JOHN F. DWYER DAVID J. NOWAK, MARY HEATHER NOBLE, AND SUSAN M. SISINNI

U.S. DEPARTMENT OF AGRICULTURE FOREST SERVICE

RANDALL S. ROSENBERGER AND JOHN B. LOOMIS

Benefit Transfer of Outdoor

Recreation Use Values

Benefit Transfer of Outdoor

Recreation Use Values

A Technical Document Supporting the

Forest Service Strategic Plan (2000 Revision)

A Technical Document Supporting the

Forest Service Strategic Plan (2000 Revision)
Abstract

Rosenberger, Randall S.; Loomis, John B. 2001. Benefit transfer of outdoor recreation use

values: A technical document supporting the Forest Service Strategic Plan (2000 revision).

Gen. Tech. Rep. RMRS-GTR-72. Fort Collins, CO: U.S. Department of Agriculture, Forest Service,

Rocky Mountain Research Station. 59 p.

We present an annotated bibliography that provides information on and reference to the literature on

outdoor recreation use valuation studies. This information is presented by study source, benefit

measures, recreation activity, valuation methodology, and USDA Forest Service region. Tables are

provided that reference the bibliography for each activity, enabling easy location of studies. The

literature review spans 1967 to 1998 and covers 21 recreation activities plus a category for wilderness

recreation. There are 163 individual studies referenced, providing 760 benefit measures. Guidelines are

provided for applying the various benefit transfer methods. Benefit transfer is the use of past empirical

benefit estimates to assess and analyze current management and policy actions. Several theoretical

and empirical issues to applying benefit transfers are identified for use in judging the relevance and

credibility of transferring specific measures. Four benefit transfer models are discussed, including value

transfers (single point estimates, average values) and function transfers (demand and benefit functions

and meta analysis benefit function). A simple example application is followed throughout the discussion

of the various benefit transfer methods. A decision tree is provided as a framework for determining how

to obtain benefit measures for recreation activities.

Keywords: Benefit transfer, meta-analysis, outdoor recreation use values

Final Report for the USDA Forest Service under Research Joint Venture Agreement #RMRS98132-RJVA, “Theories and methods for measuring environmental values and modeling

consumer and policy decision processes”: Objective #4, “Benefit transfer.”

Authors

Randall S. Rosenberger is Assistant Professor, Regional Research Institute and Division of Resource

Management, West Virginia University, Morgantown, WV 26506.

John B. Loomis is Professor, Department of Agricultural and Resource Economics, Colorado State

University, Fort Collins, CO 80523.

You may order additional copies of this publication by sending your mailing

information in label form through one of the following media. Please specify the

publication title and number.

Telephone (970) 498-1392

FAX (970) 498-1396

E-mail rschneider@fs.fed.us

Available on the Web http://www.fs.fed.us/rm

Mailing Address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526
Benefit Transfer of Outdoor Recreation Use

Values: A Technical Document Supporting

the Forest Service Strategic Plan (2000

Revision)

Randall S. Rosenberger

John B. Loomis

Contents

Executive Summary ......................................................................................... 1

Acknowledgments ............................................................................................ 1

Introduction ...................................................................................................... 2

Data ............................................................................................................ 2

Literature Review Efforts, Past and Present.............................................. 2

Data Sources and Coding Procedures ...................................................... 3

Benefit Transfer: Issues ................................................................................... 3

What Is a Benefit Transfer? ....................................................................... 3

Conditions for Performing Benefit Transfers ............................................. 4

Potential Limitations of Benefit Transfers .................................................. 5

Validity and Reliability of Benefit Transfers ............................................... 6

Benefit Transfer Methods .......................................................................... 6

Benefit Estimates ............................................................................................. 7

What Are They and What Do They Mean?................................................ 7

How Are the Study Site Values Estimated? .............................................. 8

Benefit Transfer: Methods and Application ...................................................... 9

Value Transfers ......................................................................................... 9

Benefit Function Transfers ...................................................................... 14

Recommendations and Guidance to Field Users........................................... 24

References Cited ........................................................................................... 26

Appendix A: Annotated Bibliography of Outdoor Recreation

Use Valuation Studies, 1967 to 1998 ................................................. 28

Appendix B: Summary of Multi-Estimate Studies in Appendix A,

Annotated Bibliography ...................................................................... 44

Appendix C: References to Appendix A Annotated Bibliography

Entries by Recreation Activity............................................................. 55
Executive Summary

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 1

Executive Summary

This document serves four purposes: (1) it provides access to the literature on recreation use values; (2) it provides guidelines for conducting benefit

transfers; (3) it provides a review of benefit transfer

approaches; and (4) it provides a meta analysis of

the recreation use value literature for use in benefit

transfers. Benefit transfer is the application of data

from a study site to a policy site. A study site is a

place for which we have recreation value data collected through primary research. Primary research

provides content- and context-specific estimates of

recreation value for a site. A policy site is a place for

which there is little or no data available on the

economic value of recreation. When circumstances

such as insufficient funding or time make primary

research infeasible, benefit transfer provides a means

by which the value of recreation at an unstudied site

can be estimated using information about recreation

values at other sites. Benefit transfer provides content- and context-relevant estimates of recreation

value for policy sites.

Access to the outdoor recreation use value literature

is provided via an annotated bibliography and crossreferencing of studies by recreation activity. The literature reviewed is comprised of outdoor recreation use

value studies conducted from 1967 to 1998 in the

United States and Canada. This includes 760 value

measures estimated from 163 separate empirical research efforts covering 21 recreation activities.

Guidance is provided by identifying necessary conditions for and limitations to effective benefit transfers. Necessary conditions include issues concerning

policy site needs, the quality of study site data, and

the correspondence between the study site and the

policy site. Several factors can affect benefit transfers

and limit the accuracy of value estimation. These

factors are categorized as data issues, methodological

issues, site correspondence issues, temporal issues,

and spatial issues. A decision tree is developed that

guides field personnel and resource managers through

a framework on how to obtain measures of recreation

use value.

Four benefit transfer approaches are reviewed. An

example application of each of the approaches is provided. Value transfers focus on measures of value. The

use of single point measures and measures of central

tendency for recreation values are discussed. Function

transfers focus on statistical models estimated in primary research. These models relate value measures

with measures of study site characteristics such as

demographics of the user population, attributes of the

recreation site or area, among others. The functions are

adapted to characteristics of the policy site in order to

estimate recreation values for the policy site. Demand

or willingness to pay functions and meta analysis

functions are discussed.

A meta analysis of the recreation use valuation literature is provided. Meta analysis is the statistical

summarization of research outcomes. A meta analysis

model is developed that can be applied to benefit

transfers. It is based on 701 use value estimates from

131 separate primary research studies. A backward

elimination procedure was used to optimize the meta

analysis benefit transfer function by retaining only

those 34 variables significant at the 80 percent level or

better. The variables in the model include methodological factors, Forest Service regions, physical and

political characteristics, and several recreation activities. The meta analysis benefit transfer function is used

to estimate use values for 21 recreation activities for

each of the Forest Service assessment regions and for

the United States. This meta analysis benefit transfer

function provides field personnel and resource managers with another tool for estimating use values for

outdoor recreation activities.

Acknowledgments

This project was supported by funds provided by

the Washington Office/Strategic Planning and Resource Assessment and the Rocky Mountain Research

Station, Forest Service, U.S. Department of Agriculture.

The authors acknowledge the assistance of Dennis

Black in the literature search process, Shauna Page in

the coding of the studies, Ram Shrestha for maintenance of the database, and Ross Arnold, George

Peterson, and Dan McCollum for their valuable feedback and useful suggestions in analyzing the data.

This report has benefited from a peer review by Linda

Langner, Earl Ekstrand, and Jonathan Platt, and the

USDA Forest Service General Technical Report review

process, all of whom provided insightful comments.

Any shortcoming of this report is the sole responsibility of the authors.
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Introduction

The Renewable Resources Planning Act of 1974 has

an assessment component and a program analysis

component (SPRA 2000). First, the act requires an

assessment of the supply of and demand for renewable

resources on the nation’s forests and rangelands. Second, it requires an analysis of the costs and benefits

associated with the USDA Forest Service’s programs

including the National Forest System (superceded by

the Government Performance and Results Act [GPRA]

of 1993). These requirements create the need for credible measures of benefits. In this case, we are interested

in developing credible measures of benefits for outdoor recreation. To this end, Strategic Planning and

Resource Assessment (SPRS) staff (formerly RPA staff)

supported the use of average values for various outdoor recreation activities based primarily on empirical

estimates reported in past studies.

This report serves two functions. First, it provides

information from a literature review of economic studies spanning 1967 to 1998 in the United States and

Canada. These studies estimated outdoor recreation

use values. A guide to this literature is provided through

reference to the original studies in an annotated bibliography (appendix A). Second, this report provides

guidelines on performing benefit transfers in the context of recreation use valuation. The review of the

literature and benefit transfer methods in this report

should increase the defensibility of benefit estimates

transfers when management and policy impacts on

outdoor recreation are evaluated.

We begin by discussing the source and coding of

the data collected in the literature review. Issues and

concerns surrounding benefit transfers are presented.

The obstacles to performing critical benefit transfers

highlight the need for a pragmatic approach to benefit transfer. Later we discuss theoretical aspects of

the benefit estimates in the literature, including what

the numbers mean and how they were estimated. We

give a full account of the data collected from the

literature review while examining different benefit

transfer methods.

This report is not intended to be a cookbook for

performing benefit transfers, but as a guide to the

empirical estimates available. Along the way, various

methods of benefit transfer will be discussed. An example transfer will be followed across all of the different methods. However, the many nuances of an actual

benefit transfer cannot be illustrated with a simple

example. Therefore, any plausible benefit transfer

must involve the practicioner’s use of judgment

and insight when transferring values.

Data

Literature Review Efforts, Past and

Present

We provide data on outdoor recreation use values

based on empirical research conducted from 1967 to

1998 in the United States and Canada. This data is the

compilation of four literature reviews conducted at

the bequest and under the direction of the USDA

Forest Service. The first review covered the literature

on outdoor recreation and forest amenity use value

estimation from the mid-1960s to 1982, collecting 93

benefit estimates in all (Sorg and Loomis 1984). The

second review covered outdoor recreation use valuation studies from 1968 to 1988, building on the first

review, but focusing primarily on the 1983 to 1988

period (Walsh and others 1988). This second review

increased the number of benefit estimates to 287

estimates.

A third literature review on the subject covered the

period 1968 to 1993 (MacNair 1993). This review formally coded information on the composition of the

studies. While the database developed by MacNair

(1993) includes 706 different benefit estimates, many

of the studies in the previous reviews were not included in this effort. For example, only 64 out of the 120

studies included in the second review are included in

the third review. However, the total number of benefit

estimates has significantly increased. For example, 491

estimates from the lesser 64 studies included in this

third review is larger than the total 287 estimates from

all 120 studies as reported in the second review. This is

due to the use of different criteria for including benefit

estimates.

We conducted a fourth literature review on outdoor

recreation use valuation, focusing on studies reported

from 1988 to 1998 (Loomis and others 1999). We then

merged the results of the fourth review with the MacNair

(1993) database. Our main emphasis was to improve on

coding procedures used in the past review efforts and to

focus on obtaining use value estimates for all recreation

activity categories identified by USDA Forest Service

documents. We did not emphasize fishing benefit studies since this is the effort of a separate review sponsored

by the U.S. Fish and Wildlife Service, which should be
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 3

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

available by year 2001 (Markowski and others 1997). We

did, however, include those fishing studies coded in the

MacNair (1993) database that were from the Walsh and

others (1988) review, as generally these were sufficient

in number and coverage of fishing studies for statistical

purposes. Therefore, our database includes 163 studies

providing 760 benefit estimates, covering all recreation

activity categories.

Data Sources and Coding Procedures

The focus of our literature review effort was for

outdoor recreation use valuation studies conducted

since 1988 in the United States and Canada. We concerted our efforts to locate studies on activities that

were not previously investigated, such as rock climbing, snowmobiling, and mountain biking. Computerized databases, such as American Economic

Association’s ECONLIT, were searched for published

literature along with the University of Michigan’s

Dissertation and Master’s Thesis Abstracts. Gray literature was located by using conference proceedings,

bibliographies on valuation studies (Carson and others 1994), and access to working papers. Details of

studies conducted from 1967 to 1988 were obtained

primarily from MacNair’s (1993) database that coded

the Walsh and others (1988) literature review. A few

study details were obtained directly from the Walsh

and others (1988) review that were not included in the

MacNair (1993) database.

A master coding sheet was developed that contains

126 fields. The main coding categories include reference citation to the research, benefit measure(s) reported, methodology used, recreation activity investigated, recreation site characteristics, and user or sample

population characteristics. Study reference citation

details include, in part, author identification, year of

study, and source of study results. Benefit measure(s)

details include, in part, the monetary estimate provided by the study (converted to activity day units

using information provided by the study), the units in

which the estimate is reported (e.g., day, trip, season,

or year), and temporally adjusted benefit measures for

inflationary trends to fourth-quarter 1996 dollars using the implicit price deflator. An activity day is the

typical amount of time pursuing an activity within a

24-hour period. This unit was chosen because of its

ease in being converted to other visitation/participation units (e.g., recreation visitor days, trips, seasons).

Table 1 provides summary statistics for the 21 recreation activities included in the database. All of the

benefit measures reported in table 1 are adjusted to

activity day units and fourth-quarter 1996 dollars.

Methodology details include survey mode (e.g., mail,

telephone, in-person, use of secondary data), response

rate for primary data collection studies, and sample

frame (e.g., onsite users, general population). Methodology details are further divided between the application of revealed preference (RP) and stated preference

(SP) modeling when appropriate. Details of RP modeling include, in part, identifying the model type (e.g.,

individual travel cost, zonal travel cost, random utility

models), use of travel time or substitute sites in the

model specification, and functional form (double log,

linear, semi-log, log-linear). Details of SP modeling

include, in part, identifying the model type (e.g., conjoint analysis, contingent valuation models), the elicitation technique for contingent valuation models (e.g.,

open ended, dichotomous choice, iterative bidding,

payment card), and functional form.

Details of the recreation site include, in part, its

geographic location, whether it was on public or private land, the type of public land (e.g., National Park,

National Forest, State Park, State Forest), the state, the

USDA Forest Service Region, and land type (e.g., lake,

forest, wetland, grassland, river). In many cases, specific details about the recreation site were not provided

either because of incomplete reporting or the activity

was not linked with a specific site. Details of the user

population characteristics include, in part, average

age, average income, average education, and proportion female.

The details of each study were coded to the extent

that they could be gleaned from the research-reporting

venue. However, not every study could be fully coded

according to the coding sheet. This was either because

information was not reported or was not collected for

a study. For example, coding each study for user

characteristics was severely restricted in that very few

of the studies in the literature review reported any

details about the user population. This and other factors are indicative of the lack of consistent and complete data reporting, which further limits the ability to

perform critical benefit transfers.

Benefit Transfer: Issues

What Is a Benefit Transfer?

Benefit transfer is a colloquial term referring to the

use of existing information and knowledge to new

contexts. For our present purposes, benefit transfer
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

is the adaptation and use of economic information

derived from a specific site(s) under certain resource

and policy conditions to a site with similar resources

and conditions. The site with data is typically called

the “study” site, while the site to which data are

transferred is called the “policy” site. Benefit transfer

is a practical way to evaluate management and policy

impacts when primary research is not possible or

justified because of:

1. budget constraints,

2. time limitations, or

3. resource impacts that are expected to be low or

insignificant.

Primary research is the “first-best” strategy in which

information is gathered that is specific to the action

being evaluated, including the spatial and temporal

dimensions, expected impacts, and the extent and

inclusion of affected human populations and environmental resources. However, when primary research is

not possible or plausible, then benefit transfer, as a

“second-best” strategy, is important to evaluating

management and policy impacts. The “worst-best”

strategy in economic evaluation is to not account for

recreation values, thus implying recreation has zero

value in an evaluation or assessment model.

Conditions for Performing Benefit

Transfers

Several necessary conditions should be met to

perform effective and efficient benefit transfers

(Desvousges and others 1992). First, the policy context

should be thoroughly defined, including:

1. Identifying the extent, magnitude, and quantification of expected site or resource impacts from

the proposed action.

2. Identifying the extent and magnitude of the population that will be affected by the expected site or

resource impacts.

3. Identifying the data needs of an assessment or

analysis, including the type of measure (unit,

average, marginal value), the kind of value (use,

nonuse, or total value), and the degree of certainty

surrounding the transferred data (i.e., the accuracy and precision of the transferred data).

Table 1—Summary statistics on average consumer surplus values per activity day per person from recreation demand

studies—1967 to 1998 (fourth-quarter, 1996 dollars).

Number Number of Mean of Median of Std. error Range of

Activity of studies estimates estimates estimates of mean estimates

Camping 22 40 $30.36 $24.09 5.50 $1.69 – 187.11

Picnicking 7 12 35.26 24.21 9.66 7.45 – 118.95

Swimming 9 12 21.08 18.19 4.46 1.83 – 49.08

Sightseeing 9 20 35.88 21.13 9.41 0.54 – 174.81

Off-road driving 3 4 17.43 15.85 6.27 4.37 – 33.64

Motorized boating 9 14 34.75 18.15 11.65 4.40 – 169.68

Nonmotorized boating 13 19 61.57 36.42 13.76 15.04 – 263.68

Hiking 17 29 36.63 23.21 7.87 1.56 – 218.37

Biking 3 5 45.15 54.90 8.40 17.61 – 62.88

Downhill skiing 5 5 27.91 20.90 7.07 12.54 – 52.59

Cross-country skiing 7 12 26.15 26.73 2.84 11.70 – 40.32

Snowmobiling 2 2 69.97 69.97 33.74 36.23 – 103.70

Big game hunting 35 177 43.17 37.30 2.21 4.74 – 209.08

Small game hunting 11 19 35.70 27.71 9.56 3.47 – 190.17

Waterfowl hunting 13 59 31.61 18.21 4.06 2.16 – 142.82

Fishinga 39 122 35.89 20.19 3.42 1.73 – 210.94

Wildlife viewing 16 157 30.67 28.26 1.38 2.36 – 161.59

Horseback riding 1 1 15.10 15.10 0 15.10 – 15.10

Rock climbing 2 4 52.96 48.14 11.80 29.82 – 85.74

General recreation 12 31 24.26 10.03 7.48 1.18 – 214.59

Other recreation 11 16 40.58 33.78 9.64 4.76 – 172.34

aFishing includes all types of fishing such as cold water, warm water, and salt water fishing. The number of estimates for fishing is underrepresentative of the entire body of knowledge since fishing studies were not a primary focus of the literature review.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 5

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Second, the study site data should meet certain

conditions for critical benefit transfers:

1. Studies transferred must be based on adequate

data, sound economic method, and correct empirical technique (Freeman 1984).

2. The study contains information on the statistical relationship between benefits (costs) and

socioeconomic characteristics of the affected

population.

3. The study contains information on the statistical

relationship between the benefits (costs) and physical/environmental characteristics of the study site.

4. An adequate number of individual studies on a

recreation activity for similar sites have been conducted in order to enable credible statistical inferences concerning the applicability of the transferred value(s) to the policy site.

And third, the correspondence between the study

site and the policy site should exhibit the following

characteristics:

1. The environmental resource and the change in the

quality (quantity) of the resource at the study site

and the resource and expected change in the resource at the policy site should be similar. This

similarity includes the quantifiability of the change

and possibly the source of that change.

2. The markets for the study site and the policy site

are similar, unless there is enough usable information provided by the study on own and substitute prices. Other characteristics should be considered, including similarity of demographic

profiles between the two populations and their

cultural aspects.

3. The conditions and quality of the recreation activity experiences (e.g., intensity, duration, and skill

requirements) are similar between the study site

and the policy site.

Most primary research was not conducted for future

benefit transfer applications. The information requirements expressed in the above conditions are not always met in the reporting of data and results from

primary research. In addition to weighing the benefits

of more information from expensive primary research,

the implicit cost of performing benefit transfers under

conditions of incomplete information should be accounted for. Therefore, benefit transfer practitioners

are required to be pragmatic in their applications of the

method when considering the many limitations imposed upon them by primary research.

Potential Limitations of Benefit Transfers

Several factors can be identified that affect the reliability and validity of benefit transfers. A parallel

effect that interacts with the following factors is the

benefit transfer practitioner’s judgment concerning

empirical studies, including how to code the data

reported by each study. One group of factors affect

benefit transfers generally:

1. The quality of the original study greatly affects the

quality of the benefit transfer process. This is the

garbage-in, garbage-out factor.

2. Some recreation activities have a limited number

of studies investigating their economic value, thus

restricting the pool of estimates and studies from

which to draw information.

3. Another data limitation is the documentation of

data collected and reported. This increases the

difficulty of demand estimation and benefit

transfer.

4. As we have already noted, most primary research

is not designed for benefit transfer purposes.

A second group of factors is related to methodological issues:

1. Different research methods may have been used

across study sites for a specific recreation activity,

including what question(s) was asked, how it was

asked, what was affected by the management or

policy action, how the environmental impacts

were measured, and how these impacts affect

recreation use.

2. Different statistical methods for estimating models can lead to large differences in values estimated. This also includes issues such as the overall impact of model mis-specification and choice

of functional form (Adamowicz and others 1989).

3. Substitution in recreation demand is an important

element when determining the potential impacts

of resource changes. However, there is often a lack

of data collection and or reporting on the availability of substitute sites, substitute site prices,

and the substitution relationship across sites and

among activities.

4. There are different types of values that may have

been measured in primary research, including

use values and/or passive- or non-use values.

While this report focuses on use values, the benefit transfer practitioner should be aware of what

is being measured in original research.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

A third group of factors affecting benefit transfers is

the correspondence between the study site and the

policy site:

1. Some of the existing studies may be based on

valuing recreation activities at unique sites and

under unique conditions.

2. Characteristics of the study site and the policy site

may be substantially different, leading to quite

distinct values. This can include differences in

quality changes, site quality, and site location.

A fourth factor is the issue of temporality or stability of data over time. The existing studies occurred at

different points in time. The relevant differences between then and now may not be identifiable nor

measurable based on the available data. A fifth factor

is the spatial dimension between the study site and

the policy site. This includes the extent of the implied

market, both for the extent and comparability of the

affected populations and the resources impacted

between the study site and the policy site.

The above listed factors can lead to bias or error in

and restrict the robustness of the benefit transfer process. An overriding objective of the benefit transfer

process is to minimize mean square error between the

“true” value and the “tailored” or transferred value of

impacts at the policy site. However, the original or

true values are themselves approximations and are

therefore subject to error. As such, any information

transferred from a study site to a policy site is accomplished with varying degrees of confidence in the

applicability and precision of the information. Therefore, National Forest decisionmaking involving tradeoffs of recreation, commodity production, and nature

preservation can often be improved by inclusion of

even approximate estimates of nonmarket recreation

values. Complete omission of recreation value estimates in economic analytic aids to decisionmaking

implies a zero value for recreation, in which case the

error of omission can be greater than the error of

commission in benefit transfers procedures.

Validity and Reliability of Benefit

Transfers

Several recent studies have tested the convergent

validity and reliability of different benefit transfer

methods (Loomis and others 1995; Downing and

Ozuna 1996; Kirchhoff and others 1997; Desvousges

and others 1998; Rosenberger and Loomis 2000). The

methods tested, which we will presently discuss,

include single point estimate, average value, demand

function, and meta regression analysis transfers.

While the above studies show that some of the methods are relatively more valid and reliable than other

methods, the general indication is that benefit transfer cannot replace original research, especially when

the costs of being wrong are high. In some tests of the

benefit transfer methods, several cases produced tailored values very similar to the true values (as low as

a few percentage points difference). In other cases,

the disparity between the true value and the tailored

value was quite large (in excess of 800% difference).

Therefore, the policy context and process will most

often dictate the acceptability of transferred data.

Benefit Transfer Methods

There are two broad approaches to benefit transfer:

(1) value transfer and (2) function transfer (figure 1).

Value transfers encompass the transfer of a single

(point) benefit estimate from a study site, or a measure

of central tendency for several benefit estimates from

a study site or sites (such as an average value), or

administratively approved estimates. Administratively

approved value estimates will be discussed in conjunction with the measure of central tendency discussion. Function transfers encompass the transfer of a

benefit or demand function from a study site, or a meta

regression analysis function derived from several study

sites. Function transfers then adapt the function to fit

the specifics of the policy site such as socioeconomic

characteristics, extent of market and environmental

Value Transfer Function Transfer

Single

Point

Estimate

Measure of

Central

Tendency

Benefit/

Demand

Function

Meta

Analysis

Function

Use estimate at

policy site

Adapt function to

policy site

Use tailored

estimate at

policy site

Administratively

Approved

Figure 1. Benefit transfer approaches.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 7

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

impact, and other measurable characteristics that systematically differ between the study site(s) and the

policy site. The adapted function is then used to forecast a benefit measure for the policy site.

We will discuss each of these methods in the following sections, including a simple example application

for each. However, we will first define and identify

what the benefit measures are, what they mean, and

how they were estimated.

