The name is absent



use value is positive, individuals will be observed
participating in recreation.

The probability of participation is equal to the
probability that the mean valuation function with
mean zero random error is greater than zero

(7) π1 = π [/(p1*, pi, m; τ) + ɛ > 0 ]

πι = π [/(p1*, pl, m; τ) > -ɛ ]

where π1 is π(xι > 1). The probability of participa-
tion can be estimated with one of several discrete
choice econometric models (Amemiya). The logis-
tic regression model has been recommended for
recreation economic applications and is used here
(Stynes and Peterson 1984). The logit equation
specifies the log odds of recreation participation to
depend on a linear spécification of the index variable
in equation (6) as:2

(8) In


TTlj

1 - πlj

= α + βpιj + γ ' Zj


where π1j is the probability of participation by indi-
vidual j at site 1, α and β are coefficients, γis a
coefficient vector, and z is a vector of independent
variables to account for individual differences, in-
cluding income. From the theoretical valuation
function, trip costs and income are required in esti-
mation of the participation equation. Taste and pref-
erence indicator variables should also be included in
the regression. No restrictions on functional form is
suggested by theory.

Once the maximum likelihood coefficients are
found for equation (8), the logit equation can be
solved for the unobserved probability of participa-
tion for each individual in the sample

(9) πlj =----------ʌ----.----∙

1 +exp{ - [α+ βpij + γ'zj]}

Ex-ante, each individual has a nonzero probability
of choosing to participate. An individual is expected
to participate (not participate) if the estimated prob-
ability is > (<) .50. Exploiting this information al-
lows estimation of the latent use value. First, the
probability of participation for each individual is set
to .50 to solve for the maximum trip cost that would
be tolerated by the individual

fY + V , 7∙
(10) pijlj = .5) =---
I-K

β

The maximum trip cost is the maximum willing-
ness to pay per trip. A property of maximum will-
ingness to pay per trip is that if the recreation
probability is greater (less) than .50, maximum will-
ingness to pay per trip will be greater (less) than the
observed trip cost.

Next, the difference between the estimated maxi-
mum willingness to pay and the observed trip cost
for each individual (pŋ - pŋ) can be calculated. This
value is positive for expected recreation participants.
The difference in the maximum willingness-to-pay
per trip and the observed trip cost is the use value per
trip (UVij / Ху). Use value per year is equal to use
value per trip multiplied by the number of trips taken
during the past year. For expected recreation non-
participants, use value per trip is negative. But,
because the actual trip cost is observationally equal
to the reservation price for expected recreation non-
participants, the expected number of trips and use
value must be equal to zero for this group. Therefore,
use values for expected nonusers are set equal to
zero.

EMPIRICAL ESTIMATES OF USE VALUE

The Study Area

In the western Kentucky coalfield along the lower
Ohio River, surface coal mining is a competing use
of wetlands that contributes to the conversion of
wetland acreage. These wetlands provide functions
such as fish and wildlife habitat, water quality im-
provement, flood control, and outdoor recreation.
Surface coal mining directly reduces wetland func-
tions by converting wetlands to mined areas and
indirectly reduces wetland functions by its negative
effect on the water quality of downstream wetlands.
Within the western Kentucky coalfield a three
county recreation region was identified from maps
of the area (Mitsch et al. 1983)?

Within the three-county region, the Kentucky De-
partment of Fish and Wildlife Resources (KDFWR)
manages public hunting areas, and private coal com-
panies, in cooperation with the KDFWR, manage
reclaimed surface coal mines as wildlife areas, rec-
reation areas, and waterfowl refuges. The region is
a popular deer hunting area (Shadowen et al. 1984;

2The standard conceptual model of recreation participation utilizes the household production function approach (Deyak and
Smith 1978). The valuation function approach used in this paper results in identical empirical specifications of the participation
decision.

3The counties were Hopkins, Muhlenberg, and Ohio.

115



More intriguing information

1. Learning and Endogenous Business Cycles in a Standard Growth Model
2. The name is absent
3. A parametric approach to the estimation of cointegration vectors in panel data
4. Demand Potential for Goat Meat in Southern States: Empirical Evidence from a Multi-State Goat Meat Consumer Survey
5. Imputing Dairy Producers' Quota Discount Rate Using the Individual Export Milk Program in Quebec
6. Dementia Care Mapping and Patient-Centred Care in Australian residential homes: An economic evaluation of the CARE Study, CHERE Working Paper 2008/4
7. The name is absent
8. The Functions of Postpartum Depression
9. Experimental Evidence of Risk Aversion in Consumer Markets: The Case of Beef Tenderness
10. An Incentive System for Salmonella Control in the Pork Supply Chain
11. Une Gestion des ressources humaines à l'interface des organisations : vers une GRH territoriale ?
12. The Formation of Wenzhou Footwear Clusters: How Were the Entry Barriers Overcome?
13. What Drives the Productive Efficiency of a Firm?: The Importance of Industry, Location, R&D, and Size
14. Passing the burden: corporate tax incidence in open economies
15. The name is absent
16. Surveying the welfare state: challenges, policy development and causes of resilience
17. The name is absent
18. Modelling the health related benefits of environmental policies - a CGE analysis for the eu countries with gem-e3
19. La mobilité de la main-d'œuvre en Europe : le rôle des caractéristiques individuelles et de l'hétérogénéité entre pays
20. WP RR 17 - Industrial relations in the transport sector in the Netherlands