United States Department of Agriculture

Forest 

Service

Pacific Northwest 

Research Station

General Technical Report

PNW-GTR-957 August 

2017

Recreation Economic Values for 

Estimating Outdoor Recreation 

Economic Benefits From the 

National Forest System

Randall S. Rosenberger, Eric M. White, Jeffrey D. Kline, and Claire Cvitanovich
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Authors

Randall S. Rosenberger is associate dean for undergraduate studies, Oregon State 

University, 140 Peavy Hall, Corvallis, OR 97331; Eric M. White is a research social 

scientist, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station, Forestry Sciences Laboratory, 3625 93rd Avenue SW, Olympia, WA 97512; 

Jeffrey D. Kline is a research forester, U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 3200 SW 

Jefferson Way, Corvallis, OR 97331; and Claire Cvitanovich was a research associate, Oregon State University, 321 Richardson Hall, Corvallis, OR 97331.

Cover photo by Emily Jane Davis
Abstract

Rosenberger, Randall S.; White, Eric M.; Kline, Jeffrey D.; Cvitanovich, 

Claire. 2017. Recreation economic values for estimating outdoor recreation 

economic benefits from the National Forest System. Gen. Tech. Rep. PNWGTR-957. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station. 33 p.

Natural resource professionals are often tasked with weighing the benefits and costs 

of changes in ecosystem services associated with land management alternatives and 

decisions. In many cases, federal regulations even require land managers and planners to account for these values explicitly. Outdoor recreation is a key ecosystem 

service provided by national forests and grasslands, and one of significant interest 

to the public. This report presents the most recent update of the Recreation Use 

Values Database, based on an exhaustive review of economic studies spanning 1958 

to 2015 conducted in the United States and Canada, and provides the most up-todate recreation economic values available. When combined with data pertaining 

to recreation activities and the quantity of recreation use, the recreation economic 

values can be used for estimating the economic benefits of outdoor recreation. The 

recreation economic value estimates provided in this report, whether from past 

research literature or from values constructed using our meta-analysis benefit function, are average consumer surplus per person per activity day.

Keywords: Benefit transfer, economic value, ecosystem services, outdoor 

recreation, recreation benefits, nonmarket valuation, national forest planning and 

management, NEPA.

Preface

This report was sponsored by the National Center for Natural Resource Economics Research. The center is a virtual collaborative effort of the Washington office 

and the regional research stations within U.S. Department of Agriculture, Forest 

Service, Research and Development. The center was founded to respond rapidly to 

emerging natural resource economic issues of national significance by leveraging 

expertise across the Forest Service. The center sponsors research with funding from 

client organizations and regional research station contributions.
Contents

1 Introduction

2 Recreation Economic Value

3 Benefit Transfer Methods

4 How Economic Values for NFS Recreation Were Estimated

8 Economic Values of Recreation Benefit

10 Guidance for Analysts

13 Example Applications

13 Estimating the Economic Benefit of Recreation for a Single Forest Service Region

13 Estimating the Economic Benefit of Recreation for a Single National Forest 

15 Conclusions

15 Acknowledgments

16 Literature Cited

20 Appendix

20 History of the Recreation Use Values Database

21 Consumer Surplus

22 Benefit Transfer

27 Meta-Regression Analysis Detailed Methods
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Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Introduction

Outdoor recreation is one of the most widely recognized ecosystem services 

provided by national forests and grasslands and is identified as one of five uses 

under the Multiple-Use Sustained-Yield Act of 1960. The forest reserves, which 

would eventually become the first national forests, were originally reserved in the 

late 19th century to conserve timber and water. Those places also rather quickly 

became destinations for people seeking both primitive and developed recreation 

opportunities (Waugh 1918). Today’s National Forest System (NFS) receives more 

than 148 million visits annually with visitors engaging in a variety of outdoor 

pursuits (USDA FS 2017). The continuing role of the Forest Service in providing 

sustainable recreation opportunities to the public is evident in the agency’s current 

strategic plan. Developing and maintaining sustainable recreation opportunities 

is identified as one way to achieve the agency’s strategic objectives: “Strengthen 

Communities” and “Connect People to the Outdoors” (USDA FS 2015). Meeting 

these objectives requires understanding what recreation activities occur on national 

forests and grasslands, who is involved in that recreation, and how much do they 

value their recreation experiences. Recreation activities and numbers of participants 

on national forests are tracked by the National Visitor Use Monitoring (NVUM) 

program (English et al. 2002). Other federal and state agencies have their own 

monitoring programs that also provide estimates of recreation use and activity 

participation. The economic values that people hold for specific recreation activities 

are primarily tracked through periodic updates to the Recreation Use Value Database (RUVD) (e.g., Rosenberger and Loomis 2001) and in the scientific literature. 

Natural resource professionals are often tasked with weighing the benefits 

provided by natural resources against the costs of management to produce those 

benefits. Although the social and economic values of ecosystem services, including 

outdoor recreation opportunities, are widely recognized, they can be difficult to 

quantify. Yet in many circumstances, federal regulations require land managers and 

planners to account for those values explicitly. Within the Forest Service, for example, the Renewable Resources Planning Act of 1974 (superseded by the Government 

Performance and Results Act of 1993), which informs management of national 

forests and grasslands, includes an assessment phase and a program analysis phase 

(USDA FS 2000). The assessment phase identifies the supply of, and demand for, 

renewable resources on the nation’s forests and grasslands. The program analysis 

phase evaluates the benefits and costs associated with the Forest Service’s various 

programs. These requirements demand credible benefit estimates for key ecosystem 

Natural resource 

professionals are often 

tasked with weighing 

the benefits provided 

by natural resources 

against the costs of 

management.
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GENERAL TECHNICAL REPORT PNW-GTR-957

services associated with Forest Service management and planning. More broadly, 

the need for credible benefit estimates is underscored by the President Barack 

Obama administration’s 2015 memorandum directing federal agencies to factor the 

values of ecosystem services into all federal planning and decisionmaking (Office 

of Management and Budget 2015).

The economic benefits of recreation use of NFS lands can be estimated for 

given locations using original studies or information transferred from prior studies 

conducted elsewhere. The latter method—known as “benefit transfer”—applies 

benefit estimates obtained through primary research for one location to other 

unstudied locations of interest. Benefit transfer is used by public agencies and other 

practitioners when (1) available time, funding, or expertise for conducting original 

studies are limited; (2) there are available data from existing studies conducted 

elsewhere; and (3) the application of benefit transfer, given the available studies and 

location of interest, is deemed reasonable by analysts. Benefit transfer and published recreation economic values can also be used to meet the needs of state and 

local resource management agencies, as well as nongovernment organizations and 

private consultants. 

This report is intended to meet the continuing need for current recreation 

benefit information by updating the Rosenberger and Loomis (2001) and Loomis 

(2005) databases of recreation economic values. This update reflects the most 

recent version of the RUVD, based on an exhaustive review of economic studies 

spanning 1958 to 2015 conducted in the United States and Canada. The report 

thus provides the most current and comprehensive set of recreation economic 

values available. Specifically, this report provides (1) a brief review of economic 

concepts and benefit transfer methods, (2) estimates of recreation economic values 

by primary recreation activity and Forest Service region, and (3) additional context 

and guidance for analysts using these estimates. The appendix provides technical 

information about benefit transfer and nonmarket values, and an overview of the 

RUVD itself. Additional information about the RUVD can be found online at: 

http://recvaluation.forestry.oregonstate.edu/. 

Recreation Economic Value

The economic value of any given recreation activity is a monetary measure of the 

economic benefits received by an individual or group doing that activity. For any 

one individual, the net economic value of a given recreation activity is measured as 

the maximum amount the individual is willing to pay to participate in the activity, less the actual cost incurred by the individual to participate in that activity. 

The economic value of recreation differs from the economic impact of recreation. 

The economic 

value of any given 

recreation activity is 

a monetary measure 

of the economic 

benefits received by 

an individual or group 

doing that activity.
3

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Economic impact (or economic contribution) measures how spending by recreationists affects economies within a given geography (e.g., community, region, state, or 

nation) by virtue of the influence that spending has on employment and income. 

