Revised Estimates for

Direct-Effect Recreational

Jobs in the Interior Columbia

River Basin

Lisa K. Crone and Richard W. Haynes

United States 

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

Unites States

Department of the 

Interior

Bureau of Land 

Mnagement

General Technical 

Report

PNW-GTR-483

December 1999
Authors

LISA K. CRONE and RICHARD W. HAYNES are research foresters, Forestry Sciences Laboratory, 

P.O. Box 3890, Portland, Oregon 97208-3890. Lisa Crone is located at Alaska Region, Chugach

National Forest, 3301 “C” Street, Suite 300, Anchorage, AK 99503-3998. She was at the Interior

Columbia Basin Ecosystem Management Project, 112 E. Poplar, Walla Walla, WA 99362 when this

publication was written.
Revised Estimates for Direct-Effect Recreational

Jobs in the Interior Columbia River Basin

Lisa K. Crone and Richard W. Haynes

Interior Columbia Basin Ecosystem Management

Project: Scientific Assessment

Thomas M. Quigley, Editor

U.S. Department of Agriculture

Forest Service

Pacific Northwest Research Station

Portland, Oregon

General Technical Report PNW-GTR-483

December 1999
Preface

The Interior Columbia Basin Ecosystem Management Project was initiated by the Forest Service and 

the Bureau of Land Management to respond to several critical issues including, but not limited to, forest

and rangeland health, anadromous fish concerns, terrestrial species viability concerns, and the recent

decline in traditional commodity flows. The charter given to the project was to develop a scientifically

sound, ecosystem-based strategy for managing the lands of the Interior Columbia River basin administered by the Forest Service and the Bureau of Land Management. The Science Integration Team was

organized to develop a framework for ecosystem management, an assessment of the socioeconomic and

biophysical systems in the basin, and an evaluation of alternative management strategies. This paper is

one in a series of papers developed as background material for the framework, assessment, or evaluation

of alternatives. It provides more detail than was possible to disclose directly in the primary documents.

The Science Integration Team, although organized functionally, worked hard at integrating the approaches, analyzes, and conclusions. It is the collective effort of team members that provides depth 

and understanding to the work of the project. The Science Integration Team leadership included deputy

team leaders Russel Graham and Sylvia Arbelbide; landscape ecology—Wendel Hann, Paul Hessburg,

and Mark Jensen; aquatic—Jim Sedell, Kris Lee, Danny Lee, Jack Williams, and Lynn Decker; economic—

Richard Haynes, Amy Horne, and Nick Reyna; social science—Jim Burchfield, Steve McCool, Jon

Bumstead; and Stewart Allen; terrestrial—Bruce Marcot, Kurt Nelson, John Lehmkuhl, Richard

Holthausen, Randy Hickenbottom, Marty Raphael, and Michael Wisdom; spatial analysis—Becky

Gravenmier, John Steffenson, and Andy Wilson.

Thomas M. Quigley

Editor

United States

Department of

Agriculture

Forest Service

Interior Columbia

Basin Ecosystem

Management Project

United States

Department of

the Interior

Bureau of Land

Management
Abstract

Crone, Lisa K.; Haynes, Richard W. 1999. Revised estimates for direct-effect recreational jobs in 

the interior Columbia River basin. Gen. Tech. Rep. PNW-GTR-483. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station. 29 p. (Quigley, Thomas M., ed.;

Interior Columbia Basin Ecosystem Management Project: Scientific assessment).

This paper reviews the methodology used to derive the original estimates for direct employment associated with recreation on Federal lands in the interior Columbia River basin (the basin), and details 

the changes in methodology and data used to derive new estimates. The new analysis resulted in an

estimate of 77,655 direct-effect jobs associated with recreational activities on Federal lands in the

basin. This estimate is a little over one-third of the previous estimate. The new estimated direct-effect

recreational jobs amount to 4.48 percent of the total estimated jobs in the basin in 1994. This is still

slightly larger than the estimated percentage of jobs in ranching, mining, and lumber and wood products combined (3.52 percent) in the basin. The intent of the original analysis is clarified, limitations 

of the data are brought forward, a cross-sectional analysis is conducted, and suggestions for future

research are provided.

Keywords: Recreation, employment, Columbia River basin.
Contents

1 Introduction

1 Recreational Visits and Their Geographic Distribution

2 Original Methodology for ERU Areas

5 New Methodology for ERU Areas

5 Original Methodology for BEA Areas

5 New Methodology for BEA Areas

5 Expenditure Estimates

13 Recreation-Response Multipliers

17 Direct Employment Estimates

17 Original Methodology for Direct-Effect Jobs

17 New Methodology for Direct-Effect Jobs

17 Original Methodology for Percentage of Economy Associated With Recreation

17 New Methodology for Percentage of Economy Associated With Recreation

18 Purpose of the Study

18 Limitations of the Visitation and Expenditure Data

19 Results and Discussion

27 Future Research

27 Acknowledgments

28 Literature Cited
Introduction

In the economics component of the assessment of

ecosystem components (referred to as the “economics assessment;” Haynes and Horne 1997),

estimates of the number of jobs directly associated with recreation on Federal lands in the interior

Columbia River basin (the basin) and Bureau of

Economic Analysis (BEA) functional economic

areas (described below) are reported. Many readers of the economics assessment and the draft

environmental impact statements (USDA and

USDI 1997a, 1997b) felt that the estimates were

too high. To address these concerns, a review of

the methodology and data used in the analysis

was conducted.1 Based on this review, two steps

were taken to improve the estimates: the first was

to use a more direct method to allocate recreational visits geographically, and the second was

to use more representative expenditure estimates. 

This paper presents new direct job estimates

based on the revised expenditure estimates and

the new method of allocating recreational visits

to BEA areas. We begin with a review of the

recreational use data and outline the original and

new methodology used to spatially allocate the

recreational activity. The new expenditure estimates, their sources, and their differences from

the original estimates are then presented. Next,

we provide a brief overview of what a recreationresponse multiplier is, what it measures, and how

it is calculated, followed by the original and new

estimated direct-effect employment recreationresponse multipliers. The intent of the original

analysis is clarified and limitations of the data are

discussed. The new direct employment estimates

are then displayed and analyzed, including a discussion of the sources for differences between the

original and new direct employment estimates 

1 The specific methodology is described in: Horne, Amy. 1997.

Calculation of employment supported by recreation activity in the

interior Columbia basin. Portland, OR. 2 p. Plus tables. On file

with: Interior Columbia Basin Ecosystem Management Project, 

112 W. Poplar St., Walla Walla, WA 99362.

and an analysis of sources of variation in the

new estimates across the BEA areas in the basin.

Finally, we offer suggestions for future research.

The methods used for the computation of jobs

have been used in past studies of the economic

contribution of selected sets of recreational activities (see for example Haynes and others 1992).

What differs here is that the selected set of recreational activities are broader than those used in

most previous studies. Twelve activities considered representative of the types of recreational

activities that occur on Federal lands in the basin

were used in this study.

Recreational Visits and Their 

Geographic Distribution

To estimate the amount of direct employment

associated with recreational activity on Federal

lands in the interior Columbia River basin, information on recreational use had to be gathered by

the project staff. The recreational use estimates

were obtained by contractors who contacted every

national forest, Bureau of Land Management

(BLM) district, national park, and national wildlife

refuge site in the project area. For ease, these are

referred to as “management units” in this paper.

Seventy-five percent of the management units

solicited in the project area provided data on estimated use for this study. The management units

that did not respond were given null values for

visits.2

The information was standardized and compiled,

and the average of the reported use data for 1991,

1992, and 1993 was calculated. This averaging

was done to decrease the bias of using a single

year when lower or higher use levels might have

occurred owing to external influences, such as

weather conditions or gas prices. The data were 

2 These units were given null values because we had no way of 

estimating their use levels with any degree of accuracy. All tables

and analyses were based only on data from the units that reported

their recreational use.

