Updated Outdoor Recreation 

Use Values on National Forests 

and Other Public Lands

John Loomis

United States 

Department of 

Agriculture

Forest Service

Pacific Northwest 

Research Station

General Technical 

Report

PNW-GTR-658

October 2005
The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

multiple use management of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, cooperation with the 

States and private forest owners, and management of the National Forests and National 

Grasslands, it strives—as directed by Congress—to provide increasingly greater service to a 

growing Nation.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and 

activities on the basis of race, color, national origin, age, disability, and where applicable, sex, 

marital status, familial status, parental status, religion, sexual orientation, genetic information, 

political beliefs, reprisal, or because all or part of an individual’s income is derived from any 

public assistance program. (Not all prohibited bases apply to all programs.) Persons with 

disabilities who require alternative means for communication of program information (Braille, 

large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice 

and TDD). To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, 

Room 1400 Independence Avenue, SW, Washington, DC 20250-9410 or call (800) 795-3272 

(voice) or (202) 720-6382 (TDD). USDA is an equal opportunity provider and employer.

Author

John Loomis is a professor, Department of Agricultural and Resource Economics, 

Colorado State University, Fort Collins, CO 80523. 

This work was performed under PNW-03-JV11261955-265. 
Abstract

Loomis, John. 2005. Updated outdoor recreation use values on national forests 

and other public lands. Gen. Tech. Rep. PNW-GTR-658. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station. 

26 p.

This report summarizes more than 30 years of the literature on net economic 

value of outdoor recreation on public lands. The report provides average net 

willingness to pay or consumer surplus per day for 30 recreation activities at the 

national level. Values per day by recreation activity are also presented by census 

region of the United States. Detailed tables provide the average value per day as 

well as the standard error for calculating confidence intervals. Guidance for using 

these values in performing benefit transfer to unstudied sites is also provided. 

The report provides a link to a Web site where the spreadsheet that underlies 

the averages calculated in this report is available. 

Keywords: Benefit transfer, consumer surplus, recreation use values, 

willingness to pay.
Executive Summary

This report presents updated average values per visitor-day of outdoor recreation 

opportunities commonly found at national forests, with emphasis on the Pacific 

Northwest region. The use of past valuation information for current policy analysis is called benefit transfer (Brookshire and Neill 1992). In this report, the term 

“value” is used to mean net willingness to pay or consumer surplus, a measure 

commonly used for benefit-cost analysis or economic efficiency analysis by federal 

agencies such as the U.S. Army Corps of Engineers, Bureau of Reclamation (U.S. 

Water Resources Council 1979, 1983) and U.S. Environmental Protection Agency. 

This report updates past USDA Forest Service-sponsored reviews of the literature on outdoor recreation use valuation by including recent analyses and estimates 

through the year 2003. Adding studies from this period to past reviews results in a 

database on outdoor recreation use valuation that spans 1967 to 2003; 1,239 estimates obtained from the literature provide values for 30 outdoor recreation activities. This update includes new recreation activities such as snorkeling, scuba diving, 

and birdwatching that were not part of the Rosenberger and Loomis (2001) report. 

The values presented in this report are averages of values per day from original or 

primary Contingent Valuation Method or Travel Cost Method studies (see Loomis 

and Walsh 1997 for more details on these two valuation methods). To standardize 

the units of recreation use, average values are reported per visitor-day. The average 

visitor-day value is reported for each activity by census region when available, and 

specifically broken out into greater detail for the Pacific Northwest. The complete 

spreadsheet providing the results of the individual studies is available at: http://

www.fs.fed.us/pnw/data/RecValues.htm.

Although the report provides average values for all regions of the United States, 

the values for the Pacific Northwest are separated out. Based on the existing literature, hunting on public lands in the Pacific Northwest has an average value of $35 

per day, fishing averages $42 per day, and wildlife viewing is $35 per day. Hiking 

has a value of $24 per day in the Pacific Northwest.
Contents

 1 Introduction

 1 Data

 1 Literature Review Efforts, Past and Present

 2 Data Sources and Coding Procedures 

 3 Value-Per-Day Tables by Activity and Region 

 10 Using Value Tables and Database for Benefit Transfer to 

 Unstudied Recreation Sites on National Forests

 10 Benefit Transfer Methods

 11 Single-Point Estimate Transfer

 13 Average-Value Transfer

 13 Guidance for Performing an Accurate Benefit Transfer

19 Validity and Reliability of Benefit Transfers

 20 Other Potential Limitations of Benefit Transfers

 21 A Note on Definition of Benefit Measures and Use in Policy Analyses

 22 Details of Spreadsheet Coding

 23 Summary

 23 Acknowledgments

 23 References

1

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

Introduction

The USDA Forest Service and other federal land management agencies including 

the National Park Service, U.S. Fish and Wildlife Service, and USDI Bureau of 

Land Management require information on values of recreation. Whether for land 

management planning or Government Performance and Results Act (GPRA) of 

1993, these requirements feed directly into a need for credible measures of benefits. In this case, we are interested in developing credible measures of benefits for 

outdoor recreation. 

This report is intended to serve two functions. First, it provides information 

from a literature review of economic studies conducted in the United States, spanning 1967 to 2003, that estimated outdoor recreation use values. Second, this report 

provides some basic guidelines on performing benefit transfers in the context of recreation use valuation. This report is not a cookbook for benefit transfers, but instead 

it is to be used as a guide to the empirical estimates available (a more complete discussion of benefit transfer protocols can be found in Rosenberger and Loomis 2001). 

Per federal government benefit-cost guidelines (U.S. Water Resources Council 

1979, 1983), economic value is defined as visitor’s net willingness to pay or consumer surplus (Freeman 1993). The values summarized in this report are averages 

of original or primary Contingent Valuation Method (CVM) or Travel Cost Method 

(TCM) studies (see Loomis and Walsh 1997 for more details on these two valuation 

methods). The values reported in this publication are unweighted or simple averages 

where each study and each estimate from each study is given equal weight. This is 

the same approach used by Rosenberger and Loomis (2001). However, by using the 

spreadsheet, an analyst could construct a weighted average by using any reasonable 

criteria such as study sample size or survey response rate, etc. To standardize the 

units of recreation use, average values are reported per visitor-day.

Data

Literature Review Efforts, Past and Present

We provide data on outdoor recreation use values based on empirical research 

conducted from 1967 to 2003 in the United States. This data is the compilation of 

five literature reviews conducted over the last 20 years. 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 1983–88 period 

(Walsh et al. 1988, 1992). That second review increased the number of benefit 
2

GENERAL TECHNICAL REPORT PNW-GTR-658

estimates to 287 estimates. A third literature review on the subject covered the 

period 1968–93 (MacNair 1993). A fourth literature review on outdoor recreation 

use valuation, focusing on studies reported from 1988 to 1998 (Loomis et al. 1999). 

