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An efficiency approach to choice set formation: theory and application to recreational destination choice

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  • David Scrogin
  • Richard Hofler
  • Kevin Boyle
  • J. Walter Milon

Abstract

An unresolved debate in behavioural choice research surrounds specifying individual choice sets. To overcome the limitations of deterministic and probabilistic approaches in a framework consistent with individual optimizing behaviour, this study develops an efficient frontiers approach to the choice set problem in the context of revealed preference destination choice. Stochastic frontier models relating costly inputs to utility generating outputs are estimated to measure the efficiency of sites; choice sets are then formed by retaining sites that exceed alternative efficiency thresholds. Choice set composition, site choice efficiency and probability of selection and consumer surplus are evaluated in the case of fishery site choice in Maine.

Suggested Citation

  • David Scrogin & Richard Hofler & Kevin Boyle & J. Walter Milon, 2010. "An efficiency approach to choice set formation: theory and application to recreational destination choice," Applied Economics, Taylor & Francis Journals, vol. 42(3), pages 333-350.
    • Handle: RePEc:taf:applec:v:42:y:2010:i:3:p:333-350
    DOI: 10.1080/00036840701604438
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    References listed on IDEAS

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    1. H. Spencer Banzhaf & V. Kerry Smith, 2007. "Meta-analysis in model implementation: choice sets and the valuation of air quality improvements," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1013-1031.
    2. Timothy C. Haab & Kenneth E. McConnell, 2002. "Valuing Environmental and Natural Resources," Books, Edward Elgar Publishing, number 2427.
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    Cited by:

    1. Hicks, Robert L. & Holland, Daniel S. & Kuriyama, Peter T. & Schnier, Kurt E., 2020. "Choice sets for spatial discrete choice models in data rich environments," Resource and Energy Economics, Elsevier, vol. 60(C).
    2. Backstrom, Jesse D. & Woodward, Richard T., 2017. "Using Qualitative Site Characteristics Data in Marine Recreational Fishing Models: A New Site Aggregation Approach," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258276, Agricultural and Applied Economics Association.
    3. Ke Wang & Xin Ye, 2021. "Development of alternative stochastic frontier models for estimating time-space prism vertices," Transportation, Springer, vol. 48(2), pages 773-807, April.
    4. Yuan Yuan Liu, 2017. "A Framework Identifying Factors Influencing Travel Motivations For Sustainable Tourism Destinations," Economy & Business Journal, International Scientific Publications, Bulgaria, vol. 11(1), pages 510-519.

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