United States

Department of

Agriculture

Forest Service

Southeastern Forest

Experiment Station

Research Paper

SE-275

Theory and Techniques for Assessing

the Demand and Supply of Outdoor

Recreation in the United States
The Authors:

H. Ken Cordell is Project Leader and National Assessment

Specialist, Outdoor Recreation and Wilderness Research,

Southeastern Forest Experiment Station, USDA Forest Service,

Athens, GA. John C. Bergstrom is Assistant Professor,

Department of Agricultural Economics, University of Georgia,

Athens, GA.

Acknowledgments :

We thank Greg Ashley and Carter Betz of the study team for

highly professional analytical support, Don English for the

recreation opportunity indices, and Larry Hartmann for

contributions toward development of the PARVS data set. We

especially acknowledge the PARVS Working Group which is

responsible for the creation and implementation of the PARVS

national research effort. Very helpful review comments from Dan

McCollum (Forest Service, RM), Ted McConnell (University of

Maryland), Dick Walsh (Colorado State University), and Dan

Stynes (Michigan State University) are also gratefully

acknowledged. Any remaining errors or omissions are the

responsibility of the authors. This research represents more than 6

years of effort on the part of many. Funding and guidance from

the Programs and Assessment staff of the Forest Service provided

invaluable support for completion of the project.

September 1989

Southeastern Forest Experiment Station

P.O. Box 2660

Asheville, North Carolina 26602
Abstract

Modeling and estimating consumption, demand, supply, values, scarcity, future equilibria,

and price changes have been major challenges in comprehensive recreation assessment and

planning. The results of 6 years of work reported in this Paper show research advancements

toward meeting these challenges. Accomplished as the central analysis for the 1989

Renewable Resources Planning Act Assessment, a household market model covering 37

recreational activities was computed for the United States. Equilibrium consumption and

costs were estimated, as were likely future changes in consumption and costs in response to

expected demand growth and alternative development and access policies. Except for

consumptive wildlife and fishing activities, most outdoor recreation demand and supply

equilibria will grow to much higher levels of consumption in future years. For most

activities, aggressive expansion of resource access and site development to provide more

recreation opportunities will be necessary if rapid increases of opportunity scarcity are to be

avoided.

Keywords: Outdoor recreation, market equilibrium, demand, supply, forecasts, household

production theory.

Summary

A conceptual model of outdoor recreation demand,

supply, and equilibrium consumption and costs is

described in this Paper. This model is based on

community-level, household demand and supply

functions. The aggregate demand function shows total

annual trips for an activity that a community will

demand at various trip costs. The aggregate supply

curve, based on household production theory, shows

total annual trips for an activity that a community can

produce at various trip costs, given the existing

availability of opportunities or destinations.

Equilibrium trip consumption and costs are defined

from a community-level household or consumer

perspective. Equilibrium trip consumption refers to the

number of trips consumed when aggregate demand and

supply are equal. Equilibrium trip costs are the costs or

price of a trip where the number of trips demanded by a

community is equal to the number of trips available to a

community. At the household equilibrium point, the

marginal benefits of trip consumption are equal to the

marginal costs to households of trip production.

Equilibrium consumption and costs were estimated for

several demand/supply scenarios. These scenarios

assumed moderate demand growth, combined with

either negative, zero, moderate, or high growth of

available facilities and resources. For a typical

community, equilibrium consumption of most

recreational activities was projected to increase under all

demand/supply scenarios to the year 2040. Notable

exceptions included big-game hunting, small-game

hunting, and warm-water fishing. The results, in

general, indicate that equilibrium consumption is quite

sensitive to changes in recreational facility and resource

growth rates.

The interaction of outdoor recreation demand and

supply over time is summarized in the form of changes

in market equilibrium trip costs. Increases in

equilibrium trip costs indicate that demand is increasing

faster than supply and that outdoor recreational

opportunities are becoming more scarce. Decreases in

equilibrium trip costs indicate that supply is increasing

faster than demand and that outdoor recreational

opportunities are becoming more abundant. Constant

equilibrium trip costs indicate that demand and supply

are increasing or decreasing at the same rate and that

the scarcity of recreational opportunities is remaining

about the same over time.

By summarizing the balance between demand and

supply, market equilibrium trip costs provide a broad

indicator of the relative scarcity of recreational

opportunities over space and time. The need for a

broad recreation scarcity indicator has also been

advocated in the recreation literature (Clawson 1984;

Cordell and English 1989; Harrington 1987).

Equilibrium trip costs were applied in this Paper to

assess the impact of public policy pertaining to

recreational facility and resource growth on the

provision of recreational opportunities. Results indicate

that for most activities, a moderate to high rate of public

facility and resource growth will be needed to satisfy

future demand and to prevent increases in the relative

scarcity of recreational opportunities.
Theory and Techniques for Assessing

the Demand and Supply of

Outdoor Recreation in the United States

H. Ken Cordell John C. Bergstrom

CONTENTS

The Purpose for This Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Recreational Trip Demand and Supply ........................ 1

Background Considerations ............................. 1

Demand ........................................... 1

Supply ............................................ 3

Household Market for Recreational Trips ...................... 4

Trip Consumption ....................................

Consumption Trend Lines .............................. z

Consumption Trend Lines Under Alternative Scenarios ........ 6

Equilibrium Trip Costs or Price .......................... 7

Empirical Approach ...................................... a

Trip Consumption and Cost Data ........................ 8

Defining the Representative Communities .................. 9

Resource and Facility Data ............................. 9

Other Data ......................................... 9

Organization of the Data ............................... 9

Demand Curve Estimation ............................. 10

Consumption Function Estimation ........................ 11

Household Market Costs ............................... 12

Research Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . l.3

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . l3

Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
The Purpose for This Research

The Renewable Resources Planning Act (RPA) of 1974

requires that the USDA Forest Service comprehensively

assess demands for and supplies of forest and range

resources at lo-year intervals. The next assessment

report will be published for release in 1989. The RPA

requires estimates of demand and supply trends for a

number of recreational settings, resources, and activities.

Previous recreation assessments were limited by

inadequate data, theories, and methods. This Paper

reports advancements in all three of these dimensions of

the assessment process, leading to a capability to model

and forecast within a market equilibrium framework.

This framework provides a conceptually strong, unique,

and comprehensive means for resource assessment

consistent with RPA research, planning, and policy

thrusts.

The first section provides a discussion of communitylevel household demand and supply. A conceptual

model of household equilibrium consumption and costs

for recreational trips is then presented. This conceptual

model shows that equilibrium consumption and costs can

be estimated from aggregate demand and consumption

functions. Empirical estimation of the aggregate

demand functions, aggregate consumption functions,

equilibrium consumption, and equilibrium costs are then

discussed.

Recreational Trip Demand and Supply

Background Considerations

Demand for and supply of outdoor recreation are

elusive concepts. Most outdoor recreational activities

are provided as public goods, for which the market

situation is quite different from that for other forest

products. Most other forest products are private goods,

like timber. Since market-determined recreational

opportunities as conceptualized by classical economics

are nonexistent, outdoor recreation demand and supply

must be assessed using extra market techniques.

Outdoor recreation demand and supply analysis is

further complicated by two other problems. Fist, it is

difficult to define exactly what is being demanded and

how to measure recreation demand. Second, once a

demand quantity measure has been selected, defining

theoretically appropriate demand and supply curves

tends to be very problematic.

For the RPA analysis herein reported, recreational trips

were considered to be the most conceptually appropriate

measure of quantity consumed (McConnell 1975). A

trip is defined as a purposeful commitment of time away

from home to travel to a destination(s) for enjoyment of

a particular type of recreational opportunity. Measuring

the demand for and supply of recreational trips is

consistent with the RPA goal of assessing the overall

availability of recreational opportunities to the nation,

rather than the demand for a particular site or facility by

a particular segment of the population. For the latter

purpose, a visit or an estimate of the amount of

recreation participation time is the appropriate measure.

National data on recreational trip consumption were

available for this research from the Public Area

Recreation Visitors Study (PARVS).

Demand

Recreation demand functions are generally of two

types--aggregate or individual. Aggregate demand

functions provide the basis for the zonal travel cost

method (ZTCM). In the ZTCM approach, as usually

applied, recreationists are grouped into zones around a

site. Demand curves are derived by estimating the

statistical relationship between aggregate trips from a

zone and the distance from the zone to the site

(Clawson and Knetsch 1966; Dwyer and others 1977;

Ward and Loomis 1986).

A major criticism of the ZTCM is the loss of detail on

recreation demand behavior which results from

aggregating individuals and trips within zones. A

method developed for retaining detailed information is

the individual travel cost method (ITCM) in which the

unit of observation is an individual’s consumption of

trips. ITCM demand curves are derived by estimating

the statistical relationship between an individual’s trips

and the distance traveled from residence to recreational

site (Brown and Nawas 1973; Gum and Martin 1975;

Ward and Loomis 1986).

The choice between ZTCM or the ITCM is dependent

on several considerations. For one, the ZTCM is often

selected because less data are required. A further

advantage is that it adjusts for both probability and

frequency of participation using a single equation that

can be estimated by ordinary ieast squares. Several

more complex statistical procedures have been

developed as adjustments for both the probability and

frequency of participation when the ITCM is chosen.

Much debate remains, however, over the appropriateness of these adjustments (Walsh 1986; Ward and

Loomis 1986).

There are also several perceived disadvantages of the

ZTCM which have led to increased use of the ITCM in

recent years. First, when aggregation is selected as the

appropriate analysis objective, there is difficulty in

1
reconciling its results with utility theoretic models of

individual choice. Also, the ZTCM assumes that

individual characteristics and participation rates are

constant within zones. These assumptions probably are

not realistic (Bockstael and McConnell 1981). Also, the

ZTCM eliminates within-zone variation in

socioeconomic characteristics and trip consumption

behavior. Hence, it may be difficult to estimate

statistically significant relationships between individual

socioeconomic variables and the quantity of trips

consumed (Brown and Nawas 1973; Rosenthal 1985;

Ward and Loomis 1986).

In general, if the objective of an analysis is to explain

individual consumption behavior, the ITCM is likely to

be more appropriate and efficient than the ZTCM. By

focusing on individual observations, the ITCM approach

allows for more statistically robust and theoretically

consistent analyses of individual recreation consumption

behavior. The primary disadvantages of the ITCM are

large data requirements and complicated estimation

procedures (Ward and Loomis 1986).