Economists typically use an analytical method called economic impact (or input/

output) analysis to evaluate economic impacts. In this report, we are focused only 

on the economic value of recreation benefits and not recreation economic impacts. 

The economic impacts associated with national forest recreation are reported by 

other sources (e.g., White et al. 2016). 

Benefit-cost analysis is a common method for evaluating the potential influence 

that planning and management alternatives and decisions might have on outdoor 

recreation. For example, benefit-cost analysis can be used to address such questions as: What is the relative worth (i.e., benefits generated) from investments in 

recreation opportunities, settings, and resources? Benefit-cost analysis can include 

both market and nonmarket values. Market values are those that are readily identifiable and addressed in typical market transactions and usually involve observable 

prices or the transfer of money, such as the construction costs and entrance fees. 

Nonmarket values are those that are not addressed or represented in typical market 

transactions and can include things such as the value someone has for the opportunity to view nature or the loss of well-being from residents who must endure more 

traffic from people engaging in recreation. Benefit-cost analysis can be used to 

consider present benefits and costs as well as those that might be experienced in the 

future. In this report, we focus on the computation of recreation economic values by 

developing “direct use values” representing the benefits to individual recreationists 

directly engaged in outdoor recreation activities. These values represent “access” to 

a recreation site or to an activity, relative to that location or activity not being available or accessible to recreationists. Thus, these economic values measure the total 

net benefits of doing the recreation activity rather than the total net benefits from 

changes in the quality or characteristic of that recreation. The resulting recreation 

economic values enable scientists, resource analysts, and other practitioners to 

apply benefit transfer methods to compute the economic value of recreation benefits 

based on recreation participant numbers reported or projected for a location or 

activity over a given period. The application of these average values to economic 

assessments is discussed further in the appendix.

Benefit Transfer Methods

Benefit transfer methods include value transfer and function transfer. Value transfer 

is the use of a single estimate of value or a weighted average of multiple estimates 

of value obtained from previously published studies and research literature. Value 
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GENERAL TECHNICAL REPORT PNW-GTR-957

transfer can be an attractive method for estimating recreation economic benefits 

when time, funding, and expertise are insufficient to conduct an original study. 

Moreover, new estimates of economic value based on original or primary research 

are not needed if resulting value estimates are unlikely to statistically differ from 

estimates derived from benefit transfer methods. However, original or primary 

research may provide additional information necessary to evaluate or assess 

management implications at a site—how values relate to changes in resource or 

site quality, proposed management options, or other attributes held constant in the 

benefit transfer estimation process, for example. 

Function transfer is the use of a statistical model to derive recreation economic 

values. The model is estimated from participant or survey data available from one 

or more previously published studies and is adjusted for characteristics of the site or 

collection of sites being considered. Function transfers can also rely on data summarizing value estimates reported in a body of literature (such as the RUVD), using 

a technique known as meta-analysis. Function transfer using meta-analysis can be a 

more statistically rigorous and robust method for conducting benefit transfer, but is 

dependent on the availability of information about the characteristics of a specific 

site, or collection of sites, being considered. Rosenberger and Loomis (2001, 2017) 

provide a thorough conceptual background for different benefit transfer methods. 

Additional information about the mechanics of benefit transfer methods can be 

found in the appendix of this report. 

Many research studies have tested the validity and reliability of benefit transfer 

methods, and all methods generally do well. Function transfers typically outperform value transfers in terms of validity and reliability. A summary of related 

literature shows median benefit transfer error for function transfers at 36 percent 

compared to value transfers at 45 percent (Rosenberger 2015). There is significant 

variability around both median transfer error estimates, which may in part be due 

to the experimental nature of these evaluations in academic (or research) settings. 

In actual benefit transfers conducted by economists and analysts, we feel that good 

judgment will help to avoid excessive transfer errors. The smallest transfer errors 

are generally found in benefit transfer applications where the study site and the 

policy site are similar.

How Economic Values for NFS Recreation Were 

Estimated

We developed estimates of the economic values of recreation benefits for 14 outdoor 

recreation activity sets (table 1). These recreation activity sets are based on outdoor 

recreation activities currently recognized by the Forest Service NVUM program 

Research studies have 

tested the validity and 

reliability of benefit 

transfer methods, and 

all methods generally 

do well.
5

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Table 1—Definitions and National Visitor Use Monitoring categories of primary recreation activities 

represented in the Recreation Use Values Database

Primary activity Definition

National Visitor Use Monitoring

activity represented

Backpacking Camping at primitive or dispersed backcountry sites Primitive camping, backpacking

Biking Mountain and leisure biking Bicycling

Cross-country skiing Cross-country skiing Cross-country skiing and snowshoeing

Developed camping Camping at sites with developed amenities such 

as fire pits, electricity, toilets, picnic tables, and 

parking

Developed camping

Downhill skiing Downhill skiing and snowboarding Downhill skiing and snowboarding

Fishing Freshwater fishing: all species, bodies of water, and 

angling techniques 

Fishing

Hiking Hiking, walking, jogging, and trail running that 

does not include backcountry camping

Hiking and walking

Hunting Big game, small game, and waterfowl hunting Hunting

Motorized boating All types of motorized boating Motorized water activities

Nature related Nature watching and visitor center use Nature center activities, nature study,

viewing wildlife, viewing natural features, 

visiting historic sites

Nonmotorized boating Floating, kayaking, rafting, and all types of

nonmotorized boating

Nonmotorized water activities

Off-highway vehicle 

use, snowmobiling

Snowmobiling and off-road and all-terrain

vehicle riding

Off-highway vehicle use, motorized trail 

activity, snowmobiling, other motorized 

activity

Other recreation Primary and general recreation activities not

accounted for in other categories

Relaxing, horseback riding, gathering forest 

products, resort use, other nonmotorized 

activities, other activities

Picnicking Picnicking Picnicking

(USDA Forest Service 2017). Several of the activity sets represent a narrow group 

of activities (e.g., downhill skiing and snowboarding) while others correspond to a 

mix of outdoor recreation activities (e.g., off-highway vehicle motorized trail use 

including snowmobiling). The 14 activity sets also correspond well to recreation 

activity groupings typically included in the Forest Service’s Resource Planning Act 

(RPA) assessments for recreation (e.g., Bowker et al. 2012), as well as Statewide 

Comprehensive Outdoor Recreation Plan (SCORP) reports completed for individual 

states (e.g., California State Parks 2014, Oregon Parks and Recreation Department 

2013, Washington State Recreation and Conservation Office 2013).

Data for estimating recreation economic values for the NFS were drawn from 

the RUVD. The RUVD is based on an exhaustive review of recreation economic 

value studies spanning 1958 to 2015 conducted in the United States and Canada. 
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GENERAL TECHNICAL REPORT PNW-GTR-957

The data were developed following recommended best practices for meta-analysis 

practitioners (Stanley et al. 2013). The current version of the RUVD contains 3,194 

individual recreation economic value estimates from 422 individual studies. For 

our purposes, we narrowed these estimates to the 14 NVUM recreation activity sets 

(table 2) by (1) eliminating 180 estimates for Canada; (2) eliminating 231 estimates 

for irrelevant activities (e.g., saltwater fishing and beach activities); and (3) removing 74 outlier estimates (i.e., unreasonably small or large values, which significantly 

affect average values) as less than $5 or greater than $500 per person per activity 

day. These changes resulted in the 2,709 estimates from 342 studies summarized in 

table 2. It is common for a single study to report several recreation economic value 

estimates, hence the disparity in the number of estimates and studies. 