Benefit Estimates

What Are They and What Do They Mean?

All of the benefit estimates provided by this report,

either recorded from the literature review or forecasted by adapting benefit functions, are average consumer surplus per person per activity day. In the case

of a single study, the estimate is the average consumer

surplus for the average individual in the study. In the

case of several studies, the estimate is the average of

the study samples’ average consumer surpluses from

all included studies.

Consumer surplus is the value of a recreation activity beyond what must be paid to enjoy it.1

 Figure 2

illustrates the concept of consumer surplus. Looking

just at current conditions when demand is D0, consumer surplus is the area below the demand function

(D0) and above the expenditure line (E), or area CFH.

Consumer surplus is also referred to as net willingness to pay, or willingness to pay in excess of the cost

of the good. Total economic use value is consumer

surplus plus the costs of participation, or area 0HFA

in figure 2 when demand is D0 and A is the number of

days of participation.

When the change in recreation supply or days is

small and localized, consumer surplus is equivalent

to a virtual market price for a recreation activity

(Rosenthal and Brown 1985). A general assumption

when applying the benefit estimates is that the estimates are constant across all levels of resource impacts

and perceived changes for an individual. This assumption may be plausible for small changes in visitation,

but it may be unrealistic for large changes (Morey

1994). However, this assumption is necessary for the

practical application of benefit transfers.

The valuation of management and policy impacts on

recreation can be formally described as equation (1),

following Smith and others (1999) nomenclature:

CS CS

d P dN dN S

S

= ⋅−⋅ ∆ ( ), 11 0 0 (1)

where CSP = consumer surplus estimate for evaluating management or policy impacts on

recreation;

CSS = consumer surplus gain measure reported in the literature;

di

 = the amount of recreation use in activity

days before (i = 0) and after (i = 1) the

management or policy action;

Ni = the number of people participating in

the recreation activity before (i = 0) and

after (i = 1) the management or policy

action; and

∆dS = measured change in recreation participation or affected resource in the literature providing CSS.

Simply stated, the benefit transfer estimate of a management- or policy-induced change in recreation is the

average consumer surplus estimate for the average

WTP

Cost

Price

0

A B # of days

D0

D1

 C

 H

I

F G

E

Figure 2. Consumer surplus measures for a quality-induced

change in demand.

1There are two prominent types of consumer surplus

estimated using slightly different definitions of the demand

function: Marshallian consumer surplus based on an ordinary demand function, and Hicksian consumer surplus based

on either a compensated demand function or elicited directly

using hypothetical market techniques. The difference between these measures is due to the income effect (Willig

1976). Since outdoor recreation expenditures are a relatively

small percentage of total expenditures (income), differences

between the two measures are expected to be negligible.
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

individual from the literature aggregated to the change

in use of the natural resource. The change in recreational use of a resource may be induced either through

a price change in participating in an activity (e.g., fee

change or location of the site) or through a quality

change in the recreation site.

The benefit estimates in the literature can vary according to many factors such as differences in recreation site and user population characteristics, extent of

the market, temporal and spatial differences, and

methodologically induced differences. Returning to

figure 2, the potential range in benefit estimates provided in the literature can be illustrated. If the demand

shift from D0 to D1 is due to a measurable increase in

the quality of a recreation site, then three consumer

surplus areas are identifiable: (1) CFH (existing level),

(2) CGI (improved level), and (3) IHFG (net gain).

Thus, another potential source for variability in benefit estimates provided is the context of the benefit

estimate reported. For example, study A may provide

a value for the creation of a new recreation site (area 1

or 3, depending upon expected demand level). Study

B, by contrast, may provide an estimate of the current

value of an activity with no implied change (area 1 or

3, depending on implied demand). Study C may provide an estimate of the value of a change in demand

due to a management- or policy-induced change in

implied cost or resource quality (area 2). Therefore,

benefit estimates for the same activity reported in the

literature can range from area 1 to area 3. All estimates provided in this report are of the first two types

(studies A and B) above (we are not providing incremental or marginal values).

In any case, the benefit estimate provided in the

literature will be treated as a constant per unit value

applicable to all possible levels of resource use, with no

accounting made for congestion. For example, the

same benefit measure will be used whether recreation

is affected by an increase (decrease) of 2 percent or 98

percent, measured as total activity days. Critical transfers would use estimates from a study site that best

matches the policy site context as identified in the last

section. However, because primary research is not

typically conducted for the purpose of future benefit

transfers and because of the limitations of study site

data reporting listed above (especially incomplete

and inconsistent reporting), the best match may not

be a very good match at all. This is why benefit transfers must be pragmatic in application (some value may

be better than no value at all). By being aware of the

challenges to performing critical benefit transfers and

what the estimates in the literature represent, practitioners of benefit transfer can better defend their transferred measures. There may be times when values or

functions in the literature are used to arrive at a base

value. These base values can then be adjusted up or

down based on professional judgment to account for

factors (like congestion) not accounted for in the benefit transfer. Such adjustments, if made, should be

documented by the analyst.

How Are the Study Site Values

Estimated?

There is an array of techniques used to estimate the

economic use value of outdoor recreation.2 These

approaches are traditionally called nonmarket valuation, basically because not all of the resources important to the quantity and quality of recreation experiences are traded in markets. When market prices are

not available, economic techniques may be employed

that indirectly or directly estimate virtual market

prices (as average consumer surplus or marginal willingness to pay). Revealed preference techniques are

indirect methods for estimating consumer surplus and

rely on the weak complementary between recreation

participation and market-purchased goods necessary

to recreation participation. Stated preference techniques

are direct methods to estimating consumer surplus via

constructed hypothetical markets through which

people express their willingness to pay for environmental resources or recreation opportunities. Depending on the structure of a stated preference survey, it can

also elicit information for use in indirectly estimating

consumer surplus.

The most frequently used revealed preference technique is the travel cost method. Other methods included under the revealed preference technique heading are hedonic property and random utility methods.

The travel cost method uses the variable costs of recreation participation (travel, lodging, entrance fees, equipment rentals, travel time) as a proxy for the price of

recreating in deriving a demand function. The benefit

of recreation is then the consumer surplus estimated

from the demand function as shown in figure 2.

The most frequently used stated preference technique is the contingent valuation method. Another

2There are several accessible sources to issues in

nonmarket valuation. For example, see Freeman (1993),

Loomis and Walsh (1997), Champ and others (in preparation), and the website http://www.ecosystemvaluation.org

(prepared under a cooperative agreement between U.S.

Department of Agriculture, Natural Resource Conservation

Service, U.S. Department of Commerce, NOAA-Sea Grant

Office, and University of Maryland, Center for Environmental

Science).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 9

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

stated preference approach is conjoint analysis—a multiattribute, multi-objective based method. The contingent valuation method directly solicits information from

people by asking them their maximum willingness to

pay or minimum compensation demanded for a recreation opportunity or change in a recreation experience,

all within the confines of a hypothetical market.

While revealed preference approaches have typically resulted in slightly larger benefit measures than

stated preference approaches, the approaches yield

measures that are highly correlated (Carson and others 1996). Several studies comparing revealed and

stated preference techniques for the same good have

found the two measures not to be statistically different, providing evidence that the two techniques to

nonmarket valuation exhibit convergent validity.

Benefit Transfer: Methods and

Application

This section will discuss four different benefit transfer methods—single point estimate, average value, demand and benefit function, and meta regression analysis function. A simple example of each transfer will be

presented. Specific information about the literature on

outdoor recreation benefit measures will be provided.

Value Transfers

Single point estimate transfer

A single point estimate benefit transfer is based on

using an estimate from a single relevant primary

research study (or range of point estimates if more

than one study is relevant). The primary steps to

performing a single point estimate transfer include

identifying and quantifying the management- or policyinduced changes on recreation use, and locating and

transferring a “unit” consumer surplus measure. The

text box (figure 3) provides a more detailed list of the

steps involved in single point estimate transfers.

We provide information in this report that aids in

identifying study site benefit measures from the literature.3

 An annotated bibliography of outdoor recreation use valuation studies is provided as appendix A,

with additional information on some studies in appendix B. The bibliography includes studies conducted

from 1967 through 1998 in the United States and

Canada. There are 163 studies and 760 benefit measures identified (there is a total of 786 benefit measures

provided, however, 26 of these are for wilderness recreation, some of which are a subset of other various

activities). The bibliography includes information on:

1. reference to each study,

2. identification of the recreation activity investigated,

3. geographic location of the study (Forest Service

region and RPA region),

4. original benefit measure(s) reported (adjusted to

activity day units),

5. time adjusted benefit measure (to fourth-quarter

1996 dollars), and

6. valuation methodology used to measure benefits.

Figure 3. Steps to performing a single point estimate transfer.

3Another database that contains recreation use values in

addition to other values for the environment is the Environmental Valuation Reference InventoryTM (EVRITM). This is a

subscription database and can be found at http://

www.evri.ec.gc.ca/evri/.

SINGLE POINT ESTIMATE TRANSFER

1. Identify the resources affected by a proposed action.

2. Translate resource impacts to changes in recreational use.

3. Measure recreation use changes.

4. Search the literature for relevant study sites.

5. Assess relevance and applicability of study site data.

6. Select a benefit measure from a single relevant study or a range of benefit measures if

more than one study is relevant.

7. Multiply benefit measure by total change in recreation use.
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Appendix C provides reference codes by recreation

activity to the annotated bibliography (appendix A)

for ease in locating potentially relevant studies.

It is important to note that all unit benefit measures

provided in this report are in consumer surplus per

activity day per person. Therefore, when translating

resource impacts into recreation use changes, these

impacts should be expressed in a comparable index as

changes measured in activity days or convert the activity day measures into the relevant units.

The simplicity with which the steps to performing a

single point estimate transfer are presented may be

misleading. The steps involved in finding a valid and

reliable benefit measure can be complex if taken to

their theoretical extreme. This should become apparent when the information on the conditions for and

limitations to benefit transfers are taken into account

as previously identified. See Boyle and Bergstrom

(1992) for an example of critically filtering existing

research for applicability to a policy site context. In

their example, they located five studies that measured

the benefit of white water rafting. They then filtered

the studies by three idealized technical considerations:

(1) the nonmarket commodity of the site

must be identical to the nonmarket commodity to be valued at the policy site; (2) the

populations affected by the nonmarket commodity at the study site and the policy site

have identical characteristics; and (3) the

assignment of property rights at both sites

must lead to the same theoretically appropriate welfare measure (e.g., willingness to pay

versus willingness to accept compensation)

(p. 659).

Their filtering of each study based on these considerations left them with no ideal benefit measures to

transfer to their policy site. They state that this is likely

to be the case for many transfer scenarios in which “a

small number of potential study sites are available and

the value(s) estimate at these study sites may not be

applicable to the issue at the policy site” (p. 660).

Therefore, when performing critical single point estimate benefit transfers, the original reporting of the

study results must be obtained in order to determine

its applicability to the evaluation issue at hand.

Another potentially critical aspect of benefit transfer is the defensibility of transferred values. Defensibility can be defined on two feasibility dimensions—

technical and political. Technical feasibility is inversely

related to the degree of technical and theoretical consistency between the study site context and the policy

site context. Political feasibility is highly context- and

scale-dependent, accounting for an array of social and

cultural factors. The context surrounding each benefit

transfer can be unique, meaning there is no single set

of protocols that can be objectively followed. Benefit

transfer is as much an art as it is a science. However,

quite often information can be transferred with varying levels of confidence. A confidence interval for

transferred point estimates can be calculated if the

original study reports the standard error of the estimate. This confidence interval provides the statistical

range in which we would expect the estimate to be

some large percentage of the time (e.g., a 95% confidence interval means the estimate would be within the

calculated range 95% of the time).

Example application: Background. The example that

we will follow throughout the remainder of this report

is hypothetical. We will be using this example to

illustrate some of the issues when performing benefit

transfers using the various approaches as they are

discussed. Since this report is not intended to be a short

course in nonmarket valuation, judgments concerning

the validity, applicability, and quality of the valuation

methodology used in each of the empirical studies are

left to the benefit transfer practitioner.

The example application we will use to illustrate

each of the transfer methods is to provide a per person

activity day use value estimate for mountain biking

in the Allegheny National Forest in north central

Pennsylvania. The estimate can be used to either value

current use on an existing trail or to value predicted

use for a proposed trail. The total value of mountain

biking in the forest can then enter into a resource

allocation decision, assessment of a proposed forest

plan, or accounting of the value of forest outputs. We

will assume that this use of the national forest is

important, but due to budgetary restrictions primary

research is precluded. Therefore, we will attempt to

use benefit transfer to provide a credible measure of

the net benefits of an activity day of mountain biking

in the forest. All measures will be reported in fourthquarter 1996 dollars. Inflationary indexing such as the

implicit price deflator can be used to adjust benefit

measures to current dollars.

Example of a single point estimate transfer. We

assume that the information requirements for steps 1

through 3 of figure 3 have been fulfilled. We will

therefore begin with step 4, which is “search the literature for relevant study sites.” Using appendix C, table

C9, we find three biking studies referenced. Based on

cross-referencing with the annotated bibliography,

these three studies are Bergstrom and Cordell (1991),

Fix and Loomis (1998), and Siderelis and Moore (1995).

Bergstrom and Cordell (1991) provide national zonal

travel cost models for several recreation activities.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 11

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Their models were primarily developed using PARVS

(Public Area Recreation Visitors Study) data. The

authors identify several limitations of their models

based on assumptions they have made in developing

these models from the data. In particular, they have

provided a benefit measure for biking. However, biking within the context of their study is a conglomerate

of touring, leisure riding, and mountain biking,

among others. Therefore, the benefit measure they

provide is not specific to mountain biking, but to

bicycling in general.

Siderelis and Moore (1995) investigate the net benefits of bicycling and walking on abandoned railroad

beds that have been recycled to a rail-trail for recreation and transportation purposes. Their investigation

used an onsite interview with followup mail questionnaire. Their research sites included the Heritage Trail

that traverses a rural area in Iowa, the St. Marks

Historic Railroad Trail that traverses a rural area with

small towns in Florida, and the Lafayette/Moraga

Trail that traverses a dense urban to suburban area in

California. Bicycling on these trails was the dominant

use of the trail for the Iowa and Florida trails, while

walking was the dominant use of the California trail.

Therefore, the reported values for the first two trails

are primarily measures of biking value. Biking in this

study was for leisure and transportation, not specifically mountain biking.

Fix and Loomis (1998) provide us with two estimates

specifically for mountain biking. They use the individual travel cost and the contingent valuation methods to provide benefit estimates for mountain biking at

the famed Slickrock Trail in Moab, Utah. Their data

was collected using onsite surveys.

Each of the above studies should be assessed for

relevance and applicability to the policy site issue.

Several factors that can be assessed have been listed

previously. For instance, the Bergstrom and Cordell

(1991) study provides a general benefit measure for a

generic bicycling day anywhere in the nation. Siderelis

and Moore (1995) provide benefit measures for bicycling rail-trails for a specific region of the United

States. And Fix and Loomis (1998) provide benefit

measures for mountain biking, but in a high-profile,

world-class site in Moab, Utah. Each study has positive

and negative context-dependent aspects affecting their

perceived relevance and applicability. However, for

this example, we will assume each study is relevant,

providing us with credible benefit measures.

Therefore, according to the annotated bibliography

(appendix A), the three studies provide five estimates

we could use for benefit transfer (table 2). We may

conclude that the benefit of mountain biking on the

proposed trail ranges from $18 to $63 per activity

day. We do not know where in this range, if at all, the

actual benefit of the proposed trail would be without

conducting primary research. However, we may be

able to use expert judgment concerning where in this

range we believe a defensible measure would be given

the context of the policy site and proposed action. For

example, the Allegheny National Forest site is probably closer in composition to the Iowa trail than to the

sandstone Slickrock Trail in Moab. Thus, the best

single point estimate would be in the $34 range. Whether

it would be slightly more or less than this estimate

depends on the similarity of characteristics of the trail

in the Allegheny National Forest and the confidence

interval surrounding this estimate (which is not recorded in the study report, but may be available from

the authors).

Average value transfer

An average value transfer is based on using a measure of central tendency of all or subsets of relevant

and applicable studies as the transfer measure for a

policy site issue. The primary steps to performing an

average value transfer include identifying and quantifying the management- or policy-induced changes on

recreation use, and locating and transferring a “unit”

average consumer surplus measure. The text box

(figure 4) provides a more detailed list of the steps

involved in average value transfers.

It is a common practice for federal public land agencies to use administratively approved average values

in assessing management and policy actions. The USDA

Table 2. Single point estimates from the literature for the hypothetical mountain biking

transfer.

Measure 95% Confidence Interval Source

$17.61 Not available Bergstrom and Cordell

$34.11 Not available Siderelis and Moore (Iowa trail)

$56.27 Not available Siderelis and Moore (Florida trail)

$54.90 $33–$161 Fix and Loomis (Travel cost method)

$62.88 $54–$77 Fix and Loomis (Contingent valuation method)
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Forest Service has used RPA (Resources Planning Act)

values since 1980 (USDA Forest Service 1989)4

. These

RPA values have been provided for groups of activities and Forest Service regions of the country. Along a

similar vein, the U.S. Bureau of Reclamation and the

U.S. Army Corps of Engineers have relied upon the

U.S. Water Resources Council’s “unit day values” for

decades (U.S. Water Resources Council 1973, 1979,

1983). While some of the unit day values may not have

been based directly on the emerging literature on

outdoor recreation use values and measures, they

have all been influenced to a certain degree by this

literature. Past RPA average values were provided for

each USDA Forest Service Region. However, this segregation results in two problems: (1) very small sample

sizes per activity/region cell, and (2) numerous activity/region cells with no average value (because of the

lack of primary research). To address both of these

problems, the Forest Service regions are aggregated

into RPA assessment regions with the Pacific Coast

Area and Alaska being separately reported.

Table 3 provides measures of central tendency (mean,

median, and 95% confidence intervals) of consumer

surplus per activity day per person for 21 primary

recreation activities, plus values for wilderness recreation, as defined by the USDA Forest Service (1989).

These activity day values are provided for various

regions of the United States and Canada. We report

both mean and median values for all regional estimates, and confidence intervals for recreation activity estimates based on all activity-specific data. Under

conditions of a normal distribution, mean and median

estimates will be equal. Large divergences between

these two measures indicate that the distribution of the

estimates is skewed. That is, the average value is

affected by large or small estimates. The effect of

“outlier” estimates can be large when the total number

of estimates is small. In addition, it is evident that

margin of error is related to sample size.

In addition to the average values provided in table 3,

subset average values can be calculated. For example,

upon reviewing the literature behind the average values in table 3 the benefit transfer practitioner may

determine that one or more of the inclusive studies is

not applicable or may be influenced by atypically

large values. The practitioner can then recalculate an

average value based on the individual estimates that

are judged applicable or use a rigorous statistical

test to identify potential outlier values (Barnett and

Lewis 1994).

Example of an average value transfer. To continue

with our example transfer for mountain biking benefits of a trail in Allegheny National Forest, let us

assume that none of the point estimates previously

gathered match perfectly. Instead, maybe a benefit

measure that is based on all three studies would be

preferable. Using table 3, we search the northeast for

an average value for biking. The average value is

$34.11; however, it is based on a single estimate—the

Siderelis and Moore (1995) estimate for Iowa. A measure that is based on all three studies would be the total

U.S. studies column of table 3. The average of all five

studies is $45.15. Alternatively, an average of the most

closely matching studies ($34.11 for Iowa, $56.27 for

Florida, and $17.61 for the nation) would be $36.00.

Professional judgment determines which average value

is most appropriate for the Allegheny National Forest

site.

Figure 4. Steps to performing an average value transfer.

4The Resources Planning Act office of the USDA Forest

Service is now the Strategic Planning and Resource Assessment office. However, reference to published value estimates and objectives of this group will be located under RPA.

We will use RPA when referring to its history.

AVERAGE VALUE TRANSFER

1. Identify the resources affected by a proposed action.

2. Translate resource impacts to changes in recreational use.

3. Measure recreation use changes.

4. Search the literature for relevant study sites.

5. Assess relevance and applicability of study site data.

6. Use average value provided in table 3 for that region or use an average of a subset of

study measures.

7. Multiply benefit measure by total change in recreation use.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 13

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis Table 3. Recreation activity day values per person by various geographic locations. Northeast Area studiesa Southeast Area studiesa Intermountain Area studiesa Pacific Coast Area studiesa Alaskan studiesa RPA1 RPA2 RPA3 RPA4 RPA5 Activity n Mean Median n Mean Median n Mean Median n Mean Median n Mean Median Camping 7 $24.34 $6.50 10 $21.90 $5.14 18 $25.87 $24.09 4 $86.96 $77.27 Picnicking 2 47.04 42.11 2 30.52 30.52 4 22.95 24.09 3 53.52 28.95 Swimming 3 16.37 3.52 3 22.87 16.75 1 24.62 24.62 4 22.74 18.41 Sightseeing 2 101.19 101.19 5 60.85 67.10 10 13.22 12.23 1 50.64 50.64 1 $13.20 $13.20 Off-road diving 1 4.37 4.37 1 11.76 11.76 1 33.64 33.64 Motor boating 1 66.75 66.75 2 8.40 8.40 6 47.93 29.31 4 21.69 11.48 Float boating 4 52.99 39.85 3 45.86 26.93 10 77.68 40.36 1 15.13 15.13 Hiking 3 62.65 70.54 5 61.47 17.39 5 31.85 29.66 14 26.71 22.87 1 12.93 12.93 Biking 1 34.11 34.11 1 56.27 56.27 2 58.89 58.89 Downhill skiing 3 33.02 33.93 1 20.90 20.90 Crosscountry skiing 3 28.83 28.83 7 24.90 22.79 1 40.32 40.32 Snowmobiling 2 69.97 69.97 Big game hunting 54 45.46 39.00 29 35.89 32.23 72 43.56 36.40 12 40.76 29.42 5 52.40 48.47 Small game hunting 3 36.73 30.46 13 25.75 27.71 1 27.37 27.37 Waterfowl hunting 23 32.09 18.21 11 17.70 15.41 19 37.18 18.21 5 33.19 30.82 1 60.08 60.08 Fishingb 43 31.16 15.41 13 27.74 18.21 42 40.82 21.68 15 36.97 22.41 1 39.22 39.22 Wildlife viewing 56 26.06 25.61 39 29.13 27.80 39 36.10 32.22 15 29.74 31.65 7 42.12 52.92 Horseback riding Rock climbing 1 85.74 85.74 3 42.04 45.34 General recreationc 5 14.06 7.47 7 19.66 7.25 11 42.09 14.04 2 22.27 22.27 3 8.92 10.03 Other recreationd 4 25.06 23.91 10 46.96 35.23 1 62.06 62.06 Wilderness recreation 4 19.64 20.20 3 81.95 17.39 8 49.24 29.30 13 31.29 22.53 1 12.98 12.98 Total # cases (N)e 211 135 277 84 20

aForest Service Regions per area are: Northeast Area = R9; Southeast Area = R8; Intermountain Area = R1, R2, R3, R4; Pacific Coast Area = R5, R6; and Alaskan Area = R10. bFishing values include different species, different bodies of water, and different angling techniques. The majority of the fishing benefit measures are from studies conducted between 1979

and 1988. Fishing was not a primary target of the literature review since it is the focus of a different project. Updated fishing values will be provided at the completion of the other project. cGeneral recreation is defined as a composite of recreation opportunities at a site with measure for site, not a specific activity. dOther recreation is defined as sites with recreation opportunities that are undefined or obscure, such as cliff diving and mountain running. eTotal number of cases excludes Wilderness recreation as some of these estimates are a subset of other various recreation activities (see bibliography for identification of sources). fMargin of error is calculated at the 95% confidence limit based on standard error of mean estimates (table 1). Confidence range illustrates magnitude of the range based on the data in which

a mean estimate would lie with 95% confidence.
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Average value estimates, however, are no better

than the data they are based on. All of the issues that

could be raised concerning the credibility of any single

measure are also relevant for an average value based in

part on that measure.

Benefit Function Transfers

Benefit function transfers entail the use of a model

that statistically relates benefit measures with study

factors such as characteristics of the user population

and the resource being evaluated. Benefit function

transfers usually come from two sources. First, a benefit function or demand function has been estimated

and reported for a recreation activity in a geographic

location through primary research. Second, meta regression analysis functions can be estimated from

several independent primary research projects. In either case, the transfer process entails adapting the

function to the characteristics and conditions of the

policy site, forecasting a tailored benefit measure

based on this adaptation of the function, and use of the

forecast measure for evaluating the policy site.

Demand function transfer

The transfer of an entire demand function is conceptually sounder than value transfers. This is because the

benefit estimates and use rates in recreation are a

complex function of site characteristics, user characteristics, and different spatial and temporal dimensions of recreation site quality and site choice. When

transferring a point estimate from a study site to a

policy site, it is assumed or is implied that the two sites

are identical across the various factors that determine

the level of benefits derived in recreational use of the

two sites. In the case of an average value transfer, it is

assumed that the benefits of the policy site are around

the mid-level of benefits measured for the study sites

incorporated into the average value calculation. However, based on different validity and reliability assessments of point estimate and average value transfers,

this is not always the case. The invariance surrounding

the transfer of benefit measures alone makes these

transfers insensitive or less robust to significant differences between the study site(s) and the policy site.