Rosenberger and Loomis (2001) then merged the results of the fourth review with 

the MacNair (1993) database. The main emphasis was to improve on coding procedures used in the past review efforts to focus on use value estimates for all recreation activity categories identified by USDA Forest Service documents. Fishing 

benefit studies were not emphasized, as this was the focus of a separate review 

sponsored by the U.S. Fish and Wildlife Service, and published by Industrial Economics, Inc. (Markowski et al. 1997). Fishing studies coded in the MacNair (1993) 

database that were from the Walsh et al. (1988) review were sufficient in number 

and coverage for valuation of fishing for statistical purposes. This report represents 

the fifth literature review, adding new studies from 1998 through 2003. In this new 

review, we were able to obtain 479 new observations.

Data Sources and Coding Procedures

A concerted effort was made to locate studies on activities that were not previously investigated and recreation activities of particular interest to the USDA Forest 

Service, especially the Pacific Northwest Region. Computerized databases, such as 

American Economic Association’s ECONLIT and Thomson’s ISI Web of Science 

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 et al. 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 et al. (1988, 

1992) literature review. A few study details were obtained directly from the Walsh 

et al. (1988) review that were not included in the MacNair (1993) database.

For consistency and to allow merging of the new studies with studies compiled 

by Rosenberger and Loomis (2001), the same master coding sheet was used for the 

base. The spreadsheet dataset and code sheet contains 126 fields. The main coding 

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

include, in part, the monetary estimate provided by the study (converted to activityday units by using information provided in the study report), the units in which the 

estimate is reported (e.g., day, trip, season, or year), and benefit measures temporally adjusted for inflationary trends to 2004 dollars. An activity-day represents 

Coding fields include 

reference citation to 

the research, benefit 

measure(s) reported, 

methodology used, 

recreation activity 

investigated, recreation 

site characteristics, 

and user or 

sample population 

characteristics.
3

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

the typical amount of time a person pursues 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). 

Value-Per-Day Tables by Activity and Region

New data were combined with old data to create a database of 1,239 observations 

spanning 1967 through 2003. This table (table 1) presents data for the 30 activities. 

Information that can be observed includes the number of studies, number of estimates, mean/average, standard error, and range of values. In brief, the activities 

most commonly found include hunting, fishing, wildlife viewing, and camping. 

The average estimate of consumer surplus is $47.64 per person per day across all 

1,239 observations.

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

original recreation benefit studies, 1967–2003

 Standard

Activity Studies Estimates Mean error Range of estimates

 - - - Number - - - - - - - - - - - - - - 2004 dollars - - - - - - - - - - -

Backpacking 1 6 52.10 9.29 26.82 80.34

Birdwatching 4 8 29.60 8.35 5.80 78.46

Camping 29 48 37.19 5.77 2.03 224.53

Cross-country skiing 8 12 31.38 3.41 14.05 48.38

Downhill skiing 5 5 33.49 8.48 15.05 63.11

Fishing 129 177 47.16 4.81 2.08 556.82

Floatboating/rafting/canoeing 20 81 100.91 9.56 2.70 394.82

General recreation 15 39 35.10 8.69 1.42 257.51

Going to the beach 5 33 39.43 5.06 3.78 117.82

Hiking 21 68 30.84 4.33 0.40 262.04

Horseback riding 1 1 18.12 18.12 18.12

Hunting 192 277 46.92 2.20 2.60 250.90

Motorboating 15 32 46.27 7.43 3.78 203.62

Mountain biking 7 32 73.78 12.11 20.86 295.69

Off-road vehicle driving 4 10 22.92 3.95 5.24 40.86

Other recreation 15 16 48.70 11.57 5.71 206.82

Picnicking 8 13 41.46 10.69 8.94 142.74

Pleasure driving (which may include sightseeing) 4 11 59.23 18.84 3.02 167.74

Rock climbing 4 27 56.26 6.86 26.62 135.82

Scuba diving 2 24 32.36 11.21 2.81 250.04

Sightseeing 15 28 36.84 8.80 .65 209.77

Snorkeling 1 9 30.31 15.36 5.23 135.29

Snowmobiling 3 8 36.29 13.24 10.79 124.44

Swimming 11 26 42.68 6.14 2.20 134.34

Visiting environmental education centers 1 1 6.01 6.01 6.01

Visiting arboretums 1 1 13.54 13.54 13.54

Visiting aquariums 1 1 28.31 28.31 28.31

Waterskiing 1 4 49.02 12.72 15.13 70.07

Wildlife viewing 69 240 42.36 2.64 2.40 347.88

Windsurfing 1 1 395.47 395.47 395.47
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GENERAL TECHNICAL REPORT PNW-GTR-658

Table 2 breaks down the information further by subdividing the activities by 

region. Six regions are used that roughly follow U.S. Census Regions: Alaska, 

Intermountain, Northeast, Pacific Coast (USDA Pacific Southwest and Pacific 

Northwest Regions [R5 and R6]), Southeast, and our own construct, Multiple 

Area. Multiple Area was included, as several of the studies spanned more than 

one region. Figure 1 illustrates the geographic regions used for this analysis. This 

update provides 354 observations in the Intermountain area, 306 in the Northeast, 

281 in the Southeast, 186 in the Pacific Coast, 26 in Alaska, and only 86 in the 

Multiple Area studies. Deciding upon the best degree of geographic aggregation 

is a tradeoff between greater geographic specificity, which enhances accuracy 

in benefit transfer, and smaller sample sizes within each region, which reduces 

accuracy. Considering this tradeoff, it was desirable to use regions broader than 

Forest Service administrative regions. This increased the sample size in each cell. 

Second, for some recreation activities, if smaller administrative regions were used 

it would lead to numerous blank cells, indicating no values for that activity in that 

region. Finally, the larger censuslike regions correspond to the Resources Planning Act (RPA) assessment regions, so there is some connection to Government 

Performance and Results Act and RPA regions. 

Table 2 also presents average recreation values of empirical studies conducted 

in wilderness areas by region. Of the 1,239 total studies, 108 were found to be in 

wilderness areas.

Table 3 provides more detail about each activity in each region, including 

standard error and minimum and maximum values for each activity. The region 

with the least amount of activity values was Alaska, with eight recreation activities having values. None of the regions had values for all 30 recreation activities. 

Table 4 presents averages specific to the Pacific Northwest Region (R6), 

Oregon and Washington. As can be seen, there are relatively few studies, although 

they produce a large number of benefit estimates for the different sites and variants of valuation techniques used in each study. There are quite a few fishing, 

hiking, hunting, and wildlife viewing studies and estimates. 

Many of the estimates in table 4 (specifically, camping, off-road vehicles, 

picknicking, sightseeing, and swimming) are from a USDA Forest Service-commissioned study by Bergstrom et al. (1996). Many of the hunting and fishing 

studies are from Brown and Hay (1987) from the USFWS hunting and fishing 

survey, and from Rowe et al. (1985). Most of the hiking value estimates came 

from Hilger’s (1998) master’s thesis on wilderness day hikers, and Englin and 

Shonkwiler (1995). 
5

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

Figure 1—Study regions.

