 50 0 0 0
0.074 0 50 0 0
0.204 0 0 50 0
0.297 0 0 0 50
0.099 43 0 0 0
0.074 0 43 0 0
0.204 0 0 43 0
0.297 0 0 0 43
0.099 48 0 0 0
0.074 0 48 0 0
0.204 0 0 48 0
0.297 0 0 0 48
0.099 66 0 0 0
0.074 0 66 0 0
0.204 0 0 66 0
0.297 0 0 0 66
0.099 68 0 0 0
0.074 0 68 0 0
0.204 0 0 68 0
0.297 0 0 0 68
0.099 32 0 0 0
0.074 0 32 0 0
0.204 0 0 32 0
0.297 0 0 0 32
0.099 40 0 0 0
0.074 0 40 0 0
0.204 0 0 40 0
0.297 0 0 0 40
103
OBS
250
CHOS SI
1
2
3
4
251
1
2
3
4
252
1
2
3
4
253
1
2
3
4
254
255
1
2
3
4
1
2
3
4
256
1
2
3
257
4
1
2
3
4
258
1
2
3
4
259
1
2
3
4
260
261
1
2
3
4
1
2
3
4
262
1
2
3
4
263
264
1
2
3
4
1
2
3
4
TC1
TC2
TC3
CG
Fl
FIA
Al A2 A3 A4
67.51 175.44 103.48 8.98 0.105 0.099 41 0 0 0
53.94 176.51 94.79 7.82 0.078 0.074 0 41 0 0
46.69 123.62 72.33 8.92 0.216 0.204 0 0 41 0
1 51.75 95.01 66.17
6.76 0.314 0.297 0 0 0 41
0 12.08 31.38 18.51 8.98 0.105 0.099 24 0 0 0
0
9.20 30.11 16.17 7.82 0.078 0.074 0 24 0 0
0 23.00 60.90 35.63 8.92 0.216 0.204 0 0 24 0
1 28.75 57.59
38.36 6.76 0.314 0.297 0 0 0 24
0 25.99 39.82 30.60 8.98 0.105 0.099 61 0 0 0
0 12.42 21.82 15.55 7.82 0.078 0.074 0 61 0 0
0
5.18
8.01
6.12 8.92 0.216 0.204 0 0 61 0
1
17.25 26.85 20.45 6.76 0.314 0.297 0 0 0 61
0 67.51 94.45 76.49 8.98 0.105 0.099 55 0 0 0
0 53.94 84.54 64.14 7.82 0.078 0.074 0 55 0 0
0 46.69 65.90 53.09 8.92 0.216 0.204 0 0 55 0
1
51.75 62.55 55.35 5.75 0.054 0.051 0 0 0 55
0 20.76 53.95 31.82 8.98 0.105 0.099 32 0 0 0
0
7.19 23.52 12.63 7.82 0.078 0.074 0 32 0 0
1 25.87 47.51 33.09 8.92
0.216 0.204 0 0 32 0
0 30.93 68.36 43.41 6.76 0.314 0.297 0 0 0 32
0 15.30 33.64 21.41 8.98 0.105 0.099 45 0 0 0
0
1.73 4.67
2.71 7.82 0.078 0.074 0 45 0 0
1 20.13 36.36 25.54 8.92
0.216 0.204 0 0 45 0
0 25.18 48.05 32.81 6.76 0.314 0.297 0 0 0 45
0 20.76 53.95 31.82 8.50 0.066 0.062 54 0 0 0
0
7.19 23.52 12.63 7.18 0.227 0.214 0 54 0 0
1 25.87 54.72 35.49 8.74 0.187 0.177 0 0 54 0
0 30.93 68.36 43.41 7.17 0.236 0.223 0 0 0 54
0 17.25 31.02 21.84 8.50 0.066 0.062 33 0 0 0
0
0.29
0.61
0.40 7.18 0.227 0.214 0 33 0 0
0 10.06 18.34 12.82 8.74 0.187 0.177 0 0 33 0
1 14.37 21.58 16.78
7.17 0.236 0.223 0 0 0 33
0 14.37 27.78 18.84 8.50 0.066 0.062 39 0 0 0
0
5.75 13.37
8.29 7.18 0.227 0.214 0 39 0 0
0 12.19 23.90 16.09 8.74 0.187 0.177 0 0 39 0
1 17.25 25.66 20.05 7.17 0.236 0.223 0 0
0 39
0 11.50 25.30 16.10 8.50 0.066 0.062 36 0 0 0
0
2.44
6.61
3.83 7.18 0.227 0.214 0 36 0 0
0
5.75 12.86
8.12 8.74 0.187 0.177 0 0 36 0
1 17.25
28.07 20.86 7.17 0.236 0.223 0 0 0 36
0 18.21 18.21 18.21 8.50 0.066 0.062 57 0 0 0
0
3.45
3.45
3.45 7.18 0.227 0.214 0 57 0 0
0 17.94 17.94 17.94 8.74 0.187 0.177 0 0 57 0
1
23.00 23.00 23.00 7.35 0.125 0.118 0 0 0 57
0 25.87 67.25 39.67 7.51 0.000 0.000 45 0 0 0
0
8.63 28.23 15.16 6.60 0.000 0.000 0 45 0 0
1
31.62 53.26 38.84 8.29 0.043 0.040 0 0 45 0
0 36.69 81.06 51.48 7.30 0.360 0.340 0 0 0 45
0 135.81 244.24 171.96 7.51 0.000 0.000 64 0 0 0
0 122.25 260.96 168.48 6.60 0.000 0.000 0 64 0 0
1 115.00 169.00 133.00 8.29 0.043 0.040 0 0 64 0