Therefore, the main advantage of transferring an entire demand function to a policy site is the increased

precision of tailoring a benefit measure to fit the characteristics of the policy site. It is in the adaptation stage

of forecasting a benefit measure from a study site

demand function that the additional value of the transfer method is realized (figure 5).

Disadvantages of the method are primarily due to

data collection and model specification in the original research effort. Factors in the demand function

may be relevant to the study site but not to the policy

site. Also, factors that are important to demand at

the policy site may not have been collected at the

study site or were not significant in determining

demand at the study site. These factors can have

distinct effects on the tailored benefit measures at a

policy site. This is evident in validity tests of benefit

function transfers in which error in the tailored

value ranged from as low as a few percentage points

to as high as 800% (Loomis and others 1995; Downing and Ozuna 1996; Kirchhoff and others 1997). In

comparative validity tests, demand function transfers were found to outperform (lower error) point

estimate transfers. Therefore, demand function

transfers may be an improvement over point estimate transfers but are still a second-best strategy to

recreation valuation.

Figure 5. Steps to performing a demand function transfer.

DEMAND AND BENEFIT FUNCTION TRANSFER

1. Identify the resources affected by a proposed action.

2. Translate resource impacts to changes in recreational use.

3. Measure recreation use changes.

4. Search the literature for relevant study sites.

5. Assess relevance and applicability of study site data and whether demand or benefit

function is specified.

6. Adapt demand or benefit function to policy site characteristics and forecast benefit

measure.

7. Multiply tailored benefit measure by total change in recreation use.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 15

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

We have not identified those studies in the literature

review that reported demand or benefit equations

(functions). The applicability of demand functions to

policy site transfers requires an intimate knowledge of

the policy site. In addition, the specification of individual demand functions can have significant effects

on the reliability of their use under varying circumstances. If a demand function transfer is sought, then

the transfer practitioner will have to use insight and

expert judgment concerning the applicability of

potentially transferable demand functions, the details

of which are well beyond the scope of this report. Good

illustrative examples of benefit function transfers are

provided by Loomis and others (1995), Downing and

Ozuna (1996), and Kirchhoff and others (1997).

Example of a demand function transfer. The adaptation of a demand function from a study site to a policy

site can be complex and lead to a large error. This error

can be influenced by dissimilarities between site and

user population characteristics of the study site and

policy site. Critical demand/benefit function transfer

requires strong knowledge of economic methodology

and estimation of consumer surplus. Therefore, it is

highly recommended that when attempting to perform a demand function transfer you either have the

requisite knowledge or solicit the aid of someone

who does.

A demand function relates the number of occasions

of an activity (typically as trips or days) to the price

paid (travel costs including direct variable costs and

travel time costs) (TC), characteristics of a site (SC),

socioeconomic characteristics of the user population

(SEC), and substitute site information (SubTC). This

demand function would look something like:

# of Occasions = β0 + β1*TC + β2*SubTC

+ β k*SC + βm*SEC. (2)

The adaptation entails substituting equivalently measured information relevant to the policy site for the

variables in the demand function. This adaptation

then forecasts the lefthand side of the demand equation or number of occasions. Based on this adapted

demand function, consumer surplus per day can be

calculated. In some cases, this estimation of consumer

surplus and conversion to per activity day is difficult.

For direct methods to estimating consumer surplus

via stated preference, a bid or willingness to pay

function is typically defined. The bid function relates

consumer surplus or willingness to pay to quantity

and/or quality of the activity or environmental resource (Q), characteristics of a site (SC), and socioeconomic characteristics of the user population (SEC).

This function would look something like:

WTP = b0 + b1*Q + bk*SC + bm*SEC. (3)

Returning to our mountain biking example, we

have several demand functions that could be transferred to our policy site. It can be argued that Siderelis

and Moore’s (1995) Iowa trail demand function is the

best for our purposes. This is because the Fix and

Loomis (1998) mountain biking study, although activity-specific, has noncomparable site characteristics

between their site and our policy site. Siderelis and

Moore’s (1995) Florida trail model is neither activityspecific nor of comparable site characteristics. And

Bergstrom and Cordell’s (1991) national model for

biking is not activity specific or site specific. Therefore,

Siderelis and Moore’s (1995) Iowa model is the closest

to matching the issue for our hypothetical policy site—

dirt-trail biking.

Siderelis and Moore (1995) estimate an individual

travel cost model of the form:

lnTrips = 3.62 + 1.511 – 0.033TC + 0.70W – 0.16A1 (4)

where the dependent variable (lnTrips) is the natural

logarithm of the number of trips, TC is the virtual price

or travel costs (including direct variable costs of travel

and wage rate value of travel time), W is whether the

individual was using the Iowa trail for walking (versus

bicycling), and A1 is the percentage of a group who is

26 years of age or less.5 Average consumer surplus per

trip is the area below the demand function and above

the average price line (figure 2). For a semi-log function form, an approximation of average per trip consumer surplus is –(1/βTC), where βTC is the travel cost

parameter (Adamowicz and others 1989). Thus, average consumer surplus per trip is $34.25 (adjusted to

1996 dollars).6 We are not provided with information

necessary to calculate a 95% confidence interval for

this measure.

5Siderelis and Moore (1995) estimate six different models

for the Iowa trail. They use this model to estimate consumer

surplus per trip, which is subsequently the estimate recorded

throughout this document and the database. Therefore, we

will restrict our transfer exercise to this model.

The model estimated is a count data model using a

negative binomial regression technique. Count data models

are suggested for trip data in which the dependent variable

is reported in integers (no partial trips) and restricted to be

nonnegative (no negative trips). Negative binomial specifications automatically take the natural log of the dependent

variable. For more information on count data travel cost

models, see the discussion by Siderelis and Moore (1995) or

Creel and Loomis (1990).

6The difference between this estimate and Siderelis and

Moore’s (1995) estimate of $34.11 (in comparable dollars) is

that the authors of the original research used Simpson’s rule

for approximating integrals for calculating consumer surplus.
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

One of the disadvantages to transferring a demand

function of the semi-log functional form is that consumer surplus is invariant in (or exogenous to) the

demand model. That is, changes in the levels of the

explanatory variables in the model are captured in

the predicted use levels, but not in the estimate of

average consumer surplus per trip. Adamowicz and

others (1989) identify demand specifications in which

quantity and price measures endogenously determine consumer surplus measures. Applying a benefit function with endogenously determined consumer surplus is similar to the application of the meta

analysis benefit function discussed in the next section. However, one use of the Iowa trail model would

be to predict the market area and use levels for the

Pennsylvania trail.

Meta regression analysis benefit function

transfer

Meta regression analysis is the statistical summarizing of relationships between benefit measures and

quantifiable characteristics of studies. The data for a

meta analysis is typically summary statistics from

study site reports and includes quantified characteristics of the user population, study site’s environmental

resources, and valuation methodology used. Coding

of the studies included in the literature review (as

previously described) lends itself directly to the estimation of a meta analysis benefit function. However,

interpretation of original study results can be a source

of error in meta analysis databases.

Advantages and disadvantages

Meta analysis has been traditionally concerned with

understanding the influence of methodological and

study-specific factors on research outcomes and providing summaries and syntheses of past research. A

more recent use of meta analysis is the systematic

utilization of the existing value estimates from the

literature for the purpose of benefit transfer. Essentially, meta analysis regression models can be used to

forecast benefits at policy sites. Meta analysis has

several conceptual advantages over other benefit transfer methods such as point estimate and demand function transfers:

• Meta analysis utilizes information from a greater

number of studies, thus providing more rigorous measures of central tendency that are sensitive to the underlying distribution of the study

site measures.

• Methodological differences can be controlled

when calculating a value from the meta analysis

function.

• By setting the independent variables (adapting

the function) at levels specific to the policy site, the

transfer practitioner is potentially accounting for

differences between the study sites and the policy

site.

• Multi-activity, multi-site meta analyses can provide estimates for regions in which no studies

were conducted for an activity. That is, meta

analysis can forecast estimates for new or unstudied sites.

Many of the same limitations to performing benefit

transfers in general are applicable to meta analysis

(Desvousges and others 1998):

• There should be enough original studies conducted so that statistical inferences can be made

and relationships modeled.

• A meta analysis can only be as good as the quality

of past research efforts. This quality includes the

scientific soundness of the original research and

the reporting of results and summary statistics on

original data samples that are rich in detail.

• The studies should be similar enough in content

and context that they can be combined and statistically analyzed.

Similar to demand function transfers, the main advantage of forecasting measures for a policy site via a

meta analysis benefit function is the increased sensitivity of the tailored benefit measure to characteristics

of the policy site. It is in the adaptation stage of forecasting a benefit measure from meta analysis benefit

function that the additional value of the transfer method

is realized (figure 6). An additional advantage of the

meta analysis approach over a demand function approach is its ability to discern the effect of different

factors on the level of benefit estimates provided in the

literature.

An outdoor recreation meta analysis benefit

function

As stated previously, a master coding sheet was

developed that contains 126 fields. The main coding

categories include study reference, benefit measure(s),

methodology used, recreation activity investigated,

recreation site characteristics, and user population

characteristics. Table 4 lists and defines the variables
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 17

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Figure 6. Steps to performing a meta regression analysis function transfer.

Table 4. Description of variables tested in the meta analysis.

Variable Description

Dependent variable

CS Consumer surplus (CS) per person per activity day (1996 dollars)

Method variables

METHOD Qualitative variable: 1 if stated preference (SP) valuation approach used; 0 if revealed preference

(RP) approach used

DCCVM Qualitative variable: 1 if SP and dichotomous choice elicitation technique was used; 0 if otherwise

OE Qualitative variable: 1 if SP and open-ended elicitation technique was used; 0 if otherwise

ITBID Qualitative variable: 1 if SP and iterative bidding elicitation technique was used; 0 if otherwise

PAYCARD Qualitative variable: 1 if SP and payment card elicitation technique was used; 0 if otherwise

CONJOINT Qualitative variable: 1 if SP and conjoint analysis technique was used; 0 if otherwise

SPRP Qualitative variable: 1 if SP and RP used in combination; 0 if otherwise

ZONAL Qualitative variable: 1 if RP and a zonal travel cost model was used; 0 if otherwise

INDIVID Qualitative variable: 1 if RP and an individual travel cost model was used; 0 if otherwise

RUM Qualitative variable: 1 if RP and a random utility model was used; 0 if otherwise

HEDONIC Qualitative variable: 1 if RP and a hedonic travel cost model was used; 0 if otherwise (omitted

category for METHOD) [0.024, 0.15]a

TTIME Qualitative variable: 1 if RP model included travel time; 0 if otherwise

SUBS Qualitative variable: 1 if RP model included substitute sites; 0 if otherwise

ONSITE Qualitative variable: 1 if sample frame was on-site; 0 if otherwise

MAIL Qualitative variable: 1 if primary data collection used mail survey type; 0 if otherwise

PHONE Qualitative variable: 1 if primary data collection used phone survey type; 0 if otherwise

INPERSON Qualitative variable: 1 if primary data collection used in-person survey type; 0 if otherwise

SECOND Qualitative variable: 1 if secondary data was used (omitted category for data collection) [0.063,

0.24]a

LINLIN Qualitative variable: 1 if functional form was linear on both dependent (d.v.) and independent

variables (i.v.); 0 if otherwise

LOGLIN Qualitative variable: 1 if functional form was log d.v. and linear i.v.; 0 if otherwise

LOGLOG Qualitative variable: 1 if functional form was log on both d.v. and i.v.; 0 if otherwise

LINLOG Qualitative variable: 1 if functional form was linear on d.v. and log on i.v.; 0 if otherwise (omitted

category for functional form) [0.003, 0.05]a

VALUNIT Qualitative variable: 1 if CS was originally estimated as per day; 0 if otherwise (e.g., trip, season, or

year)

TREND Qualitative variable: year when data was collected, coded as 1967 = 1, 1968 = 2,..., 1996 = 30

(cont.’d)

META ANALYSIS FUNCTION TRANSFER

1. Identify the resources affected by a proposed action.

2. Translate resource impacts to changes in recreational use.

3. Measure recreation use changes.

4. Adapt meta regression analysis benefit function (table 5) to policy site characteristics

and forecast benefit measure or select appropriate forecast measure from meta

forecasted measures (table 6).

5. Multiply tailored benefit measure by total change in recreation use.
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

tested in developing a meta regression analysis benefit

transfer function. The majority of the variables are

qualitative dummy variables coded as 0 or 1, where 0

means the study does not have a characteristic and 1

means that it does. For example, if a study was conducted on a lake in New York, then R9 and LAKE

would be coded as 1 while all other FS regions, FOREST, and RIVER would be coded as 0. The variables are

grouped in table 4 according to whether they are

methodological, site, or activity specific variables.

The user population characteristics were rarely reported with the results of a study. Other means for

obtaining data on user population characteristics, such

as contacting the researchers of the study, were not

feasible given the financial and time constraints of the

project. We did attempt to proxy user population

characteristics by using 1990 U.S. Census average values for income, gender, education, age, and race, for

the state in which the study was conducted, but found

in preliminary analysis that these proxies were broadly

Site variables

RECQUAL Qualitative variable: site quality variable coded as 1 if the author stated site was of high quality or

the site was either a National Park, National Recreation Area, or Wilderness Area; 0 if otherwise

FSADMIN Qualitative variable: 1 if the study site(s) were National Forests (i.e., administered by the U.S.

Forest Service [FS]); 0 if otherwise

R1 Qualitative variable: 1 if study sites were in FS Region 1 (Montana, No. Idaho); 0 if otherwise

R2 Qualitative variable: 1 if study sites were in FS Region 2 (Wyoming, Colorado); 0 if otherwise

R3 Qualitative variable: 1 if study sites were in FS Region 3 (Arizona, New Mexico); 0 if otherwise

R4 Qualitative variable: 1 if study sites were in FS Region 4 (Nevada, Utah, So. Idaho); 0 if

otherwise

R5 Qualitative variable: 1 if study sites were in FS Region 5 (California); 0 if otherwise

R6 Qualitative variable: 1 if study sites were in FS Region 6 (Oregon, Washington); 0 if otherwise

R8 Qualitative variable: 1 if study sites were in FS Region 8 (Southern United States east of Rocky

Mountains); 0 if otherwise

R9 Qualitative variable: 1 if study sites were in FS Region 9 (Northern United States east of Rocky

Mountains); 0 if otherwise

R10 Qualitative variable: 1 if study sites were in FS Region 10 (Alaska); 0 if otherwise

NATL Qualitative variable: 1 if study sites were the entire United States; 0 if otherwise

CANADA Qualitative variable: 1 if study sites were in Canada; 0 if otherwise (omitted category for

geographic location of study site) [0.015, 0.12]a

LAKE Qualitative variable: 1 if the recreation site was a lake; 0 if otherwise

RIVER Qualitative variable: 1 if the recreation site was a river; 0 if otherwise

FOREST Qualitative variable: 1 if the recreation site was a forest; 0 if otherwise

OCEAN Qualitative variable: 1 if the recreation site was an estuary or bay of an ocean; 0 if otherwise

(omitted category for site type) [0.169, 0.37]a

PUBLIC Qualitative variable; 1 if ownership of the recreation site was public; 0 if otherwise.

DEVELOP Qualitative variable: 1 if the recreation site had developed facilities, such as picnic tables,

campgrounds, restrooms, boat ramps, ski lifts, etc.; 0 if otherwise.

NUMACT Quantitative variable: the number of different recreation activities the site offers.

Recreation activity

variables

CAMP . . . Qualitative variables: 1 if the relevant recreation activity was studied; 0 if otherwise. Where

OTHERREC CAMP is camping, PICNIC is picnicking, SWIM is swimming, SISEE is sightseeing, OFFRD is

off-road driving, NOMTRBT is float boating, MTRBOAT is motor boating, HIKE is hiking

backpacking, BIKE is biking, DHSKI is downhill skiing, XSKI is cross-country skiing,

SNOWMOB is snowmobiling, BGHUNT is big game hunting, SMHUNT is small game hunting,

WATFOWL is waterfowl hunting, FISH is fishing, WLVIEW is wildlife viewing, HORSE is

horseback riding, ROCKCL is rock climbing, GENREC is general recreation (defined as a

composite of recreation activity opportunities at a site), and OTHERREC is other recreation (for

sites with recreation opportunities undefined or obscure—omitted category for recreation

activity) [0.015, 0.12]a

aMean and standard deviation for omitted categories reported in square brackets; N = 701.

Table 4 (Cont.’d)

Variable Description
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 19

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

insensitive to differences in benefit measures provided. Using U.S. Census average values at the county

level and for the period in which the original study

data was collected may be a viable alternative for

future investigations. However, the lack of user population characteristics will be an additional limitation

on the validity and reliability of the meta analysis

function.

The meta analysis model is of the basic form:

yi = α + β’xi + εi, (5)

where i indexes each observation, y is the dependent

variable (consumer surplus per person per activity

day adjusted to 1996 dollars), α and β are parameters

to be estimated and are respectively the intercept and

slope coefficients for the model, x is a matrix of explanatory variables including methodology, site, and

activity characteristics, and e is the classical error term

with mean zero and variance σ2

ε.

Some of the studies are not included in the meta

analysis because of the lack of reporting of key information that would enable their full coding. Therefore,

the meta analysis database consists of 701 estimates

from 131 separate studies. The number of estimates

per study ranged from 1 to 134. As identified in previous meta-analyses, the panel nature of the data can

lead to econometric problems. If there is correlation

among these multiple observations for each study,

then a classical ordinary least-squares regression will

be inefficient and inconsistent in estimated parameters. We tested for panel effects using various forms of

stratifying the data (including the most obvious stratification by study) (Rosenberger and Loomis 2000).

However, panel effects were not discernible with these

tests. Therefore, we use classical ordinary least-squares

with the robust Newey-West version of White’s consistent covariance estimator to estimate the model

(Smith and Kaoru 1990; Driscoll and Kraay 1998).

There is no precedent for choice of functional form

when conducting meta-analyses. The functional form

of the meta analysis models are linear in the dependent

variable and the quantitatively defined variables, with

the majority of the variables being qualitative dummy

variables as previously noted. We tested and rejected

logarithmic transformations of the quantitatively defined variables, finding the linear specification to be

most efficient.

The meta analysis benefit transfer model (table 5)

was optimized by retaining only those variables that

were significant at an 80% level of confidence or

better based on t-statistics. This optimization is necessary in order to reduce over-specification of the

model when retaining variables whose coefficients are

not significantly different than zero. A backward elimination procedure was used to optimize the benefit

transfer model. The procedure began with the full

specification of the model using all coded variables

and sequentially eliminated the least significant variable until all remaining variables are significant at the

80% confidence level or better (p = 0.20).

The meta analysis benefit transfer model (table 5)

has an adjusted R2

 of 0.27, meaning that 27% of the

variance in the benefit measures is explained by the

model. This is consistent with other meta analyses of

recreation valuation studies (Walsh and others 1992;

Smith and Kaoru 1990). A full meta regression analysis

of the data investigating methodological, site, and

activity factor effects and other nuances of the data can

be found in Rosenberger and others (unpublished

paper). Both models (the full and optimized meta

regression models) have a standard error of 1.22, which

means that at a 95% confidence limit they have a 7%

margin of error in prediction.

For the most part, the signs of the methodology

variables are consistent with past scientific results.

METHOD is negative, meaning that stated preference

(SP) methods yield lower benefit estimates than revealed preference (RP) methods, which is consistent

with previous meta analysis results (Walsh and others

1992) and the bulk of travel cost/contingent valuation

comparison studies (Carson and others 1996). Openended (OE), iterative bidding (ITBID), combined stated

and revealed preference (SPRP), payment card

(PAYCARD), and conjoint analysis (CONJOINT) contingent valuation elicitation techniques tend to further

increase the difference between SP and RP value estimates, which is consistent with comparison studies

(Brown and others 1996). For RP estimates, individual

and zonal travel cost methods (INDIVID and ZONAL,

respectively) and random utility models (RUM) produce relatively lower benefit estimates than hedonic

property and travel cost methods (HEDONIC). The

inclusion of substitute sites in a demand model (SUBS)

lowers the benefit estimate (Rosenthal 1987). The use

of phone surveys (PHONE) yields lower benefit estimates than either in-person or mail surveys. VALUNIT

is negative, suggesting that if the original study estimated benefits in units such as per trip or per season,

then this tended to yield higher per day estimates than

those already reported in activity day units. Therefore,

either (1) there may be a recall bias introduced when

requesting values per trip, season, or year as compared

to per day estimates, (2) per day estimates understate

the total trip or season value when aggregated, or (3)

our estimate of number of days per trip or season are

understated. The TREND variable shows that benefit

estimates generally have been increasing at a greater
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

rate than inflation over time, or annually about one

dollar per activity day per person.

USDA Forest Service administered sites (FSADMIN)

yield lower benefit estimates. These sites, however, are

juxtaposed to sites designated to be of higher quality

(e.g., National Parks, State Parks, and National Wildlife Refuges). Therefore, it is plausible that USDA

Forest Service sites would have somewhat lower recreation value than these other sites. Relatively speaking,

estimates for recreation activities for Forest Service

regions 1 and 4 are higher, while estimates for Forest

Service regions 6 and 8 are lower than the composite

base of the remaining estimates for other regions, the

nation, and Canada.

LAKE has a negative sign, meaning that lake recreation has lower values than recreation activities in

bays/oceans. This makes more sense when we consider that reservoirs were coded as lakes in this analysis. River recreation (RIVER) yields higher values

than bay/ocean recreation. Estimates for recreation

Table 5. Optimized meta analysis national benefit transfer model.

Variable Coefficient White’s standard errora Mean of variable

Constant 81.273* 15.97 —

METHOD –21.586* 10.12 0.636

DCCVM –36.981* 10.44 0.177

OE –51.762* 11.01 0.354

ITBID –46.399* 10.89 0.096

SPRP –57.796* 17.31 0.006

PAYCARD –83.192* 17.85 0.006

CONJOINT –74.028* 14.44 0.001

PHONE –15.253* 4.28 0.495

INDIVID –40.147* 12.71 0.153

ZONAL –55.699* 11.29 0.185

RUM –58.422* 11.82 0.027

SUBS –17.619* 6.33 0.264

VALUNIT –9.072* 3.92 0.412

TREND 0.980* 0.47 19.331

FSADMIN –17.822* 3.70 0.127

R1 11.407* 5.41 0.053

R4 5.529 3.32 0.111

R6 –10.838* 4.01 0.058

R8 –5.128* 2.53 0.187

LAKE –18.294* 6.06 0.048

RIVER 16.788* 8.09 0.041

FOREST –9.165 4.98 0.292

PUBLIC 13.311* 4.42 0.960

SWIM –15.513 8.14 0.010

OFFRD –17.336 12.23 0.004

NOMTRBT 13.808 8.26 0.014

BIKE –14.306 8.54 0.007

XSKI –5.937 3.72 0.006

SNOWMOB –20.919* 9.31 0.001

BGHUNT 15.387* 3.72 0.252

WATFOWL 9.894* 4.29 0.084

FISH 7.057 4.31 0.174

ROCKCL 62.027* 17.66 0.006

Adjusted R2 0.27

F-stat [33, 667] 8.76*

N 701

*Variable is statistically significant at the p < 0.05 level or better. Overall margin of error is ±7%

based on a standard error of 1.22 and 95% confidence limits. aStandard errors are corrected for heteroskedasticity and serial correlation using the robust

Newey-West version of White’s covariance consistent estimator and 11 periods (Smith and Karou

1990; Driscoll and Kraay 1998).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 21

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

activities on forested lands (FOREST) are lower than

bay/ocean recreation estimates, which is consistent

with the FSADMIN variable. PUBLIC lands provide

higher valued recreation than private areas, in general. One possible explanation is that private areas

charge substantially more for access and onsite facilities and services than public areas. Therefore, private

areas extract some of the consumer surplus from

visitors, while visitors to public areas are charged

much lower prices, retaining most of their consumer

surplus (figure 2).

The recreation activities significant in the model are

self-explanatory. Some activities (SWIM, OFFRD, BIKE,

XSKI, SNOWMOB) provide relatively lower benefits

than the composite recreation activity (composed of all

omitted or insignificant activity variables), while other

activities (NOMTRBT, BGHUNT, WATFOWL, FISH,

ROCKCL) yield relatively higher values.

Variables that were tested but not found significant

in the national meta analysis benefit transfer model are

listed in figure 7. Any variables definitive of the user

populations are necessarily left out of the model due to

the lack of data.