Tables 1, 3, and 4 present the standard error of the mean. This statistic is calculated from the standard deviation and the square root of sample size. The standard 

error of the mean is used to construct the confidence interval around the population 

mean. For example, a 95-percent confidence interval around the population mean is 

formed by adding and subtracting 1.96 standard errors from the mean. Thus in table 

1 for camping, the mean is $37.19, and the standard error is $5.77. The 95-percent 

confidence interval is $25.88 to $48.50. We expect that there is only a 5 percent 

chance, given the data we have, that the true population mean for camping lies 

outside of this range. 

Tables 1 and 3 contain maximum and minimum values for each activity and region. Although some of these maximum values may appear quite large or minimum 

values appear quite small, these study values were checked against the original 

study as were our calculations. Thus, all the values included in the report were used 

in calculating the averages. The user can access the spreadsheet data to calculate 

averages with what they consider to be outliers removed if they wish. 
6

GENERAL TECHNICAL REPORT PNW-GTR-658

Table 2—Summary statistics on average consumer surplus values per person per day by activity and region, 

1967 to 2003

 Inter- Multiple Pacific 

Alaska mountain area studies Northeast Coast Southeast Total

Activity N Mean N Mean N Mean N Mean N Mean N Mean N Mean

2004 2004 2004 2004 2004 2004 2004

dollars dollars dollars dollars dollars dollars dollars

Backpacking 6 52.10

Birdwatching 3 34.86 5 26.46

Camping 21 34.72 2 11.82 10 33.11 4 104.35 11 25.79

Cross-country skiing 7 29.88 1 15.20 3 34.60 1 48.38

Downhill skiing 3 39.62 1 23.53 1 25.08

Fishing 4 61.99 48 49.57 14 47.53 69 32.60 15 44.36 27 79.21

Floatboating/rafting/canoeing 1 18.16 22 67.70 1 34.01 6 88.32 4 27.84 47 127.46

General recreation 1 14.84 12 48.46 3 4.00 5 16.87 9 32.35 9 42.77

Going to the beach 22 42.60 11 33.12

Hiking 1 15.52 7 38.53 1 25.04 3 75.18 49 23.24 7 60.38

Horseback riding 1 18.12

Hunting 7 65.68 109 48.55 12 61.69 87 47.45 18 45.49 44 35.36

Motorboating 7 53.68 1 34.36 3 29.68 8 26.94 13 58.92

Mountain biking 6 184.48 1 21.13 1 40.93 16 49.68 8 49.62

Off-road vehicle driving 7 22.81 1 23.93 1 40.37 1 5.24

Other recreation 10 56.35 1 20.83 1 74.47 4 30.07

Picnicking 5 28.27 1 18.83 2 56.45 3 64.22 2 36.62

Pleasure driving 3 8.41 4 69.74 1 36.46 1 21.35 2 144.78

Rock climbing 3 50.45 12 26.82 1 102.89 11 85.70

Scuba diving 14 17.92 10 52.60

Sightseeing 1 15.84 11 23.58 1 17.83 2 121.43 4 20.27 9 46.06

Snorkeling 9 30.31

Snowmobiling 8 36.29

Swimming 1 29.54 1 23.56 7 22.21 4 27.29 13 60.92

Visiting environmental 

 education centers 1 6.01

Visiting arboretums 1 13.54

Visiting aquariums 1 28.31

Waterskiing 2 56.96 1 67.00 1 15.13

Wildlife viewing 8 49.33 61 37.24 29 56.36 65 31.30 23 72.48 54 40.10

Windsurfing 1 395.47

All activities in wilderness 32 41.68 17 28.46 8 25.48 46 26.22 5 118.67 108 35.38
7

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

Table 3—Detailed descriptive statistics on average consumer surplus values per person per 

day by activity and region, 1967 to 2003

 Standard

Area and activity N Mean error Minimum Maximum

2004 dollars

Alaska region:

 Fishing 4 61.99 9.22 45.60 81.94 

 Floatboating/rafting/canoeing 1 18.16 18.16 18.16

 General recreation 1 14.84 14.84 14.84

 Hiking 1 15.52 15.52 15.52

 Hunting 7 65.68 4.81 47.06 85.45

 Pleasure driving 3 8.41 3.67 3.02 15.43

 Sightseeing 1 15.84 15.84 15.84

 Wildlife viewing 8 49.33 9.49 10.69 84.40

Intermountain area studies:

 Camping 21 34.72 6.64 2.03 116.66

 Cross-country skiing 7 29.88 4.58 14.05 46.49

 Downhill skiing 3 39.62 13.88 15.05 63.11

 Fishing 48 49.57 6.96 8.96 227.28

 Floatboating/rafting/canoeing 22 67.70 14.33 2.70 316.42

 General recreation 12 48.46 20.92 7.91 257.51

 Hiking 7 38.53 7.84 12.85 75.76

 Hunting 109 48.55 3.35 2.60 169.31

 Motorboating 7 53.68 25.93 5.29 203.62

 Mountain biking 6 184.48 41.05 65.88 295.69

 Off-road vehicle driving 7 22.81 4.31 7.96 40.86

 Other recreation 10 56.35 17.36 12.17 206.82

 Picnicking 5 28.27 4.09 13.61 38.76

 Pleasure driving 4 69.74 33.23 26.41 167.74

 Rock climbing 3 50.45 7.58 35.78 61.14

 Sightseeing 11 23.58 8.65 .65 100.73

 Snowmobiling 8 36.29 13.24 10.79 124.44

 Swimming 1 29.54 29.54 29.54

 Waterskiing 2 56.96 13.09 43.87 70.07

 Wildlife viewing 61 37.24 3.30 5.26 193.91

Multiple area studies:

 Camping 2 11.82 2.00 9.82 13.82 

 Cross-country skiing 1 15.20 15.20 15.20 

 Downhill skiing 1 23.53 23.53 23.53 

 Fishing 14 47.53 10.49 2.40 126.00 

 Floatboating/rafting/canoeing 1 34.01 34.01 34.01 

 General recreation 3 4.00 2.03 1.97 8.05 

 Hiking 1 25.04 25.04 25.04 

 Horseback riding 1 18.12 18.12 18.12 

 Hunting 12 61.69 23.05 6.00 232.58 

 Motorboating 1 34.36 34.36 34.36 

 Mountain biking 1 21.13 21.13 21.13 

 Off-road vehicle driving 1 23.93 23.93 23.93 

 Other recreation 1 20.83 20.83 20.83 

 Picnicking 1 18.83 18.83 18.83 

 Pleasure driving 1 36.46 36.46 36.46 
8

Table 3—Detailed descriptive statistics on average consumer surplus values per person per 

day by activity and region, 1967 to 2003 (continued)