1
summarized as the number of visits at each of 

the management units for each of 12 recreational

activities. A visit was defined as an excursion by

one individual to a recreation area for the purpose

of participating in one or more recreational activities for any length of time. Only the primary

activity for the visitor was recorded.3 The 12 recreational activities measured were camping, day

use, fishing, hunting, motorized boating, motorized viewing, nonmotorized boating, off-highway

vehicle (OHV) use, snowmobiling, trail use, viewing wildlife, and winter sports. 

3 In the new analysis, adjustments were made to the visitation data

for one management unit, the National Bison Range, where it was

apparent that visits were being double counted into more than one

activity category. A few other minor data errors also were corrected. 

Original Methodology for

ERU Areas

In the original design of the assessment process,

the Science Integration Team divided the basin 

into 13 geographic areas called ecological reporting units (ERUs) as shown in figure 1. The ERUs

were developed by the aquatic, landscape ecology,

and terrestrial staff to facilitate the analysis and

presentation of information and results on areas

smaller than the entire basin. These ERUs are

regions in which lands have similar capacities to

produce various ecosystem goods, functions, and

conditions. As part of the integrative approach, the

original recreation methodology focused on determining the amount of estimated recreational activity occurring in each ERU. The percentage of

each management unit in each ERU was obtained 

2

Figure 1—Interior Columbia River basin and ecological reporting units. Source: Interior Columbia Basin Ecosystem Management Project GIS

staff.
3

Table 1—Regional economies of the interior Columbia basin, by BEAa area 

Area BEA area Coverage of BEA areab Counties included

Boise Boise Full OR: Harney, Malheur 

ID: Ada, Adams, Boise, Canyon, Elmore, 

Owyhee, Payette, Valley, Washington

Butte Butte Partial MT: Deer Lodge, Granite, Lewis and Clark,

Powell, Silverbow

Idaho Falls Idaho Falls Full plus ID: Bannock, Bingham, part of Bonneville,

Butte, Caribou, Clark, Custer, Fremont,

Jefferson, Lemhi, Madison, Power, 

Teton

WY: Teton

Missoula Missoula Full MT: Flathead, Lake, Lincoln, Mineral, 

Missoula, Ravalli, Sanders

Pendleton Pendleton Full WA: Columbia, Walla Walla.

OR: Baker, Gilliam, Grant, Morrow,

Umatilla, Union, Wallowa, Wheeler

Bend-Redmond Portland- Partial WA: Klickitat, Skamania

Salem OR: Crook, Deschutes, Hood River,

Jefferson, Klamath, Lake, Sherman, 

Wasco

Spokane Spokane Full WA: Asotin, Ferry, Garfield, Lincoln, Pend 

Oreille, Spokane, Stevens, Whitman

ID: Benewah, Bonner, Boundary, 

Clearwater, Idaho, Kootenai, Latah,

Lewis, Nez Perce, Shoshone

Tri-Cities Richland- Full WA: Adams, Benton, Chelan, Douglas,

Kennewick- Franklin, Grant, Yakima, Kittitas,

Pasco Okanogan

Twin Falls Twin Falls Full ID: Blaine, Camas, Cassia, Gooding,

Jerome, Lincoln, Minidoka, Twin Falls

a BEA = Bureau of Economic Analysis.

b Full = regional economy includes all the counties in the BEA area; partial = regional economy does not include every county in 

the BEA area; full plus = regional economy includes all counties within the BEA area and additional counties.

from the Project Spatial Team. The assumption

made was that activities were evenly distributed

across each management unit. Thus, for example, camping visits to the Deschutes National

Forest, which were estimated to be 362,557, were

allocated as follows:

ERU2 = 0.76573 x 362,557 = 277,621

ERU3 = 0.00018 x 362,557 = 65

ERU4 = 0.1241 x 362,557 = 44,993

ERU5 = 0.10999 x 362,557 = 39,878
Visits in each ERU were then summed across all

management units having some portion of their

land base within that ERU.

In the economics assessment, economic activities

were geographically described by using the BEA

definition of functional economies.4 A functional

economy is one large enough to include the bulk

of economic transactions or flows of trade. The BEA

defined functional economic units by identifying

economic nodes and the surrounding counties economically linked to them. For this analysis, the

BEA areas were modified to include only those

counties fully or partially contained wihin the 

4 For a detailed description of the methodology used by the BEA to

delineate these areas, see Johnson (1995). In 1995, the BEA areas

were redefined based mainly on new information on commuting patterns.This resulted in the aggregation of the Butte BEA area into the

Missoula BEA area. Because the data reported in the economic

assessment were based on the previous area definitions, those area

definitions were maintained here for consistency.

basin. Table 1 describes the extent of each regional economy and compares regional definitions

used in this study to the BEA functional economic

areas (U.S. Department of Commerce 1995). A map

of the basin and BEA areas is shown in figure 2.

Visits by ERU were converted into visits by BEA

area. To do this, conversion factors were obtained

from the Project Spatial Team to show the percentage of Forest Service- and BLM-administered lands in each ERU that lies in each BEA

area. Again the assumption was made that recreational activity was evenly distributed across the

Forest Service- and BLM-administered lands

within each ERU. For example, in the Southern

Cascades ERU (2), 0.0032 of the activity was

assumed to occur in BEA 1 (Tri-Cities), and

0.9968 in BEA 8 (Bend-Redmond). 

4

Figure 2—Interior Columbia River basin and Bureau of Economic Analysis areas. Source: Interior Columbia Basin Ecosystem Management

Project GIS staff.
New Methodology for ERU Areas

Recreational activity for each land management

unit again was assumed to be evenly distributed

across that unit. The percentage of the management unit in each county was calculated, and that

percentage was used to distribute the management unit’s recreational visits to each county.

These visits then were allocated to the BEA area

of which the county was a part. The percentages

used to allocate management unit visits to BEA

area visits are shown in table 2. The original and

new estimated recreational visits to Federal lands

for the basin and BEA areas are shown in tables

3 and 4, respectively.5

Original Methodology for

BEA Areas

In the original analysis, visits were converted into days6 to be compatible with the direct-effect

recreation-response multipliers, which were measured as jobs per thousand days of recreational

activity. To convert to days, visits were multiplied

by the average length of stay (in days) for each

activity. The estimates of average length of stay

are presented in table 5. As an example, for camping, one visit would represent an average of 3.71

days. Thus, in the Boise BEA area, which had

893,212 camping visits per year, the conversion

to days is:

Days = 893,212 visits x (3.71 days per visit) 

= 3,313,816 days.

New Methodology for BEA Areas

The new expenditure estimates for nine activities 

5 Total visits differ in the two tables because of the adjustments

noted in footnote 3.

6 A day refers to a 24-hour period.

(hunting, fishing, and wildlife viewing excluded7 ) were measured as spending per person per 

trip. We assumed that a trip was the same as a

visit and thus eliminated the need to use estimates

of average length of stay for these nine activities.

The new direct-effect recreation-response multipliers were measured in jobs per thousand recreational visits. The procedure used to convert the

response multipliers for hunting, fishing, and

wildlife viewing from jobs per thousand days to

jobs per thousand visits is discussed in the following section.

Expenditure Estimates

The original expenditure estimates were derived

from only the CUSTOMER and PARVS surveys8

conducted within the basin. Due to small sample

sizes or location-specific samples, or both, the

representativeness of these estimates has been

questioned. The new expenditure estimates for

nine of the activities (hunting, fishing, and

wildlife viewing excepted) came from CUSTOMER and PARVS expenditure data from Forest

Service sites across the country. The assumption

made here is that expenditures by recreationists

at sites outside the project area are similar to

expenditures by recreationists at sites within the

project area. These new expenditure estimates

were provided for four broad activity types rather

than for the nine specific activities listed above.