Convergent validity of meta analysis benefit

transfer model

We tested the meta analysis benefit function model

for in-sample convergent validity. That is, we tested

the accuracy of the benefit function model in forecasting the raw average values for each activity in all

regions where data existed. We found the model performed well. While the forecast values ranged from

73% to 319% of the raw average values, the median

difference was only 1%. The model forecasted within

50% of the raw average values primarily for those

activities with a relatively large amount of data

(BGHUNT, FISH, WLVIEW). Conversely, the model

forecasted in excess of 100% difference from the raw

average values for activities with relatively little data

(SWIM, NOMTRBT, MTRBOAT, XSKI, GENREC).

We also tested regional models based on assessment

region aggregation of the data and use of national

mean values versus RPA assessment region mean

values for adaptation of the models when forecasting

regional average values (Rosenberger and Loomis

2000). The national model we are presenting is more

robust to different adaptations of the model than all

other models tested.

Example of a meta analysis benefit function transfer. The meta regression analysis benefit function is

derived from information on all studies in the database. Theoretically, if a factor is significant in explaining the variation in outdoor recreation benefit measures, then the variable reflecting this factor will be

significant in the model (table 5). As stated earlier,

the overall model performance results in a grand

mean ±7% margin of error. Thus, the meta regression

Figure 7. Variables that were insignificant in developing the optimized meta analysis national benefit transfer

model.

VARIABLES INSIGNIFICANT IN META ANALYSIS MODEL

Methodology Site characteristics Recreation activity

HEDONIC RECQUAL

R2

CAMP

TTIME PICNIC

ONSITE R3 SISEE

MAIL R5 MTRBOAT

INPERSON R6 HIKE

SECOND R10 DHSKI

LINLIN NATL SMHUNT

LOGLIN CANADA WLVIEW

LOGLOG OCEAN GENREC

LINLOG DEVELOP OTHERREC

NUMACT
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

analysis model provides more robust estimates than

an average value transfer (table 3 confidence range).

The application of the meta analysis benefit function can provide three different measures of the benefit of mountain biking (forecast national and regional

average values—table 6; and policy site specific forecast average value). However, it should be noted that

the data behind the meta analysis is mostly not specific

to mountain biking. Therefore, each of the values

forecast is really an estimate for a generic biking activity. Many of the estimates that are provided in table 6

are the same. This is due to the lack of any statistically

discovered variability across these activities (i.e., the

activity-specific variable was not significant in the

optimized national model [figure 7]).

Each of the three benefit measures forecast from

the meta analysis function differ by degree of specificity to the policy site. The adaptation of the meta analysis function is essentially to substitute relevant values

for the independent variables in the regression model,

which then forecasts a benefit measure based on the

specificity of these variable values. The specificity of

each benefit measure will be identified as each of the

measures is presented.

The first measure forecasted from the meta analysis

function is the national average value. In table 6, this is

the measure reported for the United States in the last

column. For biking, this forecast value is $15.27 with a

95% confidence range of $14.20 to $16.34. The meta

analysis function was adapted by holding all independent or explanatory variables constant at their national

mean values (last column, table 5), with the exception

of the activity variables. This means that each coefficient is multiplied by the relevant national mean value

for each variable, providing the incremental consumer

surplus due to that variable. In the case of biking, the

variable BIKE was set at 1, while all other activity

variables were set to 0. This adapts the function to

Table 6. Forecasted average values using meta analysis benefit functiona.

Northeast Southeast Intermountain Pacific Coast United

Activity Areab Areab Areab Areab Alaskab States

Camping $29.95 $24.82 $34.18 $24.53 $29.95 $29.57

Picnicking 29.95 24.82 34.18 24.53 29.95 29.57

Swimming 14.44 9.31 18.67 9.02 14.44 14.06

Sightseeing 29.95 24.82 34.18 24.53 29.95 29.57

Off-road driving 12.61 7.48 16.85 7.19 12.61 12.24

Motor boating 29.95 24.82 34.18 24.53 29.95 29.57

Float boating 43.76 38.63 47.99 38.34 43.76 43.38

Hiking 29.95 24.82 34.18 24.53 29.95 29.57

Biking 15.64 10.51 19.88 10.22 15.64 15.27

Downhill skiing 29.95 24.82 34.18 24.53 29.95 29.57

Cross country skiing 24.01 18.88 28.25 18.59 24.01 23.64

Snowmobiling 9.03 3.90 13.26 3.61 9.03 8.65

Big game hunting 45.34 40.21 49.57 39.92 45.34 44.96

Small game hunting 29.95 24.82 34.18 24.53 29.95 29.57

Waterfowl hunting 39.84 34.72 44.08 34.42 39.84 39.47

Fishingc 37.01 31.88 41.24 31.59 37.01 36.63

Wildlife viewing 29.95 24.82 34.18 24.53 29.95 29.57

Horseback riding 29.95 24.82 34.18 24.53 29.95 29.57

Rock climbing 91.98 86.85 96.21 86.56 91.98 91.60

General recreationd 29.95 24.82 34.18 24.53 29.95 29.57

Other recreatione 29.95 24.82 34.18 24.53 29.95 29.57

aBenefit estimates are calculated from the meta analysis benefit function by holding each variable constant at its mean value

except for regional and activity specific variables, which are either turned on (1) or off (0) where relevant. For the United States

average value forecast, all variables are held at their national mean value except for activity variables. bForest Service Regions per area are: Northeast Area = R9; Southeast Area = R8; Intermountain Area = R1, R2, R3, R4; Pacific

Coast Area = R5, R6; and Alaskan Area = R10. cFishing values include different species, different bodies of water, and different angling techniques. The majority of the fishing

benefit measures are from studies conducted between 1979 and 1988. Fishing was not a primary target of the literature review since

it is the focus of a different project. dGeneral recreation is defined as a composite of recreation opportunities at a site with measure for site, not a specific activity. eOther recreation is defined as sites with recreation opportunities that are undefined or obscure, such as cliff diving and mountain

running.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 23

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

specifically reflect biking at the national level. All

incremental consumer surplus values are then summed

to provide the estimated consumer surplus for the

activity of interest.

The second measure forecasted from the meta analysis function is the regional average value. In table 6,

this is the measure reported for the Northeast Area.

For biking, this forecast value is $15.64 with a 95%

confidence range of $14.55 to $16.73. The meta analysis

function was adapted by holding all independent

variables at their national mean values (last column,

table 5), with the exception of the activity and region

variables. In the case of biking, the variable BIKE was

set to 1, while all other activity and region variables

were set to 0. This adapts the function to specifically

reflect biking at the regional level.

The third measure forecasted from the meta analysis

function is the average value that is most specific to the

policy site. Table 7 shows the adaptation of the function to the policy site. All methodological variables

were held at their national mean values (last column,

table 5). These variables include METHOD, DCCVM,

OE, ITBID, SPRP, PAYCARD, CONJOINT, PHONE,

INDIVID, ZONAL, RUM, SUBS, PHONE, and

VALUNIT. TREND is set to 30 to reflect 1996 dollars.

All Forest Service region variables are set to 0.

FSADMIN is set to 1 to reflect National Forest land.

LAKE and RIVER are each set to 0, reflecting that the

activity and/or policy site does not include a lake or a

river, while FOREST is set to 1 to reflect a forested

setting. PUBLIC is set to 1 to reflect that the policy site

is on public land. BIKE is set to 1 to specify biking as the

target activity, while all other activity variables are set

to 0. After adapting the model specifically to the policy

site, a benefit measure of $4.77 per activity day is

forecasted, with a 95% confidence range of $4.44 to

$5.10.

Estimates forecast from adapting the meta analysis

benefit function at the national, regional, and sitespecific levels ranged from about $6 to $16. This range

in estimates is based on information from the entire

database, including methodological, study site, and

activity factors. However, the estimated measures are

for a generic bicycling activity. Consumer surplus, or

net willingness to pay, from mountain biking at exceptional sites may be significantly larger than this generic

value, as evidenced by the measures reported in Fix

and Loomis (1998). In addition, not all relevant information about a recreation site is available in the recreation database and therefore is not reflected in the

meta analysis benefit function. Because of these two

factors, we may conclude that meta analysis forecast

values for biking are conservative measures.

Table 7. Example adaptation of meta analysis benefit

function for mountain biking.

Adaptation Incremental

Variable Coefficient value consumer surplus

Constant 81.273 1 81.27

METHOD –21.586 0.636 –13.73

DCCVM –36.981 0.177 –6.54

OE –51.762 0.354 –18.32

ITBID –46.399 0.096 –4.45

SPRP –57.796 0.006 –0.35

PAYCARD –83.192 0.006 –0.50

CONJOINT –74.028 0.001 –0.07

PHONE –15.253 0.495 –7.55

INDIVID –40.147 0.153 –6.14

ZONAL –55.699 0.185 –10.30

RUM –58.422 0.027 –1.58

SUBS –17.619 0.264 –4.65

VALUNIT –9.072 0.412 –3.74

TREND 0.980 30 29.40

FSADMIN –17.822 1 –17.82

R1 11.407 0 0

R4 5.529 0 0

R6 –10.838 0 0

R8 –5.128 0 0

LAKE –18.294 0 0

RIVER 16.788 0 0

FOREST –9.165 1 –9.16

PUBLIC 13.311 1 13.31

SWIM –15.513 0 0

OFFRD –17.336 0 0

NOMTRBT 13.808 0 0

BIKE –14.306 1 –14.31

XSKI –5.937 0 0

SNOWMOB –20.919 0 0

BGHUNT 15.387 0 0

WATFOWL 9.894 0 0

FISH 7.057 0 0

ROCKCL 62.027 0 0

Total consumer surplus $4.77

Example application: Summary. Figure 8 provides

the different benefit measures derived from applying

the various benefit transfer methods. The different

measures are relatively consistent, being within a factor or two of each other. This is not surprising given

that all of the benefit measures are essentially based on

the same data or subsets of data.

Which estimate is best, if any, depends on a number

of factors identified earlier. In addition to the factors

built into the stock of knowledge concerning recreation use values (e.g., data collection, reporting, study

site, methodology), judgments of the benefit transfer
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

practitioner can affect overall transfer results. All

judgments regarding a benefit transfer framework

affect the outcome of the process. Judgments about the

policy context frame the entire evaluation process,

including what is affected and by how much. Judgments concerning the quality and extent of the scientific body of knowledge can affect the availability of

data. Judgments concerning the gathering, coding,

and interpretation of data can affect its applicability

and relevance to the policy context. And judgments

concerning which benefit transfer approach should be

used can affect confidence in and credibility of transferred data and policy evaluations. Smith (1992) compares the Luken et al. (1992) and Desvousges et al.

(1992) uses of benefit transfer to assess the pulp and

paper industry, illustrating the affect researcher judgment can have on policy recommendations.

Recommendations and

Guidance to Field Users

We have discussed several different methods to

using existing information for benefit transfers when

primary research is prohibitive. First, single point

estimates or an average of a subset of available point

estimates are available through their listing in the

annotated bibliography (appendix A). Second, measures of central tendency for recreation activity values

provided in table 3 can be transferred to a policy site.

Third, a demand or benefit function for a study site can

be adapted to the policy site. Fourth, the national

values (last column of table 3) can be transferred to a

policy site, providing a measure of central tendency

for an activity based on all empirical research. However, locational factors are greatly ignored with this

approach. Fifth, the meta regression analysis forecasted average values (table 6) can be transferred to a

policy site. And sixth, the meta regression analysis

benefit transfer function (table 5) can be adapted to

specific characteristics of a policy site in order to forecast a site-specific benefit measure for use in evaluating a policy site. Regardless of which method is chosen, the measure(s) transferred have a certain level of

confidence surrounding them.

Figure 9 provides a flow chart of the decision process to aid in deriving a benefit measure for recreation. We do not intend to imply that any path in the

flow chart is preferred to any other. Instead, the flow

chart illustrates possible pathways to determining if

and how recreation benefit measures are to be obtained. Depending on the context of the choice among

the different methods, one method may be preferable

to another. As Desvousges and others (1998) remind

us, an important component in any benefit transfer

is the involvement and judgment of the transfer

practitioner.

The conceptual framework provided in figure 9

shows potential paths to choosing a method for obtaining recreation values when assessing management

and policy actions. The first decision to make is whether

recreation is affected by the proposed action (figure 9,

step I). If recreation is affected, then the second decision is whether the impact on recreation is expected to

be major (figure 9, step II). If the impact on recreation

is expected to be major, then path A may be followed.

If the impact on recreation is expected to be minor, then

path B may be preferred. A preliminary benefit transfer could be conducted at this stage to determine the

expected magnitude of recreation impacts. A major

impact on recreation probably warrants consideration

Figure 8. Summary of example benefit transfers.

Single point estimate transfer $17.61 to $62.88

Average value transfer

 National $45.15

 Regional $34.11

Demand function transfer $34.25

Meta analysis transfer

 National $15.27

 Regional $15.64

 Site $4.77
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 25

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

of doing primary research either through analysis of

secondary data (figure 9, step A1) or the collection of

original data through survey research (figure 9, step

A2). Step A1 is placed prior to step A2 because it

typically requires lower budget and time inputs.

Path B would be followed if either of the following

conditions exist: (1) the impact on recreation is major,

but there is no good secondary data available or budget and time constraints are prohibitive to doing primary research, or (2) the impact on recreation is minor,

thus not warranting the expense of primary analysis.

Also note, however, that benefit transfers can be as

tedious and time consuming as some primary research. A decision criterion for all benefit transfer

derived measures is their degree of defensibility in

light of political and theoretical feasibility. We cannot

determine this feasibility a priori since feasibility criteria are specific to the context of the decision.

Following path B in figure 9, the first step is to

determine if there are any studies available that resemble the recreation issue at hand. If there are, then

these studies should be gathered and filtered for applicability and acceptability of benefit transfer measures

(figure 9, step B1; studies can be located through table

3 and appendices A and C). If there are available

studies that are applicable and acceptable, then the

value transfer can be performed either as a single point

estimate or average value transfer (table 3). If there are

no studies available or the available studies are not

applicable or acceptable, then steps B2 or B3 can be

pursued.

In step B2 of figure 9, the forecast average values

from the meta regression analysis model (table 6) can

be used. If the forecast measure is acceptable, then use

this measure for benefit transfer. However, if these

values are not acceptable, then step B3 of figure 9 may

be pursued. Step B3 adapts the meta regression analysis benefit transfer model (table 5) to a policy site via

characteristics specific to that site. One then will have

to determine if this tailored benefit measure is acceptable. If the tailored measure is acceptable, then use this

measure for benefit transfer. If the measure is not

acceptable, we have then exhausted the various sources

of benefit measures based on the recreation valuation

literature. At this point, one should go back to the

beginning and reassess the criteria used in making

Figure 9. Framework for obtaining benefit measures for recreation.

NO

YES

YES NO

YES NO

YES NO

YES

NO

NO YES

NO

YES YES NO

I

Is recreation use affected

by the proposed action?

Recreation use does not

need to be accounted for.

II

Is recreation a major use

affected by the proposed

action?

A

Is there secondary data

available on visitor use and

visitor residence

A1

Estimate a simple travel

cost model

A2

Collect primary data

Is time or budget

constrained?

Contact USDA

Forest Service

Research

Go to B

B

Are there studies available

investigating similar

issues?

B1

Gather available studies (table

3, Appendices A and C)

Are any studies applicable?

Perform value

transfer (single point

or average value

from table 3)

B2

Assess meta analysis

forecast average value

(table 6)

Is measure acceptable?

B3

Adapt meta analysis

benefit transfer

function (table 5)

Is measure acceptable?

Re-assess

decision criteria

and start over

Go to B2 or B3
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

decisions about the different methods of obtaining

benefit measures. This is definitely a judgment call and

it may seem that defensibility of an accepted benefit

measure will be decreased. However, recall that benefit transfer should be pragmatic in the sense that

when benefit measures are sought, tradeoffs are

necessary in choosing the best in a “second best”

world. A rough estimate of the dollar value of recreation in economic analysis or assessment of planning

and policy objectives is better than implying a zero

value for recreation by leaving recreation out of the

economic model.

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28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Appendix A: Annotated Bibliography of Outdoor Recreation Use Valuation

Studies, 1967 to 1998

Key:

Reference #. Author. Year Publication. Title. Source.

• Recreation Activity/ Forest Service Region (FS), RPA Region where RPA1=Northeast Area, RPA2=Southeast

Area, RPA3=Intermountain Area, RPA4=Pacific Coast Area, RPA5=Alaska/ Original $ per person per

activity day [year]/ Inflation-adjusted $ for fourth Quarter, 1996/ Valuation Method (CV=contingent

valuation, TC=travel cost, RUM/MNL=random utility model/multinomial logit model). Note: an asterik

(*) prior to lead author’s name identifies studies not included in the meta-regression analysis.

1. Adamowicz, W., J. Louviere, and M. Williams. 1994. Combining revealed and stated preference methods for

valuing environmental amenities. Journal of Environmental Economics and Management 26:271-292.

• GENERAL RECREATION/ Canada/ $1.45 [1991]/ $1.64/ RUM/MNL

• GENERAL RECREATION/ Canada/ $5.94 [1991]/ $6.71/ Conjoint

• GENERAL RECREATION/ Canada/ $1.46 [1991]/ $1.65/ Combined conjoint and RUM/MNL

2. Adamowicz, W., S. Jennings, and D. Coyne. 1990. A sequential choice model of recreation behavior. Western

Journal of Agricultural Economics 15:91-99.

• BIG GAME HUNTING/ Canada/ $24.07 [1981]/ $40.11/ RUM/MNL

3. Adamowicz, W.L., and W.E. Phillips. 1983. A comparison of extra market benefit evaluation techniques.

Canadian Journal of Agricultural Economics 31:401-412.

• FISHING/ Canada/ $4.18 [1975]/ $10.92/ Individual TC

• FISHING/ Canada/ $13.99 [1975]/ $36.56/ CV

• FISHING/ Canada/ $39.44 [1975]/ $103.07/ CV

4. Adams, R.M., O. Bergland, W.N. Musser, S.L. Johnson, and L.M. Musser. 1989. User fees and equity issues in

public hunting expenditures: The case of ring-necked pheasant in Oregon. Land Economics 65:376-385.

• SMALL GAME HUNTING/ FS6, RPA4/ $20.05 [1986]/ $27.37/ CV

5. Baker, J.C. 1996. A nested Poisson approach to ecosystem valuation: An application to backcountry hiking in

California. Reno, NV: University of Nevada, Reno. 26 p.

• HIKING/ FS5, RPA4/ $25.29 [1996]/ $25.24/ Zonal TC

• HIKING/ FS5, RPA4/ $22.57 [1996]/ $22.53/ Zonal TC

• HIKING/ FS5, RPA4/ $27.12 [1996]/ $27.07/ Zonal TC

• HIKING/ FS5, RPA4/ $11.35 [1996]/ $11.33/ Zonal TC

• HIKING/ FS5, RPA4/ $14.63 [1996]/ $14.60/ Zonal TC

• HIKING/ FS5, RPA4/ $9.88 [1996]/ $9.86/ Zonal TC

• HIKING/ FS5, RPA4/ $29.59 [1996]/ $29.53/ Zonal TC

• WILDERNESS/ FS5, RPA4/ $25.29 [1996]/ $25.24/ Zonal TC

• WILDERNESS/ FS5, RPA4/ $22.57 [1996]/ $22.53/ Zonal TC

• WILDERNESS/ FS5, RPA4/ $27.12 [1996]/ $27.07/ Zonal TC

• WILDERNESS/ FS5, RPA4/ $11.35 [1996]/ $11.33/ Zonal TC

• WILDERNESS/ FS5, RPA4/ $14.63 [1996]/ $14.60/ Zonal TC

• WILDERNESS/ FS5, RPA4/ $9.88 [1996]/ $9.86/ Zonal TC

• WILDERNESS/ FS5, RPA4/ $29.59 [1996]/ $29.53/ Zonal TC

6. Balkan, E., and J.R. Kahn. 1988. The value of changes in deer hunting quality: A travel cost approach. Applied

Economics 20:533-539.

• BIG GAME HUNTING/ National/ $106.30 [1980]/ $193.82/ Zonal TC

• BIG GAME HUNTING/ National/ $104.30 [1980]/ $190.17/ Individual TC
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 29

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

7. Barrick, K. 1986. Option value in relation to distance effects and selected user characteristics for the Washakie

Wilderness, northeast Wyoming. In R.C. Lucas [comp.], Proceedings — National Wilderness Research

Conference: Current Research. Ogden, UT: USDA Forest Service, Intermountain Research Station, General

Technical Report INT-212: 411-422.

• WILDLIFE VIEWING/ FS2, RPA3/ $5.95 [1982]/ $9.33/ CV

• WILDERNESS/ FS2, RPA3/ $5.95 [1982]/ $9.33/ CV

8. Bergstrom, J.C., and H.K. Cordell. 1991. An analysis of the demand for and value of outdoor recreation in the

United States. Journal of Leisure Research 23:67-86.

• See Appendix B Table B1. VARIOUS ACTIVITIES, 16 ESTIMATES.

9. Bergstrom, J.C., J.M. Bowker, H.K. Cordell, G. Bhat, D.B.K. English, R.J. Teasley, and P. Villegas. 1996.

Ecoregional estimates of the net economic values of outdoor recreational activities in the United States:

Individual model results. Final Report submitted to Resource Program and Assessment Staff, USDA Forest

Service, Washington, DC. Athens, GA: Outdoor Recreation and Wilderness Assessment Group SE-4901,

USDA Forest Service, and Department of Agricultural and Applied Economics, University of Georgia. 68 p.

• See appendix B table B2. VARIOUS ACTIVITIES, 50 ESTIMATES.

10. Bergstrom, J.C., J.R. Stoll, J.P. Titre, and V.L. Wright. 1990. Economic value of wetlands-based recreation.

Ecological Economics 2:129-147.

• GENERAL RECREATION/ FS8, RPA2/ $15.19 [1986]/ $20.74/ CV

11. Bishop, R.C., C.A. Brown, M.P. Welsh, and K.J. Boyle. 1989. Grand Canyon recreation and Glen Canyon Dam

operations: An economic evaluation. In K.J. Boyle and T. Heekin, Western Regional Research Project W-133,

Benefits and Costs in Natural Resource Planning, Interim Report 2. Orono, ME: Department of Agricultural

and Resource Economics, University of Maine: 407-435.

• FLOAT BOATING/ FS3, RPA3/ $78.00 [1985]/ $109.26/ CV

12. Bishop, R., T. Heberlein, and M.J. Kealy. 1983. Contingent valuation of environmental assets: Comparisons

with a simulated market. Natural Resources Journal 23:619-633.

• WATERFOWL HUNTING/ FS9, RPA1/ $11.00 [1978]/ $23.78/ CV

• WATERFOWL HUNTING/ FS9, RPA1/ $21.00 [1978]/ $45.39/ CV

• WATERFOWL HUNTING/ FS9, RPA1/ $32.00 [1978]/ $69.17/ Zonal TC

13. Bishop, R., T. Heberlein, M. Welsh, and R. Baumgartner. 1984. Does contingent valuation work? Results of the

Sandhill experiment. Paper presented at the joint meetings of AERA and AAEA.

• BIG GAME HUNTING/ FS9, RPA1/ $32.00 [1983]/ $48.11/ CV

14. Bouwes, N., and R. Schneider. 1979. Procedures in estimating benefits of water quality change. American

Journal of Agricultural Economics 61:535-539.

• GENERAL RECREATION/ FS9, RPA1/ $2.54 [1979]/ $5.06/ Individual TC

15. *Bowes, M.D., and J.B. Loomis. 1980. A note on the use of travel cost models with unequal zonal populations.

Land Economics 56:465-470.

• FLOAT BOATING/ FS4, RPA3/ $19.00 [1978]/ $41.07/ Zonal TC

16. Bowes, M., and J. Krutilla. 1989. Multiple-use management: The economics of public forestlands. Washington,

DC: Resources for the Future: 177-247.

• FISHING/ FS9, RPA1/ $5.03 [1981]/ $8.37/ Zonal TC

17. Boyle, K.J., M.L. Phillips, S.D. Reiling, and L.K. Demirelli. 1988. Economic values and economic impacts

associated with consumptive uses of Maine’s fish and wildlife resources. Orono, ME: Department of

Agricultural and Resource Economics, University of Maine. 41 p.

• BIG GAME HUNTING/ FS 9, RPA1/ $74.00 [1987]/ $98.00/ CV

• SMALL GAME HUNTING/ FS 9, RPA1/ $23.00 [1987]/ $30.46/ CV

• WATERFOWL HUNTING/ FS 9, RPA1/ $72.00 [1987]/ $95.35/ CV
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

18. Boyle, K., M. Welsh, and R. Bishop. 1988. Analyzing the effects of Glen Canyon Dam releases on Colorado river

recreation using scenarios of unexperienced flow conditions. In J.B. Loomis (comp.), Western Regional

Research Publication W-133, Benefits and Costs in Natural Resources Planning, Interim Report. Davis, CA:

University of California, Davis: 111-130.

• FISHING/ FS3, RPA3/ $34.41 [1985]/ $48.20/ CV

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19. Boyle, K.J., S.D. Reiling, and M.L. Phillips. 1990. Species substitution and question sequencing in contingent

valuation surveys evaluating the hunting of several types of wildlife. Leisure Science 12:103-118.