Although consideration of the relationships between

individual socioeconomic characteristics and consumption behavior is important, the primary objective of

the RPA Assessment, for which this research was done,

is to analyze aggregate relationships and trends in

national outdoor recreation demand and supply. Estimation of aggregate demand and consumption functions

using the ZTCM is appropriate to this overall objective.

An additional consideration used to select ZTCM as the

more appropriate modeling procedure was that the data

collected for the RPA assessment through PARVS are

more compatible with an aggregate model. A final

consideration was that statistical implementation

procedures, particularly with respect to adjustments for

probability of participation, are well established in the

ZTCM. The ZTCM has been successfully employed in

hundreds of applications over the last three decades

(Walsh 1986; Ward and Loomis 1986).

For the RPA Assessment, it was necessary to modify the

classical ZTCM to account for regional recreational

opportunities. This modification was accomplished with

a region-based zonal travel cost method for multiple

destination opportunities (RZTCM) (Sorg and others

1985). RZTCM is designed to model trips for an

activity from a zone to all sites used by the population in

that zone. For the RPA Assessment, zones were

defined as counties, each assumed to be a homogeneous

community. The modeling task was therefore to develop

aggregate demand and consumption functions for trips

to all sites used by a county population for a particular

activity.

Community-level (county) recreation trip-consumption

behavior is influenced by trip costs, community

population characteristics, and the availability and

diversity of suitable recreational opportunities.

Community characteristics, such as the percentage of

households of middle-income status, affect the

proportion of the community population that participates

(analogous to probability) and the frequency of

consumption of outdoor recreational trips. Availability

of suitable recreational opportunities throughout the

region determines the cost of recreational trips and the

quantity of substitutes. Trip costs and substitutes affect

not only the frequency of participation but also the

probability of participation. Community-level recreation

demand behavior was modeled for the RPA Assessment

with the following specification:

ATRIPSD = f(P, S, SO, Z, H) , (1)

where

ATRIPSD = annual trips demanded for activity k by a

community,

P = cost or price of trips for activity k,

S = suitability of sites used for activity k,

SO = substitute recreation opportunities

available to a community,

Z = community population 12 years old and

older,

H = community characteristics.

Figure 1 illustrates a community-level demand function

for trips for activity k. This demand function holds site

suitability (a crude measure of quality), substitute

recreational opportunities, county population size, and

county population characteristics constant. Thus, it

shows the total number of trips a community will

demand at various trip costs. For example, at a cost of

$60 per trip, 100,000 trips are demanded. Trip costs

include both out-of-pocket travel expenditures and the

opportunity cost of time used for travel for a two-way

trip.

* Includes trovel and time costs for o two-way trip

Figure l-Activity k aggregate demand curve for a typical community.

2
SUPPlY

Outdoor recreation trips cannot be purchased as most

goods can at a local shopping center. Rather, trips must

be “produced,” a process carried out by the recreationists

themselves. Recreationists for example, may combine

gasoline, time, equipment, supplies, and recreational

facilities to produce a trip for the activity of hiking. The

conceptualization of recreationists as producers of trips

is derived from household production theory (Becker

1%5; Bockstael and McConnell 1981).

As travel distance or time is increased, more

recreational opportunities are available to a community

and the number of trips which can be produced or

supplied increases. The number of trips that can be

produced at a given trip cost is determined by the

availability of recreational opportunities; knowledge,

skills, and abilities (KSAs) of households in the

community; and technology available to households, such

as light-weight backpacking gear. In the absence of

measures of KSAs, household characteristics are used as

proxies. Trip production was modeled for the RPA

Assessment with an aggregate household supply function

conceptually specified as:

ATRIPS’ = h(P, RO, H) ,

where

(2)

ATRIPSS = annual number of trips supplied for activity

k by a community,

P = cost or price of trips for activity k,

RO, = recreational opportunities available to a

community for activity k,

H = household characteristics affecting trip

production technology.

Equation (2) is a modified version of Bockstael and

McConnell’s supply or marginal cost function for wildlife

recreation which focused on trip production by

individuals. In contrast to Bockstael and McConnell’s

models, equation (2) focuses on total annual trip

production by a community and is more general in that

it applies to many other activities, in addition to wildlife

recreation. This community production perspective is

consistent with the conceptual analysis of aggregate

outdoor recreation supply conducted by Harrington

(1987). Equation (2) describes trip supply for a given

time period (e.g., 1 year), during which P and ROk are

assumed to be exogenous variables.

In equation (2), recreational opportunities (ROk) refer

to specific sites and facilities. Amount and location of

these recreational opportunities are fmed in the short

run. This fured amount and location of recreational

opportunities are expressed conceptually by the

equation:

R”k = &MC,B, M) ,

where

(3)

LMCk = long-rM marginal COStS Of providing

recreational sites used as inputs for activity k,

as provided by public and private resource

managers,

B = annual recreation budget available to

resource managers,

M = current management policies.

Equation (3) implies that availability of recreational

opportunities is determined by the costs of providing

recreational sites (such as expenditures on new public

facilities), an exogenously determined budget (such as

congressional appropriations), and current attitudes and

policies (such as attitudes and policies regarding new

land acquisitions) (Hof and Kaiser 1983). The variable,

RO, , is an important variable. A change in public

management policies, for example, may change RO,. A

change in ROk , in turn, will impact the ability of a

community to produce recreational trips through

equation (2).

Aggregate household supply functions for activity k are

illustrated by the curves labeled S, and S, in figure 2.

Each curve represents a different fured amount of

recreational opportunities for activity k. The curve

labeled St indicates that at an average trip cost of $60, a

hypothetical community can produce 75,ooO trips.

Suppose a change in government management policies

increases the availability of recreational opportunities,

RO,. This increase will cause the aggregate household

supply curve to shift from St to S,. Given S,, a typical

community can now produce 150,000 trips at an average

trip cost of !$60.

v Includes travel ond time costs for o two-way trip

Figure 2-Activity k aggregate supply curves for hvo levels of

available opportunities for a typical community.
Derivation of an aggregate household supply curve is

demonstrated further by the diagrams in figure 3. The

black dot in the center of figure 3a represents the

location of a community surrounded by six recreational

sites represented by squares numbered l-6. Each site is

assumed to have the capacity to accommodate 100 trips

per year for activity k. In order to produce more trips

than those which the closest site can accommodate,

community residents must travel to sites farther away. If

residents travel up to 25 miles, they can produce a total

of 200 trips annually (100 trips each to sites 1 and 2). If

residents travel up to 50 miles, they are able to produce

a total of 400 trips annually (100 trips each to sites 1, 2,

3, and 4). At distances up to 100 miles, residents can

produce a total of 600 trips annually (100 trips each to

sites 1, 2, 3, 4, 5, and 6).

traveling to more distant sites. If a large number of

sites were located at continuous distances from the

community, the staircase-shaped curve in figure 3b

would become the smooth, upward sloping aggregate

supply curve shown in figure 2. Changes in the number,

capacity, or location of sites shift the aggregate supply

function. For example, if additional sites were provided

between 25 and 100 miles of the community shown in

figure 3a, the aggregate supply curve would shift to the

right, such as from S, to S, in figure 2. Conversely, if

sites or site capacity were reduced between 25 and 100

miles of the community, shown in figure 3a, the

aggregate supply curve would shift to the left, such as

from S, to St in figure 2.

Household Market for Recreational Trips

Trip Consumption

In the context of the Bockstael and McConnell (1981)

household production model, equilibrium consumption

and costs are defined by the household market demand

and supply curves illustrated in figure 4.

04

Figure 4-Community-level household market for activity k trips.

Figure 3-Derivation of activity k aggregate supply curve for a typical

community.

The numbers of trips that can be produced at various

distances by the community, shown in figure 3a, are

indicated by the staircase-shaped aggregate supply curve

in figure 3b. Figure 3b assumes, for convenience, that

each additional mile of travel costs exactly one more

dollar. This “staircase” supply function assumes that the

community first exhausts capacity at closer sites before

As indicated by the demand curve, at a cost of P per

trip, households in the community desire and wd4

consume Q1 trips. As indicated by the aggregate supply

curve, at a cost of P, per trip, households in the

community are able to produce Qt trips. Thus, the

quantity of trips households in a community would like

to consume is equal to the quantity of trips they can

produce. From the community’s perspective, aggregate

demand and supply are in equilibrium. At the

equilibrium number of trips, the marginal costs to the

community of producing trips are equal to the marginal

benefits to them of consuming trips. This equilibrium is

in the nature of a general equilibrium as the aggregate

demand and supply curves account for overall

recreational opportunities across multiple sites.

4
The intersection of these aggregate demand and supply

curves defines the current number of activity k trips

consumed, Qr, and the cost of these trips, P, and

represents a community-level household market

equilibrium.

The existence of a household market equilibrium

assumes that the community is not restricted to

producing and consuming trips only at local sites.

Restriction to local sites would be the case, for example,

if the community were on an island and all feasible

recreational sites were located on that island. In this

island situation, the aggregate trip supply curve would be

truncated at the point corresponding to the maximum

capacity of sites at any one time. At maximum capacity,

the aggregate supply curve effectively turns vertical,

indicating that no more trips can be produced using the

existing stock of sites (Cicchetti 1973; Loomis and Hof

1985). This situation is illustrated in figure 5.

Figure S-Community-level household market for activity k trips

(alternative case) with truncated aggregate supply function.

If the aggregate demand curve intersects the effective

aggregate supply curve in this vertical segment (point B

in figure 5) and if negligible congestion costs are

assumed, a household market disequilibrium results.

Because of capacity constraints, trip consumption is

rationed at Q1 trips. The marginal cost of producing

these trips is equal to P, per trip. Given the aggregate

demand curve in figure 5 and a marginal cost of P, per

trip, community households would desire to consume Qz

trips. Thus, there is a shortage of trips equal to Q2-Q1.

The limited number of trips denoted by Q1 are usually

rationed by nonprice mechanisms (such as first come,

fast served or reservations). If trip costs were increased

to Pz, however, the limited number of trips would be

rationed by the cost or price of a trip. One way of

increasing trip costs to Pz is to charge an entrance fee

equal to P,-P, per trip. This fee would shift the

effective aggregate supply curve from OAS to OBS, and

result in a new household market equilibrium at point B

in figure 5.