Table 2—Summary statistics for average recreation economic value estimates of consumer surplusa per 

primary activity day per person from recreation demand studies, 1958 to 2015

Activity

Number

of studiesb

Number of 

estimatesc

Mean value 

estimate

Median 

value 

estimate

Standard 

error of 

the mean

Range of value estimates

Minimum Maximum

Backpacking 6 41 $17.04 $9.83 2.44 $6.30 $60.16

Biking 13 36 $98.94 $63.48 17.43 $11.78 $499.34

Cross-country skiing 3 5 $36.84 $31.43 6.93 $20.12 $60.18

Developed camping 22 82 $22.99 $16.12 2.47 $5.08 $166.11

Downhill skiing 5 13 $77.63 $30.54 25.62 $7.85 $277.86

Fishing 120 913 $72.59 $53.27 2.22 $5.36 $464.82

Hiking 37 111 $78.19 $47.17 7.97 $5.02 $451.64

Hunting 64 618 $76.72 $63.12 2.38 $5.04 $419.60

Motorized boating 20 83 $42.48 $19.72 6.63 $5.02 $437.18

Nature related 47 431 $63.46 $47.10 2.79 $5.04 $441.26

Nonmotorized boating 23 83 $114.12 $48.95 13.54 $5.18 $473.02

Off-highway vehicle use, 

snowmobiling

14 49 $60.61 $51.19 9.58 $9.06 $462.96

Other recreation 66 220 $62.06 $30.33 5.02 $5.12 $390.74

Picnicking 8 24 $31.98 $23.62 6.62 $5.03 $149.13 a

 All value estimates in 2016 dollars. These figures are general descriptive statistics from studies contained in the Recreation Use Values Database. 

These figures are intended to give information about the range and central tendencies of values in the research literature for recreation activities 

common to national forests and grasslands. The values in this table should not be used for benefit transfer purposes; instead use the values in table 3. 

b

 Total number of studies is 342 (some studies report separate value estimates for two or more primary activities).

c

 Total number of estimates is 2,709.
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Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

The distribution of study numbers across the 14 activity sets reflects the relative 

numbers of scientific studies focused on different recreation activities and does not 

reflect the relative popularity or importance of any one activity set over another. 

Wildlife-related activities, such as fishing and hunting, have historically been the 

focus of much recreation benefit research, for example. Conversely, downhill skiing 

and backpacking have received relatively less attention in the research literature. 

Most studies included in the database focused on recreation in rural, rather than 

urban, places. There are wide ranges of recreation economic value estimates across 

most activities. The range of value estimates reflects variation across individual 

study sites (e.g., site quality, attributes, and recreation facilities) and study participants, as well as differences in study methods. Accounting for this variation is one 

reason why meta-analysis is especially attractive for developing economic estimates 

of recreation values.

We developed estimates of the average recreation economic values per person 

per day for each Forest Service region and the NFS as a whole. We developed the 

estimates by fitting a meta-regression statistical model to the economic estimates 

of values for recreation activities that are relevant to national forests, and associated 

data contained in the RUVD. The regression measured the effect or relationship of 

select independent variables from the RUVD to the recreation economic value data 

characterizing the standardized consumer surplus per person per day as:

Value/person/primary activity day = β0 + β1

X1+ β2X2 +... βkXk

where there are k explanatory variables (k = 1...K). The βs measure the statistical relationship between the variation in the explanatory variable to the variation 

in the value estimates, also known as partial effects. The estimates of economic 

value for all primary recreation activities and regions were then constructed by 

weighting the measured partial effect (coefficient) of relevant policy site features by 

database fixed values—the nonactivity and nonregion variables were held constant 

at their representation in the data (i.e., at their mean value). We then summed across 

these weighted partial effects to derive recreation economic value. This produces a 

recreation economic value estimate that adjusts the baseline estimate (by holding 

all other nonactivity and nonregion effects constant at their mean value) by activityand region-specific partial effects. 

For example, a recreation economic value for developed camping in Region 1 

(Northern Region) was derived by setting the partial effects for developed camping 

and Region 1 at their full level (weights = 1) and removing the partial effects of 

other recreation activities and regions (weights = 0), while holding all the effects 

of all other variables at their mean value. We repeated the process for all activities 

We developed 

estimates of the 

average recreation 

economic values per 

person per day for each 

Forest Service region 

and the NFS as a whole.
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GENERAL TECHNICAL REPORT PNW-GTR-957

for all regions and the NFS as a whole. The recreation economic values estimated 

in this manner are intended to be used only to represent the value associated with 

recreationists’ primary recreation activities; they do not represent the value for 

ancillary, or secondary, activities and should not be used to estimate economic 

benefits for those activities. The recreation economic values we report are robust 

to the uniqueness of any single study given they rely on contributions from all 

related studies in the metadata and are systematically adjusted based on measurable 

differences across the sites being studied. Additional details on this meta-analysis 

function, along with example applications, are provided in the appendix.

We stress that the recreation economic value estimates provided in this report 

are average values of consumer surplus per person, per primary activity day. 

Consumer surplus, or net willingness to pay (i.e., total willingness to pay minus 

cost to engage in the activity), is a measure of the welfare an individual gains by 

engaging in an activity or purchasing a good. This measure is 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. Environmental Protection Agency, and the Forest Service (see Forest Service Handbook SFH 1909.17). 

Additional technical notes on this concept are provided in the appendix. 

Economic Values of Recreation Benefit

Average recreation economic values are reported for each of the 14 primary recreation activities for each Forest Service region, and the NFS as a whole in table 3. 

Nationally, recreation economic values range from about $45 per person per day 

for camping and backpacking to about $120 per person per day for nonmotorized 

boating. On average, a day of recreating on national forest lands provides about $80 

in benefit to the recreationist. Average recreation economic values across all activities for individual Forest Service regions were calculated as the weighted average 

of the share of each region’s recreation use in each primary activity. Region-level 

recreation use was drawn from current NVUM estimates (USDA FS 2017). Average 

recreation economic values for Forest Service regions range from about $63/day for 

Region 5 (Pacific Southwest Region) national forests to about $77/day for Regions 

1 and 4 (Intermountain Region) national forests to $103 for Region 10 (Alaska 

Region) national forests. The regional-level recreation economic values are influenced by the types of activities popular in each region and the underlying values for 

those activities. 

Analysts need to pay attention to units of measure when applying the recreation economic values reported here to compute aggregate recreation benefits. We 

report the recreation economic values on an “activity day” basis (i.e., benefit per 
9

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Table 3—Estimates of the average economic value of recreation benefits (use value) by primary activity and 

Forest Service region (average consumer surplus per person per primary activity day)

Forest Service region

Primary activity R1 R2 R3 R4 R5 R6 R8 R9 R10 National

Backpacking 39.59 32.81 40.89 42.81 26.64 33.15 32.61 21.10 65.09 44.00

Biking 93.18 86.40 94.48 96.40 80.23 86.74 86.20 74.70 118.69 97.60

Cross-country skiing 62.96 56.18 64.26 66.18 50.01 56.52 55.98 44.47 88.46 67.37

Developed camping 42.06 35.28 43.36 45.27 29.11 35.61 35.07 23.57 67.56 46.47

Downhill skiing 88.67 81.89 89.97 91.88 75.72 82.23 81.68 70.18 114.17 93.08

Fishing 77.96 71.18 79.26 81.18 65.01 71.52 70.98 59.47 103.46 82.37

Hiking 90.90 84.12 92.20 94.12 77.95 84.46 83.91 72.41 116.40 95.31

Hunting 83.86 77.08 85.16 87.07 70.90 77.41 76.87 65.37 109.36 88.27

Motorized boating 64.82 58.04 66.12 68.03 51.87 58.37 57.83 46.33 90.32 69.23

Nature related 66.57 59.79 67.87 69.79 53.62 60.13 59.59 48.09 92.08 70.99

Nonmotorized boating 115.37 108.59 116.67 118.59 102.42 108.93 108.38 96.88 140.87 119.78

Off-highway vehicle 

use/snowmobiling

56.89 50.11 58.19 60.11 43.94 50.45 49.91 38.40 82.39 61.30

Other recreation 71.45 64.67 72.75 74.66 58.49 65.00 64.46 52.96 96.95 75.86

Picnicking 55.62 48.84 56.92 58.83 42.67 49.17 48.63 37.13 81.12 60.03

Weighted average 76.24 71.88 76.20 77.04 63.19 68.64 66.70 55.93 103.00 79.96

Note: All value estimates are in 2016 dollars. These estimates are computed using a statistical meta-regression model. They represent the average value 

of the economic benefit to recreationists using national forests and grasslands. These figures represent the value only for those recreationists who 

engage in the listed activities as their primary activity; these values should not be applied to secondary or ancillary activities done by recreationists. 

These values do not represent the economic activity generated by national forest recreation. 

person per day). An activity day is one person recreating for some portion of a day. 