 Standard

Area and activity N Mean error Minimum Maximum

2004 dollars

 Rock climbing 12 26.82 .04 26.62 26.92 

 Sightseeing 1 17.83 17.83 17.83 

 Swimming 1 23.56 23.56 23.56 

 Waterskiing 1 67.00 67.00 67.00 

 Wildlife viewing 29 56.36 12.38 3.00 313.99 

Northeast area:

 Birdwatching 3 34.86 22.20 5.80 78.46 

 Camping 10 33.11 6.32 6.73 66.44 

 Cross-country skiing 3 34.60 2.82 29.70 39.49 

 Fishing 69 32.60 5.46 2.08 253.13 

 Floatboating/rafting/canoeing 6 88.32 22.93 20.08 143.50 

 General recreation 5 16.87 8.08 1.97 46.69 

 Going to the beach 22 42.60 7.03 3.78 117.82 

 Hiking 3 75.18 12.83 49.80 91.10 

 Hunting 87 47.45 4.03 4.16 250.90 

 Motorboating 3 29.68 25.21 3.78 80.10 

 Mountain biking 1 40.93 40.93 40.93 

 Picnicking 2 56.45 47.51 8.94 103.96 

 Pleasure driving 1 21.35 21.35 21.35 

 Rock climbing 1 102.89 102.89 102.89 

 Scuba diving 14 17.92 3.43 2.81 45.00 

 Sightseeing 2 121.43 88.36 33.07 209.77 

 Swimming 7 22.21 6.14 2.20 50.10 

 Visiting environmental education centers 1 6.01 6.01 6.01 

 Waterskiing 1 15.13 15.13 15.13 

 Wildlife viewing 65 31.30 2.18 2.40 96.30 

Pacific coast area studies:

 Backpacking 6 52.10 9.29 26.82 80.34 

 Camping 4 104.35 45.38 7.45 224.53 

 Cross-country skiing 1 48.38 48.38 48.38 

 Downhill skiing 1 25.08 25.08 25.08 

 Fishing 15 44.36 8.68 4.43 103.50 

 Floatboating/rafting/canoeing 4 27.84 1.01 25.21 29.58 

 General recreation 9 32.35 14.38 1.42 125.57 

 Hiking 49 23.24 2.65 .40 129.62 

 Hunting 18 45.49 7.73 6.25 111.36 

 Motorboating 8 26.94 5.90 12.48 64.08 

 Mountain biking 16 49.68 2.74 31.70 78.74 

 Off-road vehicle driving 1 40.37 40.37 40.37 

 Other recreation 1 74.47 74.47 74.47 

 Picnicking 3 64.22 39.66 15.19 142.74 

 Scuba diving 10 52.60 25.86 5.23 250.04 

 Sightseeing 4 20.27 13.51 5.23 60.77 

 Snorkeling 9 30.31 15.36 5.23 135.29 

 Swimming 4 27.29 11.35 6.06 58.90 

 Wildlife viewing 23 72.48 16.90 7.09 347.88 
9

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

Table 3—Detailed descriptive statistics on average consumer surplus values per person per 

day by activity and region, 1967 to 2003 (continued)

 Standard

Area and activity N Mean error Minimum Maximum

2004 dollars

Southeast area studies:

 Birdwatching 5 26.46 6.41 9.44 43.27

 Camping 11 25.79 8.09 3.30 65.02

 Fishing 27 79.21 23.65 3.60 556.82

 Floatboating/rafting/canoeing 47 127.46 13.45 18.05 394.82

 General recreation 9 42.77 20.51 5.02 189.46

 Going to the beach 11 33.12 5.76 6.79 53.83

 Hiking 7 60.38 34.46 1.87 262.04

 Hunting 44 35.36 2.86 5.69 82.80

 Motorboating 13 58.92 9.59 6.91 134.34

 Mountain biking 8 49.62 5.39 20.86 67.52

 Off-road vehicle driving 1 5.24 5.24 5.24

 Other recreation 4 30.07 11.33 5.71 57.19

 Picnicking 2 36.62 8.06 28.56 44.69

 Pleasure driving 2 144.78 21.72 123.06 166.49

 Rock climbing 11 85.70 9.78 39.28 135.82

 Sightseeing 9 46.06 13.70 7.92 112.70

 Swimming 13 60.92 9.00 13.64 134.34

 Visiting arboretums 1 13.54 13.54 13.54

 Visiting aquariums 1 28.31 28.31 28.31

 Wildlife viewing 54 40.10 3.20 2.86 134.34

 Windsurfing 1 395.47 395.47 395.47

Table 4—Pacific Northwest (Oregon and Washington) consumer surplus per 

person per day 

Average Number of Number of Standard

Activity value estimates studies error

2004 dollars

Camping 92.72 2 2 17.44

Downhill skiing 25.08 1 1

Fishing 41.98 11 5 9.42

Hiking 23.98 40 5 3.14

Hunting 35.27 8 5 9.22

Motorboating 12.48 1 1

Mountain biking 49.68 16 1 2.73

Off-road vehicle driving 40.37 1 1

Picknicking 34.74 1 1

Sightseeing 60.77 1 1

Swimming 6.06 1 1

Wildlife viewing 35.00 6 3 2.40
10

GENERAL TECHNICAL REPORT PNW-GTR-658

Using Value Tables and Database for Benefit Transfer to 

Unstudied Recreation Sites on National Forests

Benefit transfer is a term referring to the application of existing valuation information to new sites or unstudied national forests. The two simplest types of benefit 

transfer involve either using the simple average consumer surplus or value-per-day 

information from the previous tables, or selecting from the spreadsheet data to 

more closely match the available studies to the features of the recreation site or 

national forest for which values are needed. In the nomenclature of benefit transfer, 

the site with existing valuation data is typically called the “study” site, and the site 

to which values are transferred is called the “policy” site. It would be preferable to 

value recreation at the policy site by using that site’s specific data (from campground fee receipts, wilderness permits, trail registers, etc.) to estimate a sitespecific Travel Cost Method (TCM) demand model to calculate consumer surplus, 

but this is often not possible. Therefore, benefit transfer can be used, as a “secondbest” strategy, for evaluating management and policy impacts. Including a wellprepared benefit transfer is much better than not including recreation economic 

values in the economic analysis. Some decisionmakers tend to overlook resources 

that have been omitted from economic analysis and incorrectly assume that those 

that have been included are more economically important when it may only mean 

that those included in the analysis are easier to measure. 

Thus, benefit transfer is a practical way to evaluate management and policy 

impacts when primary research is not possible or justified because of limited time 

or budget constraints.

Benefit Transfer Methods

There are two broad approaches to benefit transfer: (1) value transfer, and (2) function transfer (fig. 2). Value transfers encompass the transfer of (1-a) a single (point) 

benefit estimate from a study site, or (1-b) a measure of central tendency (such as 

an average value) for several benefit estimates from a study site or sites, or (1-c) 

administratively approved estimates. Administratively approved value estimates 

will be discussed in conjunction with the measure of central tendency discussion 

(hereafter average-value transfer will refer to both (1-b) and (1-c)). Function transfers encompass the transfer of (2-a) a function for benefit, willingness to pay, or 

demand from a study site, or (2-b) a meta-regression analysis function statistically 

estimated from several study sites. Benefit function transfers tailor the function to 

fit the specifics of the policy site by setting the values of independent variables such 

as socioeconomic characteristics, extent of market and environmental impact, and 

Benefit transfer 

can be used, as 

a “second-best” 

strategy, for evaluating 

management and 

policy impacts. 