In the more common case where a community is not on

an island, local site capacity does not restrict

consumable trips to effectively truncate the aggregate

supply curve. While it is assumed that local site capacity

is used up first, recreationists are free to move into

other regions to seek other destinations until the point is

reached where the marginal benefits and costs of

additional trips produced and consumed are equal. The

number of trips consumed is illustrated by the household

market equilibrium in figure 4. If the cost of producing

more trips using more distant sites increases rapidly, the

household market equilibrium may occur at relatively

few trips and high costs per trip.

Consumption Trend Lines

A further objective of the RPA Assessment is to

compare demand and supply of recreational

opportunities at future times. Comparisons over time

are made using consumption trend lines. The total

number of trips consumed by a community is

determined by the intersection of the aggregate demand

curve shown in equation (1) and figure 1 and the

aggregate supply curve in equation (2) and figure 2.

This intersection, illustrated in figure 5, represents the

solution to the simultaneous equation system given by

equations (1) and (2). The solution to this

demand/supply simultaneous equation system can be

estimated from the reduced form of the system:

ATRIPS = g (SO, Z, S, RO,,H) ,

where

(4)

ATRIPS = annual number of trips for activity k

consumed by a community.

The derivation of a consumption trend line is illustrated

graphically in figure 6. In panel (a) of figure 6, the base

level of consumption at a defined point in time is shown

by the intersection point a on the vertical axis, Q1 trips.

Point a corresponds to the household market

equilibrium point A in panel (b). At point A, Q1 trips

are consumed at costs of P, per trip.

Assume next that by the year 2000, demand for trips is

expected to increase from D to D, in panel (b). Given

this new demand curve, the community will desire to

consume Qz trips at the current trip costs of P,.

Consumption level Q2 is referred to as “preferred

demand.” Preferred demand is defined generally as the

number of outdoor recreational trips a community would

take if the cost or price of a trip remained what it is
today. Preferred demand in 2080 is represented by

point B in panel (b), which corresponds to point b on

the line labeled Lt in panel (a). Given the aggregate

trip supply function labeled S in panel (b), however, only

Q1 trips can be produced and consumed at a cost of P,

per trip. This constant level of trips over time is

represented by point A in panel (b), which corresponds

to point e on the line labeled La in panel (a).

p2

Pl

(a)

Ql Qs Q,

Trips

Figure 6-Conceptual derivation of consumption trend lines.

In an effort to reach a household market equilibrium, it

is assumed that recreationists will incur increased costs

to produce more trips. These trips will be produced

along the aggregate supply curve in figure 6, panel (b).

Increased trip production, for instance, may involve

travel to more distant sites and increased time spent

searching for available facilities at more local sites.

Increased trip production will continue until the

marginal benefits and costs of trip production and

consumption are equal at point F in panel (b). At this

household market equilibrium, Q3 trips are consumed at

costs equal to P per trip. Point F in panel (b) corresponds to point 1 on the line labeled 5 in panel (a).

In a household market for recreational trips, it is

assumed that temporary trip shortages caused by

increases in demand but no increase of sites and

facilities will be eliminated by increased trip production

with an associated increase in trip costs as more distant

sites are accessed. The number of trips consumed after

trip production and demand have equilibrated at higher

trip costs is mapped out by the line labeled % in figure

6. Lz 1s an example of a consumption trend hne

showmg the number of recreational trips consumed over

time with community-level household market for trips

adjusting to new equilibria at every future point in time.

Notice that there is still a gap between preferred

demand mapped by L and expected trip production and

consumption mapped by b. This gap is given by the

distance Q2 - Q3 in figure 6b and the distance b-f in

figure 6a.

Consumption Trend Lines Under Alternative Scenarios

Consumption trend lines under three alternative supply

scenarios are shown in panel (a) of figure 7. The

relationship of these consumption trend lines to the

community-level household market for trips is illustrated

in panel (b) of figure 7. The base year level of trips

(1987 for RPA) is given by point b in panel (a), Qt trips.

Point b in panel (a) corresponds to point B in panel (b).

At point B, Qt trips are consumed at costs or price

equal to P, per trip. Suppose demand for trips is

expected to increase by the year 2000 from D to D, in

panel (b). Also, assume that available recreational

facilities and resources (RO) will be held constant.

Under this assumption, the aggregate supply curve for

trips is given by the curve labeled S, in panel (b). With

this aggregate supply curve and the new aggregate

demand curve given by D,, the community will establish

a new household market equilibrium at point E in panel

(b). At point E, Qz trips are consumed at trip costs or

price equal to P,. Point E in panel (b) corresponds to

point e on the consumption trend line labeled C, in

panel (a).

Next, assume again that demand is expected to shift

from D to D, by the year 2000, but that recreational

facilities and resources available to the public are also

increased, from RO, to RO,, causing the aggregate

supply curve to shii out from St to S, in panel (b).

Given these new demand and supply curves, the

community will establish a new household market

equilibrium at point F in panel (b). At point F, Q, trips

are consumed at trip costs equal to P3. Point F in panel

(b) corresponds to point f on the higher consumption

trend line labeled C, in panel (a).
Finally, assume once more that demand will shift from

D to D, by the year 2000. Suppose, however, that

recreational facilities and resources will be reduced from

RO, to R03 causing the aggregate supply curve to shift

from S, to Ss in panel (b). Given these new demand

and supply curves, the community will establish a new

household market equilibrium at point G in panel (b).

At point G, Q, trips are consumed at trip costs equal to

Pd. Point G in panel (b) corresponds to point g on the

lower consumption trend line labeled C, in panel (a).

Trpe

Qa

Q2

Q4

Q 1 b

Figure 7-Consumption trend lines under alternative demand/supply

scenarios.

As illustrated in figure 7, different demand or supply

assumptions will result in diierent consumption trend

lines. These lines can be used to demonstrate the

impact of alternative policies affecting the amount of

available recreational facilities and resources. For

example, given an expected increase in demand from D

to D,, the consumption trend lines in panel (b) of figure

7 illustrate the impact on trip consumption and costs of

three alternative supply scenarios. Consumption trend

line C, shows trip consumption and costs, assuming that

RO

ill

quantity of recreational facilities and resources are

ava able through the year 2WO. Consumption trend line

C, shows trip consumption and costs at a relatively

higher quantity of recreational facilities and resources,

RO,. Consumption trend line C, shows trip consumption and costs at a lower quantity of recreational

facilities and resources, ROs, available without change

through 2ooo.

Equilibrium Trip Costs or Price

Changes in equilibrium trip costs or price determined by

the household market indicate the relationship between

changes in recreational demand and supply over time.

Assume that the consumption trend lines shown in

figure 7 are derived from equation (4), the consumption

function for trips for activity k. Using this consumption

function, it is estimated that under a scenario of

increased demand from D to D, and quantity RO, of

recreational facilities and resources, consumption of trips

for activity k will increase from QI in the base year to

Q, in the year 2000. Q3 trips are mdicated by point e

on consumption trend hne C, in panel (a) of figure 7.

Point e in panel (a) of figure 7 corresponds to household market equilibrium point E in panel (b) of figure 7.

Hence, the household market trip costs or price

associated with Q trips are determined by substituting

Qz trips into the 1 4 emand function for year 2000, derived

from equation (l), and then solving for trip costs. The

solution is trip costs equal to P2. Under a scenario of

increased demand and a stable quantity of recreational

facilities and resources into the future of Rot,

household market trip costs or price would increase

from P, to P,

Another possibility is that demand will increase, but that

recreational facilities and resources will be less in the

future. Under this scenario, consumption of trips for

activity k will increase to Q4 as shown in panel (b) of

figure 7. Q4 trips is indicated by point g on consumption trend line C, in panel (a) and the household market

equilibrium point G in panel (b). The equilibrium trip

costs associated with Q4 are estimated by substituting

this quantity of trips into the demand function for the

year 2000 and solving for trip costs. The solution is trip

costs equal to P,. Thus, if demand increases and

available recreational facilities and resources decrease to

quantity RO, in the future, market trip costs will

increase sharply from P, to Pd.

Next, assume that it is estimated from the consumption

function that increased demand and a higher quantity of

recreational resources and facilities will result in

increased consumption of trips for activity k to Qs in the

year 2000. Q, number of trips is indicated by pomt f on

consumption trend line C, in panel (a) of figure 7, and

the household equilibrium point F in panel (b). House7
hold market trip costs associated with QJ would, therefore, be estimated by substituting Q3 into the year 2000

demand function and solving for trip costs. The solution

is market trip costs equal to P3. Thus, if future

recreational facilities and resources were increased to a

higher level, household market trip costs would be

expected to decrease from P, to P3.

Over several future points in time, an increase in

equilibrium trip costs suggests that demand is increasing

faster than supply. A decrease in equilibrium trip costs

suggests that supply is increasing faster than demand.

Constant equilibrium trip costs over time indicate that

demand and supply are increasing or decreasing at the

same rate, or that neither is changing. Thus, changes in

equilibrium trip costs provide a convenient means for

summarizing the relationships between changes in

recreational demand and supply over time and provide a

measure of adequacy of trends in making resources,

facilities and services available for public recreational

uses.

Empirical Approach

To carry out a national analysis of the magnitude

implied by the preceding model, and to meet the RPA

Assessment mandate, extensive data and analysis were

needed to describe the factors identified in the demand,

supply, and consumption equations. The principal

source of the data on number of trips, trip costs, and

consumer characteristics was the Public Area Recreation

Visitors Study, PARVS. The National Outdoor

Recreation Supply Information System, NORSE,

provided data on resources and facilities available for

production of recreational trips. These two national

data sets were each aggregated. to the community

(county) level for this analysis, and a sample of 239

representative counties was used to construct crosssectional models.

Trip Consumption and Cost Data

The PARVS data set was developed through a cooperative effort of 17 State and Federal agencies from the

summer of 1985 through the fall and winter of 1987.

These data were assumed to be homogeneous across

time with respect to equilibrium levels of consumption,

and therefore were assumed to represent consumption

during a single year, 1987. PARVS trip consumption

data were developed through onsite interviews of visitors

at over 280 recreation sites across the country. The total

number of interviews conducted during the above period

was over 32,000, nearly 26,000 of which were usable for

this analysis, having neither recording errors nor missing

values for questionnaire items needed for modeling.

The PARVS questionnaire contains data identifying the

recreational activity that was the main reason for visiting

the destination site, location of origin, trip costs

(distance and time), profile of number of trips by activity

taken during the last 12 months prior to the interview

(including the current trip), and the respondent’s

household characteristics. Origins were recorded as

both county name and Zip Code, thus enabling identification of place of residence. From this identification, a

map plotting of Zip Codes and longitude/latitudes indicated that respondents represented almost 80 percent of

the counties in the country. Counties not represented

were mostly the very sparsely populated ones in the

Midwest and several others which were mostly

composed of public land, primarily in the West.