• BIG GAME HUNTING/ FS 9, RPA1/ $7.90 [1989]/ $9.69/ CV

• SMALL GAME HUNTING/ FS 9, RPA1/ $2.83 [1989]/ $3.47/ CV

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20. Boyle, K., S. Reiling, M. Teisel, and M. Phillips. 1990. A study of the impact of game and nongame species on

Maine’s economy. Orono, ME: Department of Agricultural and Resource Economics, University of Maine.

• BIG GAME HUNTING/ FS9, RPA1/ $50.95 [1989]/ $62.47/ CV

• BIG GAME HUNTING/ FS9, RPA1/ $37.47 [1989]/ $45.94/ CV

• BIG GAME HUNTING/ FS9, RPA1/ $68.45 [1989]/ $83.92/ CV

• SMALL GAME HUNTING/ FS9, RPA1/ $62.20 [1989]/ $76.26/ CV

• WATERFOWL HUNTING/ FS9, RPA1/ $116.49 [1989]/ $142.82/ CV

• WATERFOWL HUNTING/ FS9, RPA1/ $59.62 [1989]/ $73.10/ CV

• FISHING/ FS9, RPA1/ $172.05 [1989]/ $210.94/ CV

• FISHING/ FS9, RPA1/ $14.65 [1989]/ $17.96/ CV

21. Brooks, R. 1988. The net economic value of deer hunting in Montana. Helena, MT: Montana Department of

Fish, Wildlife, and Parks.

• BIG GAME HUNTING/ FS1, RPA3/ $54.55 [1985]/ $76.40/ Zonal TC

• BIG GAME HUNTING/ FS1, RPA3/ $19.30 [1985]/ $27.04/ Zonal TC

22. Brown, G., and J. Hammack. 1972. A preliminary investigation of the economics of migratory waterfowl. In

J.V. Krutilla (ed.), Natural Environments: Studies in Theoretical and Applied Analysis. Baltimore, MD: Johns

Hopkins University Press: 171-204.

• WATERFOWL HUNTING/ FS4, RPA3/ $25.46 [1969]/ $96.77/ CV

23. Brown, G., and M. Hay. 1987. Net economic recreation values for deer and waterfowl hunting and trout

fishing. Washington, DC: USDI Fish and Wildlife Service, Division of Policy and Directive Management.

Working paper No. 23.

• See Appendix B Table B3. BIG GAME and WATERFOWL HUNTING, and FISHING/ 134 ESTIMATES

24. *Brown, G., and M. Plummer. 1979. Recreation valuation: An economic analysis of nontimber uses of

forestland in the Pacific Northwest. Pullman, WA: Forest Policy Project, Washington State University.

• HIKING/ FS6, RPA4/ $9.40 [1976]/ $23.21/ Zonal TC

• HIKING/ FS6, RPA4/ $43.75 [1976]/ $108.02/ Zonal TC

• WILDERNESS/ FS6, RPA4/ $43.75 [1976]/ $108.02/ Zonal TC

25. Brown, T., T. Daniel, M. Richards, and D. King. 1989. Recreation participation and the validity of photo-based

preference judgments. Journal of Leisure Research 21:40-60.

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26. Brown, W., D.M. Larson, R.S. Johnston, and R.J. Wahle. 1979. Improved economic evaluation of commercially

and sport caught salmon and steelhead of the Columbia River. Corvallis, OR: Oregon State University.

• FISHING/ FS6, RPA4/ $21.77 [1974]/ $62.25/ Individual TC
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27. Cameron, T., and M. James. 1987. Efficient estimation methods for close-ended contingent valuation surveys.

Review of Economics and Statistics 69:269-276.

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28. Capel, R.E., and R.K. Pandey. 1972. Estimation of benefits from deer and moose hunting in Manitoba. Canadian

Journal of Agricultural Economics 21:6-15.

• BIG GAME HUNTING/ Canada/ $7.04 [1968]/ $28.02/ Zonal TC

• BIG GAME HUNTING/ Canada/ $9.31 [1967]/ $26.49/ Zonal TC

29. Casey, J.F., T. Vukina, and L.E. Danielson. 1995. The economic value of hiking: Further considerations of

opportunity cost of time in recreational demand models. Journal of Agricultural and Applied Economics

27:658-668.

• HIKING/ FS8, RPA2/ $213.92 [1995]/ $218.37/ Individual TC

• WILDERNESS/ FS8, RPA2/ $213.92 [1995]/ $218.37/ Individual TC

30. *Chicetti, C.J., A.C. Fisher, and V.K. Smith. 1976. An econometric evaluation of a generalized consumer

surplus measure: The Mineral King controversy. Econometrica 44:1259-1275.

• CROSS COUNTRY SKIING/ FS5, RPA4/ $12.25 [1972]/ $40.32/ Zonal TC

31. Connelly, N., and T. Brown. 1988. Estimates of nonconsumptive wildlife use on Forest Service and BLM lands.

Ithaca, NY: USDA Forest Service and Cornell University.

• See appendix B table B4. WILDLIFE VIEWING/ 42 ESTIMATES.

32. Connelly, N., and T. Brown. 1991. Net economic value of the freshwater recreational fisheries of New York.

Transactions of the American Fisheries Society 120:770-775.

• FISHING/ FS9, RPA1/ $13.68 [1988]/ $17.48/ CV

33. Cooper, J., and J. Loomis. 1991. Economic value of wildlife resources in the San Joaquin Valley: Hunting and

viewing values. In A. Dinar and D. Zilberman (eds.), The Economic and Management of Water and Drainage

in Agriculture. Boston, MA: Kluwer Academic Publishers: 447-463.

• WILDLIFE VIEWING/ FS5, RPA4/ $37.33 [1988]/ $47.70/ CV

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34. Cooper, J., and J. Loomis. 1993. Testing whether waterfowl hunting benefits increase with greater water

deliveries to wetlands. Environment and Resource Economics 3:545-561.

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35. Cordell, H.K., and J. Bergstrom. 1992. Comparison of recreation use values among alternative reservoir water

level management scenarios. Water Resources Research 29:247-258.

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36. Cory, D.C., and W.E. Martin. 1985. Valuing wildlife for efficient multiple use: Elk vs. cattle. Western Journal of

Agricultural Economics 10:282-293.

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37. Crandall, K.B. 1991. Measuring the economic benefits of riparian areas. Master’s Thesis, University of Arizona.

• WILDLIFE VIEWING/ FS3, RPA3/ $137.50 [1990]/ $161.59/ Zonal TC

38. Creel, M.D., and J.B. Loomis. 1990. Theoretical and empirical advantages of truncated count data estimators

for analysis of deer hunting in California. American Journal of Agricultural Economics 72:434-441.

• BIG GAME HUNTING/ FS5, RPA4/ $70.07 [1987]/ $92.80/ Individual TC

39. Daniels, S. 1987. Marginal cost pricing and efficient provision of public recreation. Journal of Leisure Research

19:22-34.

• CAMPING/ FS1, RPA3/ $8.71 [1984]/ $12.62/ Zonal TC
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40. Daubert, J.T., and R.A. Young. 1981. Recreational demands for maintaining instream flows: A contingent

valuation approach. American Journal of Agricultural Economics 63:666-676.

• FISHING/ FS2, RPA3/ $28.60 [1978]/ $61.82/ CV

41. Donnelly, D., J. Loomis, C. Sorg, and L. Nelson. 1983. Net economic value of recreational steelhead fishing in

Idaho. Fort Collins, CO: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station,

Resource Bulletin RM-9.

• FISHING/ FS6, RPA4/ $20.29 [1982]/ $31.81/ CV

• FISHING/ FS6, RPA4/ $14.29 [1982]/ $22.40/ Zonal TC

• FISHING/ FS6, RPA4/ $10.20 [1982]/ $15.99/ Zonal TC

42. Donnelly, D., and L. Nelson. 1983. Net economic value of deer hunting in Idaho. Fort Collins, CO: USDA Forest

Service, Rocky Mountain Forest and Range Experiment Station, Resource Bulletin RM-13.

• BIG GAME HUNTING/ FS6, RPA4/ $19.18 [1983]/ $28.84/ CV

• BIG GAME HUNTING/ FS6, RPA4/ $26.86 [1983]/ $40.39/ Zonal TC

• BIG GAME HUNTING/ FS6, RPA4/ $23.39 [1983]/ $35.17/ Zonal TC

43. Duffield, J. 1984. Travel cost and contingent valuation: A comparative analysis. In V.K. Smith and A.D. Witte

(eds.), Advances in Applied Micro-Economics, Vol. 3. Greenwich, CT: JAI Press: 67-87.

• OTHER RECREATION/ FS1, RPA3/ $6.09 [1981]/ $10.14/ Zonal TC

44. Duffield, J. 1988. The net economic value of elk hunting in Montana. Helena, MT: Report for Montana

Department of Fish, Wildlife, and Parks.

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• BIG GAME HUNTING/ FS1, RPA3/ $34.81 [1985]/ $48.76/ Zonal TC

45. Duffield, J., and C. Neher. 1990. A contingent valuation assessment of Montana deer hunting: Attitudes and

economic benefits. Helena, MT: Report for Montana Department of Fish, Wildlife, and Parks.

• BIG GAME HUNTING/ FS1, RPA3/ $61.40 [1988]/ $78.45/ CV

46. Duffield, J., and C. Neher. 1991. Montana waterfowl hunting: A contingent valuation assessment of economic

benefits and hunter attitudes. Helena, MT: Report for Montana Department of Fish, Wildlife, and Parks.

• WATERFOWL HUNTING/ FS1, RPA3/ $78.88 [1989]/ $96.71/ CV

• WATERFOWL HUNTING/ FS1, RPA3/ $89.29 [1989]/ $109.47/ CV

• WATERFOWL HUNTING/ FS1, RPA3/ $100.15 [1989]/ $122.79/ CV

47. Duffield, J., C. Neher, and T. Brown. 1992. Recreation benefits of instream flow: Application to Montana’s Big

Hole and Bitterroot Rivers. Water Resources Research 28:2169-2181.

• FISHING/ FS1, RPA3/ $88.24 [1988]/ $112.75/ CV

• OTHER RECREATION/ FS1, RPA3/ $134.88 [1988]/ $172.35/ CV

48. Duffield, J., J. Loomis, R. Brooks, and J. Holliman. 1987. The net economic value of fishing in Montana. Helena,

MT: Report for Montana Department of Fish, Wildlife, and Parks.

• FISHING/ FS1, RPA3/ $69.91 [1985]/ $97.93/ Zonal TC

• FISHING/ FS1, RPA3/ $103.20 [1985]/ $144.56/ Zonal TC

• FISHING/ FS1, RPA3/ $48.16 [1985]/ $67.46/ Zonal TC

• FISHING/ FS1, RPA3/ $32.62 [1985]/ $45.69/ Zonal TC

49. Dwyer, J., G. Peterson, and A. Darragh. 1983. Estimating value of urban forests using the travel cost method.

Journal of Arboriculture 9:182-185.

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50. Ekstrand, E.R. 1994. Economic benefits of resources used for rock climbing at Eldorado Canyon State Park,

Colorado. Ph.D. Dissertation, Colorado State University.

• ROCK CLIMBING/ FS2, RPA3/ $26.38 [1991]/ $29.82/ CV

• ROCK CLIMBING/ FS2, RPA3/ $40.11 [1991]/ $45.34/ Individual TC

• ROCK CLIMBING/ FS2, RPA3/ $45.08 [1991]/ $50.95/ Individual TC

51. Englin, J., and J.S. Shonkwiler. 1995. Estimating social welfare using count data models: An application to

long-run recreation demand under conditions of endogenous stratification. The Review of Economics and

Statistics 77:104-112.

• HIKING/ FS6, RPA4/ $16.02 [1985]/ $22.44/ Individual TC

• HIKING/ FS6, RPA4/ $24.42 [1985]/ $34.21/ Individual TC

• WILDERNESS/ FS6, RPA4/ $16.02 [1985]/ $22.44/ Individual TC

• WILDERNESS/ FS6, RPA4/ $24.42 [1985]/ $34.21/ Individual TC

52. Englin, J., and R. Mendelsohn. 1991. A hedonic travel cost analysis for valuation of multiple components of

site quality: The recreation value of forest management. Journal of Environmental Economics and Management

21:275-290.

• CAMPING/ FS6, RPA4/ $58.56 [1982]/ $91.80/ Hedonic TC

• WILDERNESS/ FS6, RPA4/ $58.56 [1982]/ $91.80/ Hedonic TC

53. Fadali, E., and W.D. Shaw. 1998. Can recreation values for a lake constitute a market for banked agricultural

water? Contemporary Economic Policy 16:433-441.

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• MOTOR BOATING/ FS5, RPA4/ $11.68 [1996]/ $11.68/ RUM/MNL

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54. Farber, S., and A. Rambaldi. 1993. Willingness to pay for air quality: The case of outdoor exercise. Contemporary

Policy Issues 11:19-30.

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55. Feltus, D.G., and E.E. Langenau. 1984. Optimization of firearm deer hunting and timber values in northern

lower Michigan. Wildlife Society Bulletin 12:612.

• BIG GAME HUNTING/ FS9, RPA1/ $2.84 [1974]/ $8.12/ Zonal TC

56. *Findeis, J.L., and E.L. Michalson. 1984. The demand for and value of outdoor recreation in the Targhee

National Forest, Idaho. Moscow, ID: University of Idaho, Agricultural Experiment Station, Bulletin No. 627.

• CAMPING/ FS4, RPA3/ $8.60 [1974]/ $28.31/ Individual TC

• CAMPING/ FS4, RPA3/ $17.93 [1974]/ $59.02/ Individual TC

57. Fisher, W. 1982. Travel cost and contingent value estimates explored. Paper presented at the Eastern Economic

Association Meeting.

• BIG GAME HUNTING/ FS9, RPA1/ $80.00 [1975]/ $209.08/ Individual TC

58. Fix, P., and J. Loomis. 1998. Comparing the economic value of mountain biking estimated using revealed and

stated preference. Journal of Environmental Planning and Management 41:227-236.

• BIKING/ FS4, RPA3/ $54.90 [1996]/ $54.90/ Individual TC

• BIKING/ FS4, RPA3/ $62.88 [1996]/ $62.88/ CV

59. Garrett, J., G. Pon, and D. Arosteguy. 1970. Economics of big game resource use in Nevada. Reno, NV:

University of Nevada, Reno, Agricultural Experiment Station.

• BIG GAME HUNTING/ FS4, RPA3/ $5.76 [1967]/ $23.92/ Zonal TC

60. Gericke, K.L. 1993. Multiple destination trips and the economic valuation of outdoor recreation sites. Ph.D.

Thesis, Virginia Polytechnic Institute and State University.

• SIGHTSEEING/ FS8, RPA2/ $61.00 [1992]/ $67.10/ Zonal TC
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61. Gibbs, K. 1974. Evaluation of outdoor recreational resources: A note. Land Economics 50:309-311.

• CAMPING/ FS8, RPA2/ $15.01 [1970]/ $54.18/ Individual TC

62. Gibbs, K., L. Queirolo, and C. Lomnicki. 1979. The valuation of outdoor recreation in a multiple-use forest.

Corvallis, OR: Forest Research Laboratory, Oregon State University.

• HIKING/ FS6, RPA4/ $12.11 [1977]/ $28.08/ Individual TC

63. *Gilbert, A.H., D.W. McCollum, and G.L. Peterson. 1988. A comparison of valuation models using crosscountry skiing data from Colorado and Vermont. Fort Collins, CO: USDA Forest Service, Rocky Mountain

Forest and Range Experiment Station Draft Paper.

• CROSS COUNTRY SKIING/ FS9, RPA1/ $21.12 [1986]/ $28.83/ CV

• CROSS COUNTRY SKIING/ FS2, RPA3/ $21.03 [1986]/ $28.71/ CV

• CROSS COUNTRY SKIING/ FS2, RPA3/ $10.94 [1986]/ $14.93/ CV

• CROSS COUNTRY SKIING/ FS2, RPA3/ $28.38 [1986]/ $38.74/ CV

• CROSS COUNTRY SKIING/ FS2, RPA3/ $26.23 [1986]/ $35.81/ CV

64. Glass, R., and T. More. 1992. Equity preferences in the allocation of goose hunting opportunities. Journal of

Environmental Management 35:271-279.

• WATERFOWL HUNTING/ FS9, RPA1/ $33.34 [1987]/ $44.15/ CV

65. Goodwin, B.K., L.A. Offenbach, T.T. Cable, and P.S. Cook. 1993. Discrete/continuous contingent valuation of

private hunting access in Kansas. Journal of Environmental Management 39:1-12.

• SMALL GAME HUNTING/ FS2, RPA3/ $4.80 [1986]/ $6.55/ CV

66. *Grubb, H., and J. Goodwin. 1968. Economic evaluation of water oriented recreation in the preliminary Texas

water plan. Dallas, TX: Texas Water Development Board.

• SWIMMING/ FS8, RPA2/ $3.80 [1965]/ $16.75/ Zonal TC

67. Halstead, J., B.E. Lindsay, and C.M. Brown. 1991. Use of tobit model in contingent valuation: Experimental

evidence from Pemigewasset wilderness area. Journal of Environmental Management 33:79-89.

• WILDLIFE VIEWING/ FS9, RPA1/ $1.92 [1989]/ $2.36/ CV

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68. Hansen, C. 1977. A report on the value of wildlife. Ogden, UT: USDA Forest Service, Intermountain Region,

Miscellaneous Publication 1365.

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ESTIMATES.

69. Hansen, W., A. Mills, J. Stoll, R. Freeman, and C. Hankamer. 1990. A case study application of the

contingent valuation method for estimating urban recreation and benefits. U.S. Army Corp of Engineers,

IWR Report 90-R-11.

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70. Harpman, D., E. Sparling, and T. Waddle. 1993. A methodology for quantifying and valuing the impacts of

flow changes on a fishery. Water Resources Research 29:575-582.

• FISHING/ FS2, RPA3/ $8.94 [1989]/ $10.96/ CV

71. Haspel, A., F.R. Johnson. 1982. Multiple destination trip bias in recreation benefit estimation. Land Economics

58:364-372.

• SIGHTSEEING/ FS3, RPA3/ $6.17 [1980]/ $11.25/ Zonal TC

• SIGHTSEEING/ FS3, RPA3/ $7.24 [1980]/ $13.21/ CV

• SIGHTSEEING/ FS3, RPA3/ $5.54 [1980]/ $10.11/ Zonal TC
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72. Hausman, J.A., G.K. Leonard, and D. McFadden. 1995. A utility-consistent, combined discrete choice and

count data model assessing recreational use losses due to natural resource damage. Journal of Public Economics

56:1-30.

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• HIKING/ FS10, RPA5/ $10.54 [1989]/ $12.93/ RUM/MNL

• WATERFOWL HUNTING/ FS10, RPA5/ $49.00 [1989]/ $60.08/ RUM/MNL

73. Hay, J.M. 1988. Net economic values of non-consumptive wildlife-related recreation. Washington, DC: USDI,

Fish and Wildlife Service. Report 85-2.

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74. Hellerstein, D.M. 1991. Using count data models in travel cost analysis with aggregate data. American Journal

of Agricultural Economics 73:861-867.

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• WILDERNESS/ FS9, RPA1/ $15.82 [1980]/ $28.84/ Zonal TC

75. Hushak, L., J. Winslow, and N. Dutta. 1984. Economic value of Lake Erie sport fishing to private-boat

anglers. Ohio State University.

• FISHING/ FS9, RPA1/ $4.16 [1981]/ $6.53/ Individual TC

• FISHING/ FS9, RPA1/ $23.79 [1981]/ $39.64/ Individual TC

76. Hushak, L., J. Winslow, and N. Dutta. 1988. Economic value of Great Lakes sportfishing: The case of privateboat fishing in Ohio’s Lake Erie. Transactions of the American Fisheries Society 117:363-373.

• FISHING/ FS9, RPA1/ $1.61 [1981]/ $2.69/ Individual TC

• FISHING/ FS9, RPA1/ $2.23 [1981]/ $3.72/ Individual TC

77. *Johnson, D.M., and R.G. Walsh. 1987. Economic benefits and costs of the fish stocking program at Blue Mesa

Reservoir, Colorado. Fort Collins, CO: Colorado Water Resources Research Institute, Colorado State University, Technical Report No. 49.

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78. *Kalter, R., and L. Gosse. 1969. Outdoor recreation in New York states: Projections of demand, economic value,

and pricing effects for the period 1970-1985. Ithaca, NY: Cornell University, Special Cornell Series No. 5.

• CAMPING/ FS9, RPA1/ $6.50 [1965]/ $28.66/ Zonal TC

• SWIMMING/ FS9, RPA1/ $9.47 [1965]/ $41.75/ Zonal TC

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• HIKING/ FS9, RPA1/ $16.00 [1965]/ $70.54/ Zonal TC

79. Kealy, M.J., and R. Bishop. 1986. Theoretical and empirical specifications issues in travel cost demand studies.

American Journal of Agricultural Economics 68:660-667.

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80. *Keith, J.E. 1980. Snowmobiling and cross-country skiing conflicts in Utah: Some initial research results.

Proceedings of the North American Symposium on Dispersed Winter Recreation. St. Paul, MN: University of

Minnesota: 57-63.

• CROSS COUNTRY SKIING/ FS4, RPA3/ $10.00 [1978]/ $21.62/ Individual TC

• SNOWMOBILING/ FS4, RPA3/ $42.00 [1976]/ $103.70/ Individual TC

81. *Keith, J., P. Halverson, and L. Fumworth. 1982. Valuation of a free flowing river: The Salt River, Arizona.

Tucson, AZ: Utah State University of Arizona.

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82. King, D., and J. Hof. 1985. Experimental commodity definition in recreation travel cost models. Forest Science

31:519-529.

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83. *King, D.A., T.C. Brown, T. Daniel, M.T. Richards, and W.P. Stewart. 1988. Personal Communication between

D.A. King and R.G. Walsh. University of Arizona, Tucson.

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84. *Klemperer, D.W., P.S. Verbyla, and L.D. Jouner. 1984. Valuing white-water river recreation by the travel cost

method. National River Recreation Symposium, Baton Rouge, LA: 709-719.

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85. *Knetsch, J., R. Brown, and W. Hansen. 1976. Estimating expected use and value of recreation sites. In

C. Gearing, W. Swart, and T. Var (eds.), Planning for Tourism Development: Quantitative Approaches.

New York, NY: Proeger.

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86. *Leuschner, W.A., P.S. Cook, J.W. Roggenbuck, and R.G. Oderwald. 1987. A comparative analysis for

wilderness user fee policy. Journal of Leisure Research 19:101-114.

• CAMPING/ FS8, RPA2/ $2.15 [1983]/ $3.23/ Zonal TC

87. Leuschner, W., and R. Young. 1978. Estimating the southern pine beetle’s impact on reservoir campsites. Forest

Science 24:527-537.

• CAMPING/ FS8, RPA2/ $1.41 [1973]/ $4.40/ Zonal TC

• CAMPING/ FS8, RPA2/ $1.89 [1973]/ $5.88/ Zonal TC

• CAMPING/ FS8, RPA2/ $0.88 [1973]/ $2.75/ Zonal TC

• CAMPING/ FS8, RPA2/ $1.15 [1973]/ $3.60/ Zonal TC

88. Loomis, J. 1979. Estimation of recreational benefits from Grand Gulch primitive area. Moad, UT: USDI Bureau

of Land Management.

• GENERAL RECREATION/ FS4, RPA3/ $92.53 [1977]/ $214.59/ Zonal TC

• WILDERNESS/ FS4, RPA3/ $92.53 [1977]/ $214.59/ Zonal TC

89. Loomis, J. 1982. Use of travel cost models for evaluation lottery rationed recreation: Application to big game

hunting. Journal of Leisure Research 14:117-124.

• BIG GAME HUNTING/ FS4, RPA3/ $20.77 [1979]/ $41.37/ Zonal TC

90. Loomis, J., D. Donnelly, C. Sorg, and L. Oldenburg. 1985. Net economic value of hunting unique species in

Idaho: Bighorn sheep, mountain goat, moose, and antelope. Fort Collins, CO: USDA Forest Service, Rocky

Mountain Forest and Range Experiment Station, General Technical Report RM-10.

• BIG GAME HUNTING/ FS4, RPA3/ $90.00 [1982]/ $141.09/ Zonal TC

• BIG GAME HUNTING/ FS4, RPA3/ $19.12 [1982]/ $29.97/ Zonal TC

• BIG GAME HUNTING/ FS4, RPA3/ $27.80 [1982]/ $43.58/ Zonal TC

• BIG GAME HUNTING/ FS4, RPA3/ $38.58 [1982]/ $60.48/ Zonal TC

• BIG GAME HUNTING/ FS4, RPA3/ $48.00 [1982]/ $75.25/ Zonal TC

• BIG GAME HUNTING/ FS4, RPA3/ $14.83 [1982]/ $23.25/ Zonal TC

• BIG GAME HUNTING/ FS4, RPA3/ $31.16 [1982]/ $48.85/ Zonal TC

• BIG GAME HUNTING/ FS4, RPA3/ $10.24 [1982]/ $16.05/ Zonal TC

91. Loomis, J., D. Updike, and W. Unkel. 1989. Consumption and nonconsumption values of a game animal: The

case of California deer. Transactions of the North American Wildlife and Natural Resource Conference 54:640-650.