Including a wellprepared benefit 

transfer is much 

better than not 

including recreation 

economic values in 

the economic analysis.
11

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

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

and the policy site to the values at the policy site. The adapted or tailored benefit 

function is then used to “forecast” a benefit measure for the policy site. 

In this section we define and identify what the benefit measures are, what they 

mean, and how they were estimated.

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) obtained from the spreadsheet data. The primary steps to performing 

a single-point estimate transfer include identifying and quantifying the effect of 

management- or policy-induced changes on recreation use, and locating and transferring a “unit” consumer surplus measure. The detailed list of the steps involved 

in single-point estimate transfers were given by Rosenberger and Loomis (2001) as: 

1. Identify the resources affected by a proposed action or alternative.

2. Translate resource impacts to changes in recreational use.

3. Estimate recreation use changes. 

4. Search the spreadsheet data 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.

Value transfer Function transfer

Single 

point 

estimate

(1-a) 

Measure 

of central 

tendency

(1-b) 

Administratively 

approved

(1-c) 

Benefit/

demand 

function

(2-a) 

Metaanalysis 

function

(2-b) 

Use estimate 

at policy site

Adapt function

to policy site

Use tailored estimate 

at policy site

Figure 2—Benefit transfer approaches (from Rosenberger and Loomis 2001).
12

GENERAL TECHNICAL REPORT PNW-GTR-658

We provide information in this report that aids in identifying study site benefit 

measures from the literature.1 2 The spreadsheet includes studies conducted from 

1967 through 2003 in the United States and Canada. There are 593 studies and 

1,239 benefit measures identified. The spreadsheet includes a full reference, recreation activity, geographic region, methodology used, etc., for each observation. 

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 activity days. 

The simplicity with which the steps to performing a single-point estimate 

transfer are presented may be misleading. This will become apparent when the 

information on the conditions for benefit transfers are taken into account as identified below. See Boyle and Bergstrom (1992) for an example of how to critically 

filter 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 (Boyle and 

Bergstrom 1992: 659): 

(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).

Their filtering of each study based on these considerations left them with no 

ideal benefit measures to transfer to their policy site. They stated 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.

1

 Another 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/.

2

 Use of trade or firms names in this publication is for reader information and does not 

imply endorsement by the U.S. Department of Agriculture of any product or service.
13

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

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. 

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. Rosenberger 

and Loomis (2001) provided a detailed list of the steps involved in average-value 

transfers:

1. Identify the resources affected by a proposed action.

2. Translate resource impacts to changes in recreational use.

3. Estimate recreation use changes. 

4. Search the spreadsheet for relevant study sites. 

5. Assess relevance and applicability of study site data. 

6. Use average-value provided in table 2 for that activity in that region 

or calculate an average of a subset of applicable study values.

7. Multiply benefit value by total change in recreation use.

Guidance for Performing an Accurate Benefit Transfer

There are several conditions required for performing an accurate benefit transfer 

(Desvousges et al. 1992). This section illustrates the application of these conditions 

for a hypothetical benefit transfer. For each condition we provide the name of the 

relevant variables in the spreadsheet. The exact definition of each of these variables 

is given in table 5. 

The purpose of checking the correspondence of variables for the candidate 

studies to be transferred against the policy site in need of values is to ensure they 

are reasonably similar in most characteristics that affect the value of recreation 

(e.g., determinants of demand and supply). Accuracy in benefit transfer would be 

improved if there is a good match between the natural environment (e.g., forest) at 

the sites with values and the sites for which you need values (e.g., forest). This point 

can best be illustrated by an example. If one only had values in the spreadsheet for 

mountain biking in the high desert of Moab, Utah, and needed values for mountain 

biking in the evergreen forests of the El Dorado National Forest near Sacramento in 

northern California, there would be a mismatch between the natural environment 

(as well as differences between a small rural town of Moab versus a large urban 

city of Sacramento, on the demand side). The following factors or variables are 

worth checking in the spreadsheet to determine whether the average value from the 

table can be transferred or whether the analyst should select a subset of studies from 
14

GENERAL TECHNICAL REPORT PNW-GTR-658

Table 5—Variable definitions in spreadsheet 

Code # Variable Coding

General study characteristics

 V000 STUDYID Study number

 V001 ORIGDATA 1,0; 1 = This is the first study to use this data

 V002 AUTHOR(s) Name(s)

 V003 STUDY TITLE Text

 V004 SOURCE/VOL/PAGES Text

 V005 PUBDATE Month (if available) and year of publication

 V005A DATAANAL Year of publication

 V006 PUBLISHER Text

 V007 DOCUMENT TYPE 1 = journal; 2 = book; 3 = proceedings; 4 = report; 5 = thesis or 

 dissertation; 6 = working paper

 V008 CTRY NAME USA, Canada

Benefit measures

 V009 BENMEAS 1 = willingness to pay (WTP); 2 = willingness to accept (WTA)

 V010 MEAN/MED 1,0; 1 = mean, 0 = median (mean should be reported where possible)

 V011 DOLVALUE Value converted to per person per day in 2004 dollars

 V011org ORIGVAL Original value printed in report

 V012 YEARVAL Year of data 

 V012a YEARVALUSED Year that the given values are based on

 V013 ORIGVALUNITS 1 = day; 2 = trip; 3 = year; 4 = season

 V013a AVGTRIP Average days per trip

 V013aa REPESTASK Reported, estimated, or asked author

 V013b ORIGNUM Original number of people per group for ORIGVAL

 V014 STD ER Standard error of mean/median WTP for $ value or study 

 average value

 V015 CI’S 1,0; 1 = confidence interval included in report

 V016 NATIONAL 1,0; 1 = national

 V017 MULTI-STATE 1,0; 1 = multistate

 V018 STATE 1,0; 1 = state

 V019 ST NAMES Type in two-letter state abbreviation (e.g., CO for Colorado).