The principal dependent variable for empirical

estimation of demand and consumption models was

annual number of trips by residents in the 239 representative counties. Computation of total trips by

residents of these counties was accomplished by weighting each respondent’s reported trips, costs, and characteristics such that the percentages of respondents

across 32 socioeconomic strata (representing age, race,

gender, and rural-urban residence) were proportionately

adjusted to match the socioeconomic makeup of participant respondents to the 1983 National Recreation

Survey (NRS). The NRS was designed by the Bureau of

Census and weighted to reflect the most current profile

of the United States population and thus could serve as

a base for weighting the PARVS data. Thus the

PARVS records, each representing individual-level trips,

were weighted to proportionately represent the U.S.

population’s profile of trips by activity. These weighted

individual trip totals were then aggregated to the

community (county) level and multiplied by the ratio of

number of participants in outdoor recreation in the

community to the weighted number of PARVS respondents in the community. This same weighting, and as

appropriate, extrapolations, were performed for all

variables in the PARVS data set.

Trip costs, described in more detail later, were

developed by combining transportation costs to travel

the distances adjusted to reflect route circuity from each

origin to each destination with the wage value of travel

time. The total number of origin-by-destination

combinations with relevant travel distances for the

activities considered was over 7,200. Relevant travel

distances were computed across reported single destination trips as the distance at the 95th percentile,

separately developed for each activity. All sites on

which PARVS interviewing had been conducted and

which lay within these relevant-travel-distance radii

composed the set of representative trip destinations for

people living in the 239 representative origins

8
(communities equivalent to counties). Trips and trip

costs between this set of representative origins and

destinations formed the consumption and price

observations for this analysis.

Defining the Representative Communities

Respondents to the PARVS survey represented almost

80 percent of the counties in the United States, except

for those which are very sparsely populated. Subregions

within the four major assessment regions (North, South,

Rocky Mountains, and Pacific Coast) were identified by

combining counties with similar rural-urban proportions

and physiographic characteristics and lying adjacent to

one another. To assure sufficiency in number of

PARVS respondents per subregion, counties with

PARVS respondents were added until a minimum of 90

respondents was achieved. These subregions, therefore,

did not overlap, and counties within them were combined in such a manner as to result in a block or circle

of adjacent counties, rather than a strip or band.

Selection of 90 as the minimum number of respondents

was based on a preliminary analysis to identify the

minimum sample size needed to achieve reasonable stability of standard deviations about trip consumption and

travel means. Within each of the 239 thusly defined

subregions (excluding Alaska and Hawaii), the one

county nearest the center which had a high percentage

of the subregion’s respondents was selected as the single

origin to represent the subregion. All respondents, trip

consumption, and travel were assumed to originate from

this central county (community). The maps in figure 8

show the locations of these representative communities.

All data aggregations and descriptions of resident

populations used in this study pertain to these 239

counties.

Figure &Location of the 239 representative communities (counties)

used estimating national models.

Resource and Facility Data

The NORSIS data set includes primary- and secondarysource data describing over 400 different dimensions of

public and private areas, facilities, and services available

for or servicing public recreational uses. Secondarysource data ranged in vintage from 1982 to 1987, always

using the most recent available source. Primary data

were collected in 1986 and included national studies of

private lands and municipal and county governments.

The coverage of the NORSIS data set included

wilderness and other remote wild lands to the most

highly developed and accessible of resorts, theme parks,

and high-visitation facilities, including urban parks.

The NORSIS, aggregated at the county level and

representing all counties in the United States, provided

data describing opportunities directly relevant to the

activities modeled in this research, as well as an index of

substitute opportunities. An example of direct opportunities includes Federal, State, local, and private camping

sites. The substitute indices were computed using

approximately the method proposed by Clawson (1984)

and described by Cordell and others (in press). These

substitute indices measured comprehensively the

magnitude of “other” opportunities available within a

distance that community residents were willing to travel,

adjusted to reflect (1) how many other people were

competing for use of these opportunities and (2) how

distant they were from the community’s geographic

center. This distance was adjusted with a straight-line

distance-decay function to down weight the more distant

opportunities (Cordell and others, in press).

Other Data

Data describing household characteristics for each of the

239 representative communities were obtained from the

Bureau of the Census County Data File. These data

reflected the most recent census, 1980. Measures of the

recreational suitabilities of each destination site were

obtained through a survey of site managers, including

State and national parks, reservoirs, State and national

forests, and wildlife management areas. Suitabilities for

each of 16 recreational activity categories were scored

on a scale of 0 to 10 by each manager for their own site.

In developing models for any one activity, sites rated as

not suitable were excluded as destinations; for example,

a State forest with no reservoir or river was rated at

zero suitability for water-skiing.

Organization of the Data

All data were organized around the 239 representative

U.S. communities. Trips were aggregated to express
countywide totals of trips for each of the 37 activities for

which there was a sufficient number of respondents

interviewed at PARVS sample sites to provide statistically reliable estimates of distance and time of travel.

Activity specific recreational opportunities and substitute

opportunities for these communities represented the full

set of opportunities within relevant travel radii of each

community centroid, typically including,adjacent, and

sometimes quite distant, other counties.

The resulting final data set provided a 239 by 310 matrix

of data values. Each community was identified by the

Federal Information Processing Standards (FIPS) code

and longitude and latitude of its centroid. All data were

triple checked for accuracy of entry.

Demand Curve Estimation

Trips for activity k are expected to vary across

communities because of differences in costs of producing

trips; in availabilities of opportunities; in suitabilities of

available, directly relevant sites; in population size; and

in demographic and socioeconomic characteristics. The

relationships between each of these variables and trips

for activity k were estimated by the general model:

TRIPS& = exp (B,, - B, PRICE . .

t B, INC3Q5i + B, BCT18TMDi

+ B, CCPOP86, - B, PCTFARM,

- B, SUBEROS, t B, SUITkii) , (5)

where

TRIPQ - annual trips for activity k demanded

from community i to site j,

PRICEkii - cost of trips for activity k from

community i to site j,

INC345i = percent of community i population

with annual income of at least $30,000,

PCT18TMDi = percent of community i population age

18 to 32,

CCPOP86t = total community i population (12 years

old and older),

PCTFARMi = percent of community i population

living on farms,

SUBEROS, = an index of substitute recreational

opportunities available to community i

and which compete with activity k for

available household time and money,

SUIT& = suitability of site j for activity k.

The dependent variable in equation (5), TRIPS .., was

constructed in several steps. First, trips per cap17 a for

each of the 239 subregions, covering the entirety of the

coterminous 48 States, was calculated by dividing the

estimate of total number of trips for activity k by the

respective population of each subregion (12 years old or

older). As discussed by Walsh (1986), this procedure

accounts for both the probability of participation and the

frequency of participation. Hence, the estimates of trips

per capita account for both participants and nonparticipants in outdoor recreation.

Total trips for activity k from each representative

community within each subregion were estimated by

multiplying trips per capita for the subregion by the

resident population of the community (12 years old or

older). Because it is a function of trips per capita, the

specification of total trips as the dependent variable for

equation (5) implicitly accounts for both participants and

nonparticipants (Walsh 1986; Ward and Loomis 1986).

Thus, the problem of excluding nonparticipants in the

demand analysis, which is often encountered in

applications of the individual travel cost model, was

avoided (Ward and Loomis 1986).2

In equation (5), site j refers to a specific recreational

site (e.g., a State park) used by community i for activity

k. Sites used by community i for any of the 37 types of

outdoor recreation modeled were identified from

destinations reported in the PARVS data.

For each site, the probability that it was visited for

activity k by persons living in community i was

calculated. This probability was a function of the

distance to the site from the geographic center of the

community and the suitability of that site for a particular

activity. This estimate of probability that site j was

visited by community i for activity k served to allocate a

percentage of the total trips for activity k from

community i. These allocated trips, or TRIPS&, were

the observations for the dependent variable for equation

(5) and contained trips for activity k to each site visited

by people from each of the 239 communities (zones in

the ZTCM context), approximately 7,200 observations.

’ The probability of participation in activity k is given by the total

number of participants in community i divided by total population of

community i (participants and nonparticipants). The frequency of

participation is given by the total number of activity k trips from

community i/total number of participants in community i. Trips per

capita from community i, or TRIP!&, is given by.

TRfPSki = narticiuants

total population ’

total tliDS total trios

participants ’ total population

2 Results reported later (e.g., consumption and cost indices) can still

only be extrapolated back to the population of activity k participants,

not the general population. Estimates of activity k participants in the

United States, which were derived from several data sources, are

shown in tables 5, 7, 9, and 11.

10
The price variable in equation (5), PRICE .., was

derived by first calculating the straight-line %istance

from community i to site j and then applying a calculated

circuity factor.

These circuity factors were computed as the ratio of

reported travel distance to straight-line distance across all

PARVS respondents, about 32,008. Travel miles were

then converted to transportation costs, including the

opportunity cost of travel time. Transportation cost was

based on vehicle operating costs as reported by the U.S.

Department of Transportation. The assumed vehicle

operating cost was $0.278 per mile, the cost of operating

an intermediate-size automobile. The opportunity cost

of travel time was valued at one-half the reported wage

rate for a community, as recommended by Rosenthal

(1985). The average travel speed was calculated by

travel time and distance as reported in the PARVS data.

The substitute variable in equation (5), SUBEROS,,

was derived from the effective recreational opportunity

supply data base discussed previously. This substitute

variable combines recreational service, facility, and

resource variables, indicating the range of opportunities

for activities that compete with activity k for a

household’s recreation time and money. Effectiveness of

substitute opportunities accounts for distance of the

opportunities from community i and number of people

in other communities who compete for these

recreational opportunities.

The suitability variable in equation (5), SUITu,

represents the suitability of site j for activity k. The

suitability variable is based on responses to the

nationwide survey of site managers discussed previously.

Socioeconomic variables in equation (5), including

INC345, PCTlSTMD, CCPOP86., and PCTFARM,

were obtained from U.S. Census data.

The semilog functional form of equation (5) has been

recommended in previous studies. This functional form

has been found to be theoretically consistent with

recreation demand behavior and reduces heteroscedasticity (Rosenthal 1985; Ward and Loomis 1986; Ziemer

and others 1980). Equation (5) was estimated by

ordinary least squares for 37 recreational activities.

