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.     

Two Nested Constant-Elasticity-of-Substitution Models of Recreational Participation and Site Choice: An "Alternatives" Model and an "Expenditures" Model

Two demand models of recreational participation and site choice are developed: an alternatives model and an expenditures model. Both assume maximization of utility over the year, so allow for diminishing marginal utility. They do not impose the restrictive assumption that where one goes on a trip is independent of where one plans to go on other occasions. Estimation is with a nested constant-elasticity-of-substitution preference ordering: it is relatively easy to estimate because of global regularity, it allows sites to be complements, and it has the potential to be locally flexible. The application is to Atlantic salmon fishing.     

A Joint Latent-Class Model: Combining Likert-Scale Preference Statements With Choice Data to Harvest Preference Heterogeneity

In addition to choice questions (revealed and stated choices), preference surveys typically include other questions that provide information about preferences. Preference-statement data include questions on the importance of different attributes of a good or the extent of agreement with a particular statement. The intent of this paper is to model and jointly estimate preference heterogeneity using stated-preference choice data and preference-statement data. The starting point for this analysis is the belief that the individual has preferences, and both his/her choices and preference statements are manifestations of those preferences. Our modeling contribution is linking the choice data and preference-statement data in a latent-class framework. Estimation is straightforward using the E-M algorithm, even though our model has hundreds of preference parameters. Our estimates demonstrate that: (1) within a preference class, the importance anglers associate with different Green Bay site characteristics is in accordance with their responses to the preference statements; (2) estimated across-class utility parameters for fishing Green Bay are affected by the preference-statement data; (3) estimated across-class preference-statement response probabilities are affected by the inclusion of the choice data; and (4) both data sets influence the number of classes and the probability of belonging to a class as a function of the individual’s type.     

Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model

A latent-class model of environmental preference groups is developed and estimated with only the answers to a set of attitudinal questions. Economists do not typically use this type of data in estimation. Group membership is latent/unobserved. The intent is to identify and characterize heterogeneity in the preferences for environmental amenities in terms of a small number of preference groups. The application is to preferences over the fishing characteristics of Green Bay. Anglers answered a number of attitudinal questions, including the importance of boat fees, species catch rates, and fish consumption advisories on site choice. The results suggest that Green Bay anglers separate into a small number of distinct classes with varying preferences and willingness to pay for a PCB-free Green Bay. The probability that an angler belongs to each class is estimated as function of observable characteristics of the individual. Estimation is with the expectation–maximization (E–M) algorithm, a technique new to environmental economics that can be used to do maximum-likelihood estimation with incomplete information. As explained, a latent-class model estimated with attitudinal data can be melded with a latent-class choice model. Edward Morey and Jennifer Thacher are equal authors and rotate authorship across articles.