 V019b REGION U.S. Forest Service Regions 1 thru 10 (11 is all regions); 100 for 

 U.S. national, 101 for Canada

 V019cc Region for Tables 1 = NE (Forest Service area R9); 2 = SE (R8); 3 = Intermountain 

 (R1, R2, R3, R4); 4 = Pacific Coast (R5, R6); 5 = Alaska (R10); 

 6 = Multiple area studies (R11); there is no region 7

 V019ccc Region for Category 1 = Northeast; 2 = Southeast; 3 = Intermountain; 4 = Pacific Coast;

 5 = Alaska; 6 = Multiple area studies (R11)

 V19b1 CENSUSREG Census regions of the USA, 1 thru 5 (and 6 is all regions); 

 100 is U.S. National, 101 is for Canada

 V020 ESTSELEC 1 = author recommendation; 0 = other

 V021 AVGSITIME Average onsite time per trip, in hours (convert multiple days by using 

 12 hours/day)

 V022 GROUPSIZE Average number of people in group

 V023 TOTSITEVIS Number of visits to the area/site per year in total or per person

 V023a TOTSITDES Description of the units of number of visits data

 V024 SEASLNGTH Season length converted to days (e.g., hunting period allowed)

 V025 ALL/NO-SQ 1,0; 1 = Yes, valued for existing condition; 0 = No
15

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

Table 5—Variable definitions in spreadsheet (continued)

Code # Variable Coding

 V026 CHGVAL 1,0; 1 = Yes, valued for change in quality; 0 = No 

 V026b DOLVALCHG $ value of change

 V027 CHGDESCRIP Text description of change

 V028 CHGSIZE % change, absolute change

 V029 STDYSPONSOR 0 = industry; 1 = university; 2 = government; 3 = environmental/

 conservation; 4 = multiple category of sponsors; 5 = others

 V030 NUMSVYSRET Number of surveys returned

 V030a NUMUSE Number of usable surveys

 V031 RESPRATE Response rate percentage

 V0331a RESUSE Response rate of usable surveys

 V032 MAILSVY 1,0; 1 = some studies will have more than 1 survey mode; mail

 survey includes those mailed out to people but also those that were

 given to people and needed to be mailed back

 V033 PHONESVY 1,0; 1 = phone survey used in the study

 V034 INPERSON 1,0; 1 = in person used in the study

 V035 SAMPFRAME 1 = on-site; 2 = user list; 3 = general population; 4 = others;

 V036 VALMETHOD 1 = contingent valuation method, 0 =travel cost method, 2 = both

 V037 GEOGAREA Geographic area of visitor origin (average one-way distance in miles

Details of CVM application

 V038 PAYVEHICLE 1 = trip cost; 2 = entrance fee/license; 3 = annual pass; 4 = others

 V039 OECVM 1,0; 1 = open-ended CVM question

 V040 ITBID 1,0; 1 = iterative bidding used

 V041 CONJOINT 1,0; 1 = conjoint (rating scale approach)

 V042 ST&RP 1,0; 1 = combined stated and revealed preference

 V043 PAYCARD 1,0; 1 = payment card

 V044 MIDPTS 1 = midpoint; 2 = amount circled (refers to payment card)

 V045 PCCAMHUPLF 1,0; 1 = Cameron-Huppert likelihood function (refers to payment card)

 V046 DCCVM 1,0; 1 = dichotomous choice or referendum

 V047 SB 1 = (SB) single bound; 2 = (DB,MB) double bound or multiple bound 

 V048 DCSTAT 1 = logit; 2 = probit; 3 = nonparametric; 4 = semi-nonparametric

 V049 CVWTPEQ 1,0; 1 = WTPEQ, if equation estimate for any CVM, 0 = no; equation 

 (refers to open-ended CVM)

 V050 CVEQTYPE 1 = OLS; 2 = 2SLS; 3 = TOBIT; 4 = others (refers to open-ended CVM)

 V051 HNNEGMEAN 1 = no neg (log of Bid or 1/B*(ln(1+expBo)); 2 = neg allowed.

 V052 CVUPTRUNC 1,0; 1 = upper limit; 0 = no upper limit of integration

 V053 CVOUTLIE 1,0; 1 = removed or “trimmed” outliers; 0 = if not or full sample

 V054 PROTESTR 1,0; 1 = protest responses removed; 0 = all observations used

Details of TCM application

 V055 TCMTYPE 1 = zonal; 2 = individual; 3 = RUM/MNL

 V056 TCMEQTYPE 1 = OLS; 2 = 2SLS or SUR; 3 = TOBIT; 4 = count data 

 (POISSON, neg binomial); 5 = others (includes MNL, NMNL, 

 when TCMTYPE = 3)

 V057 TRUNCADJ 1,0; 1 = truncation adjustment

 V058 ENDOGSTRT 1,0; 1 = corrected for endogenous stratification

 V059 TRAVTIMEVAR 1,0; 1 = separate variable given for travel time

 V060 OPCOSTIME Wage rate in percent
16

GENERAL TECHNICAL REPORT PNW-GTR-658

Table 5—Variable definitions in spreadsheet (continued)

Code # Variable Coding

 V060a OPCTINC 1,0; 1 = V060 has value coded; 0 otherwise

 V061 COSTMILE $ per mile used in study year

 V061a COSTKM $ per km used in study year

 V062 SUBS 1,0; 1 = price of substitute or availability of substitute variable included in 

 demand function

 V063 SITEQUAL 1,0; 1 = site quality or facility (indicated by author)

 V064 HEDTCM 1,0; 1 = hedonic TCM

 V065 LHSFUNCFRM 1 = linear; 2 = log; Poisson, negative binomial; 3 = other

 V066 RHSFUNCFRM 1 = linear; 2 = log; 3 = other

 V067 EXPENDAT 1,0; 1 = expenditure data included in the study/report 

 (e.g., lodging, food, equipment, etc.)

 V068 TCMWTPTRUNC 1,0; 1 = upper limit of integration truncated, at max observed TC

 V069 TCMOUTLIE 1,0; 1 = outliers or multidestination trips explicitly removed

Study location

 V070A GENDES General description of area studied

 V070 COUNTY 1,0; 1 = county

 V071 CTY NAME County name

 V072 SITE NAME Name of site

 V073 LAKE/RESERVOIR 1,0; 1 = lake/reservoir

 V074 LAKE NAME Text

 V075 ESTBAY 1,0; 1 = site is estuary or bay

 V076 OCEAN 0 if not ocean; 1 = Atlantic; 2 = Pacific; 3 = Gulf of Mexico

 V077 RIVER 1,0; 1 = recreation site is river based

 V078 RIVNAME Name of the river

 V079 GREAT LAKES 1,0; 1 = great lakes

 V080 AREASIZE Size of recreation area in acres

 V081 NAT FOREST 1,0; 1 = national forest

 V082 NFNAME Name of national forest

 V083 NATPARK 1,0; 1 = national park

 V084 N.P.NAME Name of national park

 V084bbb NP,NF,Other Whether in national park, national forest, or other

 V085 NRAREA 1,0; 1 = national recreation area

 V086 NRANAME Name of national recreation area

 V087 W/L AREA 1,0; (1 = wildlife refuge or game management area)

 V088 W/L AREA NAME Name of refuge or mgmt area

 V089 WILDERNESS 1,0; 1 = recreation use is in wilderness area

 V090 WILDNAME Name of wilderness area

 V091 STPARKFOR 1,0; 1 = recreation use is in state park or state forest

 V092 STPKNAME Name of state park

 V093 PUBLIC 1,0; 1 = public land including federal, state, county/city

 V094 PRIVATE 1,0; 1 = private land

 V095 W/L SPECIES 1 = BGAME (deer, elk, etc.); 2 = SGAME (rabbit, quail, dove, etc.); 

 3 = WTRFWL (duck, geese); 4 = threatened and endangered; 

 5 = songbirds; 6 = raptors, hawks, eagles, etc.; 7 = fish; 8 = general wildlife

 V095a W/L SPECIES 2

 V095b W/L SPECIES 3

 V095c W/L SPECIES 4
17

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

Table 5—Variable definitions in spreadsheet (continued)

Code # Variable Coding

 V096 FOREST 1,0; 1 = recreation area in forest; 0 = otherwise

 V097 ENV TYPE 1 = wetland, 2 = riparian; 0 = otherwise

 V098 WATERQUAL 1,0; 1 = water quality was valued or focus of study

 V099 AIRQUAL 1,0; 1 = air quality was valued or focus of study

 V100 DEVELOP 1,0; 1 = site studied had developed recreation facilities (such as 

 arranged tables etc., e.g., camping, boating, etc.)