These estimated individual community-by-site equations

or demand functions are shown in table 1.

After estimating equation (5) for each of the 37

activities, an aggregate or market demand function

across sites, corresponding to equation (l), was derived

for each activity. These demand functions were derived

by substituting mean values for all independent

variables, except cost, into equation (8) and solving for a

composite constant term representing the sum of the

products of these means multiplied by their respective

partial regression coefficients. This simplified equation

was then multiplied by the average number of sites used

for activity k across all communities so that the equation

represented total trips consumed by a typical community

at each average trip cost. The results of this operation

were aggregate demand curves for each activity for a

typical U.S. community. These aggregate demand

functions are shown in table 2.

Estimation of the aggregate demand functions depicted

by equation (1) assumed that the cross-sectional

modeling approach correctly identified the communityby-site demand functions. The identification question

requires consideration of the community-level household

market for trips illustrated in figures 4 and 5. In figure

5, the observed cost-quantity relationship would be P,

and Q1 trips. However, this consumption point is not on

the aggregate demand function. Hence, if observed

consumption data corresponded to the household market

disequilibrium situation shown in figure 5, the aggregate

demand function would not be identified.

The vertical portion of the effective aggregate supply

curve in figure 5 implies that trip production was limited

to local sites. Such would be the case if each community

were located on an island. In order to identify the

aggregate demand curve, it was assumed that local site

capacity did not truncate the aggregate supply curve.

Rather, it was assumed that recreationists were free to

move into other regions (to other destinations) until the

point was reached where the marginal benefits and costs

of the last trip produced and the last one consumed

were equal. This situation is illustrated in figure 4.

The availability of public recreational facilities and

resources varied across the 239 U.S. communities used

in this study. Variations in public recreational facilities

and resources lead to variations in the aggregate supply

function. Other things being equal, changes in

consumption caused by changes in the aggregate supply

function identify the aggregate demand function for trips

(Cicchetti 1973; Kalter and Gosse 1970).

Consumption Function Estimation

As with the estimation of the aggregate demand

functions, estimation of consumption functions assumed

that community-level household markets for trips were

in equilibrium. Household market equilibrium

consumption is explained generally by equation (4). For

estimation purposes, equation (4) was specified as:

11
TRIPS, = exp (B,, + B, INC345t

+ Bz PCT18TMDi + B, OCPOP86,

- B4 PCTFARM, - B, SUBEROS,

+ B, [FACILITYkt * SUIT,]) , (6)

where

TRIPS, = annual trips for activity k consumed by

community i

FACILITYki = quantity of recreational facilities

directly relevant to activity k and

available to community i.

All other variables are as defined for equation (5).

Equation (6) represents the reduced form for the

recreation demand/supply system given generally by

equation (4).

The calculation of the dependent variable in equation

(6) was discussed previously. The variable RO, was

obtained from the extensive recreational resource and

facility data set maintained by the Forest Service’s

Outdoor Recreation and Wilderness Assessment Group.

This data set enabled computation of the quantity of

various types of recreational facilities and resources

available for each of the 37 activities and found within

the relevant travel radii of each community in the

United States. Specific facilities and resources which are

relevant to activity k were selected from this data set.

As indicated by equation (6), these facilities and

resources were then weighted by the average suitability

of sites used by community i for activity k. The data set

only shows recreational facilities and resources within

60- to X&mile travel radii of each community. These

travel radii represented the mean plus one standard

deviation of the actual distances reported by PARVS

respondents across 12 categories of resources relevant to

the 37 recreational activities modeled.

Equation (6) was estimated by ordinary least squares for

37 recreation activities. In some equations the

PCTFARM, variable was deleted because of collinearity.

The estimated consumption functions are shown in table

3. The recreational resource and facility variables used

in the consumption functions are defined in table 4.

Estimated consumption functions were used to project

numbers of trips for activity k from a typical community

to the year 2040.

In tables 5, 7, 9, and 11, future trip consumption indices

relative to the 1987 base year are projected for four

possible future recreation opportunity scenarios. Across

all four scenarios, demand determinants, such as income,

were projected to increase the same in the future at a

likely, moderate rate as provided by the Bureau of the

Census. Thus, differences in consumption across the

four scenarios are the result only of differences in

growth of availability of opportunities across time, and

simulate the effects of alternative future supply policies,

independent of demand growth.

In the low-supply growth scenario, facilities and

resources for activity k decrease in the future. In the

zero-supply growth scenario, facilities and resources for

activity k remain at current levels into the future.

Facilities and resources for activity k increase at a

moderate growth rate (about 0.5 percent annually) in

the medium-supply growth scenario. Finally, in the

high-growth scenario, facilities and resources for activity

k increase at a growth rate of about 1 percent annually.

The indices in tables 5, 7,9, and 11 indicate that future

consumption of recreational trips is sensitive to the

availability of recreational facilities and resources for

most activities. The strength of recreational facility and

resource supply effects varies across the different

recreational activities.

Household Market Costs

Future household market trip costs were estimated by

substituting future trip-consumption estimates into the

future aggregate demand functions and solving for costs.

The appropriate future aggregate demand function is

determined by shifting the current aggregate demand

function (see table 2) to reflect probable future demand

schedules based upon assumed futures for the variables

shown in equation (5). Indices of future household

market costs relative to the 1987 base year are shown in

tables 6,8, 10, and 12. The four demand/supply

scenarios shown correspond to the scenarios explained

above for the consumption indices.

The cost or price indices (tables 6, 8, 10, and 12)

summarize the relationship between outdoor recreation

supply and demand over time. An index greater than

100 indicates that trip demand is growing faster than

supply. An index equal to 100 indicates that trip

demand and supply are increasing at about the same

rate. An index less than 100 indicates that supply is

increasing faster than demand.

The cost or price indices also summarize the impact on

recreation consumption of public policy on growth of

available recreational facilities and resources. With

decreasing or zero public-facility growth, household

market costs would increase for most activities. Such

increases in household market costs, in general, would

be most pronounced for land-based activities. The

implication of increasing household market costs is that

recreational opportunities would become more and more

scarce. People, therefore, would have to travel greater

distances or spend increased time searching for available

recreational opportunities.

12
Under the assumption of medium public-recreationalfacility growth, household market costs remain about

constant for many activities. The implication is that

growth of recreational opportunities at this medium rate

would just maintain a balance between demand and

supply. Thus, for many activities, maintenance of the

status quo with respect to recreation demand and supply

requires at least a moderate increase (about l/2 of 1

percent per year) in public recreational facilities and

resources. For some activities, maintenance of the

status quo with respect to demand and supply requires a

1 percent growth of public recreational facilities and

resources.

Household market costs decrease for several activities

under the assumption of high public-recreational-facility

growth. The implication in this case is that rapid growth

of recreational opportunities would cause them to

become more abundant over time relative to demand

growth. People would typically be able to travel shorter

distances and spend less time searching for available

recreational opportunities. Thus, improvements in the

availability of recreational opportunities would require a

relatively high rate of public-recreational-facility growth

for a number of activities, adding about 45 percent to

the total stock over the next 50 years.

Research Conclusions

More research is needed to improve estimates of

aggregate demand, supply, equilibrium consumption, and

costs or prices. A particular need is to collect sufficient

data compatible with conceptual models so that

complicated, time-consuming, and sometimes

questionable procedures for adjusting data to satisfy

model assumptions can be avoided. For the most part,

the abundance of the data used to support the work

reported here demonstrates this point. In addition to

improving the quality of available data, additional

research is needed to identify the most appropriate and

cost-effective statistical procedures for estimating

aggregate demand and supply curves, and for estimating

equilibrium consumption and costs. There is also a

specific need to develop procedures for including

congestion costs, and the effects of varying quality of

recreational opportunities. Finally, much more

conceptual and empirical work, and convincing of policy

makers, are needed to identify the public welfare and

policy implications of changes in equilibrium

consumption and costs, not only over time but also

across spatial dimensions.

For previous RPA Assessment efforts, the economic

analyses, using existing data, technology, and theory,

were unable to produce recreation demand/supply and

price trend comparisons equivalent to those produced

for commodity outputs, such as timber. With the

accomplishment of the research described above,

measurable improvements in the appropriateness,

accuracy, and believability of aggregate outdoor

recreation demand/supply analyses have been achieved.

The theory and techniques described in this Paper, it is

argued, provide a stronger foundation for advanced

economic assessment of the demand and supply situation

of outdoor recreation in the United States.

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13
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14
Tables

Table l--Estimated community demand functions for recreational activities

Table 2--Abbreviated demand functions for a typical U.S. community, by activity, 1987

Table 3--Estimated community consumption functions for recreational activities

Table C-Recreational resource and facility variables used in activity consumption functions

Table 5--Future market-clearing trip indices under low public-supply-growth and medium-demand-growth

assumptions, by activity

Table 6--Future market-clearing price indices under low public-supply-growth and medium-demand-growth

assumptions for land activities

Table 7--Future market-clearing trip indices under zero public-supply-growth and medium-demand-growth

assumptions for land activities

Table &-Future market-clearing price indices under zero public-supply-growth and medium-demand-growth

assumptions for land activities

Table g--Future market-clearing trip indices under medium public-supply-growth and medium-demandgrowth assumptions for land activities

Table lo--Future market-clearing price indices under medium public-supply-growth and medium-demandgrowth assumptions for land activities

Table 11--Future market-clearing trip indices under high public-supply-growth and medium-demand-growth

assumptions for land activities

Table 12--Future market-clearing price indices under high public-supply-growth and medium-demand-growth

assumptions for land activities

15

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l 911’

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l 190

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Table l--Estimated community demand functions for recreational activities--Continued

Activity INI’ERCEFT PRICEijk INC34Si

Parameter estimates (standard error)

Adjusted

PCIX3TMDi ccPoP86i PCTFARMi SUBERO$ SUIT, N F-value R2

Pool swimming

Motorized boating

Water-skiing

Rafting/tubing

Canoeing/kayaking

Rowing/other boating

Stream/lake swimming

Saltwater fishing

Warm-water fmhing

Cold-water fishing

Anadromous fishing

Downhill skiing

Crcsscountry skiing

7.76.5*

‘f-4g

(lb)

0.059. *

‘:Z

(-Q-1

0.001**

(:gg)

(Jw

0.3388

(.037)