The four activity groupings were camping and

picnicking (includes camping and day use activities), trail use (includes trail use and nonmotorized boating), mechanized travel (includes motor 

(Text continues on page 11.)

7 The 1991 national survey of fishing, hunting and wildlife-associated recreation (USDI 1993) estimates were used for these activities

because these estimates were derived from a sampling technique

and statistical methodology that was applied consistently across the

lands in the basin.

8 CUSTOMER is an acronym that stands for customer use and 

survey techniques for operations, management evaluation, and

research. PARVS is an acronym that stands for public area recreation visitors survey. Details on the survey techniques and data sets

may be obtained from: USDA Forest Service Outdoor Recreation

and Wilderness Research Group, 320 Green St. Athens, GA 30602-

2044.

5
6

Table 2—Percentages used to allocate management unit visits to BEAa areas by management

unit

Management unit BEA allocation by BEA area

Percent

National Forest System:

Deschutes 100 Bend-Redmond

Fremont 100 Bend-Redmond

Malheur 80 Pendleton, 20 Boise

Mount Hood 100 Bend-Redmond

Ochoco 51 Bend-Redmond, 27 Boise, 22 Pendleton

Umatilla 90 Pendleton, 10 Spokane

Wallowa-Whitman 100 Pendleton

Colville 100 Spokane

Gifford Pinchot 100 Bend-Redmond

Okanagan 100 Tri-Cities

Wenatchee 100 Tri-Cities

Winema 100 Bend-Redmond

Boise 100 Boise 

Caribou 100 Idaho Falls

Challlis 100 Idaho Falls

Clearwater 100 Spokane

Idaho Panhandle 86 Spokane, 14 Missoula

Nez Perce 100 Spokane

Payette 65 Boise, 35 Spokane

Salmon 92 Idaho Falls, 4 Boise, 4 Spokane

Sawtooth 76 Twin Falls, 16 Idaho Falls, 8 Boise

Targhee 100 Idaho Falls

Bitteroot 70 Missoula, 30 Spokane

Deerlodge 100 Butte

Flathead 90 Missoula, 10 Butte

Helena 100 Butte

Kootenai 97.5 Missoula, 2.5 Spokane

Lolo 80 Missoula, 20 Butte

Humboldt 100 Boise

Bridger-Teton 100 Idaho Falls

BLMb districts:

Burns 97.1 Boise, 2.7 Bend-Redmond, 0.2 Pendleton

Lakeview 79 Bend-Redmond, 21 Boise

Prineville 80 Bend-Redmond, 20 Pendleton

Vale 91.4 Boise, 8.4 Pendleton, 0.2 Spokane

Spokane 78 Tri-Cities, 17 Spokane, 5 Bend-Redmond,

Boise 100 Boise 

Burley 100 Twin Falls

Coeur d’Alene 100 Spokane
7

Table 2—Percentages used to allocate management unit visits to BEAa areas by management

unit (continued)

Management unit BEA allocation by BEA area

Percent

Idaho Falls 100 Idaho Falls

Salmon 100 Idaho Falls

Shoshone 100 Twin Falls 

Garnet 91 Butte, 9 Missoula

Nevada, all 65 Boise, 35 Twin Falls

National Park Service:

Crater Lake 100 Bend-Redmond

John Day Fossil Beds 100 Pendleton

Lake Chelan and North Cascades 100 Tri-Cities

Coulee Dam 100 Spokane

Whitman Mission National Historic Site 100 Pendleton 

City of Rocks 100 Twin Falls

Craters of the Moon 75 Idaho Falls, 25 Twin Falls

Hagerman Fossil Beds 100 Twin Falls

Nez Perce National Historic Park 100 Spokane 

Glacier 100 Missoula 

Grant-Kohrs Ranch 100 Butte

Fish and Wildlife Service:

Klamath Basin 100 Bend-Redmond

Malheur NWRc 100 Boise

Columbia NWR 100 Tri-Cities

Conboy Lake 100 Bend-Redmond

Deer Flat NWR 100 Boise

Kootenai 100 Spokane

South East Idaho NWR Complex 66 Twin Falls, 34 Idaho Falls 

Lee Metcalf NWR 100 Missoula

National Bison Range 100 Missoula

a BEA = Bureau of Economic Analysis.

b BLM = Bureau of Land Management.

c NWR= National Wildlife Refuge.
8

Table 3—Original estimated recreational visits to Federal lands for the basin and BEAa areas (1991-93 avera

BEA area

Recreational Bend- Tri- activity Boise Butte Idaho Falls Missoula Pendleton Redmond Spokane Cities 

Number of visits

Camping 893,212 158,193 1,046,705 954,681 573,370 882,821 1,149,585 921,339 Day use 1,781,576 472,178 2,973,321 3,236,890 1,066,828 2,501,171 3,550,249 1,388,957 Fishing 877,076 132,340 850,538 781,316 560,749 968,710 987,831 278,132 Hunting 490,748 176,174 474,294 553,630 256,421 226,949 590,229 154,106 Motor boating 328,496 51,355 286,956 483,494 44,698 61,711 454,878 102,516 Motor viewing 2,350,408 394,588 3,515,045 2,247,798 758,330 2,371,552 3,335,255 3,441,879 Nonmotor boating 134,576 20,083 311,525 93,320 60,280 421,218 170,498 56,951 OHVb use 308,370 48,266 413,126 194,364 59,230 74,708 338,800 181,919 

Snowmobiling 178,426 97,863 432,835 222,892 74,635 99,023 253,945 137,575 Trail use 595,418 151,902 1,017,444 738,976 386,781 579,625 875,846 861,180 Viewing wildlife 150,746 175,167 160,278 340,024 181,344 633,374 231,623 114,438 Winter sports 899,670 103,074 1,423,247 385,346 481,581 858,272 730,714 569,374 

Total 8,988,724 1,981,132 12,905,225 10,232,732 4,504,248 9,679,134 12,669,453 8,208,365 

a BEA = Bureau of Economic Analysis.b OHV = off-highway vehicle.

Source: Derived from Haynes and Horne 1997.
9

Table 4—New estimated recreational visits to Federal lands for the basin and BEAa areas (1991-93 average), 

BEA area

Recreational Bend- Tri- activity Boise Butte Idaho Falls Missoula Pendleton Redmond Spokane Cities 

Number of visits

Camping 985,337 166,316 666,279 855,914 704,173 881,755 1,273,616 889,630 Day use 2,046,412 606,364 2,431,983 4,030,341 1,252,786 2,256,114 3,385,908 837,665 Fishing 877,392 177,742 780,286 459,433 491,883 1,278,290 1,168,916 116,603 Hunting 340,536 264,424 443,529 435,774 312,095 246,718 756,298 95,754 Motor boating 577,891 52,177 111,166 122,980 48,953 63,916 818,087 22,049 Motor viewing 4,316,792 603,795 1,781,463 1,681,373 814,556 2,445,429 2,962,801 3,184,284 Nonmotor boating 148,805 26,222 247,588 78,241 51,035 485,513 222,652 25,315 OHVb use 446,927 84,705 237,224 156,644 60,554 80,854 355,961 166,030 

Snowmobiling 295,141 179,708 325,292 119,186 90,796 102,172 255,060 121,803 Trail use 621,122 212,057 773,180 668,781 478,334 613,131 888,815 1,176,156 Viewing wildlife 127,977 50,318 96,825 165,514 154,116 829,720 206,238 51,614 Winter sports 1,228,730 151,348 805,263 405,536 627,450 902,533 318,280 518,945 

Total 12,013,062 2,575,176 8,700,078 9,179,717 5,086,731 10,186,146 12,612,632 7,205,848 

a BEA = Bureau of Economic Analysis.b OHV = off-highway vehicle.
10

Table 5—Estimated average length of stay,

original analysis, by activity

Recreational activity Days

Camping 3.71

Day use 2.96

Hunting 5.13

Fishing 3.14

Motorized boating 4.41

Motorized viewing 2.18

Nonmotorized boating 2.64

OHVa use 2.72

Snowmobiling 1.79

Trail use 2.99

Viewing wildlife 2.96

Winter sports 1.66

a OHV = off-highway vehicle.