 V101 DISPERSED 1,0; 1 = site studied was dispersed recreation with no formal site or 

 facilities (e.g., hunting, hiking, etc.)

 V102 ROSCLASS 1 = primitive; 2 = SPNM (semiprimitive nonmotorized); 3 = SPM 

 (semiprimitive motorized); 4 = RN (roaded natural); 5 = rural; 

 6 = urban; 7 = various

 V103 ACT TYPE 1 1 = camping; 2 = picnicking; 3 = swimming; 4 = sightseeing; 5 = off-road 

 vehicle driving; 6 = motorboating; 7 = floatboating/rafting/canoeing; 

 8 = hiking; 9 = mountain biking; 10 = downhill skiing; 11 = cross-country 

 skiing; 12 = snowmobiling; 13 = snowplay; 14 = hunting; 15 = fishing; 

 16 = wildlife viewing; 17 = horseback riding; 18 = resort; 19 = rock 

 climbing; 20 = general recreation; 21 = other recreation; 22 = visiting 

 wilderness; 23 = waterskiing; 24 = pleasure driving (can include 

 sightseeing); 25 = visiting arboretums; 26 = going to the beach; 

 27 = relaxing outdoors; 28 = visiting aquariums; 29 = scuba diving; 

 30 = windsurfing; 31 = bird watching; 32 = snorkeling; 33 = backpacking; 

 34 = visiting environmental education centers

 V104 ACT TYPE 2 One of the above categories of ACTTYPE except one already chosen

 V105 ACT TYPE 3 One of the above categories of ACTTYPE except one already chosen

 V106 ACT TYPE 4 One of the above categories of ACTTYPE except one already chosen

 V107 NUMACT Number of activities site offers or typical visitor could participate in at site

 V108 AVGINC Average income of visitors

 V109 AVGED Average education of visitors

 V110 AVGAGE Average age of visitors

 V111 AVGSEX (% female); 1 = female; 0 = male; or percent female for group

 V112 RESIDENTS 1,0; 1 = residents only; 0 = both

 V113 USEEXP 1,0; 1 = very experienced (level of user experience with site); 0 = otherwise

 V114 SUCESRATE Percentage of success rate in hunting

 V115 BAG Number of animals (in hunting)

 V116 HOUR 1,0; 1 if bag reported is per hour, zero otherwise

 V117 DAY 1,0; 1 if bag reported is per day

 V118 TRIP 1,0; 1 if bag reported is per trip

 V118a YEAR 1,0; 1 if bag reported is per year

 V119 HIQUAL 1,0; 1 = author states site is of high quality (e.g., popular, unique, well-known, 

 only in the region, etc.)

 V120 DATAYEAR Year data collected

 V121 SAMPSIZE Total sample size used in analysis

 V122 NUMTCZONES Number of zones or origins in zonal TCM.

 V123 MULTSITE 1,0; 1 = yes

 V124 NUMSITES Number of sites modeled in multisite or RUM models

 V125 CHOICEOC Number of choice occasions (frequency)

 V126 COMMENTS Text field where coder can write anything special or unusual about study or

 (COMMENTS2 details about recreation site or area where study was performed

 and COMMENTS3)
18

GENERAL TECHNICAL REPORT PNW-GTR-658

the spreadsheet data from which to calculate average value based on studies that 

more closely match the study site.

1. The activities to be valued should be identical, or at least similar; see 

spreadsheet variables, ACT TYPE1, ACT TYPE 2, and ACT TYPE 3. 

2. The general geographic region of the study sites and the policy site should 

be identical or at least similar; see spreadsheet variables ST NAMES, 

REGION (USFS Regions 1 through 10) and CTY NAME (when available). 

3. The type of public land at the study sites and the policy site should be 

identical or at least similar; see spreadsheet variables PUBLIC, PRIVATE, 

NAT FOREST, NATPARK, NRAREA (national recreation area), W/L 

AREA (state or federal wildlife area), WILDERNESS, STPARKFOR 

(state park or state forest). 

4. For wildlife recreation, similar species should be valued in both cases. For 

example, for valuation of big game hunting, one should use existing big 

game hunting studies, not waterfowl or upland game bird hunting studies; 

see spreadsheet variables W/L SPECIES, W/L SPECIES 2, W/L SPECIES 3. 

5. The type of population and magnitude of the human population at the study 

site and policy site should be similar (i.e., rural to rural, or urban to urban); 

see spreadsheet variables AVGED, AVGAGE, RESIDENTS.

6. Level of facility development and recreation opportunity spectrum 

classification should be similar between the study sites and the policy 

site; see spreadsheet variables DEVELOP, DISPERSED, ROSCLASS.

7. The environmental resource and the natural setting of the resource at the 

study site and the resource at the policy site should be similar. As mentioned 

in the example above, it would be desirable to transfer values of a particular recreation activity that occurred in the same environmental setting or 

ecosystem type. Thus camping in a forest might yield different values than 

camping at the beach. See spreadsheet variables FOREST, ENV TYPE, 

LAKE/RESERVOIR, ESTBAY (estuary/bay), OCEAN, RIVER, GREAT 

LAKES. 

8. The markets or determinants of demand (similarity of demographic profiles 

between the two populations and their cultural aspects) for the study site 

and the policy site should be similar. That is, similar levels of income, racial 

composition, degree of ruralness. Unfortunately, most studies did not report 

demographics, but check spreadsheet variable AVGINC. If there are no observations for this demographic variable, inspection of spreadsheet variables 

such as ST NAMES and CTY NAME (when available) may be instructive. 

For example, a camping study in North Dakota might not yield accurate 
19

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

values for camping at the Angeles National Forest outside of Los Angeles 

owing to differences in income levels and racial composition of the two 

populations. 

9. 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. It is not accurate to transfer the value per day 

for rafting down the Colorado River in Grand Canyon National Park 

to rafting down the Colorado River in the White River National Forest 

paralleling I-70 in Glenwood Canyon. 