For example, an individual whose primary recreation activity is picnicking and 

who engages in that activity for 2 hours on one day is one primary activity day of 

picnicking. Six people with the primary activity of picnicking who each spent 2 

hours on one day doing that activity is six primary activity days of picnicking. One 

individual with the primary activity of camping who camps overnight for one night 

would equal two primary activity days of camping. 

Currently, recreation use estimates for most federal agencies managing outdoor 

recreation opportunities are reported in terms of “visits.” For the Forest Service, 

a national forest visit is defined as “one person participating in one or more recreation activities on a national forest or grassland for an unspecified period of time” 

(USDA FS 2017). A visit begins when someone enters the national forest and ends 

when the individual leaves the national forest for the last time that day. A national 

forest visit may last 1 hour or several days. Analysts will need to convert visits to 

primary activity days to obtain a quantity of recreation use with which to multiply 

by the recreation economic values. We provide conversion factors for doing this in 
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GENERAL TECHNICAL REPORT PNW-GTR-957

table 4 and example computations in the next section of this report. The conversion 

factors were computed using the NVUM data by estimating the average number 

of calendar days per visit reported by visitors engaged in each NVUM recreation 

activity. The values presented here should only be applied to the primary activities 

of visitors. For instance, recreationists whose primary activity is hiking likely participate in other activities (e.g., viewing nature, viewing wildlife, and photography) 

during their hikes. However, for those visitors, only the recreation economic value 

of “hiking” counts for their visit. 

Guidance for Analysts

The recreation economic values provided in table 3 may be used in a variety of 

ways. By themselves, the values show the average economic value of recreation 

benefit (i.e., consumer surplus) per activity day that accrues to an individual 

engaged in a type of recreation activity within a Forest Service region. These 

average value estimates are what we would expect the economic benefit to be, 

conditional on available information and holding all else constant. This expected, or 

average, value is an estimate within the distribution of all estimates with the highest 

likelihood of being observed. Thus, these recreation economic value estimates 

may be multiplied by the number of activity days a location receives to derive the 

aggregate benefit of recreation. Applications at national, regional, and forest-level 

aggregations include a mix of recreation sites with different qualities and characteristics, and the use of average values is typically most appropriate at this level of 

analysis. 

To apply the recreation economic values, analysts will multiply the value 

per person per day by the estimated annual activity days in that primary activity. For national forests under current conditions, the number of activity days can 

be estimated using visit estimates by activity provided by NVUM reports and 

conversion factors to translate visits into activity days reported in table 4. Other 

reliable information on the number of recreation visits can also be used. Reliable 

information on visits may include counts of recreation use (in per-person activity 

days) estimated from fee envelopes or permits where all use is covered by those 

measures, studies by university or agency scientists where the methods are clearly 

described and replicable, and “engineered” estimates that clearly show assumptions 

and describe data sources. 

We urge users to not interpret the relative economic values of activities as 

indicative of which activities are “best” to promote through management. Just 

because the average economic value for nonmotorized boating is larger than the 

average economic value for picnicking, for example, does not necessarily mean 
11

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Table 4—Activity days per national forest visit, by primary activity and Forest Service region

Forest Service region

Primary activity R1 R2 R3 R4 R5 R6 R8 R9 R10 National

Backpacking 2.4 2.5 2.1 2.7 2.8 2.6 2.4 2.5 2.7 2.4

Bicycling 1.1 1.1 1.1 1.2 1.1 1.2 1.1 1.1 1.1 1.1

Cross-country skiing 1.0 1.0 1.0 1.0 1.1 1.1 1.0 1.0 2.0 1.0

Developed camping 2.7 2.7 2.6 2.5 2.8 2.8 2.8 2.9 2.5 2.7

Downhill skiing 1.0 1.0 1.0 1.0 1.1 1.1 1.0 1.0 1.1 1.0

Driving for pleasure 1.1 1.1 1.1 1.1 1.1 1.1 1.0 1.0 1.0 1.1

Fishing 1.3 1.2 1.3 1.5 1.3 1.3 1.1 1.3 1.3 1.3

Gathering forest products 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.0 1.1

Hiking, walking 1.1 1.1 1.0 1.1 1.1 1.1 1.1 1.1 1.1 1.1

Horseback riding 1.3 1.3 1.1 1.4 1.2 1.4 1.6 1.4 1.0 1.3

Hunting 1.3 1.3 1.6 1.6 1.5 1.5 1.2 1.2 1.5 1.3

Motorized trail activities 1.3 1.3 1.2 1.4 1.3 1.3 1.1 1.1 1.0 1.3

Motorized water activities 1.3 1.1 1.1 1.2 1.3 1.4 1.2 1.1 1.1 1.3

Nature center activities 1.0 1.0 1.0 1.0 1.1 1.1 1.0 1.0 1.0 1.0

Nonmotorized water activities 1.7 1.1 1.2 1.7 1.4 1.3 1.2 1.3 1.1 1.7

Off-highway vehicle use 1.2 1.2 1.2 1.5 1.2 1.3 1.2 1.2 1.0 1.2

Other motorized activities 1.5 1.2 1.1 1.0 1.2 1.2 1.1 1.1 1.1 1.5

Other nonmotorized 1.1 1.2 1.0 1.2 1.2 1.1 1.1 1.1 1.1 1.1

Picnicking 1.2 1.1 1.1 1.1 1.2 1.2 1.1 1.1 1.2 1.2

Primitive camping 2.8 2.4 2.4 2.5 2.3 2.6 2.3 2.7 2.0 2.8

Relaxing 1.6 1.5 1.4 1.5 1.5 1.5 1.3 1.4 1.4 1.6

Resort use 2.5 2.1 2.6 2.5 3.2 2.3 3.1 2.2 3.1 2.5

Snowmobiling 1.0 1.2 1.0 1.1 1.2 1.2 1.0 1.1 1.1 1.0

Viewing natural features 1.1 1.1 1.1 1.2 1.2 1.1 1.1 1.0 1.1 1.1

Viewing wildlife 1.1 1.1 1.1 1.2 1.1 1.2 1.1 1.1 1.1 1.1

Visiting historic sites 1.1 1.1 1.1 1.1 1.1 1.1 1.0 1.0 2.9 1.1

Other activities 1.1 1.2 1.1 1.2 1.1 1.1 1.0 1.2 1.1 1.1

No activity reported 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Weighted activity average 1.2 1.1 1.2 1.3 1.2 1.2 1.2 1.2 1.2 1.2

Conversion coefficients are the average number of calendar days per national forest visit. These figures can be used to convert Forest 

Service national forest visits into activity days. The values in the weighted activity average row are average values for each region 

weighted by the percentage of visits for each primary activity for each region as estimated from National Visitor Use Monitoring. Those 

values can be used to convert aggregate regional or national level visit estimates to activity days without needing to account for primary 

activity type. 
12

GENERAL TECHNICAL REPORT PNW-GTR-957

that management efforts should focus on nonmotorized boating at the expense of 

opportunities for picnicking. Additionally, managers should also consider the supply of different recreation opportunities. There may be many nonmotorized boating 

opportunities, and few or no picnic facilities, implying that the incremental benefit 

from additional picnic sites may be relatively high compared to adding boating 

sites. Further, there may be numerous people who picnic compared to people who 

participate in nonmotorized boating activities, meaning that, in aggregate, the total 

benefit from picnicking is much great than that of boating, despite the average 

recreation economic value for boating being comparatively large. 

These average recreation economic values may not always be appropriate for 

site-level analyses (e.g., those focused on a specific lake, campground, or trail), but 

they can be a starting point. The average values here are computed from a wide 

range of studies conducted in actual recreation settings with varying characteristics 

and quality. These average economic values may not always be representative of the 

conditions (including quality) at an individual recreation site or specific recreation 

setting. The average recreation economic values reported here could be reasonably 

applied for site-specific analyses if that site was similar to an “average” site studied 

in the RUVD. In cases where greater specificity is required in the economic value 

estimate, analysts may want to scale up or down the average value. We recommend 

that analysts considering rescaling of average values lean toward making conservative alterations, as very low and very high estimates of recreation economic values 

are the rarest kinds estimated from primary research. An alternative approach 

would be to use a single point estimate transfer by matching specific studies in 

the RUVD with the policy site of interest (see the appendix for a description of the 

steps for conducting point estimate transfers).