Source: USDA Forest Service, Outdoor Recreation and

Wilderness Assessment Group, Southeast Research 

Station, 320 Green St., Athens, GA 30602-2044.

Table 6—Estimated average spending per person per trip within 50 miles of visited site,

for all visitors to Forest Service CUSTOMER and PARVS sitesa

Expenditure Camping, Trail Mechanized Winter

item picnicking use travel activities

1993 dollars

Lodging, private 5.88 6.67 8.76 17.25

Lodging, public 6.26 2.53 3.10 .01

Food 9.57 7.59 8.13 4.84

Beverages 2.32 1.77 1.80 1.01

Restaurants 3.35 5.95 8.00 15.20

Gasoline and oil 6.63 6.32 7.89 2.62

Air fares 1.07 3.02 2.96 .94

Car rental .07 .18 .17 .01

Other trans. 1.47 2.48 2.82 1.19

Rec. equipment 1.64 1.64 2.69 1.33

Rec. services 1.63 1.46 1.55 6.01

All other 3.13 5.03 4.55 2.44

Total 43.02 44.63 52.42 52.85

a CUSTOMER = customer use and survey techniques for operations, management evaluation, and research;

PARVS = public area recreation visitors survey.

Source: USDA Forest Service, Outdoor Recreation and Wilderness Assessment Group, Southeast Research Station, 

320 Green St., Athens, GA 30602-2044.
viewing, OHV use, snowmobiling, and motorized

boating), and winter activities (includes winter 

sports except snowmobiling). The previous response multipliers were based on average expenditures per person per day by residents, where

“resident” was defined as a resident of the project area. The new response multipliers are based

on average expenditures per person per trip within 50 miles of the visited site for all visitors in

the sample. Once again, the assumption was made

that the percentages of resident and nonresident

recreational visitors to all CUSTOMER and

PARVS sites would be representative of these

percentages for recreational visitors to Federal

sites within the project area. These expenditure

profiles (measured in constant 1993 dollars) are

shown in table 6.

The new expenditure estimates for hunting, fishing, and wildlife viewing were derived from a

1991 national survey of fishing, hunting, and

wildlife-associated recreation conducted by the

U.S. Fish and Wildlife Service (USDI 1993).

Estimates were given for four types of hunting

(big game, small game, migratory bird, and other

game), freshwater fishing, and nonconsumptive

wildlife recreation (observing, feeding, or 

photographing fish and other wildlife). Expenditure 

profiles were reported for each activity for each

state in two categories: residents of the state and

people who travel to the state (nonresidents) to

participate in activities within the state. The expenditures included are only those that are trip

related and take place within the state. Average

expenditures are reported per person per day. For

illustrative purposes, tables 7 to 9 show the average expenditure profiles (in constant 1993 dollars)

for these activities for one state, Idaho. Table 10

shows the previous and new total average expenditure estimates per person per visit (in constant

1993 dollars) for each activity.

The estimated number of days for each activity

and spender type (e.g., resident big game hunter)

also were reported for each state. To estimate the

response multipliers, these estimates were used to

allocate the percentage of a thousand days that

would fall into each category of activity and

11

Table 7—Estimated average spending per 

person per day for nonresident and resident

wildlife viewers in Idaho, 1991

Wildlife viewing

Expenditure item Nonresident Resident

1993 dollars

Food 11.74 5.14

Lodging 7.29 .53

Package fee .06 .04

Public transportation 12.03 .02

Private transportation 6.42 5.37

Rentals .00 .24

Total 37.54 11.34

Source: USDI 1993.

Table 8—Estimated average spending per 

person per day for nonresident and resident

anglers in Idaho, 1991

Fishing

Expenditure item Nonresident Resident

1993 dollars

Food 15.70 5.14

Lodging 6.21 .65

Package fee .04 .23

Public transportation 7.32 .03

Private transportation 6.28 6.00

Bait .81 .64

Boat fuel .54 1.14

Boat launch fees .01 .05

Boat mooring fees .86 .77

Guide fees .16 —

Ice .37 .38

Rentals .31 .05

Total 38.74 17.85

Source: USDI 1993.
12

Table 9—Estimated average spending per person per day for nonresident and resident hunters

in Idaho, 1991

Nonresident Resident

Expenditure Big game Migratory Small game Other game Big game Migratory Small game Other game

item hunting bird hunting hunting hunting hunting bird hunting hunting hunting

1993 dollars

Food 20.11 21.00 14.85 1.67 10.97 7.99 4.94 5.13

Lodging 2.50 2.56 .13

Package fee 2.23 .01 .15

Rentals .65 .05

Private Transportation 24.28 34.26 15.04 3.33 8.54 9.03 5.51 6.32

Total 49.12 55.27 32.45 5.00 20.30 17.02 10.65 11.45

Source: USDI 1993.

Table 10—Estimated average spending per person per trip by activity

Recreational Original New

activity estimate estimate

1993 dollars

Camping 95.79 43.02

Day use 99.22 43.02

Fishing 120.20 29.86,a 60.54,b 26.11,c 21.68d

Hunting 667.67 23.56,a 58.16,b 29.51,c 19.82d

Motorized boating 125.11 52.42

Motorized viewing 119.38 52.42

Nonmotorized boating 1007.55 44.63

Off-highway vehicle use 27.88 52.42

Snowmobiling 192.60 52.42

Trail use 91.40 44.63

Viewing wildlife 41.82 45.38,a 100.74,b 35.57c, 30.31d

Winter sports 77.80 52.85

a Estimate for Idaho.

b Estimate for Montana.

c Estimate for Oregon.

d Estimate for Washington.

Sources: Original estimates (based on data only from sites in the basin) from USDA, Outdoor Wilderness 

and Assessment Group, Southeast Research Station, 320 Green St., Athens, GA 30602-2044. Source 

for new estimates same as original (based on data from sites nationwide) except for hunting, fishing, and 

wildlife viewing, which were derived from USDI 1993.
spender type for each state. In Idaho, for example,

48.6 percent of the hunting days were estimated

to be by resident big game hunters, 6.1 percent by

nonresident big game hunters, 10 percent by resident migratory bird hunters, 0.5 percent by nonresident migratory bird hunters, 23 percent by

resident small game hunters, 3.5 percent by nonresident small game hunters, 8.2 percent by resident other game hunters, and 0.1 percent by

nonresident other game hunters. Some BEA areas

included counties from two states. For these BEA

areas, the estimated expenditure and activity percentages used were from the state containing the

most counties in that BEA area.

Because the response multipliers for these activities were in jobs per thousand days, but the recreational use data was reported in visits, we had to

convert the response multipliers to jobs per thousand visits. The 1991 U.S. Fish and Wildlife survey (USDI 1993) reports the estimated average

days per trip as well as the estimated number of

trips by each activity and spender type. The percentage of trips by each activity and spender type

was multiplied by the average days per trip for

that activity and spender type. These numbers

were summed to derive the overall estimated

days per visit for each broad activity category

(hunting, fishing, and wildlife viewing). The response multipliers per thousand days were then

multiplied by the estimated days per visit to

obtain response multipliers per thousand visits.