0 127.08 203.83 152.66 7.30 0.360 0.340 0 0 0 64
0 11.50 29.89 17.63 7.51 0.000 0.000 27 0 0 0
0
5.18 16.94 9.10 6.60 0.000 0.000 0 27 0 0
0
4.69 12.43 7.27 8.29 0.043 0.040 0 0 27 0
1
5.75 12.96 8.15 7.30 0.360 0.340 0 0 0 27
0 11.50 25.30 16.10 7.51 0.000 0.000 25 0 0 0
0
5.75 15.56 9.02 6.60 0.000 0.000 0 25 0 0
0 13.00 29.06 18.35 8.29 0.043 0.040 0 0 25 0
1 17.25 28.07 20.86 7.30 0.360 0.340 0
0 0 25
0
0
0
104
OBS
265
266
CHOS SI
1
2
3
0
4
1
1
0
0
0
2
3
4
0
0
1
TC1
TC2
TC3
11.50 25.30 16.10
5.18 14.00
8.12
4.69 10.50
6.63
5.75 11.16
7.55
14.72 38.26 22.57
1.15
3.76 2.02
8.63 22.84 13.36
12.94 27.36 17.74
CG
7.51
6.60
8.29
7.30
7.51
6.60
8.29
5.87
Fl
0.000
0.000
0.043
0.360
0.000
0.000
0.043
0.164
FIA
Al A2 A3 A4
0.000 30 0 0 0
0.000 0 30 0 0
0.040 0 0 30 0
0.340 0 0 0 30
0.000 24 0 0 0
0.000 0 24 0 0
0.040 0 0 24 0
0.155 0 0 0 24
105
APPENDIX 2
THE 1988 WILLANETTE RUN
SPRING CHINOOK SURVEY
106
SPRING CHINOOK WI LLAMETTE/CLACKAMAS RIVERS INTERCEPT SURVEY
Interviewer
Mode:
Date
Time
Sex:
1 - X1e
Location:
2 - Female
Nuzbr
1 - Boat
2 - Bank
- Lower
2 - Middle
3 - Upper
Status:
1 - Complete
2 - Not complete
3 - Validity
Keying:
-7 - Protest
-8 - Don't know
-9 - Refused
2.
4 -. Clackamas
WE
MD I REPRESENT THE RESEARCH GROUP.
HELLO, MY NAME IS
I 'D
ARE INTERVIEWING SPRING CHINOOK FISHERMEN FOR AN ECONOMIC SURVEY.
LIKE TO ASK YOU A FEW QUESTIONS ABOUT YOUR FISHING. THE INFORMATION
YOU PROVIDE IS STRICTLY CONFIDENTIAL AND WILL NOT BE IDENTIFIED WITH YOU
IN ANY WAY.
A-I. What was th. primary type of fish you were fishing for today and
how many of this and other fish did you land?
Primary TvDe
I. - Spring chinook
2 - Sturgeon
3 - Shad
TERMINATE SURVEY
4 - Stesihead
5 - Other (speci±y)
A-2. Including today, how many spring chinook have you landed on the
Willamette and Clackamas rivers this season?
).-3. Including today, how many times have you gone fishing for spring
chinook on the Willamette and Clackamas rivers this season?
How many years have you been fishing at least once per year for
spring chinook on the Willamette and Clackamas rivers?
To the nearest half-hour, how many hours have you spent spring chinook
fishing today? That is, with your gear actually in the water?
Have you completed your fishing today?
1 - Yes
No
How much additional time will you expect to have your
gear in the water for spring chinook today?
107
B-i. Was th. primary purpos. of your trip away from home for fishing or
some other activity?
1 - Fishing
other activity
8-2. What percent of your time will be devoted to
fishing?
How many days will you be away from your residence on this trip?
(If staying 1 night away from residence, then answer 2 days. If not
staying overnight, then answer 1 day)
What is the ;ip coda of your residence?
B-S. How many round trip miles is that from here?
B-6. To the nearest half-hour, how many hours will you spend traveling
to this site and back home.
8-7. How many people came with you on your fishing trip today?
3-8. What best describes your employment status?