The average recreation economic values reported here are likely inappropriate 

for analyses that involve changes in the quality of recreation sites and settings or the 

cost of accessing them. For example, the recreation economic values reported here 

would not be helpful in estimating the benefits to recreationists from a project to 

increase the screening between campsites that improved the quality of the camping 

experience. To do that analysis, a primary study would have to be done, or the analyst would need to find a study in the RUVD that covered a comparable site. The 

recreation economic values reported here might be appropriate for a study focused 

on added benefit from increasing the number of sites in a campground that was at 

full capacity (and therefore increases the number of visits) if the addition of sites 

did not change the quality or cost of camping there. Finally, the recreation economic 

values here are likely inappropriate to estimate the benefit (or loss) to visitors from 

a change in fees to access a recreation site.
13

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Example Applications 

We provide two examples of how the recreation economic values reported in table 

3 can be used to compute aggregated economic benefits of recreation. The first 

example is an estimate of the aggregated economic benefits of recreation provided 

collectively by the national forests in each Forest Service region; the second is an 

estimate of the aggregated economic benefit of recreation provided by a single 

national forest. 

Estimating the Economic Benefit of Recreation for a Single 

Forest Service Region

We use Forest Service Region 2 (Rocky Mountain Region) as an example for 

computing aggregated economic benefits for an entire Forest Service region. The 

aggregate benefit to users who recreate on national forests in Region 2 can be computed by multiplying the number of recreation visits by the conversion coefficient 

from table 4 and by the average recreation economic value estimate for the region 

from table 3 as:

Region 2

NVUM 2015

use estimate 

(1,000s)

X

Conversion 

coefficient 

(table 4)

X

Economic 

value

(table 3)

=

Aggregated

recreation 

benefit value 

($1,000s)

or:

28,291 visits X 1.1 X $71.88 = $2,236,913

Given these inputs, the economic benefit to individuals who recreated on 

Region 2 national forests in 2015 is computed as $2.24 billion. That means that the 

money spent by federal agencies to provide recreation opportunities in Region 2 

national forests provided $2.24 billion in well-being to those people who recreated. 

The $2.24 billion figure does not represent the economic contribution or economic 

activity generated by recreation at Region 2 national forests; computing economic 

contribution would require an economic impact analysis.

Estimating the Economic Benefit of Recreation for a Single 

National Forest

 We use the Medicine Bow National Forest to show the procedure for estimating 

the aggregate economic benefit of recreation for an individual national forest (table 

5). The computation begins with the estimate of total annual recreation use on the 

Medicine Bow National Forest (534,871 visits) and the percentage distribution of 

that use by primary activity. Both the recreation use figure and the distribution 

of use by recreation activity are drawn from NVUM estimates (USDA FS 2017). 
14

GENERAL TECHNICAL REPORT PNW-GTR-957

Table 5—Estimate of the annual, aggregate economic benefits accruing to individuals recreating on the 

Medicine Bow National Forest 

Primary activity

Primary 

activity

National 

forest visits

Conversion 

coefficient 

(table 4)

Primary 

activity days

Use value 

(table 3)

Economic 

benefita

Percent - - - - - Dollars - - - - -

Backpacking 0.0 161 2.5 403 32.81 13,209

Bicycling 2.5 13,372 1.1 14,709 86.40 1,270,853

Cross-country skiing 16.8 90,034 1.0 90,034 56.18 5,058,131

Developed camping 0.9 4,804 2.7 12,972 35.28 457,654

Downhill skiing 9.6 51,105 1.0 51,105 81.89 4,185,002

Driving for pleasure 6.0 32,092 1.1 35,301 64.67 2,282,947

Fishing 2.6 14,072 1.2 16,887 71.18 1,201,981

Gathering forest products 0.2 919 1.1 1,010 64.67 65,343

Hiking/walking 15.0 80,231 1.1 88,254 84.12 7,423,903

Horseback riding 1.9 9,976 1.3 12,969 64.67 838,724

Hunting 7.2 38,767 1.3 50,397 77.08 3,884,575

Motorized trail activities 1.7 9,253 1.3 12,029 50.11 602,786

Motorized activities 0.2 918 1.1 1,010 58.04 58,633

Nature center activities 0.0 0 1.0 0 64.67 0

Nature study 0.1 501 1.1 551 64.67 35,605

No activity reported 0.2 1,303 1.0 1,303 64.67 84,258

Nonmotorized water activities 0.2 964 1.1 1,061 108.59 115,183

Off highway vehicle use 4.1 22,094 1.2 26,512 50.11 1,328,540

Other motorized activities 0.2 856 1.2 1,027 50.11 51,461

Other nonmotorized activities 0.6 3,170 1.2 3,804 64.67 246,023

Picnicking 1.0 5,286 1.1 5,814 48.84 283,971

Primitive camping 0.8 4,258 2.4 10,220 32.81 335,302

Relaxing 4.3 22,999 1.5 34,499 64.67 2,231,062

Resort use 0.0 0 2.1 0 64.67 0

Snowmobiling 9.0 48,138 1.2 57,766 50.11 2,894,658

Other activities 6.0 32,092 1.2 38,511 64.67 2,490,488

Viewing natural features 8.0 42,790 1.1 47,069 59.79 2,814,234

Viewing wildlife 0.9 4,716 1.1 5,187 59.79 310,145

Visiting historic sites 0.0 0 1.1 0 59.79 0

Total 100.0 534,871 620,404 40,564,669 a

 Economic benefit values are in 2016 dollars. Visitation figures are from National Visitor Use Monitoring round 3 (fiscal years 2009 to 2014).
15

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Practitioners should focus on the primary recreation activity of visits rather than 

any secondary (or “participating”) activities. 

The number of visits by recreation activity is computed by multiplying the 

appropriate primary activity percentage by the estimate of total use on the national 

forest. The visits-by-activity figure is then multiplied by the conversion coefficient 

for each activity for Region 2 (where the Medicine Bow National Forest is located) 

drawn from table 4 to compute the number of activity days for each activity. The 

appropriate economic benefit estimate for each activity is drawn from table 3 

using the crosswalk to NVUM activities shown in table 2. The economic benefit 

for each activity is calculated by multiplying activity days by the use value figure. 

The aggregate economic benefit of recreation on the entire Medicine Bow National 

Forest is the sum of all the benefit values for each primary activity. 

Recreationists on the Medicine Bow National Forest receive in total $40.6 million in economic benefits from recreating there. Again, that figure does not represent the economic impact or economic activity generated from recreation on the 

national forest, but rather the economic value of the benefit to those who recreated. 

Conclusions

Outdoor recreation has been, and likely will continue to be, an important use of 

national forests, and one that connects the U.S. public and international tourists 

with the many benefits that public forest lands have to offer. Characterizing and 

understanding recreation uses of national forests thus will continue to be a necessary step in managing national forests to meet their multiple-use mandate. The 

economic value estimates reported here thus provide a critical resource for forest 

planners, managers, and policymakers charged with developing and implementing 

the stewardship of U.S. public forest lands. 

Acknowledgments

This project was directly supported by funds provided by the U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station. It also was 

indirectly supported through collaborative arrangements with the U.S. Geological 

Survey’s project updating the Benefit Transfer Toolkit funded by the U.S. Department of the Interior, Bureau of Land Management. We thank J.M. Bowker, Ross 

Arnold, Chris Miller, Kawa Ng, and David Simpson for their helpful reviews and 

suggestions. 

Characterizing 

and understanding 

recreation use will 

continue to be a 

necessary step in 

managing national 

forests.
16

GENERAL TECHNICAL REPORT PNW-GTR-957

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GENERAL TECHNICAL REPORT PNW-GTR-957

Appendix

This appendix provides additional technical information about the methods and 

techniques described in this document. It begins with a history of the Recreation 

Use Value Database (RUVD), and then summarizes key economic concepts. A 

more detailed discussion of benefit transfer methods and how to conduct them is 

provided, followed by the technical details of the meta-analysis function transfer 

used in constructing table 3.