The assumptions made here were that the statewide estimates of average expenditures, percentage of day, percentage of trips, and average days

per trip—by activity and spender type—are representative of these activities on Federal lands in

the project area within each state.

Recreation-Response 

Multipliers9

Economic multipliers typically describe the rate 

of change in one parameter, such as employment

or income, with respect to another, such as the

demand for a good or service. Input-output (I-O)

models include multipliers of this type. The I-O

models are systems of multipliers describing the

production response (e.g., the response of producers in terms of required labor inputs, earnings, etc.)

in an economy to the demand for various goods

and services. The I-O models are usually sufficiently detailed in their specifications that it is possible to determine the multiplier for any of several

hundred different types of goods and services.

The activity of recreation unfortunately is not

and cannot be classified as a particular good or

service. Recreation is an activity engaged in by

individuals and can be viewed as an activity in

which a variety of goods and services are consumed concurrently. Thus, what is required is not

a multiplier relating the production response to the

demand for a single good or service, but rather

an aggregate multiplier relating the production

response to the demand for a collection of goods

and services. Such aggregate multipliers are

referred to as “recreation-response multipliers”

and can be derived for units of various recreational activities, which may have different 

patterns of demand for goods and services.

Multipliers, either for a particular good or aggregate multipliers for recreational activities, also

are typically described as being composed of

three parts: the direct effect (the rate at which the

primary producers of the demanded good or service are affected), the indirect effect (the rate at

which the suppliers of primary producers are 

9 This section is based in part on: Alward, Greg. 1997. Methods 

used to derive the recreation response multipliers for the Columbia

River basin ecosystem assessment. Fort Collins, CO: Inventory 

and Monitoring Institute, USDA Forest Service. 3 p. Plus tables. 

On file with: Interior Columbia Basin Ecosystem Management

Project, 112 E. Poplar, Walla Walla, WA 99362.

13
affected), and the induced effect (the largest indirect effect and the rate at which worker earnings

affect producers). Addition of the three parts of a

multiplier yields the total multiplier. For example,

if a recreationist purchases a meal at a restaurant,

the direct employment effect would be the restaurant labor needed to provide that meal. Indirect

employment effects would include the labor needed to produce the other inputs (e.g., fish, electricity, napkins) that the restaurant must purchase to

provide the meal. Induced employment effects

would include the labor needed to produce the

goods and services purchased with the wages

paid to the employees of the restaurant, as well

as other input suppliers that produced the other

items necessary to provide the meal. 

Only the direct-effect portions of the aggregate

multipliers are reported and used in this analysis.

Thus for the restaurant meal example, only the

restaurant labor needed to provide the meal is included as direct-effect employment.

Many people were concerned that total effect

multipliers were used in the previous analysis:

They were not. Some of the confusion may have

arisen from the fact that all I-O models use producer prices (those paid at the factory door) but

expenditures are measured in purchaser prices

(those paid at the retail level). In working with

the producer-priced I-O model, the purchaser

prices must be assigned back to the producing

economic sector. Some sectors keep only a part

of each dollar spent and pass the rest directly

through to the primary commodity sectors. This

process is called margining. The following explanation of margining is excerpted from Minnesota

IMPLAN Group, Inc. (MIG; 1997: 93-96):

Margins represent the difference between producer and purchaser prices. Margining assigns

direct expenditures to the correct I-O sectors.

It splits a purchaser price into the appropriate

producer values, each value impacting a specific industry. Margins allow us to be more

specific as to the economic activity triggered

by a retail purchase. Only retail stores that

buy goods from manufacturers use margins.

Any purchases made by consumers from service-oriented stores do not have margins.

Service businesses produce the service at the

same time it is purchased so there is no markup. Eating and drinking establishments also

have no margins, producing their consumables

at the time of purchase.

An example, from Johnson and others (1995: 20):

. . . if a recreationist buys a dollar’s worth 

of milk, the grocer may keep a margin of

$0.25 to cover business expenses and use the

remaining $0.75 to buy milk from the wholesale dealer. The wholesaler may keep $0.25 to

cover business expenses but use the remaining

$0.50 to buy milk from the local dairy. Therefore, the dollar spent is margined off to three

sectors.

The I-O models were estimated for the nine BEA

functional regional economies listed in table 1.

These I-O models were derived by using the

IMPLAN modeling system (e.g., Alward and 

others 1993, MIG 1997). These models are based

on highly detailed economic accounts for the

counties within each area. Specifically, the I-O

models were constructed from economic data for

calendar year 1991. 

The direct-effect recreation-response multipliers10

for employment (full- and part-time jobs) were

estimated for each functional regional economy

within the basin, for each of 12 recreational

activities.11 The original and new direct-effect

recreation-response multipliers for employment

for each activity in each BEA area are presented

in tables 11 and 12, respectively.12

10 In strict I-O terminology, the direct effect is not considered a

multiplier but a response coefficient. For consistency with earlier

documentation (see footnote 7), we use the term “multiplier” here.

11 These response multipliers were provided by Greg Alward and

Susan Winter, USDA Forest Service Inventory and Monitoring

Institute, 240 W. Prospect Rd., Fort Collins, CO 80526-2098.

12 For ease of comparison, the original direct-effect recreationresponse multipliers were converted from jobs per thousand days 

to jobs per thousand visits.

14
15

Table 11—Original estimates of recreation-response multipliers for direct-effect jobs for the BEA

activity 

BEA area

Recreational Bend- activity Boise Butte Idaho Falls Missoula Pendleton Redmond Spokane 

Jobs per thousand visits

Camping 1.86 1.82 2.04 1.95 1.76 1.77 1.64 Day use 1.99 1.91 2.20 2.07 1.89 1.88 1.72 Fishing 2.82 2.51 2.90 2.66 2.65 2.51 2.35 Hunting 15.72 16.09 16.77 16.85 15.24 15.53 13.90 Motorized boating 2.52 2.41 2.72 2.54 2.39 2.35 2.13 Motorized viewing 2.19 2.06 2.33 2.19 2.07 1.98 1.88 Nonmotor boating 22.39 23.00 24.14 24.00 21.30 22.06 20.41 OHVb use .46 .44 .51 .46 .47 .43 .39 

Snowmobiling 4.44 4.59 4.74 4.60 4.31 4.17 3.91 Trail use 2.26 2.26 2.44 2.37 2.08 2.18 2.00 Viewing wildlife .87 .90 .94 .92 .86 .86 .77 Winter sports 2.48 2.39 2.71 2.60 2.31 2.31 2.20 

a BEA = Bureau of Economic Analysis.b OHV = off-highway vehicles.Source: Derived from Haynes and Horne 1997.
16

Table 12—New estimates of recreation-response multipliers for direct-effect jobs for the BEAa are

BEA area

Recreational Bend- activity Boise Butte Idaho Falls Missoula Pendleton Redmond Spokane 

Jobs per thousand visits

Camping 1.00 0.89 0.97 0.96 0.88 0.85 0.86 Day use .52 .98 .54 1.02 .73 .71 .48 Fishing .60 1.44 .62 1.49 .54 .53 .55 Hunting .45 1.39 .48 1.43 .55 .53 .41 Motor boating 1.39 1.25 1.35 1.34 1.24 1.19 1.20 Motor viewing 1.39 1.25 1.35 1.34 1.24 1.19 1.20 Nonmotor boating 1.11 1.00 1.08 1.08 .98 .95 .96 OHVb use 1.39 1.25 1.35 1.34 1.24 1.19 1.20 

Snowmobiling 1.39 1.25 1.35 1.34 1.24 1.19 1.20 Trail use 1.11 1.00 1.08 1.08 .98 .95 .96 Viewing wildlife .91 1.91 .92 1.98 .83 .78 .82 Winter sports 1.89 1.74 1.83 1.85 1.76 1.68 1.66 

a BEA = Bureau of Economic Analysis.b OHV = off-highway vehicles.
Direct Employment Estimates

Original Methodology for DirectEffect Jobs

To estimate the number of direct-effect jobs associated with recreational activity, the estimated

days for each recreational activity in each BEA

area were multiplied by the direct-effect response

multipliers for each BEA area for each activity

(measured in jobs per thousand days in the original analysis).

