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

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Abstract

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.

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Authors and Affiliations

  1. Department of Economics, University of Colorado-Boulder, 256 UCB, Boulder, CO, 80309–0256, USA

    Edward Morey

  2. Department of Economics, University of New Mexico, Albuquerque, USA

    Jennifer Thacher

  3. Stratus Consulting Inc, Boulder, CO, USA

    William Breffle

Authors
  1. Edward Morey
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  2. Jennifer Thacher
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  3. William Breffle
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Correspondence to Edward Morey.

Additional information

Edward Morey and Jennifer Thacher are equal authors and rotate authorship across articles.

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Morey, E., Thacher, J. & Breffle, W. Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model. Environ Resource Econ 34, 91–115 (2006). https://doi.org/10.1007/s10640-005-3794-7

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  • DOI: https://doi.org/10.1007/s10640-005-3794-7

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