• BIG GAME HUNTING/ FS5, RPA4/ $69.00 [1987]/ $91.38/ CV

• WILDLIFE VIEWING/ FS5, RPA4/ $15.00 [1987]/ $19.87/ CV

92. Loomis, J., and J. Cooper. 1988. The economic value of antelope hunting in Montana. Montana Department

of Fish, Wildlife, and Parks.

• BIG GAME HUNTING/ FS1, RPA3/ $62.00 [1985]/ $86.85/ Zonal TC
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 37

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

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

assessment of economic benefits to hunter. Helena, MT: Montana Department of Fish, Wildlife, and Parks.

• BIG GAME HUNTING/ FS1, RPA3/ $39.90 [1986]/ $54.47/ CV

• BIG GAME HUNTING/ FS1, RPA3/ $14.24 [1986]/ $19.44/ CV

94. Loomis, J., M. Creel, and J. Cooper. 1989. Economic benefits of deer in California: Hunting and viewing values.

Davis, CA: College of Agricultural and Environmental Sciences, University of California.

• BIG GAME HUNTING/ FS5, RPA4/ $36.96 [1987]/ $48.95/ CV

• BIG GAME HUNTING/ FS5, RPA4/ $13.18 [1987]/ $17.45/ Individual TC

• WILDLIFE VIEWING/ FS5, RPA4/ $5.10 [1987]/ $6.76/ CV

95. Loomis, J., and M. Feldman. 1995. An economic approach to giving “equal consideration” to environmental

values in FERC hydropower reliscensing. Rivers 5:96-108.

• SIGHTSEEING/ FS4, RPA3/ $0.95 [1994]/ $0.99/ CV

• SIGHTSEEING/ FS4, RPA3/ $0.52 [1994]/ $0.54/ CV

• SIGHTSEEING/ FS4, RPA3/ $1.54 [1994]/ $1.61/ CV

96. Markstrom, D., and D. Rosenthal. 1987. Demand and value of firewood permits as determined by the travel

cost method. Western Journal of Applied Forestry 2:48-50.

• GENERAL RECREATION/ FS2, RPA3/ $16.07 [1982]/ $25.18/ Zonal TC

97. Martin, W., F. Bollman, and R. Gum. 1982. Economic value of Lake Mead fishing. Fisheries 7:20-24.

• FISHING/ FS3, RPA3/ $11.74 [1978]/ $25.39/ Individual TC

98. Martin, W., R. Gum, and A. Smith. 1974. The demand for and value of hunting, fishing, and general rural

outdoor recreation in Arizona. Tucson, AZ: Agricultural Experiment Station, University of Arizona.

• CAMPING/ FS3, RPA3/ $7.09 [1970]/ $25.59/ Individual TC

• BIG GAME HUNTING/ FS3, RPA3/ $3.21 [1970]/ $11.60/ Individual TC

• BIG GAME HUNTING/ FS3, RPA3/ $3.49 [1970]/ $12.60/ Individual TC

• SMALL GAME HUNTING/ FS3, RPA3/ $1.45 [1970]/ $5.24/ Individual TC

• WATERFOWL HUNTING/ FS3, RPA3/ $0.60 [1970]/ $2.17/ Individual TC

• FISHING/ FS3, RPA3/ $12.22 [1970]/ $44.09/ Individual TC

• FISHING/ FS3, RPA3/ $5.56 [1970]/ $20.08/ Individual TC

99. May, J.A. 1997. Measuring consumer surplus of Wyoming snowmobilers using the travel cost method.

Master’s Thesis. University of Wyoming.

• SNOWMOBILING/ FS2, RPA3/ $36.30 [1996]/ $36.23/ Individual TC

100. McCollum, D., A. Gilbert, and G. Peterson. 1990. The net econmic value of day use cross country skiing in

Vermont: A dichotomous choice contingent valuation approach. Journal of Leisure Research 22:341-352.

• CROSS COUNTRY SKIING/ FS9, RPA1/ $18.69 [1987]/ $24.75/ CV

• CROSS COUNTRY SKIING/ FS9, RPA1/ $24.85 [1987]/ $32.91/ CV

101. McCollum, D.W., G.L. Peterson, J.R. Arnold, D.C. Markstrom, and D.M. Hellerstein. 1990. The net economic

value of recreation on the national forests: Twelve types of primary activity trips across nine Forest Service

regions. Fort Collins, CO: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station,

Research Paper RM-89.

• See appendix B table B7. VARIOUS ACTIVITIES/ 33 ESTIMATES.

102. McCollum, D.W., R.C. Bishop, and M.P. Welsh. 1988. A probabilistic travel cost model. Madison, WI:

Department of Agricultural Economics, University of Wisconsin.

• BIG GAME HUNTING/ FS9, RPA1/ $42.80 [1984]/ $62.01/ Zonal TC

• BIG GAME HUNTING/ FS9, RPA1/ $40.04 [1984]/ $58.01/ Zonal TC

• BIG GAME HUNTING/ FS9, RPA1/ $42.80 [1984]/ $62.01/ Zonal TC

• BIG GAME HUNTING/ FS9, RPA1/ $69.70 [1984]/ $100.99/ Zonal TC

• BIG GAME HUNTING/ FS9, RPA1/ $35.11 [1984]/ $50.87/ Zonal TC
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

103. McCollum, D.W., and S.M. Miller. 1994. Alaska voter, Alaska hunters and Alaska non-resident hunters: Their

wildlife related trip characteristics and economics. Anchorage, AK: Alaska Department of Fish and Game.

• BIG GAME HUNTING/ FS10, RPA5/ $42.88 [1991]/ $48.47/ CV

• BIG GAME HUNTING/ FS10, RPA5/ $49.35 [1991]/ $55.78/ CV

• BIG GAME HUNTING/ FS10, RPA5/ $41.88 [1991]/ $47.34/ CV

• WILDLIFE VIEWING/ FS10, RPA5/ $46.82 [1991]/ $52.92/ CV

• WILDLIFE VIEWING / FS10, RPA5/ $53.63 [1991]/ $60.62/ CV

• WILDLIFE VIEWING / FS10, RPA5/ $62.22 [1991]/ $70.33/ CV

104. McConnell, K. 1979. Values of marine recreational fishing: Measurement and impact of management.

American Journal of Agricultural Economics 61:921-925.

• FISHING/ FS9, RPA1/ $85.35 [1978]/ $184.48/ Individual TC

105. Mendelsohn, R. 1987. Measuring the value of recreation in the White Mountains. Appalachia 46:73-84.

• CAMPING/ FS9, RPA1/ $4.54 [1985]/ $6.36/ Zonal TC

106. Menz, F., and D. Wilton. 1983a. Alternative ways to measure recreation values by the travel cost method.

American Journal of Agricultural Economics 65:332-336.

• FISHING/ FS9, RPA1/ $25.68 [1976]/ $63.41/ Individual TC

107. Menz, F., and D. Wilton. 1983b. An economic study of the muskellunge fishery in New York. New York Fish

and Game Journal 30:??.

• FISHING/ FS9, RPA1/ $14.89 [1976]/ $36.77/ Zonal TC

• FISHING/ FS9, RPA1/ $19.03 [1976]/ $46.99/ Zonal TC

• FISHING/ FS9, RPA1/ $17.52 [1976]/ $43.26/ Zonal TC

108. Michaelson, E. 1977. An attempt to quantify the esthetics of wild and scenic rivers in Idaho. St. Paul, MN:

USDA Forest Service, North Central Forest Experiment Station, General Technical Report NC-28: 320-328.

• CAMPING/ FS4, RPA3/ $31.20 [1971]/ $97.22/ Individual TC

• CAMPING/ FS4, RPA3/ $9.00 [1971]/ $30.88/ Individual TC

• FLOAT BOATING/ FS4, RPA3/ $10.36 [1971]/ $35.55/ Individual TC

• FLOAT BOATING/ FS4, RPA3/ $76.85 [1971]/ $263.68/ Zonal TC

109. *Michaelson, E., and C. Gilmour. 1978. Estimating the demand for outdoor recreation in the Sawtooth Valley,

Idaho. Moscow, ID: Agricultural Experiment Station, University of Idaho, Research Bulletin No. 107.

• CAMPING/ FS4, RPA3/ $3.73 [1971]/ $12.80/ Individual TC

110. Miller, J., and M. Hay. 1984. Estimating sub-state values for fishing and hunting. Transactions of the North

American Wildlife and Natural Resources Conference 49:345-355.

• BIG GAME HUNTING/ FS9, RPA1/ $35.00 [1980]/ $63.82/ Individual TC

• SMALL GAME HUNTING/ FS2, RPA3/ $30.00 [1980]/ $54.70/ Individual TC

• FISHING/ FS9, RPA1/ $23.00 [1980]/ $41.94/ Individual TC

• FISHING/ FS3, RPA3/ $35.00 [1980]/ $63.82/ Individual TC

• FISHING/ FS9, RPA1/ $29.00 [1980]/ $52.88/ Individual TC

• FISHING/ FS4, RPA3/ $27.00 [1980]/ $49.23/ Individual TC

• FISHING/ FS8, RPA2/ $30.00 [1980]/ $54.70/ Individual TC

111. Moncur, J.E. 1975. Estimating the value of alternative outdoor recreation facilities within a small area. Journal

of Leisure Research 7:301-311.

• GENERAL RECREATION/ FS5, RPA4/ $0.36 [1972]/ $1.18/ Zonal TC

112. Morey, E. 1985. Characteristics, consumer surplus, and new activities. Journal of Public Economics 26:221-236.

• DOWNHILL SKIING/ FS2, RPA3/ $3.15 [1968]/ $12.54/ RUM/MNL
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 39

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

113. Morey, E., R. Rowe, and M. Watson. 1991. An extended discrete-choice model of Atlantic salmon fishing: With

theoretical and empirical comparisons to standard travel-cost models. Boulder, CO: Department of Economics, University of Colorado.

• FISHING/ FS9, RPA1/ $78.36 [1988]/ $100.12/ RUM/MNL

114. Mullen, J., and F. Menz. 1985. The effect of acidification damages on the economic value of the Adirondack

fishing to New York anglers. American Journal of Agricultural Economics 67:112-119.

• FISHING/ FS9, RPA1/ $12.67 [1976]/ $31.28/ Zonal TC

• FISHING/ FS9, RPA1/ $20.98 [1976]/ $51.80/ Zonal TC

115. Palm, R., and S. Malvestuto. 1983. Relationships between economic benefit and sport-fishing effort on West

Point reservoir, Alabama-Georgia. Transactions of the American Fisheries Society 112:71-78.

• FISHING/ FS8, RPA2/ $8.90 [1980]/ $16.23/ Individual TC

116. Park, T., J. Loomis, and M. Creel. 1991. Confidence intervals for evaluating benefits estimates from dichotomous choice contingent valuation studies. Land Economics 67:64-73.

• BIG GAME HUNTING/ FS1, RPA3/ $29.73 [1986]/ $40.61/ CV

117. Peterson, G.L., and J.R. Arnold. 1987. The economic benefits of mountain running the Pike’s Peak marathon.

Journal of Leisure Research 19:84-100.

• OTHER RECREATION/ FS2, RPA3/ $34.25 [1984]/ $49.62/ Zonal TC

• OTHER RECREATION/ FS2, RPA3/ $36.51 [1984]/ $52.90/ Zonal TC

• OTHER RECREATION/ FS2, RPA3/ $18.48 [1984]/ $26.78/ Zonal TC

118. *Peterson, G.L., R.G. Walsh, and J.R. McKean. 1988. The discriminatory impact of recreation price. Fort Collins,

CO: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Unpublished paper.

• CAMPING/ FS9, RPA1/ $6.34 [1980]/ $11.56/ Zonal TC

• CAMPING/ FS9, RPA1/ $19.64 [1980]/ $35.81/ Zonal TC

• WILDERNESS/ FS9, RPA1/ $6.34 [1980]/ $11.56/ Zonal TC

• WILDERNESS/ FS9, RPA1/ $19.64 [1980]/ $35.81/ Zonal TC

119. *Prince, R. 1988. Estimating recreation benefits under congestion, uncertainty, and disequilibrium.

Harrisonburg, VA: Department of Economics, James Madison University.

• HIKING/ FS8, RPA2/ $12.00 [1984]/ $17.39/ CV

• WILDERNESS/ FS8, RPA2/ $12.00 [1984]/ $17.39/ CV

120. Ribaudo, M., and D. Epp. 1984. The importance of sample discrimination in using the travel cost method to

estimate the benefits of improved water quality. Land Economics 60:397-403.

• SWIMMING/ FS9, RPA1/ $3.52 [1982]/ $5.52/ Individual TC

121. Richards, M., D.B. Wood, and D. Coyler. 1985. Sport fishing at Lees Ferry, Arizona: User differences and

economic values. Flagstaff, AZ: Northern Arizona University.

• FISHING/ FS3, RPA3/ $120.82 [1982]/ $189.40/ Individual TC

122. Richards, M., and T. Brown. 1992. Economic value of campground visits in Arizona. Fort Collins, CO: USDA

Forest Service, Rocky Mountain Forest and Range Experiment Station, Research Paper RM-305.

• CAMPING/ FS3, RPA3/ $8.16 [1985]/ $11.43/ Zonal TC

123. Roberts, K., M. Thompson, and P. Pawlyk. 1985. Contingent valuation of recreational diving at petroleum rigs,

Gulf of Mexico. Transactions of the American Fisheries Society 114:214-219.

• OTHER RECREATION/ FS8, RPA2/ $28.60 [1981]/ $47.66/ CV

124. Rosenthal, D. 1987. The necessity for substitute prices in recreation demand analysis. American Journal of

Agricultural Economics 69:828-837.

• MOTOR BOATING/ FS2, RPA3/ $4.04 [1982]/ $6.33/ Zonal TC

• MOTOR BOATING/ FS2, RPA3/ $2.81 [1982]/ $4.41/ Zonal TC
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

125. *Rosenthal, D.H., and H.K. Cordell. 1984. Pricing river recreation: Some issues and concerns. National River

and Recreation Symposium, Baton Rouge, LA: School of Landscape Architecture, Louisiana State University:

272-284.

• FLOAT BOATING/ FS9, RPA1/ $8.40 [1979]/ $16.73/ Zonal TC

• FLOAT BOATING/ FS4, RPA3/ $49.75 [1979]/ $99.09/ Zonal TC

126. Rosenthal, D., and R. Walsh. 1986. Hiking values and the recreation opportunity spectrum. Forest Science

32:405-415.

• HIKING/ FS2, RPA3/ $23.05 [1981]/ $38.41/ CV

127. Rowe, R., E. Morey, A. Ross, and W.D. Shaw. 1985. Valuing marine recreational fishing on the Pacific coast.

Washington, DC: USDC National Marine Fisheries Service, Report LJ-85-18C.

• FISHING/ FS6, RPA4/ $6.67 [1981]/ $11.11/ RUM/MNL

• FISHING/ FS5, RPA4/ $2.21 [1981]/ $3.69/ RUM/MNL

• FISHING/ FS5, RPA4/ $51.76 [1981]/ $86.25/ RUM/MNL

• FISHING/ FS6, RPA4/ $42.11 [1981]/ $70.17/ RUM/MNL

• FISHING/ FS6, RPA4/ $48.62 [1981]/ $81.03/ RUM/MNL

• FISHING/ FS6, RPA4/ $5.41 [1981]/ $9.02/ RUM/MNL

128. Samples, K., and R. Bishop. 1985. Estimating the value of variations in anglers’ success rates: An application

of the multiple-site travel cost method. Marine Resource Economics 21:55-74.

• FISHING/ FS9, RPA1/ $0.80 [1978]/ $1.73/ Zonal TC

129. Sanders, L., R. Walsh, and J. McKean. 1991. Comparable estimates of the recreational value of rivers. Water

Resources Research 27:1387-1394.

• OTHER RECREATION/ FS2, RPA3/ $24.38 [1983]/ $36.66/ CV

• OTHER RECREATION/ FS2, RPA3/ $23.41 [1983]/ $35.21/ CV

• OTHER RECREATION/ FS2, RPA3/ $23.44 [1983]/ $35.25/ CV

130. Shafer, E., and M. Wang. 1989. Economic amenity values of fish and wildlife resources. State College, PA: Penn

State University.

• FISHING/ FS9, RPA1/ $9.96 [1988]/ $12.73/ Individual TC

• WILDLIFE VIEWING/ FS9, RPA1/ $7.74 [1988]/ $9.89/ CV

• WILDLIFE VIEWING/ FS9, RPA1/ $2.64 [1988]/ $3.37/ CV

• GENERAL RECREATION/ FS9, RPA1/ $1.28 [1988]/ $1.64/ CV

131. Shaw, W. D., and P. Jakus. 1996. Travel cost models of the demand for rock climbing. Paper presented at the

Western Regional Research Publication W-133, Benefits and Costs in Natural Resources Planning.

• ROCK CLIMBING/ FS9, RPA1/ $80.00 [1993]/ $85.74/ RUM/MNL

132. Siderelis, C., G. Brothers, and P. Rea. 1995. A boating choice model for the valuation of lake access. Journal of

Leisure Research 27:264-282.

• MOTOR BOATING/ FS8, RPA2/ $5.24 [1992]/ $5.76/ RUM/MNL

• MOTOR BOATING/ FS8, RPA2/ $10.03 [1992]/ $11.03/ RUM/MNL

133. Siderelis, C., and R. Moore. 1995. Outdoor recreation net benefits of rail-trails. Journal of Leisure Research

27:344-359.

• BIKING/ FS9, RPA1/ $30.18 [1991]/ $34.11/ Individual TC

• BIKING/ FS8, RPA2/ $49.78 [1991]/ $56.27/ Individual TC

• HIKING/ FS5, RPA4/ $4.81 [1991]/ $5.44/ Individual TC

134. Silberman, J., and M. Klock. 1989. The behavior of respondents in contingent valuation: Evidence on starting

bids. Journal of Behavioral Economics 18:51-60.

• SWIMMING/ FS9, RPA1/ $1.00 [1980]/ $1.83/ CV
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Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

135. Smith, V.K., and R. Kopp. 1980. A regional recreation demand and benefits model. Land Economics 56:64-72.

• WILDLIFE VIEWING/ FS5, RPA4/ $1.80 [1972]/ $5.91/ Zonal TC

• WILDERNESS/ FS5, RPA4/ $1.80 [1972]/ $5.91/ Zonal TC

136. SMS Research. 1983. Experimental valuation of recreational fishing in Hawaii. Washington, DC: USDC

National Marine Fisheries Service, Report H-83-11C.

• FISHING/ FS5, RPA4/ $41.00 [1985]/ $57.43/ CV

137. Sorg, C., J. Loomis, D. Donnelly, G. Peterson, and L. Nelson. 1985. Net economic value of cold and warm water

fishing in Idaho. Fort Collins, CO: USDA Forest Service, Rocky Mountain Forest and Range Experiment

Station, Resource Bulletin RM-11.

• FISHING/ FS4, RPA3/ $26.20 [1982]/ $41.07/ Zonal TC

• FISHING/ FS4, RPA3/ $14.25 [1982]/ $22.34/ CV

• FISHING/ FS4, RPA3/ $12.95 [1982]/ $20.30/ CV

• FISHING/ FS4, RPA3/ $25.55 [1982]/ $40.06/ Zonal TC

138. Sorg, C., and L. Nelson. 1986. Net economic value of elk hunting in Idaho. Fort Collins, CO: USDA Forest

Service, Rocky Mountain Forest and Range Experiment Station, Resource Bulletin RM-12.

• BIG GAME HUNTING/ FS4, RPA3/ $35.15 [1982]/ $55.10/ Zonal TC

• BIG GAME HUNTING/ FS4, RPA3/ $22.57 [1983]/ $33.94/ CV

• BIG GAME HUNTING/ FS4, RPA3/ $22.24 [1982]/ $34.87/ Zonal TC

139. Stoll, J., and L.A. Johnson. 1984. Concepts of value, nonmarket valuation and the case of the whooping crane.

Transactions of the North American Wildlife and Natural Resources Conference 49:382-393.

• WILDLIFE VIEWING/ FS8, RPA2/ $1.52 [1982]/ $2.38/ CV

140. Strong, E. 1983. A note on the functional form of travel cost models with zones of unequal populations. Land

Economics 59:342-349.

• FISHING/ FS6, RPA4/ $21.06 [1977]/ $48.83/ Zonal TC

141. Sublette, W., and W. Martin. 1975. Outdoor recreation in the Salt-Verde Basin of central Arizona: Demand and

value. Tucson, AZ: Agricultural Experiment Station, University of Arizona.

• CAMPING/ FS3, RPA3/ $2.75 [1972]/ $9.06/ Individual TC

• CAMPING/ FS3, RPA3/ $0.51 [1972]/ $1.69/ CV

• FISHING/ FS3, RPA3/ $15.85 [1972]/ $52.18/ Individual TC

• FISHING/ FS3, RPA3/ $8.43 [1972]/ $27.75/ Individual TC

• FISHING/ FS3, RPA3/ $16.15 [1972]/ $53.14/ Individual TC

142. Sutherland, R. 1982. The sensitivity of travel cost estimates of recreation demand to the functional form and

definition of origin zones. Western Journal of Agricultural Economics 7:87-98.

• MOTOR BOATING/ FS6, RPA4/ $5.22 [1979]/ $10.40/ Zonal TC

143. Teasley, R.J., and J.C. Bergstrom. 1992. Estimating revenue-capture potential with public area recreation.

Athens, GA: University of Georgia.

• GENERAL RECREATION/ FS8, RPA2/ $3.80 [1992]/ $4.18/ CV

• GENERAL RECREATION/ FS8, RPA2, $4.52 [1992]/ $4.97/ Zonal TC

144. Vaughan, W., and C. Russell. 1982. Valuing a fishing day: An application of a systematic varying parameter

model. Land Economics 58:450-463.

• FISHING/ National/ $24.09 [1979]/ $47.98/ Individual TC

• FISHING/ National/ $16.03 [1979]/ $31.93/ Individual TC

• FISHING/ National/ $24.09 [1979]/ $47.98/ Individual TC

• FISHING/ National/ $10.62 [1979]/ $21.15/ Individual TC
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

145. Waddington, D.G., K.J. Boyle, and J. Cooper. 1991. 1991 Net economic values for bass and trout fishing, deer

hunting, and wildlife watching. Washington, DC: USFWS Division of Federal Aid.

• See appendix B table B8. BIG GAME HUNTING AND WILDLIFE VIEWING/ 96 ESTIMATES.

146. *Wade, W., G.M. McCollister, R.J. McCann, and G.M. Jones. 1988. Estimating recreation benefits for

instream and diverted users of waterfowls of the Sacramento-San Joaquin rivers watershed. Paper

presented at the Western Regional Research Publication W-133, Benefits and Costs in Natural Resources

Planning, Monterey, CA.

• SWIMMING/ FS5, RPA4/ $15.84 [1985]/ $22.19/ Zonal TC

• SWIMMING/ FS5, RPA4/ $35.04 [1985]/ $49.08/ Zonal TC

• MOTOR BOATING/ FS3, RPA3/ $34.64 [1985]/ $48.52/ Zonal TC

• MOTOR BOATING/ FS3, RPA3/ $24.28 [1985]/ $34.01/ Zonal TC

147. Walsh, R.G., F.A. Ward, and J.P. Olienyk. 1989. Recreation demand for trees in National Forests. Journal of

Environmental Management 28:255-268.

• GENERAL RECREATION/ FS2, RPA3/ $7.70 [1980]/ $14.04/ CV

• GENERAL RECREATION/ FS2, RPA3/ $8.37 [1980]/ $15.26/ Zonal TC

• GENERAL RECREATION/ FS2, RPA3/ $6.56 [1980]/ $11.96/ Zonal TC

148. *Walsh, R.G., and G.J. Davitt. 1983. A demand function for length of stay on ski trips to Aspen. Journal of Travel

Research 22:23-29.

• DOWNHILL SKIING/ FS2, RPA3/ $24.33 [1978]/ $52.59/ CV

149. *Walsh, R.G., J.B. Loomis, and R.S. Gillman. 1984. Valuing option, existence, and bequest demand for

wilderness. Land Economics 60:14-29.

• CROSS COUNTRY SKIING/ FS2, RPA3/ $12.50 [1980]/ $22.79/ CV

150. *Walsh, R.G., and J.P. Olienyk. 1981. Recreation demand effects of mountain pine beetle damage to the quality

of forest recreation resources in the Colorado Front Range. Fort Collins, CO: Department of Economics,

Colorado State University.