1
2
3
4
5
6
-
Retired
Student
Homemaker
Unemployed
Employed part-time
Employed full-time
8-9. Did you take time off from work to
go fishing today, and if so, did you
lose any income from your job?
$
B-ia. How important is fishing to you as compared to other forms of
recreation?
1
2
3
4
5
-
Extremely important
Very important
Moderately important
Somewhat important
Not at all important
108
C-i. What is the estimated replacement costs for the equipment you use
for fishing, and what is its percent of use for just spring chinook
fishing on the Willamette and Clackamas rivers ? Replaçmen Percent
Use
Q1
Fishing tackle inventory (rods, rels, nets,
$
tackle, etc.)
Boating equipment (boat, trailer, motors,
$
fathometers, heaters, accessories)
sDecia automobil. used for fishing (motor home,$
camping trailers, pickup and camper, etc.)
Camping/lodging equipment (tent, coolers,
$
cooking equip., sleeping bags, etc.)
Annual repair and maintenanc, on equipment
$
Special clothing (rain gear, etc.)
$
Annual licenses, boat registration, insurance
S
Annual guide fees
$
Annual permanent moorage or dry storage for
boat
Other (specify)
S
XXXXXXX %
XXXXXXX %
S
C-2. What are your costs for one typical spring chinook fishing trip
n route pestin
$one
on the Wiliamette and Clackamas rivers ?
Transportation
$
S
S
$
$
S
Food and drink purchased at stores
$
$
Food and drink purchased in restaurants
$
$
$
Guide fees
$
$
S
Boat gas/oil
S
S
Rental of boat and/or fishing equipment
$
S
S
Fishing tackle and bait
$
$
$
$
S
$
Camping/lodging (overnight
I. Supplies (ice, etc.)
ccommodations)
$
$
j. Other (launching fees, transient moorage)
S
daily licenses, etc.)
THE TOTAL TYPICAL TRIP COST THEREFORE IS $
(For those that prefer to mail equipment and trip Costs, what is their
phone or address?)
PHONE
k 0 ORE S S
109
CONSIDER THIS A HYPOTHETICAL SITUATION: SUPPOSE A FUND WAS SET tiP FOR THE
BENEFIT OF RECREATIONAL FISHERMEN TO BE USED SPECIFICALLY TO INCREASE
THE RUN SIZE OF SPRING CHINOOK ON THE WILLAMETE AND CLACKAMAS RIVERS BY
ALSO SUPPOSE THAT THE NUMBER OF FISHERMEN WOULD INCREASE BY
_____%.
ALL FISHERMEN WOULD BE REQUIRED TO PAY INTO THE FUND IN ORDER TO
_____%.
FISH FOR THE SPRING CHINOOK.
How marty more fishing trips would you take per season because of the
increase in run size? (Probe for numbers.)
How marty more fish would you expect to catch in a season because of
the increase in run size? (Probe for numbers.)
What is the maximum you would be willing to pay into the fund annually
for the privilege to fish for spring chinook with that run size
increase?
1.
- Nothing at all, i.e. SO
2 - $1. to 3
3
4
5
6
-
$4 to 5
$6 to 10
$11 to 15
$16 to 20
NEXT, I WOULD LII
7
8
9
10
11
12
13
-
$21 to 30
$31 to 45
$46 to 60
$61 to 75
$26 to 100
$100 or greater
Protest. Why
TO ASK YOU SOME PERSONAL INFORMATION.
What year were you born?
How much education have you completed?
1
2
3
4
5
-
Grade school
Some high school
High school
Technical/vocational school
Some college
6
7
8
9
10
-
Associate degree
Bachelor degree
Master's degree
Doctorate degree
Other (specify)
E-3. What was your total household income before taxes last year?
1 - less than $ 5,000
5 - $20,000 - $24,999
2 - $ 5,000 - $ 9,999
6 - $25,000 - $34,999
3 - $10,000 - $14,999
7 - $35,000 - $49,999
4 - $15,000 - $19,999
8
- $50,000 - $74,999
9 - $75,000 and over
110
F-i. Were you satisfied with your fishing trip today?
1 - Satisfied
Not satisfied (say have multiple reasons)
2
3
4
5
6
7
8
9
10
11
12
13
14
15
F-2.
-
number of fish landed
number of spring chinook
siz, of spring chinook
congestion
facilities (launching, moorage, access, parking)
fishing equipment
allocation between gill-netters and sports fishery
habitat
enforcement
licenses and tags
bag limits
seasons
gear restrictions
other
Would you like to make any comments about this survey?
1. No commment
Comments (may have multiple comments)
2
3
4
5
6
7
8
9
10
11
12
13
14
15
-
number of fish landed
number of spring chinook
size of spring chinook
congestion
facilities (launching, moorage, access, parking)
fishing equipment
allocation between gill-netters and sports fishery
habitat
enforcement
licenses and tags
bag limits
seasons
gear restrictions
other
THANI( YOU FOR YOUR ASSISTANCE.