857 131.410 0.43

1537 176.174 .41

15.53 136.448 34

1379 %.158 .29

2381 455.052 53

2413 248.152 .%

2678 521.61 54

664 109.797 .45

2290 316.040 .45

1290 132.738 38

991 31.171 .l5

138 22.706

2656 231.917

0.49

34

*Significant at 0.01 level; **Significant at 0.05 level; *** Significant at 0.10 level.
Table 2--Abbreviated demand functions for a typical U.S. community, by activity, 1987

Parameter estimates

Activity Intercept

cost

(standard error)*

Developed camping

Picnicking

Sightseeing

Family gathering

Pleasure driving

Visiting historic sites

Attending events

Visiting museums

Off-road driving

Biking

Running/h&%

waking

Cutting firewood

Collecting berries

Visit prehistoric sites

Photography

Day hiking

Horseback riding

Small-game hunting

Big-game hunting

Nature study

Backpacking

Primitive camping

Wildlife observation

Pool swimming

Motorized boating

Water-skiing

Rafting/tubing

Canoeing/kayaking

Other boating/rowing

Stream/lake swimming

Saltwater fishing

Warm-water fishing

Cold-water fishing

LAND

13.2226 -0.0182 (0.00042)

15.1128 -.0499 (0.00110)

14.9591 -.0183 (0.00029)

14.2297 -.0232 (0.00043)

15.1268 -.0359 (0.00109)

13.5640 -.0231 (0.00050)

13.2127 -.0286 (0.00076)

13.3323 -.0227 (0.00073)

13.0965 -a437 (0.00380)

14.1572 -.0315 (0.00106)

15.1549 -.1356 (0.01373)

15.1723 -.0271 (0.00066)

12.3011 -.0319 (0.00447)

11.9465 -.0244 (0.00338)

11.2391 -.0261 (0.00140)

13.2304 -.0221 (0.ooo9o)

13.5763 -.0393 (0.00125)

12.5163 -.0459 (0.00142)

13.1748 -.0632 (0.00334)

13.1341 -.0345 (0.00103)

12.6636 -.0289 (0.00093)

10.6442 -.0124 (0.00110)

12.6221 -.0294 (0.00070)

13.7336 -.0220 (O.ooo46)

WATER

14.6800 -0.0364 (0.00208)

13.7019 -.0383 (0.00156)

12.4289 -.0275 (0.00152)

6.0026 -.0326 (0.00204)

12.7674 -4484 (0.00122)

11.6530 -.0237 (0.00116)

14.9339 -.0341 (0.00073)

11.4034 -.0194 (0.00123)

14.6418 -.0490 (0.00129)

13.1901 -.0272 (0.00151)

SNOW AND ICE

Cross-country skiing

Downhill skiing

8.6520 -0.0340 (0.00237)

11.2286 -.0308 (0.oo46o)

*All models significant at p c 0.01.

19
s Table 3--Estimated community consumption functions for recreational activities

Activity

Parameter estimates (standard error)

Adjusted

INTERCEP INC345 PCIXI’MD CCPOP86 SUBEROS PCTFARM ROl R02 N F-value R2

Developed camping

Picnicking

Sightseeing

Family gatherings

Pleasure driving

Visiting historical sites

Attending events

Visiting museums

Off-toad driving

Biking

Running/jogging

Walking

Cutting firewood

Collecting berries

Visiting prehistoric sites

Photography

Day hiking

Horseback riding

Small-game hunting

Big-game hunting

Nature study

Backpacking

Primitive camping

Wildlife observation

8x3*

6.913*

7.618*

8.873’

E?

(:729)

-0.189.

(.018)

-.205*

(.019)

-.219* l

$gi

(.020)

-.194*

(.016)

.ooo59*

(.t3803)

800021**

C~>

239

239

239

239

239

239

239

239

239

239

239

239

239

239

239

239

239

239

239

239

239

.000000076*** 239

(444 E-08) 239

.00677’ 239

(.0022)

49.488 0.50

54.607 53

80.838 .67

57.467 54

51.895 52

87.398 .a

56.683 54

57.763 54

14.905 23

53.677 52

25.164 34

58.998 55

53.924 57

55.490 58

77.633 66

59.638 55

97.476 .71

22.482 35

9.984 .16

16544 25

30.993 .43

21.337 34

45.018 A8

49.075 55
Table 3-Estimated community consumption functions for recreational activities--Continued

’ Parameter estimates (standard error)

Activity INTERCEP INC345 PCTlSTMD CCPOP86

Adjusted

SUBEROS PCI’FARM ROl R02 N F-value R2

Pool swimming

Motorized boating

Water-skiing

Rafting/tubing

Canoeing/kayaking

Sailing/rowing

Stream/lake swimming

Saltwater fishing

Warm-water fishing

Cold-water fishing

Anadromous fishing

Downhill skiing

Cross-country skiing

3.091*

ygz

&’

(:903)

-12.7608

‘:.gq$

$992

(:919)

9.2w

i-2

s::;*

p:i

,(yg1

(4.851)

11.45s

(2.146)

7570**

(4.061)

0.00143*

‘:Ei*

(.0003)

.00144*

(:E&*

‘:Eb

$339**

wQ3)

.000876*

(003)

.00%09*

(.ow

.001225*

‘:x7*

WV

.OOOs14*

ww

SNOW AND ICE

0.OOOOO11* 0.0013** -0.2.56* .001086*

w-73 (-0003)

.OOcQ33*

(S.06 E-07) (.000007)

0.109*

‘-g;19**

coo@w

.9ss3**

(-gJg*

co@w

.ooo622*

mw

.00128*

~-0oo4)

239

239

239

239

239

239

239

239

239

239

239

239

239

58521 0.59

31.265 A3

29.446 .37

6.431 .12

33.973 .4s

32.137 A0

43.275 52

21.944 .31

32.607 A0

20.027 39

12.451 .19

41.848

23.268

52

.32

*Significant at 0.01 level; **Significant at 0.05 level; ***Significant at 0.10 level. ’ For these activities, age variable was MEDAGE = medium age of central county population.
Table 4--Recreational resource and facility variables used in activity consumption functions

Activity ROl R02

LAND

Developed camping Federal road mileage converted to acres and

Federal and State land located within l/2 mile of

a road*

Picnicking

Sightseeing

Family gatherings

Pleasure driving

Federal and State land located within l/2 mile of

a road, and State forest land open to recreation*

Federal road mileage converted to acres, and

Federal and State land located within l/2 mile of

a road*

Federal road State, local and private

campgrounds*

Federal road mileage converted to acres, and

Federal and State land located within l/2 mile of

a road

Visiting historical sites Federal road mileage converted to acres, and

Federal and State land located with l/2 mile of a

road*

Attending events Federal road mileage converted to acres, and

Federal and State land located within l/2 mile of

a road*

Visiting museums

Off-road driving

Federal road mileage converted to acres, and

Federal and State land located within l/2 mile of

a road*

Federal road mileage (except for U.S. Army

Corps of Engineers and Tennessee Valley

Authority) converted to acres, National

Recreational Trail mileage open to motorcycles

converted to acres, and Federal and State land

located within l/2 mile of a road*

Federal road mileage converted to acres, Federal

and State land located within l/2 mile of a road,

and State forest acres open to recreation*

Federal road mileage converted to acres, Federal

and State land located within l/2 mile of a road,

and State forest acres open to recreation*

--

_-

--

--

--

--

__

22
Table 4--Recreational resource and facility variables used in activity consumption functions--Continued

Activity ROl R02

walking

Cutting firewood

Collecting berries

Visiting prehistoric sites

Photography

Day hiking

Horseback riding

Small-game hunting

Federal road mileage converted to acres, and

Federal and State land located within l/2 mile of

a road* --

Federal land located within l/2 mile of a road,

Federal and State land located within l/2 to 3

miles of a road, and acres of nonindustrial forest

land open to recreation, both leased and

nonleased* --

Industrial and nonindustrial forest lands*

Federal road mileage converted to acres, Federal

and State land located within l/2 mile of a road,

Federal and State land located within l/2 to 3

miles of a road, and rural transportation use

acres*

Federal and State land located within l/2 mile of

a road, and State Forest acres open to recreation* - -

Federal and State land located within l/2 mile of

a road, Federal and State land located l/2 to 3

miles of a road, and Federal wilderness* - -

Federal and State land located within l/2 to 3

miles of a road, and nonwilderness land more

than 3 miles from a road

Federal land located within l/2 mile of a road,

Federal land located within l/2 to 3 miles of a

road, industrial forest-land acres, and

nonindustrial private-land acres open to

recreation, both leased and not leased*

Miles of National

Recreational Trails open

to horseback riding

__

Federal land located within l/2 mile of a road,

Federal land located l/2 to 3 miles of a road,

industrial forest-land acres, and nonindustrial

private-land acres open to recreation, both leased

and not leased

Big-game hunting

23
Table 4--Recreational resource and facility variables used in activity consumption functions--Continued

Activity ROl R02

Nature Study Acres of water in river/streams up to 660 feet

wide, and acres of flat-water bodies

Backpacking Federal and State land located within l/2 to 3

miles of a road, nonwilderness land located over 3

miles from a road, and Federal wilderness acres*

Primitive camping Federal and State land located within l/2 mile of

a road, and State forest acres open to recreation* --

Wildlife observation Federal and State land located within l/2 mile of Acres of water in

a road, Federal and State land located with l/2 to rivers/streams up to 660

3 miles from a road, nonwilderness land located feet wide, acres of flatmore than 3 miles from a road, Federal water bodies, and acres

wilderness acres, The Nature Conservancy acres, of Federal water bodies

and State fBh and game land* open to recreation*

Pool swimming

Motorized boating

Water-skiing

Rafting/tubing

WATER

Public and private swimming pools, State parks

with some swimming facilities, and tourist

accommodations

Acres of flatwater bodies and acres of Federal

water open to recreation

Acres of flatwater bodies and acres of Federal

water open to recreation*

Miles of Federal wild and scenic rivers, miles of

rivers designated by States as being significant for

historic, cultural, scenic or recreational reasons,

and miles of Bureau of Land Management

recreational rivers*

Canoeing/kayaking Acres of flatwater bodies, and acres of water in

river/streams up to 660 feet wide*

Sailing/rowing Acres of flatwater bodies, acres of water in

rivers/streams up to 660 feet wide, and acres of

Federal water bodies open to recreation*

Federal and State land

located within l/2 mile

to 3 miles of a road,

nonwilderness land

located over 3 miles

from a road, and Federal

wilderness acres*

National Recreation

Trail State park trail

miles*

--

Number of boat ramps*

--

Indicator variable for

presence of mountains

(0 = no mountains;

1 = mountains)*

Canoe rental firms and

canoe outfitters*

24
Table 4--Recreational resource and facility variables used in activity consumption functions--Continued

Activity ROl RO2

Stream/lake/ocean

swimming

Saltwater fishing

Warm-water fishing

Cold-water fishing

Anadromous fishing

Downhill skiing

Cross-country skiing

Federal developed swimming areas* Miles of public ocean

beach*

Miles of public ocean beach*

Acres of flatwater bodies, acres of water in

rivers/streams up to 660 feet wide, and acres of

Federal water bodies open to recreation*

Acres of water in rivers/streams up to 660 feet

wide, and acres of Federal water bodies open to

recreation*

Miles of Federal wild and scenic rivers, and miles

of rivers designated by States as being significant

recreational resources* a

SNOW AND ICE

Daily ski-lift capacity

Federal and State lands located within l/2 mile of

a road, Federal and State lands located within l/2

to 3 miles of a road, and acres of rural

transportation use*

*Resource and facility variables are weighted by the average suitability of sites used by a community for an

activity. Average suitability was derived from responses to a survey sent to site managers.