History of the Recreation Use Values Database 

The RUVD summarizes recreation economic value estimates from more than 50 

years of economic research (work published from 1958 to 2015) characterizing the 

value of outdoor recreation in the United States and Canada. The RUVD includes 

all documented estimates of recreation economic values published in journal 

articles, technical reports, book chapters, working papers, conference proceedings, 

or graduate theses (Stanley 2001). Included studies encompass a variety of methods, 

regional and activity foci, sample sizes, and site characteristics. 

The RUVD is the result of seven separate literature reviews, although it was 

completely reconstructed in 2006. The first review covered literature on outdoor 

recreation and forest amenity use values from the 1960s to 1982, with 93 benefit 

estimates (Sorg and Loomis 1984). The second literature review covered 1968 

to 1988, (Walsh et al. 1988) increasing the benefit estimate count to 287. A third 

literature review, conducted by MacNair (1993), covered estimates from 1968 

to 1993 and formally coded information on study attributes. A fourth literature 

review, conducted by Loomis and others (1999), used an expanded coding protocol 

and merged with the MacNair database. Kaval and Loomis (2003) updated this 

expanded database, with emphasis on underrepresented recreation activities. In 

2006, the RUVD was rebuilt using an expanded coding protocol with new variables 

and the database was again updated with new and overlooked valuation studies. 

Finally, in 2015 the RUVD was updated to include studies from 2006 to 2015. This 

effort, following the best practice guidelines established by Stanley et al. (2013), 

brought the number of studies included to 422 and estimates to 3,194.

Primary studies were included if (1) they estimated access values (i.e., with 

vs. without access to the resource or activity); (2) they followed well-established 

economic practices for stated or revealed preference, or mixed estimation models 

(e.g., Champ et al. 2017); (3) they were conducted in the United States or Canada; 

and (4) they reported an economic value that could be converted into a standardized 

The RUVD summarizes 

recreation economic 

value estimates from 

more than 50 years of 

economic research.
21

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

consumer surplus dollar value per person per day. The RUVD includes the standardized economic value as well as identified information on the document source 

and study, site, activity, and methodology attributes of each study. Additional 

information about the RUVD, including studies and coding protocol, can be found 

at http://recvaluation.forestry.oregonstate.edu/.

Consumer Surplus

Consumer surplus is the economic value of a recreation activity above what must 

be paid by the recreationist to enjoy the activity (fig. 1). Looking at conditions when 

demand is D0, consumer surplus is the area below the demand function (D0) and 

above the price or expenditure line (B), or area BCD. 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 0ACD in figure 1 when demand is D0

 and A is the number of days 

of participation. Consumer surplus is generally estimated in primary research by 

inferring it from revealed preference data (i.e., generate the demand function and 

then calculate consumer surplus), or directly estimated using stated preference data 

(i.e., where people state their maximum net willingness to pay within constructed 

market conditions). For more information on nonmarket valuation methods, see 

Champ et al. (2017).

Figure 1—Consumer surplus in demand.
22

GENERAL TECHNICAL REPORT PNW-GTR-957

Benefit Transfer 

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 benefit 

estimate from a study site, or (1-b) a measure of central tendency (e.g., average or 

median) for several benefit estimates from a study site or sites, or (1-c) administratively approved estimates. Administratively approved value estimates are 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 are the transfer 

of (2-a) a benefit or demand function from a study site, or (2-b) a meta regression 

analysis function derived from several study sites. Function transfers are adapted 

to fit the context of the policy site with respect to socioeconomic characteristics, 

extent of market and environmental impact, and other measurable characteristics 

that may capture or define the differences between sites with this information and 

the one where it is needed (i.e., being transferred to). The adapted function is then 

used to construct a benefit measure for the policy site. 

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

VALUE TRANSFER FUNCTION TRANSFER

Singlepoint

estimate

(1-a)

Measure of 

central 

tendency

(1-b)

Benefit/ 

demand

function

(2-a)

Metaanalysis

function

(2-b)

Use estimate at 

policy site

Adapt function to 

policy site

Use constructed 

estimate at policy 

site

Administratively 

approved

(1-c)
23

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Applications of benefit transfer methods may or may not be structurally

(i.e., directly) related to underlying utility theoretic approaches. The continuum 

of structural linkages is identified in Bergstrom and Taylor (2006). Additional discussions and comprehensive information on benefit transfers are found in Johnston 

(2015) and others, including Johnston and Rosenberger (2010), and Rosenberger and 

Loomis (2017).

Value transfer methods—

Single-point-estimate transfer—A single-point-estimate benefit transfer uses an 

estimate from a single relevant primary research study (or range of point estimates 

if more than one study is relevant). The steps to performing a single-point-estimate 

transfer include identifying and quantifying the management or policy-induced 

changes on recreation use, and locating and transferring an appropriate “unit” consumer surplus measure. The following is a more detailed list of the steps involved in 

single-point-estimate transfers: 

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. 

1. Assess relevance and applicability of study site data. 

5. Select a benefit measure from a single relevant study or a range of benefit 

measures if more than one study is relevant.

6. Multiply benefit measure by total change in recreation use.

The simplicity with which these steps are presented may be misleading. Finding 

a valid and reliable benefit measure can be complex and require the analyst to make 

many judgments on the comparative structure between two or more sites. These 

judgments often rely on limited available information about the original study 

context and may require additional information be gathered about the sites and 

study methods. 

Similarity of sites is a key element in the defense of point-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 universal protocol that can be objectively followed in any situation. However, quite often information can be transferred with varying levels of 

confidence (Johnston and Rosenberger 2010). 

The context 

surrounding each 

benefit transfer can 

be unique, meaning 

there is no universal 

protocol that can be 

objectively followed in 

any situation.
24

GENERAL TECHNICAL REPORT PNW-GTR-957

Average value transfer methods—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 policyinduced changes on recreation use, and locating and transferring a “unit” average 

consumer surplus measure. The following is a more 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. 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 for the region or use an average of a subset of

study measures.

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

Federal public land agencies commonly use administratively approved average values in assessing management and policy actions. The U.S. Department of 

Agriculture Forest Service has used Resources Planning Act (RPA) values since 

1980 (USDA FS 1991). These RPA values have been provided for groups of activities and Forest Service regions of the country. Similarly, the U.S. Bureau of Reclamation and U.S. Army Corps of Engineers have relied on U.S. Water Resources 

Council (1973, 1979, 1983) “unit day values” for decades. Although some of the unit 

day values may not have been based directly on the emerging literature on outdoor 

recreation economic values and measures, they have all been influenced to a certain 

degree by this literature. Average value estimates, however, are no better than the 

data on which they are based. All 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-transfer methods—Benefit-function transfers use a model to 

statistically relate benefit measures to 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, a meta-analysis function 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, constructing a benefit measure based on this adaptation of the function, and using the 

measure for evaluating the policy site.
25

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Demand-function transfer—The transfer of an entire demand function is conceptually more sound than value transfers, because recreation benefit estimates and use 

rates are a complex function of site and user characteristics, and 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 implied that the two sites 

are identical across the various factors that determine benefit derived in recreational 

use of the two sites. An average value transfer assumes 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, 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 relevance of tailoring a benefit measure to fit the characteristics 

of the policy site. It is in the adaptation stage of constructing a benefit measure from 

a study site demand function that the additional value of the transfer method is realized. The following is a more detailed list of steps for demand- and benefit-function 

transfers:

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 construct 

benefit measure.

7. Multiply constructed benefit measure by total change in recreation use.

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 influence 

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 significantly 

affect the constructed benefit measures at a policy site. 

The specification of demand functions can significantly affect the reliability of 

their use under varying circumstances. To employ a demand function transfer, the 

analyst must use insight and judgment concerning the applicability and transferability of demand functions, the details of which are beyond the scope of this report. 
26

GENERAL TECHNICAL REPORT PNW-GTR-957

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.

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 are generally summary 

statistics from study site reports and include quantified characteristics of the user 

population, study site’s environmental resources, and valuation methodology used. 

Coding of the studies included in the literature review 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 (Stanley et al. 2013).