New Methodology for Direct-Effect

Jobs

The new methodology is essentially the same except that the new response multipliers are based

on the new expenditure estimates and are measured in jobs per thousand visits.

Original Methodology for

Percentage of Economy Associated

With Recreation

To calculate the percentage of the economy represented by direct-effect jobs associated with

recreational activity on Federal lands, the estimated number of direct-effect recreational jobs

was divided by the total number of jobs projected

by the BEA in 1995 for each BEA area and multiplied by 100. For example, in the Bend-Redmond

area where the BEA projected 122,200 jobs in

1995, the estimated percentage of jobs directly

associated with camping was 1.28 percent:

1,569/122,200 x 100 = 1.28 .

New Methodology for Percentage of

Economy Associated With Recreation

The new methodology used to calculate the estimated percentage of the economy represented 

by direct-effect jobs associated with recreational

activity on Federal lands is similar to the original 

17

Figure 3—Original methodology for calculating direct-effect recreational jobs. ERU = ecological research unit; BEA =

Bureau of Economic Analysis. 

Visits to management 

units by activity

Visits in each ERU

by activity

Days in each BEA

region by activity

Visits in each BEA

region by activity

Estimated direct-effect

recreational jobs in each 

BEA region by activity

Estimated percentage of 

total jobs in each BEA

region that are direct-effect

recreational jobs by activity

Allocate visits to ERUs

Allocate visits 

to BEA regions

Multiply visits by 

average length of stay

Multiply by recreation-response multipliers 

for direct-effect jobs for each BEA region

Divide by total jobs 

in each BEA region
18

Figure 4—New methodology for calculating direct-effect recreational jobs. BEA = Bureau of Economic Analysis. 

Visits to management 

units by activity

Visits in each BEA

region by activity

Estimated direct-effect

recreational jobs in each 

BEA region by activity

Estimated percentage of 

total jobs in each BEA

region that are direct-effect

recreational jobs by activity

Allocate visits to counties 

and then to BEA regions

Multiply by recreation-response

multipliers for direct-effect jobs

for each BEA region

Divide by total jobs 

in each BEA region,

then multiply by 100

methodology, except that actual 1994 total employment figures were used instead of 1995 projected employment figures.13

Flow charts summarizing the original and new

methodologies used to calculate direct-effect

recreational jobs are provided in figures 3 and 4,

respectively.

Purpose of the Study

Many readers of the original analysis had a misconception regarding its design and intent. This

misconception was that it was an economic base

analysis wherein only recreational expenditures

representing an inflow of new dollars to the

economies of the basin were considered. The

actual intent of the analysis was to look at the

contribution of expenditures by recreational visitors to Federal lands to economic activity in the 

13 These latter figures were used in the earlier analysis because they

were available as part of the BEA data sets, and the 1994 

figures were not available at that time.

area, regardless of whether the expenditures represented an inflow of new money to the area or a

recirculation of money already there. This type of

analysis has been referred to as both a “contribution analysis”14 and a “significance analysis.”15

This contribution analysis was not an attempt to

measure the value of recreational activities to the

individuals engaging in those activities on Federal

lands.

Limitations of the Visitation

and Expenditure Data

Given the present state of data collection on recreational use, we cannot specify levels of confidence

14 Alward, Greg. 1998. Economic impact analysis. Presentation at

national workshop on obtaining recreation values and economic

impacts; 1998 March 10-12; [Chattanooga, TN]. On file with:

Warnell School of Forest Resources, University of Georgia, Athens,

GA 30602.

15 Stynes, Daniel. 1998. Economic impacts of recreation. Presentation at national workshop on obtaining recreation values and economic impacts; 1998 March 10-12; [Chattanooga, TN]. On file with:

Warnell School of Forest Resources, University of Georgia, Athens,

GA 30602.
for the accuracy of these data. The reliability of

the data may differ by land management agency,

by management units within a single agency,

and by recreational activity occurring at a single

management unit. Visitation data, for example,

may be more accurate for controlled and monitored access areas such as national parks, national

historic sites, and developed campgrounds. Estimates for activities such as motorized viewing

and other forms of dispersed recreation occurring

in areas with multiple entry and exit points may

rely more on human judgment and thus be less

reliable. For increased confidence, future visitation estimates should be generated under statistically valid sampling techniques applied

consistently across the basin.

Because the original expenditure estimates were

derived from samples subsequently deemed too

small or location specific to be representative of

recreational expenditures across the basin, the new

analysis used a new set of expenditure profiles

drawn from larger samples with less locationspecific influences. Two new problems accompany these estimates. First, because the new

expenditure profiles were derived from the more

generalized grouping of recreational activities

(i.e., mechanized travel), variations in expenditure

patterns and levels among the activities in each

group are not revealed. Second, as discussed

above, because the surveys used to derive the new

expenditure estimates included samples from outside the basin, we must assume that the expenditures by recreationists in those samples are representative of expenditures by recreationists in the

basin. In the absence of statistically valid basinwide expenditure surveys, we cannot determine

the accuracy of this assumption. 

Results and Discussion

The original and new estimates of direct-effect

jobs associated with each recreational activity on

Federal lands in each BEA area and for the entire

basin are shown in tables 13 and 14, respectively.

The original and new estimates of direct-effect

recreational jobs as a percentage of total jobs for

each BEA area and the basin are presented in

tables 15 and 16, respectively. The new estimated

percentage of direct-effect recreational jobs along

with the estimated percentage of jobs in ranching,

lumber and wood products, mining, and natural

resources (the previous three categories, combined) for each BEA area and the basin are shown

in table 17.16

The new estimate of direct-effect jobs associated

with recreational activities on Federal lands is a

little greater than one-third of the previous estimate. The estimated percentage of total jobs that

are direct-effect recreational jobs is still slightly

more, however, than the percentage of estimated

jobs in natural resources for the entire basin.

Previously, estimated direct-effect recreational

jobs exceeded estimated natural resource jobs in

every BEA area. Now, estimated natural resource

jobs exceed estimated direct-effect recreational

jobs in the Pendleton, Bend-Redmond, and

Spokane BEA areas. Readers should note that the

ranching, mining, and wood products job estimates are estimates of all jobs in these sectors, 

not just those stemming from activities on or outputs from Federal lands; and the estimated directeffect recreational jobs are not estimates of all

jobs associated with outdoor recreational activities, because many of these activities take place

on non-Federal lands. Moreover, usage of U.S. 

Army Corps of Engineer recreational sites was not

included, and as noted above, not all the Federal

management units solicited provided estimates of

their recreational visits. 

16 These percentages are not directly comparable. The mining and

lumber and wood products estimates are based on actual job counts

in the Standard Industrial Classification (SIC) categories for these

industries. Because there is no SIC category specifically for ranching (a subset of the agriculture and agriculture services categories),

that estimate is based on an estimated direct-effect multiplier that

relates the number of ranching jobs to the number of animal unit 

months necessary to support the estimated cattle and sheep inventories in the basin. As discussed above in “Recreation-Response

Multipliers,” because recreation is an activity in which a variety of

goods are consumed, an aggregate multiplier is used to estimate the

direct-effect recreational jobs. Although most recreational expenditures typically are concentrated in food, lodging, and transportation

purchases, a small portion of direct-effect recreational jobs may

occur in the lumber and wood products, mining, and ranching

industries because of margining.

