Estimating Recreational User Counts

This article outlines a methodology for estimating the number of individual visitors to a set of recreational sites, as well as counts of specific visitor groups. The model is designed for sets of sites characterized by: (a) unrestricted recreation from a wide and partially unknown geographic market; (b) individuals who may visit more than one site; (c) accurate visitation (gate) counts from each site. The model provides consistent estimates of the total number of individual visitors, based on information embedded in site-level count and survey data. Monte Carlo analysis and an empirical application illustrate the properties of visitor count estimates. Copyright 2003, Oxford University Press.

Hiking in the Alps: exploring substitution patterns of hiking destinations.

Tourism in the Alps used to rely on a network of facilities maintained in part by the military Alpine Corps. Hiking has been growing in popularity, while the national draft is no longer compulsory. This situation calls for a renewed approach to management of the maintenance of alpine facilities. The authors explore the use of destination choice models which allow for various substitution hypotheses and highlight how single mountain sites can be substitutes for others, although located in a different geographical area. The results supply helpful information for local policy decision makers as they provide insights about the redistribution of visits following the implementation of different policy scenarios. The authors investigate such redistributions following the variation of availability to hikers in terms of alpine shelters, length of trails, site access and the application of access fees. They also estimate changes in welfare for selected variations of alpine facilities and availability of destinations. The findings highlight the sensitivity of results to the use of different specifications of demand models to guide local policy strategies

Valuing a Change in a Fishing Site without Collecting Characteristics Data on All Fishing Sites: A Complete but Minimal Model

Resource economists are often asked to value a proposed change at one, and only one, recreational site; the model we develop and estimate is applicable for those cases. The application is valuing the elimination of fish consumption advisories on a large bay on Lake Michigan. The model is minimal but complete: complete in that the choice set is not restricted, minimal in that only two conditional indirect utility functions are estimated. It is utility-theoretic and one does not have to collect characteristic data on all of the other fishing sites in the region. Data include the number of trips each individual currently takes to Green Bay, answers to “would you prefer to fish Green Bay under conditions A or B?” and how often each angler says they would fish Green Bay under different sets of conditions. Copyright 2006, Oxford University Press.

Urban Influence on Costs of Production in the Corn Belt

This article determines the relative technical efficiency of rural- and urban-influenced crop/livestock enterprises in the Corn Belt. Farmers in urban-influenced locations are less technically efficient than farmers in rural locations. During 1998–2000, stochastic production frontier procedures indicate that a 10% increase in urban influence leads to a close to 4% decrease in technical efficiency. The most successful urban-influenced farms have controlled costs as effectively as rural farms. They have tended to de-emphasize that nondairy livestock activities—particularly beef and hogs—do not rely extensively on off-farm income, and have relatively large, less residential/lifestyle operations compared to less successful urban-influenced farmers. However, our statistical analysis clearly bears out the refrain in popular literature that urban proximity raises the cost for, and decreases the viability of, traditional farms. Copyright 2006, Oxford University Press.

Age effects in monetary valuation of reduced mortality risks: the relevance of age-specific hazard rates

Health risk prevention, Value of statistical life, Age effects, Hazard rate, Contingent valuation, D81, J17, Q26,