__

Indicator variable for

presence of mountains

(0 = no mountains;

1 = mountains)*

a States where no anadromous fishing occurs were assigned an average suitability of zero.

25
Table J--Future market-clearing trip indices under low public-supply-growth and medium-demand-growth

assumptions, by activity

(1987 base level = 100)

Activity

Baseline

consumption

in U.S.

(million)

Year

2010 2020 2030 2040

Developed camping

Picnicking

Sightseeing

Family gathering

Pleasure driving

Visiting historic sites

Attending events

Visiting museums

Off-road driving

Biking

Running/jogging

walking

Cutting firewood

Collecting berries

Visiting prehistoric sites

Photography

Day hiking

Horseback riding

Small-game hunting

Big-game hunting

Nature study

Backpacking

Primitive camping

Wildlife observation

Pool swimming 221.0 130 155 181 208 228

Motorized boating 219.5 103 107 111 116 119

Water-skiing 107.5 109 117 125 133 139

Rafting/tubing 8.9 9 8 106 113 131 143

Canoeing/kayaking 39.8 107 115 123 133 140

Rowing/paddling/other boating 61.8 110 120 130 142 150

Stream/lake swimming 238.8 102 106 110 114 118

Saltwater fishing 77.3 103 109 116 127 134

Warm-water fishing 239.5 9 0 8 7 8 4 8 4 8 3

Cold-water fishing 83.8 111 121 130 141 148

Downhill skiing 64.3 141 173 205 238 260

Cross-country skiing 9.7 133 152 164 171 158

LAND

60.6 116 130 144 158 168

262.0 105 111 118 125 130

292.7 115 129 146 167 190

74.4 112 123 134 146 154

421.6 112 122 133 145 153

73.1 118 135 155 182 210

73.7 111 121 132 144 152

9.7 115 129 143 160 171

80.2 104 109 114 120 124

114.6 121 140 160 182 198

83.7 126 149 173 200 220

266.5 113 125 137 151 161

30.3 110 119 131 147 165

19.0 110 121 134 153 175

16.7 128 150 177 209 244

42.0 118 135 152 170 183

91.0 125 149 177 211 247

63.2 116 129 142 154 162

58.6 9 0 8 4 8 0 7 8 7 6

55.2 9 3 9 0 8 8 8 8 8 8

70.8 102 107 112 120 125

26.0 128 152 178 205 224

38.1 110 120 129 139 146

69.5 111 122 133 145 153

WATER

SNOW AND ICE

2 6
Table 6--Future market-clearing price indices under low public-supply-growth and medium-demand-growth

assumptions, by activity

(1987 base level = 100)

Activity

Baseline

cost per

day (1987)

Year

2010 2020 2030 2040

Developed camping

Picnicking

Sightseeing

Family gathering

Pleasure driving

Visiting historic sites

Attending events

Visiting museums

Off-road driving

Biking

Running/jogging

walking

Cutting firewood

Collecting berries

Visiting prehistoric sites

Photography

Day hiking

Horseback riding

Small-game hunting

Big-game hunting

Nature study

Backpacking

Primitive camping

Wildlife observation

Pool swimming

Motorized boating

Water-skiing

Rafting/tubing

Canoeing/kayaking

Rowing/paddling/other boating

Stream/lake swimming

Saltwater fishing

Warm-water fishing

Cold-water fishing

Downhill skiing

Cross-country skiing

LAND

$21.39

39.69

65.61

67.52

2;

54:49

57.08

34.04

40.82

12.00

44.91

26.09

30.06

14.11

31.42

32.35

33.16

32.87

47.77

35.17

29.26

23.61

41.47

WATER

102

101

101

103

101

102

102

102

101

102

103

102

102

103

102

103

103

103

102

102

102

105

102

103

$46.52

39.85

41.04

46.91

38.61

39.79

57.28

88.38

40.58

40.97

103

101

101

107

102

101

101

101

101

101

SNOW AND ICE

27.81 106

42.59 107

104

102

102

105

102

103

103

103

101

103

104

103

104

105

103

105

105

104

103

103

103

108

103

104

105

102

102

114

104

102

102

103

101

102

109

110

105

103

103

106

103

104

104

104

102

105

106

104

105

107

104

107

106

106

104

103

104

111

104

106

106

103

103

119

105

103

102

104

102

103

112

113

106

103

105

108

104

106

105

105

102

106

108

105

106

108

106

108

108

107

105

104

105

114

105

107

108

104

104

126

107

104

103

105

102

104

115

114

107

104

106

109

105

107

106

106

103

107

108

105

108

110

107

109

110

108

105

104

106

115

106

108

109

104

105

130

108

105

104

106

102

104

116

114

27
Table ‘I-Future market-clearing trip indices under zero public-supply-growth and medium-demand-growth

assumptions, by activity

(1987 base level= 100)

Activity

Baseline

consumption

in U.S.

(million)

Year

2010 2020 2030 2fl48

LAND

Developed camping 68.6

Picnicking 262.0

Sightseeing 292.7

Family gathering 74.4

Pleasure driving 421.6

Visiting historic sites 73.1

Attending events 73.7

Visiting museums 9.7

Off-road driving 80.2

Biking 114.6

Running/jogging 83.7

walking 266.5

Cutting firewood 30.3

Collecting berries 19.0

Visiting prehistoric sites 16.7

Photography 42.0

Day hiking 91.0

Horseback riding 63.2

Small-game hunting 58.6

Big-game hunting 55.2

Nature study 70.8

Backpacking 26.0

Primitive camping 38.1

Wildlife observation 69.5

Pool swimming 221.0 131 158 186 215 236

Motorized boating 219.5 105 110 115 120 124

Water-skiing 107.5 110 118 127 135 142

Rafting/tubing 8.9 108 124 141 169 190

Canoeing/kayaking 39.8 110 128 131 144 153

Rowing/paddling/other boating 61.8 111 121 132 144 152

Stream/lake swimming 238.8 104 109 114 119 124

Saltwater fishing 77.3 104 112 120 133 141

Warm-water fEhing 239.5 91 88 85 85 85

Cold-water fishing 83.8 111 121 132 142 150

WATER

117

107

116

116

113

118

112

116

105

122

127

114

111

112

129

120

127

119

E

105

l31

112

114

SNOW AND ICE

Downhill skiing 64.3 146 182 219 258 283

Cross-country skiing 9.7 137 160 176 185 173

132 147 162

114 121 130

130 148 169

130 144 158

124 136 148

136 157 184

123 135 148

130 146 162

110 115 121

142 162 186

152 178 207

127 140 155

122 134 151

124 139 159

152 179 212

139 157 178

152 181 217

133 148 161

88 85 83

95 95 96

111 117 126

157 186 215

122 133 144

127 139 153

173

135

193

:“:

213

157

174

125

202

228

165

170

182

248

192

255

171

82

97

132

237

151

163

28
Table &-Future market-clearing price indices under zero public-supply-grow& and medium-demand-growth

assumptions, by activity

(1987 base level = 100)

Activity

Baseline

cost per

day (1987)

Year

2010 2020 2030 2040

Developed camping $21.39 101

Picnicking 39.69 101

Sightseeing 65.61 101

Family gathering 67.52 101

Pleasure driving 40.26 101

Visiting historic sites 59.97 101

Attending events 54.49 101

Visiting museums 57.08 101

Off-road driving 34.04 100

Biking 40.82 102

Running/jot3&3 12.00 102

walking 44.91 101

Cutting firewood 26.09 101

Collecting berries 30.06 101

Visiting prehistoric sites 14.11 103

Photography 31.42 102

Day hiking 32.35 102

Horseback riding 33.16 102

Small-game hunting 32.87 101

Big-game hunting 47.77 100

Nature study 35.17 100

Backpacking 29.26 103

Primitive camping 23.61 101

Wildlife observation 41.47 101

Pool swimming $46.52 102

Motorized boating 39.85 100

Water-skiing 41.04 101

Rafting/tubing 46.91 102

Canoeing/kayaking 38.61 101

Rowing/paddling/other boating 39.79 101

Stream/lake swimming 57.28 100

Saltwater ftshing 88.38 100

Warm-water fishing 40.58 101

Cold-water fishing 40.97 101

Downhill skiing 27.81 104

Cross-country skiing 42.59 104

LAND

WATER

SNOW AND ICE

102

101

102

102

102

103

102

102

101

103

103

102

102

102

104

103

104

103

101

100

101

105

102

102

104

101

102

105

102

102

101

101

101

102

106

107

103 104 105

102 102 102

103 104 105

103 104 104

102 103 103

104 105 106

103 103 104

103 104 105

101 102 102

104 105 106

105 106 107

103 103 104

103 104 105

103 104 105

106 108 109

104 105 105

105 107 108

104 105 105

101 102 102

100 100 100

101 102 103

106 108 109

103 103 104

103 104 104

105

101

102

108

103

103

101

102

101

102

107

102

103

116

104

104

102

104

101

103

108

108

106

102

103

113

104

103

101

103

101

103

110

109

111

108

29
Table 9--Future market-clearing trip indices under medium public-supply-growth and medium-demandgrowth assumptions, by activity

(1987 base level = 100)

Activity

Baseline

consumption

in U.S.