19
20

Table 13—Original estimates of direct-effect recreational jobs for the basin and BEAa areas, 1993, by activ

BEA area

Recreational Bend- Tri- activity Boise Butte Idaho Falls Missoula Pendleton Redmond Spokane Cities 

Jobs

Camping 1,664 289 2,136 1,863 1,010 1,569 1,885 1,514 Day use 3,549 900 6,539 6,707 2,018 4,694 6,106 2,401 Fishing 2,473 332 2,470 2,080 1,484 2,433 2,326 705 Hunting 7,716 2,835 7,954 9,330 3,908 3,525 8,203 2,191 Motorized boating 826 124 780 1,226 107 145 971 225 Motorized viewing 5,139 815 8,176 4,920 1,567 4,705 6,267 6,468 Nonmotor boating 3,014 462 7,521 2,239 1,284 9,294 3,481 1,159 OHVb use 143 21 209 89 28 32 132 74 

Snowmobiling 792 499 2,053 1,026 322 413 994 552 Trail use 1,344 344 2,479 1,752 804 1,263 1,752 1,741 Viewing wildlife 131 158 151 313 156 546 179 89 Winter sports 2,233 246 3,860 1,001 1,057 1,988 1,611 1,246 

Total 29,023 6,974 44,329 32,547 13,745 30,607 33,906 18,365 

a BEA = Bureau of Economic Analysis.b OHV = off-highway vehicles.Source: Derived from Haynes and Horne 1997.
21

Table 14—New estimates of direct-effect recreational jobs for the basin and BEAa areas, 1993, by activity

BEA area

Recreational Bend- Tri- activity Boise Butte Idaho Falls Missoula Pendleton Redmond Spokane Cities 

Jobs

Camping 985 147 645 823 622 749 1,094 734 Day use 2,046 538 2,354 3,877 1,107 1,916 2,909 691 Fishing 525 253 483 686 264 672 638 41 Hunting 155 367 211 622 186 130 311 35 Motorized boating 800 65 150 165 61 76 980 25 Motorized viewing 5,979 753 2,399 2,259 1,012 2,902 3,552 3,662 Nonmotor boating 165 26 267 84 50 460 214 23 OHVb use 619 106 319 210 75 96 427 191 

Snowmobiling 409 224 438 160 113 121 306 140 Trail use 687 212 835 722 469 581 852 1,082 Viewing wildlife 117 96 89 327 127 651 170 30 Winter sports 2,324 263 1,476 749 1,104 1,518 529 825 

Total 14,811 2,990 9,665 10,684 5,190 9,872 11,981 7,480 

a BEA = Bureau of Economic Analysis.b OHV = off-highway vehicles.
22

Table 15—Original estimates of direct-effect recreational jobs as a percentage of total jobs in the basin an

by activity

BEA area

Recreational Bend- Tri- activity Boise Butte Idaho Falls Missoula Pendleton Redmond Spokane Cities 

Percentage of total jobs

Camping 0.70 0.53 1.47 1.70 1.06 1.28 0.52 0.51 Day use 1.49 1.65 4.51 6.14 2.13 3.84 1.68 .80 Fishing 1.04 .61 1.70 1.90 1.56 1.99 .64 .24 Hunting 3.25 5.19 5.49 8.54 4.12 2.88 2.26 .73 Motor boating .35 .23 .54 1.12 .11 .12 .27 .08 Motorized viewing 2.16 1.49 5.64 4.50 1.65 3.85 1.73 2.16 Nonmotor boating 1.27 .85 5.19 2.05 1.35 7.61 .96 .39 OHVb use .06 .04 .14 .08 .03 .03 .04 .02 

Snowmobiling .33 .82 1.42 .94 .34 .34 .27 .18 Trail use .57 .63 1.71 1.60 .85 1.03 .48 .58 Viewing wildlife .06 .29 .10 .29 .16 .45 .05 .03 Winter sports .94 .45 2.66 .92 1.11 1.63 .44 .42 

Total 12.22 12.77 30.57 29.78 14.48 25.05 9.35 6.13 

a BEA = Bureau of Economic Analysis.b OHV = off-highway vehicles.Source: Haynes and Horne 1997.
23

Table 16—New estimates of direct-effect recreational jobs as a percentage of total jobs in the basin and B

BEA area

Recreational Bend- Tri- activity Boise Butte Idaho Falls Missoula Pendleton Redmond Spokane Cities 

Percentage of total jobs

Camping 0.34 0.25 0.43 0.62 0.61 0.48 0.26 0.21 Day use .71 .90 1.58 2.92 1.09 1.22 .70 .20 Fishing .18 .43 .32 .52 .26 .43 .15 .01 Hunting .05 .62 .14 .47 .18 .08 .08 .01 Motorized boating .28 .11 .10 .12 .06 .05 .24 .01 Motorized viewing 2.07 1.27 1.61 1.70 1.00 1.85 .86 1.06 Nonmotor boating .06 .04 .18 .06 .05 .29 .05 .01 OHVb use .21 .18 .21 .16 .07 .06 .10 .06 

Snowmobiling .14 .38 .29 .12 .11 .08 .07 .04 Trail use .24 .36 .56 .54 .46 .37 .21 .44 Viewing wildlife .04 .16 .06 .25 .13 .42 .04 .01 Winter sports .80 .44 .99 .56 1.09 .97 .13 .24 

Total 5.12 5.13 6.50 8.04 5.13 6.30 2.89 2.17 

a BEA = Bureau of Economic Analysis.b OHV = off-highway vehicles.
The two recreational activities having the largest

decreases in estimated direct-effect jobs were

hunting (decreased by 45,686 jobs) and nonmotorized boating (decreased by 27,747 jobs). These

large decreases were the result of much smaller

estimates of average expenditures per visit for

these activities. The estimated number of jobs

associated with off-highway vehicle use increased

as a result of a larger estimate of average expenditures per visit. Using the original estimates,

hunting, motorized viewing, and day use accounted for the largest amounts of the estimated directeffect jobs associated with recreation. Based on 

the new estimates, motorized viewing, day use,

and winter sports account for the largest amounts

of estimated direct-effect jobs associated with

recreation.

In the original analysis, the top five BEA areas in

total estimated direct-effect jobs associated with

recreation on Federal lands were, in descending

order, Idaho Falls, Spokane, Missoula, BendRedmond, and Boise. In the new analysis, the order is Boise, Spokane, Missoula, Bend-Redmond,

and Idaho Falls. The switch in order for Idaho

Falls and Boise is primarily the result of the

change in methodology used to allocate visits to

BEA areas. Total estimated recreational visits in

the Idaho Falls BEA area decreased by 4,205,147,

and estimated visits to the Boise BEA area increased by 3,024,338. The top four BEA areas in

terms of the percentage of total jobs estimated to

be direct-effect recreational jobs in the previous

analysis were, in descending order, Missoula,

Idaho Falls, Bend-Redmond, and Pendleton. In

the new analysis the top four are Missoula, Idaho

Falls, Bend-Redmond, and Twin Falls. 