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 metaanalysis is the systematic use of the existing value estimates from the literature 

for benefit transfer. Essentially, meta-analysis regression models can be used to 

construct benefits at policy sites. Meta-analysis has several conceptual advantages 

over other benefit-transfer methods such as point-estimates and demand-function 

transfers, which generally revolve around the advantages of broader and more 

diverse data for adapting meta-regression models to specific policy site valuation 

needs. The specific steps to conducting a meta-regression analysis function transfer 

are as follows: 

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 to policy site characteristics 

and construct benefit measure.

5. Multiply constructed benefit measure by total change in recreation use.

Meta-analysis has many advantages over unit transfer: it uses information from 

many studies, providing more rigorous value measures sensitive to the underlying 

distribution of estimates; multiactivity, multisite meta-analyses can construct estimates for regions in which no studies were conducted for an activity; and methodological differences can be controlled when calculating a value. An example of this 

Meta-regression 

analysis is 

the statistical 

summarizing of 

relationships between 

benefit measures 

and quantifiable 

characteristics of 

studies.
27

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

method is provided in this report. It is the method used to construct the economic 

values in table 3. 

Meta-Regression Analysis Detailed Methods

Panel data and model specification—

Quantitative literature reviews such as meta-analysis may utilize pools of data with 

panel characteristics (Rosenberger and Loomis 2000). The RUVD includes many 

empirical studies (e.g., single observations) that provide several estimates of recreation economic value, fewer studies that provide only one estimate, and a handful of 

studies that provide many (greater than 20) estimates of value. Using a fixed-effects 

model to correct for intrastudy panel effects, or a random-effects model to correct 

for interpanel effects is one option. However, these options can add complexity to 

modeling and decrease degrees of freedom. Random-effects models assume the random error associated with each panel (e.g., primary study) is uncorrelated with other 

variables, for example region or valuation method. Past meta-analysis has also elected to use only one estimate per study or to average all estimates into one weighted 

estimate per study (Nelson and Kennedy 2009). However, this approach leaves a lot 

of information out of the meta-regression. Where individual studies publish multiple 

estimates, these estimates generally represent different activities at one site, different user groups at one or more sites, or the same activity at multiple sites. 

Identification of panel effects or stratification within any panel data can be 

difficult. In this case, we use a simple correction to identify potential panel effects 

by publication. A cluster-robust covariance estimator with pooled ordinary least 

squares (OLS) corrects for potential nonindependence without requiring any 

assumptions about the error. Clustering covariances by activity, region, or document (individual publication) increased the standard error (SE) of some variables 

and decreased SE of others but made little difference in the significance of most 

variables. This indicates there may be some within-group correlation by region, 

activity, or even publication but not enough to prevent the use of OLS. 

Meta-regression—

Ordinary least-squares linear regression is a widely used method for relating the 

distribution of a dependent variable, here the estimates of use value in the RUVD, 

with the variation in one or more independent variables. Conventional OLS assumes the dependent variable has similar variance across the range of independent 

variable values; observations of the dependent variable are independent from one 

another; and the explanatory variables have no linear relationship. The independent variables included in the model are described in table 6 and include aspects of 

survey methodology and site characteristics. Our OLS model uses a linear-linear 
28

GENERAL TECHNICAL REPORT PNW-GTR-957

Table 6—Meta-regression analysis variables definitions (continued)

Variable name Description

Dependent variable:

Value Consumer surplus per person per activity day (2016 dollars)

Sample characteristics variables:

Nonresidents = 1 if sample contains nonresident visitors only; = 0 otherwise

Residentsa = 1 if sample contains local resident visitors only; = 0 otherwise

Mixed residents/nonresidents =1 if sample contains a mix of resident and nonresident visitors; = 0 otherwise

User sample =1 if sample derived from user list (e.g., fishing/hunting license holders);

= 0 otherwise

Onsite sample =1 if visitors sampled on-site; = 0 otherwise

General population sample =1 if sample derived from a general population (e.g., random sample of state 

residents); = 0 otherwise 

Methodology variables:

Revealed preference =1 if revealed preference valuation method used; = 0 otherwise

Stated preference =1 if stated preference valuation method; = 0 otherwise

Substitutes modeled =1 if substitute sites included in valuation model; = 0 otherwise

Zonal travel cost =1 if zonal travel cost method used; = 0 otherwise

Individual travel cost =1 if individual travel cost method used; = 0 otherwise

Resource/site variables:

Lake =1 if value reported for a lake/reservoir environment; = 0 otherwise

Forest = 1 if value reported for a forested environment; = 0 otherwise

Wetland = 1 if value reported for a wetland environment; = 0 otherwise

River = 1 if value reported for a river/stream environment; = 0 otherwise

Regional variables:

Forest Service (FS) Region 1 = 1 if value reported for FS Region 1; = 0 otherwise

FS Region 2 = 1 if value reported for FS Region 2; = 0 otherwise

FS Region 3 = 1 if value reported for FS Region 3; = 0 otherwise

FS Region 4 = 1 if value reported for FS Region 4; = 0 otherwise

FS Region 5 = 1 if value reported for FS Region 5; = 0 otherwise

FS Region 6 = 1 if value reported for FS Region 6; = 0 otherwise

FS Region 8 = 1 if value reported for FS Region 8; = 0 otherwise

FS Region 9 = 1 if value reported for Forest Service Region 9; = 0 otherwise

FS Region 10 = 1 if value reported for FS Region 10; = 0 otherwise

National = 1 if value reported for national level; = 0 otherwise

Multiple regions = 1 if value reported for multiple FS Regions; = 0 otherwise
29

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Table 6—Meta-regression analysis variables definitions (continued)

Variable name Description

NVUM primary recreation

activity variables

Developed camping = 1 if value reported for developed camping; = 0 otherwise

Backpacking = 1 if value reported for backpacking; = 0 otherwise

Picnicking = 1 if value reported for picnicking; = 0 otherwise

Nature related = 1 if value reported for nature-related; = 0 otherwise

Cross-country skiing = 1 if value reported for cross-country skiing; = 0 otherwise

Fishing = 1 if value reported for fishing; = 0 otherwise

Hunting = 1 if value reported for hunting; = 0 otherwise

Off-highway vehicle use/snowmobiling = 1 if value reported for off-highway vehicle use use/snowmobiling; = 0

otherwise

Nonmotorized boating = 1 if value reported for nonmotorized boating; = 0 otherwise

Motorized boating = 1 if value reported for motorized boating; = 0 otherwise

Hiking = 1 if value reported for hiking; = 0 otherwise

Biking = 1 if value reported for biking; = 0 otherwise

Downhill skiing = 1 if value reported for downhill skiing; = 0 otherwise

Other recreation activity = 1 if value reported for other recreation activity; = 0 otherwise

Note: Omitted variables are bold.

NVUM = National Visitor Use Monitoring.

functional form to relate the dependent and independent variables as follows.

Equation: CS/Day = ∑βXik = β1

Xi1+ β2Xi2+... βJXiK + εi (2)

where there are i estimates, j individual studies and k explanatory variables

(k = 1...K) that explain consumer surplus per day (CS/Day). The meta-regression 

follows the simple equation above where i = 2,709, j =342, and K = 32, where 

regional and activity comprised 23 of the explanatory independent variables. All 

statistical analysis was performed in Stata (SE version 14). 

Data coding and independent variable selection—

The RUVD includes a master coding sheet with 126 fields. The main coding categories include study, benefit measure, methodology specifics, activity, site characteristics, and user demographics. Table 6 lists and defines the variables from this pool that 

were included in the meta-regression. Most 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. Independent variables were included in the optimized metaregression if they were significant at an 80 percent level of confidence or better. A 

general-to-specific process was used, which began with the full specification of the 
30

GENERAL TECHNICAL REPORT PNW-GTR-957

model using all coded variables. Least significant variables were removed sequentially until remaining variables were significant at the 80 percent confidence level or 

better (p ≤0.20). The choice of the minimum significance level is arbitrary, but it does 

reduce the risk of not detecting a difference even though Type I errors are increased at 

an equal rate. This optimization reduces overspecification of the model when retaining variables whose coefficients are not significantly different than zero. Regional and 

activity category variables were retained regardless of significance as the purpose of 

this meta-regression is to construct values for benefits transfer by region and activity, not to study the influence of region and activity on consumer surplus values. The 

results of this model are presented in table 7. 