• CAMPING/ FS2, RPA3/ $5.59 [1980]/ $10.19/ CV

• CAMPING/ FS2, RPA3/ $7.99 [1980]/ $14.57/ CV

• PICNICKING/ FS2, RPA3/ $6.22 [1980]/ $11.34/ CV

• OFF ROAD DRIVING/ FS2, RPA3/ $6.45 [1980]/ $11.76/ CV

• HIKING/ FS2 RPA3/ $9.51 [1980]/ $17.34/ CV

151. *Walsh, R.G., L.D. Sanders, and J.B. Loomis. 1985. Wild and scenic river economics: Recreation use and

preservation values. Fort Collins, CO: Department of Agricultural and Resource Economics, Colorado State

University.

• GENERAL RECREATION/ FS2, RPA3/ $22.00 [1983]/ $33.08/ Individual TC

• GENERAL RECREATION/ FS2, RPA3/ $24.00 [1983]/ $36.09/ CV

• WILDERNESS/ FS2, RPA3/ $22.00 [1983]/ $33.08/ Individual TC

• WILDERNESS/ FS2, RPA3/ $24.00 [1983]/ $36.09/ CV

152. *Walsh, R.G., L.D. Sanders, and J.R. McKean. 1987. The value of travel time as a negative function of distance.

Fort Collins, CO: Department of Agricultural and Resource Economics, Colorado State University.

• SIGHTSEEING/ FS2, RPA3/ $9.10 [1983]/ $13.68/ CV

153. Walsh, R.G., and L. Gilliam. 1982. Benefits of wilderness expansion with excess demand for Indian Peaks.

Western Journal of Agricultural Economics 7:1-12.

• SIGHTSEEING/ FS2, RPA3/ $18.29 [1979]/ $36.42/ CV

• SIGHTSEEING/ FS2, RPA3/ $10.31 [1979]/ $20.53/ CV

• WILDERNESS/ FS2, RPA3/ $18.29 [1979]/ $36.42/ CV

• WILDERNESS/ FS2, RPA3/ $10.31 [1979]/ $20.53/ CV
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 43

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154. Walsh, R.G., N. Miller, and L. Gilliam. 1983. Congestion and willingness to pay for expansion of skiing

capacity. Land Economics 59:195-210.

• DOWNHILL SKIING/ FS2, RPA3/ $18.61 [1980]/ $33.93/ CV

155. *Walsh, R.G., O. Radulaski, and L. Lee. 1984. Value of hiking and cross-country skiing in roaded and

nonroaded areas of a national forest. In F. Kaiser, D. Schweitzer, and P. Brown (eds.), Economic Value Analysis

of Multiple-Use Forestry: 176-187.

• HIKING/ FS2, RPA3/ $5.88 [1980]/ $10.71/ CV

• CROSS COUNTRY SKIING/ FS2, RPA3/ $6.42 [1980]/ $11.71/ CV

156. *Walsh, R.G., R. Aukeman, and R. Milton. 1980. Measuring benefits and the economic value of water in

recreation on high country reservoirs. Fort Collins, CO: Colorado Water Resources Research Institute,

Colorado State University.

• CAMPING/ FS2, RPA3/ $10.90 [1978]/ $23.56/ CV

• CAMPING/ FS2, RPA3/ $22.00 [1978]/ $47.55/ CV

• PICNICKING/ FS2, RPA3/ $10.90 [1978]/ $23.56/ CV

• HIKING/ FS2, RPA3/ $13.72 [1978]/ $29.66/ CV

157. *Walsh, R.G., R. Ericson, D. Arosteguy, and M. Hansen. 1980. An empirical application of a model for

estimating the recreation value of instream flow. Fort Collins, CO: Colorado Water Resources Research

Institute, Colorado State University.

• FLOAT BOATING/ FS2, RPA3/ $12.65 [1978]/ $27.34/ CV

• FLOAT BOATING/ FS2, RPA3/ $10.94 [1978]/ $23.65/ CV

158. *Walsh, R.G., R. Gillman, and J. Loomis. 1981. Wilderness resource economic: Recreation use and preservation

values. Fort Collins, CO: Department of Economics, Colorado State University.

• OTHER RECREATION/ FS2, RPA3/ $14.00 [1980]/ $25.53/ Individual TC

• WILDERNESS/ FS2, RPA3/ $14.00 [1980]/ $25.53/ Individual TC

159. *Ward, F. 1982. The demand for and value of recreational use of water in southeastern New Mexico, 1978-79.

Los Cruces, NM: Agricultural Experiment Station, New Mexico State University, Research Report No. 465.

• CAMPING/ FS3, RPA3/ $11.39 [1978]/ $24.62/ Individual TC

• PICNICKING/ FS3, RPA3/ $11.39 [1978]/ $24.62/ Individual TC

• SWIMMING/ FS3, RPA3/ $11.39 [1978]/ $24.62/ Individual TC

• MOTOR BOATING/ FS3, RPA3/ $11.39 [1978]/ $24.62/ Individual TC

160. Weithman, S., and M. Haas. 1982. Socioeconomic value of the trout fishery in Lake Tanneycomo, Missouri.

Transactions of the American Fisheries Society 111:223-230.

• FISHING/ FS9, RPA1/ $8.81 [1979]/ $17.55/ Zonal TC

161. Wilman, E. 1984. Benefits to deer hunters from forest management practices which provide deer habitat.

Transactions of the North American Wildlife and Natural Resources Conference 49:334-344.

• BIG GAME HUNTING/ FS2, RPA3/ $33.69 [1980]/ $61.43/ Individual TC

162. Young, J., D. Donnelly, C. Sorg, J. Loomis, and L. Nelson. 1987. Net economic value of upland game hunting

in Idaho. Fort Collins, CO: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station,

Resource Bulletin RM-15.

• SMALL GAME HUNTING/ FS4, RPA3/ $28.50 [1982]/ $44.68/ Zonal TC

• SMALL GAME HUNTING/ FS4, RPA3/ $22.45 [1982]/ $35.20/ CV

• SMALL GAME HUNTING/ FS4, RPA3/ $19.02 [1982]/ $29.82/ Zonal TC

163. Ziemer, R., W. Musser, and C. Hill. 1980. Recreation demand equations: Functional form and consumer

surplus. American Journal of Agricultural Economics 62:136-141.

• FISHING/ FS8, RPA2/ $26.46 [1971]/ $90.79/ Individual TC
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Appendix B: Summary of Multi-Estimate Studies in Appendix A, Annotated

Bibliography

Appendix B Table B1. Bibliography Entry #8, Bergstrom, J.C., and H.K. Cordell, 1991, An analysis of

the demand for and value of outdoor recreation in the United States.

Activity Regionsa $ Original [1987]b $ Adjusted [1996]b Methodc

Camping National $8.70 $11.52 Zonal TC

Picnicking National 11.85 15.69 Zonal TC

Swimming National 14.82 19.63 Zonal TC

Sightseeing National 11.22 14.86 Zonal TC

Off Road Driving National 15.06 19.94 Zonal TC

Motor Boating National 21.62 28.63 Zonal TC

Float Boating National 21.40 28.34 Zonal TC

Hiking National 15.76 20.87 Zonal TC

Biking National 13.30 17.61 Zonal TC

Downhill Skiing National 14.81 19.61 Zonal TC

Cross Country Skiing National 9.57 12.67 Zonal TC

Big Game Hunting National 12.07 15.98 Zonal TC

Small Game Hunting National 11.98 15.87 Zonal TC

Wildlife Viewing National 12.88 17.06 Zonal TC

Horseback Riding National 11.40 15.10 Zonal TC

Other Recreation National 13.11 17.36 Zonal TC

aFS=USDA Forest Service Region, CR=Census Region.

bValues in per person per activity day.

cTC=travel cost method, CV=contingent valuation method.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 45

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Appendix B Table B2. Bibliography Entry #9, Bergstrom, J.C., et al., 1996, Ecoregional estimates of

the net economic values of outdoor recreational activities in the United States.

Activity Regionsa $ Original [1992]b $ Adjusted [1996]b Methodc

Camping FS9, RPA1 $43.25 $47.58 Individual TC

Camping FS9, RPA1 31.65 34.82 Individual TC

Camping FS8, RPA2 34.29 37.72 Individual TC

Camping FS8, RPA2 47.03 51.73 Individual TC

Camping FS8, RPA2 46.62 51.28 Individual TC

Camping FS6, RPA4 57.03 62.73 Individual TC

Camping FS5, RPA4 170.10 187.11 Individual TC

Picnicking FS9, RPA1 78.75 86.63 Individual TC

Picnicking FS8, RPA2 33.85 37.24 Individual TC

Picnicking FS8, RPA2 21.64 23.80 Individual TC

Picnicking FS2, RPA3 29.36 32.30 Individual TC

Picnicking FS6, RPA4 26.32 28.95 Individual TC

Picnicking FS5, RPA4 108.14 118.95 Individual TC

Swimming FS8, RPA2 36.81 40.49 Individual TC

Swimming FS6, RPA4 4.59 5.05 Individual TC

Sightseeing FS9, RPA1 158.92 174.81 Individual TC

Sightseeing FS8, RPA2 85.38 93.92 Individual TC

Sightseeing FS8, RPA2 75.70 83.27 Individual TC

Sightseeing FS8, RPA2 34.75 38.23 Individual TC

Sightseeing FS8, RPA2 19.75 21.73 Individual TC

Sightseeing FS6, RPA4 46.04 50.64 Individual TC

Off Road Driving FS8, RPA2 3.97 4.37 Individual TC

Off Road Driving FS6, RPA4 30.58 33.64 Individual TC

Motor Boating FS2, RPA3 154.25 169.68 Individual TC

Float Boating FS9, RPA1 46.23 50.85 Individual TC

Float Boating FS9, RPA1 105.04 115.54 Individual TC

Float Boating FS8, RPA2 86.93 95.62 Individual TC

Float Boating FS8, RPA2 24.48 26.93 Individual TC

Float Boating FS2, RPA3 91.91 101.10 Individual TC

Hiking FS9, RPA1 69.02 75.92 Individual TC

Hiking FS8, RPA2 12.82 14.10 Individual TC

Hiking FS8, RPA2 50.85 55.94 Individual TC

Hiking FS2, RPA3 57.39 63.13 Individual TC

Hiking FS6, RPA4 11.22 12.34 Individual TC

Downhill Skiing FS6, RPA4 19.00 20.90 Individual TC

Big Game Hunting FS8, RPA2 29.30 32.23 Individual TC

Big Game Hunting FS8, RPA2 4.31 4.74 Individual TC

Big Game Hunting FS2, RPA3 43.52 47.87 Individual TC

Big Game Hunting FS5, RPA4 18.56 20.42 Individual TC

Big Game Hunting FS6, RPA4 4.74 5.21 Individual TC

Wildlife Viewing FS8, RPA2 13.74 15.11 Individual TC

Wildlife Viewing FS2, RPA3 58.05 63.86 Individual TC

General Recreation FS9, RPA1 35.37 38.91 Individual TC

General Recreation FS8, RPA2 6.59 7.25 Individual TC

General Recreation FS8, RPA2 15.16 16.68 Individual TC

General Recreation FS8, RPA2 70.80 77.88 Individual TC

General Recreation FS2, RPA3 90.00 99.00 Individual TC

General Recreation FS5, RPA4 39.41 43.35 Individual TC

Other Recreation FS8, RPA2 29.42 32.36 Individual TC

Other Recreation FS6, RPA4 56.42 62.06 Individual TC

aFS=USDA Forest Service Region, CR=Census Region.

bValues in per person per activity day.

cTC=travel cost method, CV=contingent valuation method.
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Appendix B Table B3. Bibliography Entry #23, Brown, G., and M. Hay, 1987, Net economic

recreation values for deer and waterfowl hunting and trout fishing.

Activity Regionsa $ Original [1985]b $ Adjusted [1996]b Methodc

Big Game Hunting FS9, RPA1 $22.00 $30.82 CV

Big Game Hunting FS9, RPA1 15.00 21.01 CV

Big Game Hunting FS9, RPA1 14.00 19.61 CV

Big Game Hunting FS9, RPA1 20.00 28.01 CV

Big Game Hunting FS9, RPA1 18.00 25.21 CV

Big Game Hunting FS9, RPA1 13.00 18.21 CV

Big Game Hunting FS9, RPA1 15.00 21.01 CV

Big Game Hunting FS9, RPA1 21.00 29.42 CV

Big Game Hunting FS9, RPA1 14.00 19.61 CV

Big Game Hunting FS9, RPA1 18.00 25.21 CV

Big Game Hunting FS9, RPA1 21.00 29.42 CV

Big Game Hunting FS9, RPA1 22.00 30.82 CV

Big Game Hunting FS9, RPA1 19.00 26.61 CV

Big Game Hunting FS9, RPA1 20.00 28.01 CV

Big Game Hunting FS9, RPA1 15.00 21.01 CV

Big Game Hunting FS9, RPA1 17.00 23.81 CV

Big Game Hunting FS9, RPA1 20.00 28.01 CV

Big Game Hunting FS9, RPA1 18.00 25.21 CV

Big Game Hunting FS9, RPA1 20.00 28.01 CV

Big Game Hunting FS9, RPA1 16.00 22.41 CV

Big Game Hunting FS8, RPA2 17.00 23.81 CV

Big Game Hunting FS8, RPA2 16.00 22.41 CV

Big Game Hunting FS8, RPA2 17.00 23.81 CV

Big Game Hunting FS8, RPA2 21.00 29.42 CV

Big Game Hunting FS8, RPA2 19.00 26.61 CV

Big Game Hunting FS8, RPA2 15.00 21.01 CV

Big Game Hunting FS8, RPA2 18.00 25.21 CV

Big Game Hunting FS8, RPA2 20.00 28.01 CV

Big Game Hunting FS8, RPA2 14.00 19.61 CV

Big Game Hunting FS8, RPA2 26.00 36.42 CV

Big Game Hunting FS8, RPA2 16.00 22.41 CV

Big Game Hunting FS8, RPA2 15.00 21.01 CV

Big Game Hunting FS8, RPA2 15.00 21.01 CV

Big Game Hunting FS4, RPA3 28.00 39.22 CV

Big Game Hunting FS4, RPA3 20.00 28.01 CV

Big Game Hunting FS4, RPA3 20.00 28.01 CV

Big Game Hunting FS3, RPA3 24.00 33.62 CV

Big Game Hunting FS3, RPA3 28.00 39.22 CV

Big Game Hunting FS2, RPA3 18.00 25.21 CV

Big Game Hunting FS2, RPA3 22.00 30.82 CV

Big Game Hunting FS2, RPA3 23.00 32.22 CV

Big Game Hunting FS2, RPA3 16.00 22.41 CV

Big Game Hunting FS2, RPA3 24.00 33.62 CV

Big Game Hunting FS1, RPA3 23.00 32.22 CV

Big Game Hunting FS1, RPA3 25.00 35.02 CV

Big Game Hunting FS6, RPA4 17.00 23.81 CV

Big Game Hunting FS6, RPA4 15.00 21.01 CV

Big Game Hunting FS5, RPA4 25.00 35.02 CV

Big Game Hunting FS10, RPA5 28.00 39.22 CV

Waterfowl Hunting FS9, RPA1 7.00 9.81 CV

Waterfowl Hunting FS9, RPA1 8.00 11.21 CV

Waterfowl Hunting FS9, RPA1 12.00 16.81 CV

Waterfowl Hunting FS9, RPA1 10.00 14.01 CV

(cont’d.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 47

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Appendix B Table B3. (Cont’d.)

Activity Regionsa $ Original [1985]b $ Adjusted [1996]b Methodc

Waterfowl Hunting FS9, RPA1 19.00 26.61 CV

Waterfowl Hunting FS9, RPA1 10.00 14.01 CV

Waterfowl Hunting FS9, RPA1 9.00 12.61 CV

Waterfowl Hunting FS9, RPA1 15.00 21.01 CV

Waterfowl Hunting FS9, RPA1 11.00 15.41 CV

Waterfowl Hunting FS9, RPA1 16.00 22.41 CV

Waterfowl Hunting FS9, RPA1 13.00 18.21 CV

Waterfowl Hunting FS9, RPA1 17.00 23.81 CV

Waterfowl Hunting FS9, RPA1 8.00 11.21 CV

Waterfowl Hunting FS9, RPA1 8.00 11.21 CV

Waterfowl Hunting FS9, RPA1 8.00 11.21 CV

Waterfowl Hunting FS8, RPA2 13.00 18.21 CV

Waterfowl Hunting FS8, RPA2 9.00 12.61 CV

Waterfowl Hunting FS8, RPA2 11.00 15.41 CV

Waterfowl Hunting FS8, RPA2 9.00 12.61 CV

Waterfowl Hunting FS8, RPA2 10.00 14.01 CV

Waterfowl Hunting FS8, RPA2 11.00 15.41 CV

Waterfowl Hunting FS8, RPA2 12.00 16.81 CV

Waterfowl Hunting FS8, RPA2 19.00 26.61 CV

Waterfowl Hunting FS8, RPA2 18.00 25.21 CV

Waterfowl Hunting FS8, RPA2 16.00 22.41 CV

Waterfowl Hunting FS8, RPA2 11.00 15.41 CV

Waterfowl Hunting FS4, RPA3 20.00 28.01 CV

Waterfowl Hunting FS4, RPA3 13.00 18.21 CV

Waterfowl Hunting FS4, RPA3 10.00 14.01 CV

Waterfowl Hunting FS2, RPA3 12.00 16.81 CV

Waterfowl Hunting FS2, RPA3 15.00 21.01 CV

Waterfowl Hunting FS2, RPA3 13.00 18.21 CV

Waterfowl Hunting FS2, RPA3 10.00 14.01 CV

Waterfowl Hunting FS2, RPA3 15.00 21.01 CV

Waterfowl Hunting FS1, RPA3 11.00 15.41 CV

Waterfowl Hunting FS1, RPA3 13.00 18.21 CV

Waterfowl Hunting FS6, RPA4 13.00 18.21 CV

Waterfowl Hunting FS6, RPA4 10.00 14.01 CV

Waterfowl Hunting FS5, RPA4 22.00 30.82 CV

Fishing FS9, RPA1 17.00 23.81 CV

Fishing FS9, RPA1 9.00 12.61 CV

Fishing FS9, RPA1 9.00 12.61 CV

Fishing FS9, RPA1 10.00 14.01 CV

Fishing FS9, RPA1 14.00 19.61 CV

Fishing FS9, RPA1 13.00 18.21 CV

Fishing FS9, RPA1 7.00 9.81 CV

Fishing FS9, RPA1 7.00 9.81 CV

Fishing FS9, RPA1 9.00 12.61 CV

Fishing FS9, RPA1 10.00 14.01 CV

Fishing FS9, RPA1 9.00 12.61 CV

Fishing FS9, RPA1 8.00 11.21 CV

Fishing FS9, RPA1 13.00 18.21 CV

Fishing FS9, RPA1 20.00 28.01 CV

Fishing FS9, RPA1 8.00 11.21 CV

Fishing FS9, RPA1 11.00 15.41 CV

Fishing FS9, RPA1 9.00 12.61 CV

Fishing FS9, RPA1 11.00 15.41 CV

Fishing FS9, RPA1 9.00 12.61 CV

Fishing FS9, RPA1 7.00 9.81 CV

(cont’d.)
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Fishing FS8, RPA2 10.00 14.01 CV

Fishing FS8, RPA2 13.00 18.21 CV

Fishing FS8, RPA2 9.00 12.61 CV

Fishing FS8, RPA2 20.00 28.01 CV

Fishing FS8, RPA2 15.00 21.01 CV

Fishing FS8, RPA2 11.00 15.41 CV

Fishing FS8, RPA2 24.00 33.62 CV

Fishing FS8, RPA2 8.00 11.21 CV

Fishing FS8, RPA2 20.00 28.01 CV

Fishing FS8, RPA2 12.00 16.81 CV

Fishing FS4, RPA3 10.00 14.01 CV

Fishing FS4, RPA3 11.00 15.41 CV

Fishing FS4, RPA3 11.00 15.41 CV

Fishing FS3, RPA3 11.00 15.41 CV

Fishing FS3, RPA3 14.00 19.61 CV

Fishing FS2, RPA3 17.00 23.81 CV

Fishing FS2, RPA3 13.00 18.21 CV

Fishing FS2, RPA3 13.00 18.21 CV

Fishing FS2, RPA3 12.00 16.81 CV

Fishing FS2, RPA3 15.00 21.01 CV

Fishing FS1, RPA3 12.00 16.81 CV

Fishing FS1, RPA3 12.00 16.81 CV

Fishing FS6, RPA4 11.00 15.41 CV

Fishing FS6, RPA4 12.00 16.81 CV

Fishing FS5, RPA4 16.00 22.41 CV

Fishing FS10, RPA5 28.00 39.22 CV

aFS=USDA Forest Service Region, CR=Census Region.

bValues in per person per activity day.

cTC=travel cost method, CV=contingent valuation method.

Appendix B Table B3. (Cont’d.)

Activity Regionsa $ Original [1985]b $ Adjusted [1996]b Methodc
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 49

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Appendix B Table B4. Bibliography Entry #31, Connelly, N., and T. Brown, 1988, Estimates of

nonconsumptive wildlife use on Forest Service and Bureau of Land Management lands.

Activity Regionsa $ Original [1985]b $ Adjusted [1996]b Methodc

Wildlife Viewing FS9, RPA1 $35.30 $49.45 CV

Wildlife Viewing FS9, RPA1 37.18 52.07 CV

Wildlife Viewing FS9, RPA1 16.84 23.59 CV

Wildlife Viewing FS9, RPA1 11.93 16.70 CV

Wildlife Viewing FS9, RPA1 21.73 30.44 CV

Wildlife Viewing FS9, RPA1 24.38 34.15 CV

Wildlife Viewing FS9, RPA1 15.41 21.59 CV

Wildlife Viewing FS9, RPA1 15.13 21.19 CV

Wildlife Viewing FS9, RPA1 11.99 16.80 CV

Wildlife Viewing FS9, RPA1 14.06 19.69 CV

Wildlife Viewing FS9, RPA1 20.07 28.11 CV

Wildlife Viewing FS9, RPA1 14.46 20.26 CV

Wildlife Viewing FS9, RPA1 11.53 16.15 CV

Wildlife Viewing FS8, RPA2 18.20 25.50 CV

Wildlife Viewing FS8, RPA2 15.02 21.04 CV

Wildlife Viewing FS8, RPA2 19.85 27.80 CV

Wildlife Viewing FS8, RPA2 19.46 27.26 CV

Wildlife Viewing FS8, RPA2 21.48 30.09 CV

Wildlife Viewing FS8, RPA2 21.37 29.94 CV

Wildlife Viewing FS8, RPA2 22.22 31.12 CV

Wildlife Viewing FS8, RPA2 29.62 41.50 CV

Wildlife Viewing FS8, RPA2 19.26 26.98 CV

Wildlife Viewing FS8, RPA2 19.05 26.68 CV

Wildlife Viewing FS8, RPA2 20.83 29.18 CV

Wildlife Viewing FS8, RPA2 15.41 21.58 CV

Wildlife Viewing FS8, RPA2 16.27 22.79 CV

Wildlife Viewing FS4, RPA3 24.36 34.12 CV

Wildlife Viewing FS4, RPA3 27.68 38.78 CV

Wildlife Viewing FS4, RPA3 37.64 52.72 CV

Wildlife Viewing FS3, RPA3 28.93 40.52 CV

Wildlife Viewing FS3, RPA3 16.32 22.86 CV

Wildlife Viewing FS2, RPA3 12.68 17.76 CV

Wildlife Viewing FS2, RPA3 11.64 16.31 CV

Wildlife Viewing FS2, RPA3 16.53 23.15 CV

Wildlife Viewing FS2, RPA3 21.22 29.72 CV

Wildlife Viewing FS2, RPA3 24.46 34.27 CV

Wildlife Viewing FS1, RPA3 15.16 21.24 CV

Wildlife Viewing FS1, RPA3 21.67 30.35 CV

Wildlife Viewing FS6, RPA4 20.04 28.06 CV

Wildlife Viewing FS6, RPA4 25.52 35.75 CV

Wildlife Viewing FS5, RPA4 31.27 43.80 CV

Wildlife Viewing FS10, RPA5 9.34 13.09 CV

aFS=USDA Forest Service Region, CR=Census Region.

bValues in per person per activity day.

cTC=travel cost method, CV=contingent valuation method.
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Appendix B Table B5. Bibliography Entry #68, Hansen, C., 1977, A report on the value of wildlife.