The BEA area with the largest absolute decrease

(34,664 jobs) and percentage of decrease (78 percent) in estimated jobs directly associated with

recreation was Idaho Falls; the Twin Falls area

24

Table 17—Estimated percentage of jobs for each BEAa area and the basin, 1994

Lumber and Natural Direct-effect

BEA area Ranching wood products Mining resource recreationb

Percentage of total jobs

Boise 1.09 1.82 0.22 3.13 5.12

Butte .89 .99 1.50 3.39 5.13

Idaho Falls 1.34 .49 .70 2.53 6.50

Missoula .56 4.14 .31 5.01 8.04

Pendleton 1.82 3.40 .13 5.35 5.13

Bend-Redmond .96 6.94 .18 8.08 6.30

Spokane .39 2.69 .40 3.48 2.89

Tri-Cities .27 .96 .12 1.35 2.17

Twin Falls 2.23 .30 .30 2.83 5.66

Total basin .82 2.37 .33 3.52 4.48

a BEA = Bureau of Economic Analysis.

b Based on the estimated direct-effect jobs associated with recreation for the Federal land management units that 

reported their recreational use data.

Source: Estimates for ranching, lumber and wood products, and mining are from Horne and Haynes (1999) and 

represent estimates of all jobs in these sectors.
had the smallest absolute decrease (2,896 jobs)

and percentage of decrease (37 percent) in jobs.

These relative decreases are primarily attributable to the fact that Idaho Falls had the largest

percentage of decrease (33 percent) in estimated

recreation visits and Twin Falls had the largest

percentage of increase (77 percent) in estimated

recreation visits with the new visit-allocation

methodology.

Debate continues over the current and potential

roles of recreation in stimulating economic

growth in rural and less diverse regions of the

basin. Much of this debate is nested within the

larger question regarding the primary determinants in location decisions made by people and

businesses. The traditional view is that businesses locate near resources and that people are then

attracted to these locations by job opportunities

(Richardson 1979); i.e., people follow jobs. An

alternative view is that people locate in highamenity areas based on quality-of-life considerations and that businesses follow in the belief that

workers will accept lower wages to remain in high

amenity areas; i.e., jobs follow people.17 Niemi

and Whitelaw (1997:31) use the phrase “second

paycheck” to represent “the value to residents of

the various amenities contributing to the quality

of life in the area, including access to social, cultural, and environmental amenities, access they

would not enjoy if they lived elsewhere.”

Another aspect of this debate centers on the 

issue of whether jobs associated with recreation

provide lower average incomes, offer fewer 

benefits, and provide less stimulus to economic

growth than other types of jobs. Smith (1989) 

17 Writings in support of this latter view, and additional references,

can be found in Niemi and Whitelaw 1997, Power 1996, Rasker

1995, Rudzitis and Johansen 1989, and Whitelaw and Niemi 1989.

Writings critical of this view include Fawson 1997, Marston 1996,

Miller, J.R. 1997,* Polzin and Daubert 1997, and Sommers 1996. 

*Miller, J.R. 1997. The growth follows amenities doctrine and the

Pacific Northwest. Paper presented at the Pacific Northwest

Regional Economic Conference. April 24-26. Spokane, WA.

writes in support of this view, and Christensen

and Nickerson (1995) offer a contrary view. Although both studies focus on the tourism industry,

many of their findings are probably applicable to

direct-effect recreational employment.

Because our analysis examined only one—recreational opportunities on Federal land—of the

many amenities that may attract people and businesses to an area, and at only a single point in

time, it cannot be used to fully address these

larger issues. Given the generality and broadscale nature of our estimates of BEA area directeffect recreational jobs, these estimates also

cannot be used for a detailed analysis of the 

significance of recreation at the county or local

level. A simple linear regression does provide

some interesting insights, however, regarding the

variation of these estimates across the basin. The

dependent variable in this regression, %Recjobs,

is the new estimate of direct-effect recreational

jobs as a percentage of total jobs in the BEA

area. The following variables are included as

regressors:

%Federal The percentage of the total acreage 

of a BEA area that is National Forest

System, Bureau of Land Management,

National Park Service, or Fish and

Wildlife Service land. 18

%Rural The percentage of the 1990 population

in a BEA area not classified as urban

by the Bureau of the Census. A simplified definition is that urban residents live in places with a population

of 2,500 or more.19

18 Derived from county figures in McGinnis 1996, table 2.

19 Derived from county figures in McGinnis 1996, table 1.

25
Pcapinc The per capita total personal income

in a BEA area in 1992, measured in

1990 dollars.20 Total personal income

includes transfer payments (including Social Security and government

retirement payments, medicare and

medicaid, unemployment insurance

benefits, income maintenance payments [including inkind payments,

such as food stamps], and others),

property income (dividends, interest,

and rent), farm income, and nonfarm

earnings.

The results of this regression are given in table

18. The adjusted R-squared indicates that the

model explains 88 percent of the variation in

%Recjobs for the BEA areas in the basin. The

most significant explanatory variable is %Federal

(significant at α =0.003 level). This variable has

a positive coefficient and can be interpreted as,

for a 1-percent increase in BEA area in Federal

ownership (as defined above), a 0.077-percent

increase in the total jobs in a BEA area that are

estimated to be direct-effect recreational jobs, all

other things constant. This result is not surprising

because %Federal can be viewed as a proxy for

the supply of recreational opportunities in the

BEA area. 

20 Derived from county figures in McGinnis 1996, tables 1 and 9.

The next most significant variable is %Rural

(significant at α =0.020). This variable also has 

a positive coefficient, which can be interpreted as,

for a 1-percent increase in the population in a

BEA area classified as rural, a 0.063-percent increase in total jobs in the BEA area that are estimated to be direct-effect recreational jobs, all

other things constant. This supports a hypothesis

that the more rural the BEA area, the larger the

role that recreation may play in the economy of

the area.

Finally, the Pcapinc variable is significant at 

α =0.024. The negative coefficient on this variable

can be interpreted as, for a $1.00 increase in per

capita income in a BEA area, a 0.0009-percent

decrease in total jobs in the BEA area that are

estimated to be direct-effect recreational jobs, all

other things equal. This suggests that the percentage of jobs in recreation are higher in BEA areas

where per capita incomes are lower. The degree

that lower per capita incomes may be offset by

lower costs of living or larger second paychecks

cannot be discerned in the present analysis. Additionally, as mentioned above, because this is a

static analysis, the dynamic issue of economic

growth cannot be addressed.

26

Table 18—Ordinary least squares regression results for BEAa area 

direct-effect recreational jobs 

Dependent variable %Recjobs

Number of observations 9

R2 0.93

Adjusted R2 0.88

Variable Estimated coefficient t-statistic Significance level

Constant 12.7630 2.58 .0496

%Federal .0772 5.37 .0030

%Rural .0635 3.38 .0196

Pcapinc - .0009 - 3.19 .0242

a BEA = Bureau of Economic Analysis.
27

Future Research

If a broad-scale assessment of the direct economic activity associated with recreation on Federal

lands is undertaken in the future, we offer the

following suggestions to improve the reliability

of such estimates.

1. Ensure that the recreational visitation data

are collected in a consistent and statistically

valid manner across management units and

agencies. Information on the proportions of

resident versus nonresident and local versus

nonlocal recreationists is a critical data element.

2. Collect expenditure data by using the same

categories and units of measurement in

which the recreational data are recorded

(e.g., expenditures per primary visit by nonlocal motorized viewing recreationists).

3. Stratify the expenditure survey samples by

the estimated visitation proportions. If it is

estimated, for example, that 70 percent of the

off-highway vehicle users are locals, about

70 percent of the expenditure survey sample

for off-highway vehicle use should be locals.

4. Collect visitation and expenditure data with

as much geographic specificity as possible,

so that expenditures and the resultant associated economic activities are attributed as

closely as possible to the areas where they

are actually occurring.

Acknowledgments

Susan Winter and Greg Alward, U.S. Department

of Agriculture, Forest Service, Land Management

and Planning, Fort Collins, CO, calculated the

various response coefficients used in this report.
28

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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.

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of Civil Rights, Room 326-W, Whitten Building, 14th and

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(202) 720-5964 (voice and TDD). USDA is an equal opportunity

provider and employer.

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