Outliers—

Outliers are a common occurrence in metadata (Nelson 2015) and the economic 

values within the RUVD vary widely. Outliers can become influential data points, 

affecting the meta-regression and weighted means in ways that cloud inference. 

Based on examination of the methods behind these outliers, and some reasonable 

assumptions about daily recreation economic values, consumer surplus per day

estimates below $5 and above $500 were removed from the meta-analysis 

Results—

Table 7 provides results of the meta-regression model fit to the data and used in 

constructing the values in table 3. The next section provides examples of how average values are constructed, with particular attention to treatment of the region and 

activity-specific variables. However, as noted elsewhere, the first eight variables, 

measuring partial effects of study methods and modeling assumptions, population, 

and site characteristics, are held constant at their mean values. In general, the model 

accounts for more than 20 percent of the observed variation in the benefit estimates, 

which is consistent with prior meta-analyses of recreation benefits (Rosenberger 

and Loomis 2001).

The meta-regression analyzes information on all studies in the database 

and relates independent variables of interest, such as activity, region, or survey 

methodology, to the dependent variable, estimated recreation benefit (measured as 

consumer surplus). Theoretically, when a variable helps explain the variation in recreation benefit measures, its regression coefficient will be significant in the model. 

Combining these significant variables in a multivariate model provides a transparent and consistent way to estimate average values based on a policy site’s specific 

characteristics. Given the large sample size, the overall model performance has a 

grand mean—that is, the mean of the sample means—with ±2.5 percent margin of 

error. Thus, the meta-regression analysis model provides more robust estimates than 

an average value transfer (e.g., table 3 values).
31

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Table 7–Optimized meta-analysis benefit-transfer model

Variable Coefficient Robust SEa Mean of variable

Nonresidents 45.05b 9.94 0.07

User sample 22.25b 8.27 0.21

Revealed preference 28.06b 8.83 0.48

Substitutes modeled -15.95b 6.25 0.25

Zonal travel cost -47.78b 9.53 0.21

Lake -23.15b 7.13 0.19

Forest -11.84 8.85 0.16

Wetland 187.47b 8.87 0.01

Forest Service (FS) Region 1 15.50 11.87 0.04

FS Region 2 8.72 9.51 0.09

FS Region 3 16.80 12.53 0.07

FS Region 4 18.72 12.96 0.09

FS Region 5 2.55 12.04 0.04

FS Region 6 9.06 12.65 0.06

FS Region 8 8.52 8.74 0.19

FS Region 9 -2.98 8.59 0.31

FS Region 10 41.01 22.87 0.03

National 19.92 13.13 0.03

Developed camping -29.39b 10.22 0.02

Backpacking -31.85b 10.63 0.03

Picnicking -15.83b 7.90 0.01

Nature related -4.87 9.02 0.16

Cross-country skiing -8.48 9.96 0.01

Fishing 6.51 9.00 0.34

Hunting 12.41 10.10 0.23

Off-highway vehicle use/snowmobiling -14.55 13.45 0.02

Nonmotorized boating 43.92 30.99 0.03

Motorized boating -6.63 16.15 0.03

Hiking 19.45 12.63 0.04

Biking 21.74 27.72 0.01

Downhill skiing 17.22 35.75 0.01

Constant 54.77b 12.89 1

Summary statistics: N = 2,709, adjusted R2 = 0.20, Root mean squared error = 61.44.

a

 Cluster robust standard error computed in Stata 14.1 using individual study as cluster (n = 342).

b

 Variable is statistically significant at the p < 0.05 level or better. Overall margin of error is ±2.5 percent.
32

GENERAL TECHNICAL REPORT PNW-GTR-957

Also keep in mind that many qualitative variables reflecting other attributes 

of the study, site and resource, methods, and values estimates do not exceed the 80 

percent significance threshold when specifying the meta-regression model. Empirically these variables are not related to variations in consumer surplus for this set of 

data, but they may be theoretically significant. Unfortunately, retaining all variables 

would result in increased multicollinearity and overspecification of the model. 

Please keep this in mind when conducting single-study transfers where assessing the degree of similarity between sites depends greatly on their characteristics 

regardless of their significance in the meta-regression model.

The estimated parameters show the partial effect of each variable on the 

variation in the dependent variable—value per person per day. For example, people 

who travel greater distances (nonresidents) from home to recreation sites have 

higher values, ceteris paribus, than local residents. However, the total aggregate 

benefits to local residents are likely higher owing to the ability to visit more often at 

lower overall cost, but people who generally travel greater distances have selected 

their destination over other sites and activities that are generally closer to home. 

Also along this same line of reasoning, studies that incorporate substitute sites 

(substitutes modeled) generally produce lower estimated values, ceteris paribus, as 

economic theory would expect (see Loomis and Walsh 1997, Rosenthal 1987). 

Additional detail and application—

The meta-analysis function is used to construct values by holding all independent 

or explanatory variables constant at their mean values (last column, table 7), except 

for the relevant regional and activity variables. These effects are weighted by their 

mean values—each variable’s coefficient is multiplied by its weight, providing 

the partial consumer surplus owing to that variable. These partial values are then 

summed along with the constant (intercept) to construct values. To construct estimates for a particular region, that region’s variable would be equal to 1, and the full 

value of its coefficient would be summed into the constructed value. 

This procedure is illustrated in the examples presented in table 8 where we 

calculate the average value of a day of hiking in California (FS Region 5 [Pacific 

Southwest Region]) and a day of camping in Georgia (FS Region 8 [Southern 

Region]). The example predictions in table 8 may look simplistic—this is because 

we have averaged out the many other nonregion and nonactivity variables in the 

model. However, note that the data behind the meta-analysis is not all specific to 

hiking or camping, or California or Georgia. Therefore, each of the constructed 

average values is an estimate for a generic activity similar to hiking in California 

or to camping in Georgia. There is often a direct correlation between the degree 

of specificity in the constructed value and the overall representation of a variable 

Total aggregate 

benefits are likely 

greater for locals than 

nonlocals. 
33

Recreation Economic Values for Estimating Outdoor Recreation Economic Benefits from the National Forest System

Table 8—Example adaptation of meta-analysis benefit function

Hiking in California Camping in Georgia

Variable Coefficient

Adaption 

value

Partial 

CS

Adaption 

value

Partial 

CS

FS Region 1 15.50 0 0 0 0

FS Region 2 8.72 0 0 0 0

FS Region 3 16.80 0 0 0 0

FS Region 4 18.72 0 0 0 0

FS Region 5 2.55 1 2.55 0 0

FS Region 6 9.06 0 0 0 0

FS Region 8 8.52 0 0 1 8.52

FS Region 9 -2.98 0 0 0 0

FS Region 10 41.01 0 0 0 0

Developed camping -29.39 0 0 1 -29.39

Backpacking -31.85 0 0 0 0

Picnicking -15.83 0 0 0 0

Nature related -4.87 0 0 0 0

Cross-country skiing -8.48 0 0 0 0

Fishing 6.51 0 0 0 0

Hunting 12.41 0 0 0 0

OHV use/snowmobiling -14.55 0 0 0 0

Nonmotorized boating 43.92 0 0 0 0

Motorized boating -6.63 0 0 0 0

Hiking 19.45 1 19.45 0 0

Biking 21.74 0 0 0 0

Downhill skiing 17.22 0 0 0 0

Constant 54.77 1 54.77 1 54.77

Net of all other variables NA NA 1.17 NA 1.17

Total $77.94 $35.07

CS = consumer surplus, FS = Forest Service, NA = not applicable, and OHV = off-highway vehicle.

in the database. This is due to the statistically discovered variability across these 

activities, or lack thereof. For example, there are 111 estimates for hiking and 82 

estimates for camping included in the database, not all of which are in Region 5 or 

Region 8. Therefore, the constructed averages take into account the distribution of 

all values for hiking or camping relative to all values for Regions 5 and 8. These 

example applications illustrate the degree to which these constructed values are 

generic estimates when holding everything in the model constant except for region 

and activity.
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