(million)

Year

2010 2020 2030 2040

Developed camping 60.6 120 137 155 173 186

Picnicking 262.0 110 119 129 139 147

Sightseeing 292.7 117 132 151 174 198

Family gathering 74.4 120 137 155 174 187

Pleasure driving 421.6 115 128 141 155 165

Visiting historic sites 73.1 119 138 160 189 219

Attending events 73.7 115 128 142 157 167

Visiting museums 9.7 118 134 150 169 182

Off-road driving 80.2 105 111 117 124 128

Biking 114.6 124 145 168 193 211

R~dbgg;lg 83.7 131 158 188 221 246

walking 266.5 116 131 146 163 176

Cutting firewood 30.3 112 124 138 157 176

Collecting berries 19.0 114 128 144 167 192

Visiting prehistoric sites 16.7 131 155 184 219 258

Photography 42.0 125 147 170 195 214

Day hiking 91.0 130 157 190 231 273

Horseback riding 63.2 122 139 157 174 186

Small-game hunting 58.6 95 93 91 90 89

Big-game hunting 55.2 100 102 104 106 108

Nature study 70.8 109 119 129 141 149

Backpacking 26.0 136 167 202 238 265

Primitive camping 38.1 115 127 140 154 163

Wildlife observation 69.5 120 137 155 174 188

Pool swimming 221.0 133

Motorized boating 219.5 107

Water-skiing 107.5 111

Rat&g/tubing 8.9 134

Canoeing/kayaking 39.8 116

Rowing/paddling/other boating 61.8 112

Stream/lake swimming 238.8 107

Saltwater fishing 77.3 108

Warm-water fuhing 239.5 92

Cold-water f=hing 83.8 113

Downhill skiing 64.3 151

Cross-country skiing 9.7 147

LAND

WATER

SNOW AND ICE

102

114

121

175

131

123

113

119

90

123

192

179

193 224 246

120 127 132

131 141 148

225 298 355

147 165 177

134 147 156

120 128 133

130 146 157

88 88 88

134 146 154

237

205

283

223

314

212

30
Table lo--Future market-clearing price indices under medium public-supply-growth and medium-demandgrowth assumptions, by activity

(1987 base level = 100)

Activity

Baseline

cost per

day (1987)

Year

2010 2024) 2030 2040

Developed camping $21.39 100 100

Picnicking 39.69 9 9 9 9

Sightseeing 65.61 101 101

Family gathering 67.52 9 9 9 9

Pleasure driving 40.26 100 100

Visiting historic sites 59.97 101 102

Attending events 54.49 100 100

Visiting museums 57.08 100 101

Off-road driving 34.04 100 100

Biking 40.82 101 101

Running/hw3h3 12.00 101 101

walking 44.91 100 100

Cutting firewood 26.09 100 100

Collecting berries 30.06 9 9 9 9

Visiting prehistoric sites 14.11 102 103

Photography 31.42 9 9 9 8

Day hiking 32.35 101 101

Horseback riding 33.16 100 100

Small-game hunting 32.87 9 9 9 9

Big-game hunting 47.77 9 9 9 8

Nature study 35.17 98 9 7

Backpacking 29.26 9 8 9 7

Primitive camping 23.61 100 100

Wildlife observations 41.47 9 8 9 7

Pool swimming !§46.52 101 102 103 104 104

Motorized boating 39.85 9 9 9 9 9 8 9 8 9 8

Water-skiing 41.04 100 100 100 100 100

Rafting/tubing 46.91 9 1 8 7 8 4 8 4 8 3

Canoeing/kayaking 38.61 9 9 9 8 98 98 9 8

Rowing/paddling/other boating 39.79 100 101 101 101 102

Stream/lake swimming 57.28 9 9 99 9 9 9 9 9 9

Saltwater fishing 88.38 9 9 99 9 8 9 9 9 9

Warm-water fishing 40.58 100 100 100 100 100

Cold-water fishing 40.97 100 101 101 101 102

Downhill skiing 27.81 102 103 104 105 105

Cross-country skiing 42.59 100 99 9 8 97 95

LAND

WATER

SNOW AND ICE

100

9 9

102

9 9

100

103

100

101

100

102

102

100

100

9 9

104

9 7

102

101

9 9

9 7

iz

100

%

100 100

9 9 9 9

103 104

9 9 9 9

100 101

104 105

100 100

102 102

101 101

103 103

103 103

100 100

100 101

9 9 100

105 106

9 7 9 7

103 104

101 101

9 8 9 8

% %

% 9 5

% %

100 100

% 9 5

31
Table 11--Future market-clearing trip indices under high public-supply-growth and medium-demand-growth

assumptions, by activity

(1987 base level = 100)

Activity

Baseline

consumption

in U.S.

(million)

Year

2010 2020 2030 2040

Developed camping 60.6 123 142 163

Picnicking 262.0 113 125 137

Sightseeing 292.7 118 134 154

Family gathering 74.4 125 146 168

Pleasure driving 421.6 117 131 147

\Usiting historic sites 73.1 121 140 163

Attending events 73.7 117 132 149

Visiting museums 9.7 119 137 155

Off-road driving 80.2 106 113 119

Biking 114.6 125 148 173

Running/jogging 83.7 134 165 199

walking 266.5 118 135 153

Cutting firewood 30.3 114 127 142

Collecting berries 19.0 116 131 150

Visiting prehistoric sites 16.7 132 158 189

Photography 42.0 129 156 184

Day hiking 91.0 133 163 200

Horseback riding 63.2 126 146 168

Small-game hunting 58.6 98 97 97

Big-game hunting 55.2 104 108 113

Nature study 70.8 114 127 141

Backpacking 26.0 141 178 220

Primitive camping 38.1 118 133 149

Wildlife observation 69.5 126 148 172

Pool swimming 221.0 135 166 199 233 258

Motorized boating 219.5 110 118 126 134 140

Water-skiing 107.5 113 124 135 146 154

Rafting/tubing 8.9 167 248 358 522 665

Canoeing/kayaking 39.8 122 143 164 189 206

Rowing/paddling/other boating 61.8 113 125 137 151 160

Stream/lake swimming 238.8 109 118 127 136 143

Saltwater fshing 77.3 112 126 141 161 175

Warm-water fishing 239.5 93 91 90 91 92

Cold-water fuhing 83.8 114 125 137 150 158

Downhill skiing 64.3 157 204 256 310 348

Cross-country skiing 9.7 158 200 238 267 260

LAND

WATER

SNOW AND ICE

184

150

178

191

163

194

166

175

126

237

172

162

175

227

215

245

188

97

118

157

267

165

198

199

159

204

207

174

225

179

189

131

220

265

186

183

202

268

237

292

202

98

122

169

2 %

176

217

32
Table 12~-Future market-clearing price indices under high public-supply-growth and medium-demandgrowth assumptions, by activity

(1987 base level= 100)

Activity

Baseline

cost per

day (1987)

Year

2010 2820 2030 2040

Developed camping $21.39 98 97

Picnicking 39.69 98 97

Sightseeing 65.61 100 100

Family gathering 67.52 98 96

Pleasure driving 40.26 99 99

Visiting historic sites 59.97 100 101

Attending events 54.49 98 98

Visiting museums 57.08 100 99

Off-road driving 34.04 100 99

Biking 40.82 100 100

Running/jogging 12.00 99 99

walking 44.91 99 98

Cutting fuewood 26.09 98 98

Collecting berries 30.06 97 96

Visiting prehistoric sites 14.11 100 101

Photography 31.42 % 93

Day hiking 32.35 99 99

Horseback riding 33.16 99 98

Small-game hunting 32.87 98 97

Big-game hunting 47.77 97 95

Nature study 35.17 95 93

Backpacking 29.26 94 90

Primitive camping 23.61 99 98

Wildlife observation 41.47 95 92

Pool swimming $46.52 101

Motorized boating 39.85 98

Water-skiing 41.84 99

Rafting/tubing 46.91 80

Canoeing/kayaking 38.61 97

Rowing/paddling/other boating 39.79 99

Stream/lake swimming 57.28 98

Saltwater fishing 88.38 97

Warm-water fishing 48.58 100

Cold-water fuhing 40.97 100

WATER

SNOW/ICE

Downhill skiing 27.81 100 100 100

Cross-country skiing 42.59 95 92 88

101

97

98

70

95

99

97

z

100

%

97

101

95

98

102

97

99

99

100

99

98

97

95

101

91

99

98

%

93

91

87

zi

102

%

98

60

94

99

97

95

99

100

%

%

101

94

98

103

97

100

99

100

99

97

97

94

102

89

100

97

95

92

89

84

97

88

102

95

97

55

93

99

%

94

99

100

99

85

%

%

102

%

98

104

96

100

99

101

100

97

97

94

103

88

100

97

94

91

88

82

z

102

94

97

51

92

100

%

94

98

100

99

81

33

L

Cordell, H. Ken; Rergstrom, John C. 1989. lluxuy and techniques

for assessing the demand and supply of outdoor recreation in the

United States. Res. Pap. SE-275. Athens, Gk U.S. Department of

Agriculture, Forest Service, Southeastern Forest Experiment Station.

33 PP.

As the central analysis for the 1989 Renewable Resources planning

Act Assessment, a household market model covering 37 recreational

activities was computed for the United States. Equilibrium

consumption and costs were estimated, as uwe likely future changes

in consumption and costs in response to expected demand growth

and alternative development and access policies.

Keywords: outdoor recreation, market equilibrium, demand, supply,

forecasts, household production theory.

Cordell, H. Ken; Retgstrom, John C. 1989. Theory and techniques

for assessing the demand and supply of outdoor recreation in the

United States. Res. pap. SE-275. Athens, GA: US. Department of

Agriculture, Forest Service, Southeastern Forest Experiment Station.

33 pp. i

As the central analysis for the 1989 Renewable Resources planning

Act Assessment, a household market model covering 37 recreational

activities was computed for the United States. Equilibrium

consumption and costs were estimated, as were likely future changes

in consumption and costs in response to expected demand growth

and aitemative development and access policies.

Keywords: Outdoor recreation, market equilibrium, demand, supply,

forecasts, household production theory.

.
The Forest Service, 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.

USDA policy prohibits discrimination because of race,

color, national origin, sex, age, religion, or handicapping

condition. Any person who believes he or she has been

discriminated against in any USDA-related actixity should

immediately contact the Secretary of Agriculture,

Washington, DC 20250.

: .