Keep in mind that most of the original research studies reported in the database 

were not designed 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 need to be pragmatic in their applications 

of the method when considering the many limitations imposed upon them by the 

limited availability of existing studies. It is this author’s opinion that in many cases, 

even a rough approximation of the average value per day from a conservative benefit 

transfer is better than simply ignoring the economic value of recreation in forest 

plans or environmental impact statements. 

Validity and Reliability of Benefit Transfers

There are at least two sources of error in benefit transfer that influence the reliability 

and validity of the resulting benefit estimates. First is the underlying variability in 

the original study estimates. If the original study reports the standard error of the 

estimate, then a confidence interval for transferred point estimates can be calculated. This confidence interval provides the statistical range in which we would expect 

the original estimate to be some large percentage of the time (e.g., a 95-percent confidence interval means the estimate would be within the calculated range 95 percent 

of the time). However, this confidence interval does not account for the additional 

error associated with transferring the estimate from the original study site to the 

policy site. 

Several recent studies have tested the convergent validity and reliability of different benefit-transfer methods (Desvousges et al. 1998, Downing and Ozuna 1996, 

Kirchhoff et al. 1997, Loomis et al. 1995, Rosenberger and Loomis 2000). The 

methods tested include single-point estimate, average-value, demand-function, and 

meta-regression-analysis transfers. Although the above studies show that some of 
20

GENERAL TECHNICAL REPORT PNW-GTR-658

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 tests of the benefit-transfer methods within the 

same geographic region, transferred values were very similar to the “true” values 

and errors were in the range of 4 to 40 percent when using benefit-function transfer (Loomis 1992). In other cases, the disparity between the “true” value and the 

“tailored” value was quite large. These errors were typically in the range of 50 to 

80 percent when using meta-regression benefit transfer as compared with in-sample 

study values used to estimate the meta-regression (Rosenberger and Loomis 2000) 

and a comparison to new out-of-sample study values not used to estimate the original meta-regression (Shrestha and Loomis 2003). 

Other Potential Limitations of Benefit Transfers

Several other factors can also influence the accuracy of any particular benefit 

transfer. Factors that affect the accuracy of any specific benefit transfer include:

• The quality of the original study.

• A limited number of studies investigating an activity’s economic value, 

thus restricting the pool of estimates and studies from which to draw 

information.

• Different research methods across study sites for a specific recreation 

activity, including differences in 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.

• Different statistical methods used for estimating models, which 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 

of the demand function (Adamowicz et al. 1989).

• Unique sites and conditions of existing studies used for valuing recreation 

activities. See the variables SITE NAME, LAKE NAME, N.P. NAME, W/

L AREA NAME, STPKNAME to ensure there is similarity of the 

study site and 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 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 
21

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

transferred from a study site to a policy site is accomplished with varying degrees 

of confidence in the applicability and precision of the information. 

Nonetheless it is our belief that national forest decisionmaking involving 

tradeoffs between types of recreation (motorized vs. nonmotorized), and other 

multiple-use tradeoffs can often be improved by inclusion of even approximate 

estimates of nonmarket recreation values. 

A Note on Definition of Benefit Measures and Use in 

Policy Analyses

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 of the average individual values reported 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.3

 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 some of the simple approaches 

to benefit transfers such as point-estimate or average-value transfer. If the analyst 

is evaluating a large-scale ecosystem change, then an original study will often be 

necessary (and warranted), or a benefit-function transfer approach that incorporates 

the quality of the resource would be necessary to accurately capture the change 

in benefits. Such a benefit-function transfer approach would be to apply a demand 

curve that contains a resource quality variable or apply a contingent valuation 

method willingness-to-pay equation that contains the relevant resource quality 

variables for the change being evaluated. 

3

 There are two prominent types of consumer surplus estimated by using slightly different 

definitions of the demand function: Marshallian consumer surplus based on an ordinary 

demand function, and Hicksian surplus based on either a compensated demand function 

or elicited directly by using hypothetical market techniques. The difference between these 

measures is due to the income effect (Willig 1976). Because outdoor recreation expenditures are a relatively small percentage of total expenditures (income), differences between 

the two measures are expected to be negligible.
22

GENERAL TECHNICAL REPORT PNW-GTR-658

Simply stated, the benefit-transfer estimate of a management- or policyinduced change in recreation is the average consumer surplus estimates for the 

average individual from the literature aggregated for the particular change in 

use of the natural resource. The change in recreational use of a resource may 

be induced either through a price change for participating in an activity (e.g., fee 

change or location of the site) or through a quality change in the recreation site.

Details of Spreadsheet Coding 

Often times in performing benefit transfer, it is more appropriate to compute an 

average value per visitor-day from empirical studies that closely match the policy 

site, rather than just using an overall average for the region. To facilitate doing this, 

the spreadsheet contains numerous details about each of the studies. 

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 because 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.

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, semilog, 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. 

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 

(table 5). This was either because information was not reported or was not collected 

for a study. For example, 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 that further limits the ability to 

perform critical benefit transfers. 
23

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

Summary

This report provides updated average values and a spreadsheet that gives information on outdoor recreation use valuation studies, including study source, benefit 

measures, recreation activity, valuation methodology, and geographic region. This 

literature review spans 1967 to 2003 and covers more than 20 recreation activities.

Guidance on performing various benefit-transfer methods is also provided 

in this report. Benefit transfer is the use of past empirical benefit estimates to 

assess and analyze current management and policy actions. Two benefit-transfer 

approaches (single-point estimates, average values) were discussed in detail.

A research effort such as this is really never complete, as new studies appear 

every year. Some of these studies could fill important gaps in the existing literature 

or increase the small sample of valuation studies for that activity in that region. 

Augmenting this database with new studies every 5 years is probably a worthwhile 

undertaking to keep the database current and of greatest use for field personnel. 

Acknowledgments

I thank Pam Kaval, Randy Rosenberger, and Ram Shrestha for their original 

participation in the earlier databases. This report also builds upon and draws from 

earlier work with Randy Rosenberger and Pam Kaval. The suggestions of three 

reviewers improved the clarity and exposition of this report. I especially thank 

Roger Fight for his attention to details in the spreadsheet and the editor for catching several important details in this report. Any errors are those of the author. 

References 

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Bergstrom, J.C.; Bowker, J.M.; Cordell, H.K.; Bhat, G.; English, D.B.K.; 

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Boyle, K.J.; Bergstrom, J.C. 1992. Benefit transfer studies: myths, pragmatism, 

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Augmenting this 

database with new 

studies every 5 years is 

probably a worthwhile 

undertaking to keep the 

database current and 

of greatest use for field 

personnel. 
24

GENERAL TECHNICAL REPORT PNW-GTR-658

Brookshire, D.S.; Neill, H.R. 1992. Benefit transfers: conceptual and empirical 

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25

Updated Outdoor Recreation Use Values on National Forests and Other Public Lands

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26

GENERAL TECHNICAL REPORT PNW-GTR-658

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