Activity Regionsa $ Original [1975]b $ Adjusted [1996]b Methodc

Big Game Hunting FS4, RPA3 $8.30 $21.68 CV

Big Game Hunting FS4, RPA3 5.10 13.32 CV

Big Game Hunting FS4, RPA3 4.29 11.22 CV

Big Game Hunting FS4, RPA3 45.84 119.81 CV

Big Game Hunting FS4, RPA3 4.36 11.38 CV

Big Game Hunting FS4, RPA3 9.47 24.75 CV

Big Game Hunting FS4, RPA3 9.30 24.32 CV

Big Game Hunting FS4, RPA3 17.54 45.85 CV

Big Game Hunting FS4, RPA3 9.03 23.60 CV

Big Game Hunting FS4, RPA3 32.22 84.21 CV

Big Game Hunting FS4, RPA3 16.20 42.33 CV

Big Game Hunting FS2, RPA3 2.95 7.70 CV

Big Game Hunting FS2, RPA3 9.37 24.50 CV

Big Game Hunting FS2, RPA3 6.17 16.13 CV

Big Game Hunting FS2, RPA3 14.40 37.63 CV

Small Game Hunting FS4, RPA3 10.60 27.71 CV

Small Game Hunting FS4, RPA3 4.96 12.96 CV

Small Game Hunting FS4, RPA3 6.51 17.02 CV

Small Game Hunting FS4, RPA3 16.41 42.88 CV

Small Game Hunting FS4, RPA3 7.86 20.54 CV

Small Game Hunting FS2, RPA3 3.23 8.43 CV

Small Game Hunting FS2, RPA3 11.11 29.04 CV

Waterfowl Hunting FS4, RPA3 6.88 17.97 CV

Waterfowl Hunting FS4, RPA3 10.78 28.16 CV

Waterfowl Hunting FS4, RPA3 3.21 8.39 CV

Waterfowl Hunting FS2, RPA3 14.92 39.00 CV

Fishing FS2, RPA3 3.08 8.05 CV

Fishing FS4, RPA3 5.32 13.91 CV

Fishing FS4, RPA3 4.01 10.47 CV

Fishing FS4, RPA3 4.79 12.53 CV

Fishing FS4, RPA3 2.86 7.47 CV

aFS=USDA Forest Service Region, CR=Census Region.

bValues in per person per activity day.

cTC=travel cost method, CV=contingent valuation method.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 51

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Appendix B Table B6. Bibliography Entry #73, Hay, J.M., 1988, Net economic values of nonconsumptive wildlife-related recreation.

Activity Regionsa $ Original [1985]b $ Adjusted [1996]b Methodc

Wildlife Viewing FS9, RPA1 19.00 26.61 CV

Wildlife Viewing FS9, RPA1 25.00 35.02 CV

Wildlife Viewing FS9, RPA1 13.00 18.21 CV

Wildlife Viewing FS9, RPA1 14.00 19.61 CV

Wildlife Viewing FS9, RPA1 28.00 39.22 CV

Wildlife Viewing FS9, RPA1 21.00 29.42 CV

Wildlife Viewing FS9, RPA1 24.00 33.62 CV

Wildlife Viewing FS9, RPA1 10.00 14.01 CV

Wildlife Viewing FS9, RPA1 20.00 28.01 CV

Wildlife Viewing FS9, RPA1 20.00 28.01 CV

Wildlife Viewing FS9, RPA1 14.00 19.61 CV

Wildlife Viewing FS9, RPA1 18.00 25.21 CV

Wildlife Viewing FS9, RPA1 28.00 39.22 CV

Wildlife Viewing FS9, RPA1 16.00 22.41 CV

Wildlife Viewing FS9, RPA1 13.00 18.21 CV

Wildlife Viewing FS9, RPA1 24.00 33.62 CV

Wildlife Viewing FS9, RPA1 21.00 29.42 CV

Wildlife Viewing FS9, RPA1 18.00 25.21 CV

Wildlife Viewing FS9, RPA1 15.00 21.01 CV

Wildlife Viewing FS9, RPA1 17.00 23.81 CV

Wildlife Viewing FS8, RPA2 13.00 18.21 CV

Wildlife Viewing FS8, RPA2 12.00 16.81 CV

Wildlife Viewing FS8, RPA2 16.00 22.41 CV

Wildlife Viewing FS8, RPA2 26.00 36.42 CV

Wildlife Viewing FS8, RPA2 15.00 21.01 CV

Wildlife Viewing FS8, RPA2 10.00 14.01 CV

Wildlife Viewing FS8, RPA2 12.00 16.81 CV

Wildlife Viewing FS8, RPA2 14.00 19.61 CV

Wildlife Viewing FS8, RPA2 11.00 15.41 CV

Wildlife Viewing FS8, RPA2 34.00 47.63 CV

Wildlife Viewing FS8, RPA2 34.00 47.63 CV

Wildlife Viewing FS8, RPA2 24.00 33.62 CV

Wildlife Viewing FS8, RPA2 21.00 29.42 CV

Wildlife Viewing FS4, RPA3 21.00 29.42 CV

Wildlife Viewing FS4, RPA3 25.00 35.02 CV

Wildlife Viewing FS4, RPA3 41.00 57.43 CV

Wildlife Viewing FS3, RPA3 29.00 40.62 CV

Wildlife Viewing FS3, RPA3 30.00 42.02 CV

Wildlife Viewing FS2, RPA3 11.00 15.41 CV

Wildlife Viewing FS2, RPA3 13.00 18.21 CV

Wildlife Viewing FS2, RPA3 14.00 19.61 CV

Wildlife Viewing FS2, RPA3 23.00 32.22 CV

Wildlife Viewing FS2, RPA3 26.00 36.42 CV

Wildlife Viewing FS1, RPA3 22.00 30.82 CV

Wildlife Viewing FS1, RPA3 29.00 40.62 CV

Wildlife Viewing FS6, RPA4 15.00 21.01 CV

Wildlife Viewing FS6, RPA4 20.00 28.01 CV

Wildlife Viewing FS5, RPA4 27.00 37.82 CV

Wildlife Viewing FS5, RPA4 32.00 44.82 CV

Wildlife Viewing FS10, RPA5 24.00 33.62 CV

aFS=USDA Forest Service Region, CR=Census Region.

bValues in per person per activity day.

cTC=travel cost method, CV=contingent valuation method.
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Appendix B Table B7. Bibliography Entry #101, McCollum, D.W., G.L. Peterson, J.R. Arnold, D.C.

Markstrom, and D.M. Hellerstein, 1990, The net economic value of recreation on the national

forests: Twelve types of primary activity trips across nine Forest Service regions.

Activity Regionsa $ Original [1986]b $ Adjusted [1996]b Methodc

Camping FS9, RPA1 4.11 5.61 Zonal TC

Camping FS8, RPA2 3.07 4.19 Zonal TC

Camping FS3, RPA3 4.26 5.82 Zonal TC

Camping FS6, RPA4 4.55 6.21 Zonal TC

Picnicking FS9, RPA1 5.46 7.45 Zonal TC

Swimming FS8, RPA2 8.33 11.37 Zonal TC

Swimming FS5, RPA4 10.72 14.63 Zonal TC

Sightseeing FS9, RPA1 20.19 27.56 Zonal TC

Sightseeing FS10, RPA5 9.67 13.20 Zonal TC

Hiking FS9, RPA1 30.40 41.50 Zonal TC

Big Game Hunting FS8, RPA2 7.56 10.32 Zonal TC

Big Game Hunting FS1, RPA3 4.61 6.29 Zonal TC

Big Game Hunting FS2, RPA3 4.18 5.71 Zonal TC

Big Game Hunting FS4, RPA3 4.35 5.94 Zonal TC

Big Game Hunting FS6, RPA4 5.56 7.59 Zonal TC

Fishing FS9, RPA1 8.35 11.40 Zonal TC

Fishing FS1, RPA3 24.08 32.87 Zonal TC

Fishing FS2, RPA3 10.44 14.25 Zonal TC

Fishing FS4, RPA3 7.47 10.20 Zonal TC

Wildlife Viewing FS10, RPA5 6.53 8.91 Zonal TC

General Recreation FS9, RPA1 5.47 7.47 Zonal TC

General Recreation FS8, RPA2 4.33 5.91 Zonal TC

General Recreation FS1, RPA3 7.30 9.97 Zonal TC

General Recreation FS2, RPA3 9.49 12.95 Zonal TC

General Recreation FS3, RPA3 6.90 9.42 Zonal TC

General Recreation FS4, RPA3 4.83 6.59 Zonal TC

General Recreation FS5, RPA4 7.35 10.03 Zonal TC

General Recreation FS6, RPA4 3.20 4.37 Zonal TC

General Recreation FS10, RPA5 9.06 12.37 Zonal TC

Wilderness FS8, RPA2 7.40 10.10 Zonal TC

Wilderness FS2, RPA3 13.47 18.39 Zonal TC

Wilderness FS5, RPA4 3.09 4.22 Zonal TC

Wilderness FS10, RPA5 9.51 12.98 Zonal TC

aFS=USDA Forest Service Region, CR=Census Region.

bValues in per person per activity day.

cTC=travel cost method, CV=contingent valuation method.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 53

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Appendix B Table B8. Bibliography Entry #145, Waddington, D.G., K.J. Boyle, and J. Cooper,

1991, 1991 Net economic values for bass and trout fishing, deer hunting, and wildlife watching.

Activity Regionsa $ Original [1991]b $ Adjusted [1996]b Methodc

Big Game Hunting FS9, RPA1 $25.00 $28.26 CV

Big Game Hunting FS9, RPA1 50.00 56.51 CV

Big Game Hunting FS9, RPA1 22.00 24.87 CV

Big Game Hunting FS9, RPA1 48.00 54.25 CV

Big Game Hunting FS9, RPA1 38.00 42.95 CV

Big Game Hunting FS9, RPA1 61.00 68.95 CV

Big Game Hunting FS9, RPA1 53.00 59.91 CV

Big Game Hunting FS9, RPA1 27.00 30.52 CV

Big Game Hunting FS9, RPA1 41.00 46.34 CV

Big Game Hunting FS9, RPA1 55.00 62.17 CV

Big Game Hunting FS9, RPA1 61.00 68.95 CV

Big Game Hunting FS9, RPA1 63.00 71.21 CV

Big Game Hunting FS9, RPA1 52.00 58.78 CV

Big Game Hunting FS9, RPA1 45.00 50.86 CV

Big Game Hunting FS9, RPA1 33.00 37.30 CV

Big Game Hunting FS9, RPA1 49.00 55.38 CV

Big Game Hunting FS9, RPA1 47.00 53.12 CV

Big Game Hunting FS9, RPA1 39.00 44.08 CV

Big Game Hunting FS9, RPA1 31.00 35.04 CV

Big Game Hunting FS9, RPA1 36.00 40.69 CV

Big Game Hunting FS8, RPA2 45.00 50.86 CV

Big Game Hunting FS8, RPA2 50.00 56.51 CV

Big Game Hunting FS8, RPA2 41.00 46.34 CV

Big Game Hunting FS8, RPA2 44.00 49.73 CV

Big Game Hunting FS8, RPA2 58.00 65.56 CV

Big Game Hunting FS8, RPA2 36.00 40.69 CV

Big Game Hunting FS8, RPA2 35.00 39.56 CV

Big Game Hunting FS8, RPA2 36.00 40.69 CV

Big Game Hunting FS8, RPA2 47.00 53.12 CV

Big Game Hunting FS8, RPA2 34.00 38.43 CV

Big Game Hunting FS8, RPA2 32.00 36.17 CV

Big Game Hunting FS8, RPA2 53.00 59.91 CV

Big Game Hunting FS8, RPA2 33.00 37.30 CV

Big Game Hunting FS4, RPA3 45.00 50.86 CV

Big Game Hunting FS4, RPA3 45.00 50.86 CV

Big Game Hunting FS4, RPA3 95.00 107.38 CV

Big Game Hunting FS3, RPA3 66.00 74.60 CV

Big Game Hunting FS3, RPA3 81.00 91.55 CV

Big Game Hunting FS2, RPA3 34.00 38.43 CV

Big Game Hunting FS2, RPA3 36.00 40.69 CV

Big Game Hunting FS2, RPA3 44.00 49.73 CV

Big Game Hunting FS2, RPA3 66.00 74.60 CV

Big Game Hunting FS2, RPA3 72.00 81.38 CV

Big Game Hunting FS2, RPA3 83.00 93.81 CV

Big Game Hunting FS1, RPA3 56.00 66.72 CV

Big Game Hunting FS6, RPA4 59.00 66.69 CV

Big Game Hunting FS6, RPA4 52.00 58.78 CV

Big Game Hunting FS10, RPA5 63.00 71.21 CV

Wildlife Viewing FS9, RPA1 27.00 30.52 CV

Wildlife Viewing FS9, RPA1 23.00 26.00 CV

Wildlife Viewing FS9, RPA1 12.00 13.56 CV

Wildlife Viewing FS9, RPA1 32.00 36.17 CV

Wildlife Viewing FS9, RPA1 23.00 26.00 CV

(cont’d.)
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Wildlife Viewing FS9, RPA1 14.00 15.82 CV

Wildlife Viewing FS9, RPA1 21.00 23.74 CV

Wildlife Viewing FS9, RPA1 29.00 32.78 CV

Wildlife Viewing FS9, RPA1 22.00 24.87 CV

Wildlife Viewing FS9, RPA1 29.00 32.78 CV

Wildlife Viewing FS9, RPA1 36.00 40.69 CV

Wildlife Viewing FS9, RPA1 28.00 31.65 CV

Wildlife Viewing FS9, RPA1 23.00 26.00 CV

Wildlife Viewing FS9, RPA1 26.00 29.39 CV

Wildlife Viewing FS9, RPA1 17.00 19.21 CV

Wildlife Viewing FS9, RPA1 16.00 18.08 CV

Wildlife Viewing FS9, RPA1 27.00 30.52 CV

Wildlife Viewing FS9, RPA1 12.00 13.56 CV

Wildlife Viewing FS9, RPA1 59.00 66.69 CV

Wildlife Viewing FS9, RPA1 71.00 80.25 CV

Wildlife Viewing FS8, RPA2 37.00 41.82 CV

Wildlife Viewing FS8, RPA2 67.00 75.73 CV

Wildlife Viewing FS8, RPA2 39.00 44.08 CV

Wildlife Viewing FS8, RPA2 27.00 30.52 CV

Wildlife Viewing FS8, RPA2 24.00 27.13 CV

Wildlife Viewing FS8, RPA2 30.00 33.91 CV

Wildlife Viewing FS8, RPA2 28.00 31.65 CV

Wildlife Viewing FS8, RPA2 25.00 28.26 CV

Wildlife Viewing FS8, RPA2 21.00 23.74 CV

Wildlife Viewing FS8, RPA2 31.00 35.04 CV

Wildlife Viewing FS8, RPA2 41.00 46.34 CV

Wildlife Viewing FS4, RPA3 22.00 24.87 CV

Wildlife Viewing FS4, RPA3 29.00 32.78 CV

Wildlife Viewing FS4, RPA3 45.00 50.86 CV

Wildlife Viewing FS3, RPA3 34.00 38.43 CV

Wildlife Viewing FS3, RPA3 50.00 56.51 CV

Wildlife Viewing FS2, RPA3 23.00 26.00 CV

Wildlife Viewing FS2, RPA3 28.00 31.65 CV

Wildlife Viewing FS2, RPA3 34.00 38.43 CV

Wildlife Viewing FS2, RPA3 49.00 55.38 CV

Wildlife Viewing FS1, RPA3 10.00 11.30 CV

Wildlife Viewing FS1, RPA3 21.00 23.74 CV

Wildlife Viewing FS1, RPA3 21.00 23.74 CV

Wildlife Viewing FS6, RPA4 27.00 30.52 CV

Wildlife Viewing FS6, RPA4 28.00 31.65 CV

Wildlife Viewing FS5, RPA4 28.00 31.65 CV

Wildlife Viewing FS5, RPA4 29.00 32.78 CV

Wildlife Viewing FS10, RPA5 49.00 55.38 CV

aFS=USDA Forest Service Region, CR=Census Region.

bValues in per person per activity day.

cTC=travel cost method, CV=contingent valuation method.

Appendix B Table B8. (Cont’d.)

Activity Regionsa $ Original [1991]b $ Adjusted [1996]b Methodc
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 55

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Appendix C Table C1. CAMPING: Empirical Studies

Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 9, 78, 101, 105, 118

RPA2 FS8 9, 61, 86, 87, 101

RPA3 FS1 39

FS2 150, 156

FS3 25, 83, 98, 101, 122, 141, 159

FS4 56, 108, 109

RPA4 FS5 9

FS6 9, 52, 101

RPA5 FS10 —

NATIONAL 8

CANADA —

Appendix C: References to Appendix A Annotated Bibliography Entries by

Recreation Activity

Appendix C Table C2. PICNICKING: Empirical Studies

Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 9, 101

RPA2 FS8 9

RPA3 FS1 —

FS2 9, 150, 156

FS3 159

FS4 —

RPA4 FS5 9, 85

FS6 9

RPA5 FS10 —

NATIONAL 8

CANADA —

Appendix C Table C3. SWIMMING: Empirical Studies

Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 78, 120, 134

RPA2 FS8 9, 66, 101

RPA3 FS1 —

FS2 —

FS3 159

FS4 —-

RPA4 FS5 101, 146

FS6 9

RPA5 FS10 —

NATIONAL 8

CANADA —

Appendix C Table C4. SIGHTSEEING: Empirical Studies

Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 9, 101

RPA2 FS8 9, 60

RPA3 FS1 —

FS2 77, 152, 153

FS3 71

FS4 95

RPA4 FS5 —

FS6 9

RPA5 FS10 101

NATIONAL 8

CANADA —
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Appendix C Table C5. OFF ROAD DRIVING: Empirical

Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 —

RPA2 FS8 9

RPA3 FS1 —

FS2 150

FS3 —

FS4 —

RPA4 FS5 —

FS6 9

RPA5 FS10 —

NATIONAL 8

CANADA —

Appendix C Table C6. MOTOR BOATING: Empirical

Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 78

RPA2 FS8 132

RPA3 FS1 —

FS2 9, 124

FS3 146, 159

FS4 —

RPA4 FS5 —

FS6 142

RPA5 FS10 —

NATIONAL 8

CANADA —

Appendix C Table C7. FLOAT BOATING: Empirical

Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 9, 74, 125

RPA2 FS8 9, 84

RPA3 FS1 —-

FS2 9, 157

FS3 11, 18, 81

FS4 15, 108, 125

RPA4 FS5 —

FS6 —

RPA5 FS10 72

NATIONAL 8

CANADA —

Appendix C Table C8. HIKING: Empirical Studies

Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 9, 78, 101

RPA2 FS8 9, 29, 69, 119

RPA3 FS1 —

FS2 9, 126, 150, 155, 156

FS3 —

FS4 —

RPA4 FS5 5, 133

FS6 9, 24, 51, 62

RPA5 FS10 72

NATIONAL 8

CANADA —

Appendix C Table C9. BIKING: Empirical Studies

Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 133

RPA2 FS8 133

RPA3 FS1 —

FS2 —

FS3 —

FS4 58

RPA4 FS5 —

FS6 —

RPA5 FS10 —

NATIONAL 8

CANADA —

Appendix C Table C10. DOWNHILL SKIING: Empirical

Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 —

RPA2 FS8 —

RPA3 FS1 —

FS2 112, 148, 154

FS3 —

FS4 —

RPA4 FS5 —

FS6 9

RPA5 FS10 —

NATIONAL 8

CANADA —
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 57

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Appendix C Table C11. CROSS COUNTRY SKIING:

Empirical Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 63, 100

RPA2 FS8 —

RPA3 FS1 —

FS2 63, 149, 155

FS3 —

FS4 80

RPA4 FS5 30

FS6 —

RPA5 FS10 —

NATIONAL 8

CANADA —

Appendix C Table C12. SNOWMOBILING: Empirical

Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 —

RPA2 FS8 —

RPA3 FS1 —

FS2 99

FS3 —

FS4 80

RPA4 FS5 —

FS6 —

RPA5 FS10 —

NATIONAL —

CANADA —

Appendix C Table C13. BIG GAME HUNTING: Empirical

Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 913, 17, 19, 20, 23, 55,

 57, 102, 110, 145

RPA2 FS8 9, 23, 101, 145

RPA3 FS1 21, 23, 44, 45, 92, 93,

 101, 116, 145

FS2 9, 23, 68, 101, 145, 161

FS3 23, 36, 98, 145

FS4 23, 59, 68, 89, 90, 101,

 138, 145

RPA4 FS5 9, 23, 38, 91, 94

FS6 9, 23, 42, 101, 145

RPA5 FS10 23, 103, 145

NATIONAL 6, 8

CANADA 2, 28

Appendix C Table C14. SMALL GAME HUNTING:

Empirical Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 17, 19, 20

RPA2 FS8 —

RPA3 FS1 —

FS2 65, 68, 110

FS3 98

FS4 68, 162

RPA4 FS5 —

FS6 4

RPA5 FS10 —

NATIONAL 8

CANADA —
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001

Rosenberger and Loomis Benefit Transfer of Outdoor Recreation Use Values

Appendix C Table C15. WATERFOWL HUNTING:

Empirical Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 12, 17, 19, 20, 23, 64

RPA2 FS8 23

RPA3 FS1 23, 46

FS2 23, 68

FS3 98

FS4 22, 23, 68

RPA4 FS5 23, 33, 34

FS6 23

RPA5 FS10 72

NATIONAL —

CANADA —

Appendix C Table C16. FISHING: Empirical Studies

Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 16, 20, 23, 32, 75, 76,

 79, 101, 104, 106,

 107, 110, 113, 114,

 128, 130, 160

RPA2 FS8 23, 110, 115, 163

RPA3 FS1 23, 47, 48, 101

FS2 23, 40, 68, 70, 101

FS3 18, 23, 82, 97, 98,

 110, 121, 141

FS4 23, 68, 101, 110, 137

RPA4 FS5 23, 127, 136

FS6 23, 26, 41, 127, 140

RPA5 FS10 23

NATIONAL 144

CANADA 3, 27

Appendix C Table C17. WILDLIFE VIEWING: Empirical

Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 31, 67, 73, 130, 145

RPA2 FS8 9, 31, 73, 139, 145

RPA3 FS1 31, 73, 145

FS2 7, 9, 31, 73, 145

FS3 31, 37, 73, 145

FS4 31, 73, 145

RPA4 FS5 31, 33, 73, 91, 94,

 135, 145

FS6 31, 73, 145

RPA5 FS10 31, 73, 101, 103, 145

NATIONAL 8

CANADA —

Appendix C Table C18. HORSEBACK RIDING:

Empirical Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 —

RPA2 FS8 —

RPA3 FS1 —

FS2 —

FS3 —

FS4 —

RPA4 FS5 —

FS6 —

RPA5 FS10 —

NATIONAL 8

CANADA —
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-72. 2001 59

Benefit Transfer of Outdoor Recreation Use Values Rosenberger and Loomis

Appendix C Table C19. ROCK CLIMBING: Empirical

Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 131

RPA2 FS8 —

RPA3 FS1 —

FS2 50

FS3 —

FS4 —

RPA4 FS5 —

FS6 —

RPA5 FS10 —

NATIONAL —

CANADA —

Appendix C Table C20. GENERAL RECREATION:

Empirical Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 9, 14, 49, 101, 130

RPA2 FS8 9, 10, 101, 143

RPA3 FS1 101

FS2 9, 96, 101, 147, 151

FS3 101

FS4 88, 101

RPA4 FS5 9, 101, 111

FS6 101

RPA5 FS10 101

NATIONAL —

CANADA 1

Appendix C Table C21. OTHER RECREATION:

Empirical Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 —

RPA2 FS8 9, 35, 54, 123

RPA3 FS1 43, 47

FS2 117, 129, 158

FS3 —

FS4 —

RPA4 FS5 —

FS6 9

RPA5 FS10 —

NATIONAL 8

CANADA —

Appendix C Table C22. WILDERNESS RECREATION:

Empirical Studies Estimating Economic Use Values.

Forest

 RPA Service Bibliography

Region Region Reference Number

RPA1 FS9 67, 74, 118

RPA2 FS8 29, 101, 119

RPA3 FS1 —

FS2 7, 101, 151, 153, 158

FS3 —

FS4 88

RPA4 FS5 5, 101, 135

FS6 24, 51, 52

RPA5 FS10 101

NATIONAL —

CANADA —
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ROCKY MOUNTAIN RESEARCH STATION

RMRS

The Rocky Mountain Research Station develops scientific information and technology to improve management, protection, and use of

the forests and rangelands. Research is designed to meet the needs

of National Forest managers, Federal and State agencies, public and

private organizations, academic institutions, industry, and individuals.

Studies accelerate solutions to problems involving ecosystems,

range, forests, water, recreation, fire, resource inventory, land reclamation, community sustainability, forest engineering technology,

multiple use economics, wildlife and fish habitat, and forest insects

and diseases. Studies are conducted cooperatively, and applications

may be found worldwide.

Research Locations

Flagstaff, Arizona Reno, Nevada

Fort Collins, Colorado* Albuquerque, New Mexico

Boise, Idaho Rapid City, South Dakota

Moscow, Idaho Logan, Utah

Bozeman, Montana Ogden, Utah

Missoula, Montana Provo, Utah

Lincoln, Nebraska Laramie, Wyoming

*Station Headquarters, Natural Resources Research Center,

2150 Centre Avenue, Building A, Fort Collins, CO 80526
UNITED STATES DEPARTMENT OF AGRICULTURE

FOREST SERVICE

ROCKY MOUNTAIN RESEARCH STATION

GENERAL TECHNICAL REPORT RMRS-GTR-72

APRIL 2001