SOUTHERN JOURNAL OF AGRICULTURAL ECONOMICS DECEMBER 1992
MEASURING USE VALUE FROM RECREATION PARTICIPATION
John C. Whitehead
Abstract
Recreation demand studies have traditionally util-
ized a two-step valuation method, estimating condi-
tional recreation participation probabilities and then
intensity of use decisions. These two steps of analy-
sis are combined to estimate the use value of natural
resource recreation sites. The purpose of this paper
is to provide a method by which use value can be
estimated solely from the participation decision.
The one-step resource valuation method allows esti-
mation of use values from coefficients of the logistic
regression recreation participation equation. The
benefits of the method are the reduced data and effort
required to value natural resource areas.
Key words: logistic regression, recreation
participation, use value.
Estimated on-site natural resource use value is one
type of information that is useful when decisions
about allocation of natural resources must be made.
Use value can be determined from hypothetical,
constructed markets (contingent valuation) or from
revealed behavior (travel cost) recreation demand
studies (Forster 1989). Recreation demand studies
have traditionally utilized a two-step valuation
method, estimating conditional recreation participa-
tion probabilities and then intensity of use decisions
(McConnell 1985; Rockel and Kealy 1991). These
two steps of analysis are combined to estimate the
value of the resource site (Clawson and Knetsch
1966; Cicchetti 1973; Charbonneau and Hay 1978).
The purpose of this study was to provide a method
by which use value can be estimated solely from the
participation decision.
The two-step outdoor recreation study can be used
to forecast recreation demand and value recreational
activities and sites. The participation decision, the
first step, is the choice of whether or not to travel to
a natural resource site and engage in a recreational
activity while there. The decision is usually mod-
eled based on reduced form household demand and
supply equations with socioeconomic characteristics
and resource supply variables influencing the par-
ticipation decision (Charbonneau and Hay 1978;
Deyak and Smith 1978; Hay and McConnell 1979,
1984; Miller and Hay 1981 ; Walsh et al. 1989). The
intensity of use decision is the choice of how many
trips (days, hours, etc.) to take to the resource site
conditional on the decision to participate. Relative
to the participation decision, the travel cost demand
model has received much more attention in the rec-
reation economics literature (Forster 1989).
The travel cost recreation demand model can be
used to directly estimate the value of a recreation trip
or day. The value of recreation trips or days can then
be combined with information on the forecasted
number of visitors, determined from the first-step,
participation equation, to estimate the value of a
natural resource site. For instance, Miller and Hay
(1981) value the economic loss to waterfowl hunters
of wetlands conversion by multiplying the estimated
loss in hunter days by an estimate of consumer’s
surplus per hunting day. Recently, information on
recreation nonparticipants has been combined with
travel cost models and jointly estimated to measure
the value of natural resource sites (Zeimer et al.
1982; Smith 1988; Bockstael et al. 1990).
A limitation of the two-step approach is that com-
putation of the second-step demand function re-
quires the extra computing expense of estimating
visitation with varying travel costs. In contrast to the
two-step valuation strategy described above, this
study presents a one-step resource valuation method
based solely on the recreation participation decision.
A benefit of this approach is the reduced data and
effort required for valuing recreation sites.
THEORETICAL AND EMPIRICAL
Modelsofvalue
Defining Use Value
Assume that individuals possess a utility function
u(xi,y) where u(∙) is the utility function, xi is recrea-
tional visits to resource site i (i = l,...,n), and y is a
John C. Whitehead is an Assistant Professor in the Department OfEconomics at East Carolina University. This paper uses data collected
for a project supported by the U.S. Department of the Interior, Office of Surface Mining Reclamation and Enforcement, Pittsburgh, PA
(Grant Number GR996212). The author acknowledges several constructive comments made on this paper by Glenn Blomquist, Pete
Groothuis, participants in the Economics Seminar at East Carolina University, and three anonymous reviewers. The views expressed,
however, are the author’s alone.
Copyright 1992, Southern Agricultural Economics Association.
113