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Using geographically weighted choice models to account for spatial heterogeneity of preferences

Author

Listed:
  • Wiktor Budziński

    () (Faculty of Economic Sciences, University of Warsaw)

  • Danny Campbell

    () (University of Stirling, Stirling Management School)

  • Mikołaj Czajkowski

    () (Faculty of Economic Sciences, University of Warsaw)

  • Urška Demšar

    () (University of St Andrews, School of Geography and Geosciences)

  • Nick Hanley

    (University of St Andrews, School of Geography and Geosciences)

Abstract

In this paper we investigate the prospects of using geographically weighted choice models for modelling of spatially clustered preferences. The data used in this study comes from a discrete choice experiment survey regarding public preferences for the implementation of a new country-wide forest management and protection program in Poland. We combine it with high-resolution geographical information system data related to local forest characteristics. Using locally estimated discrete choice models we obtain location-specific estimates of willingness to pay (WTP). Variation in these estimates is explained by the socio-demographic characteristics of respondents and characteristics of the forests in their place of residence. The results are compared with those obtained from a more typical, two stage procedure which uses Bayesian posterior means of the mixed logit model random parameters to calculate individual-specific estimates of WTP. The latter approach, although easier to implement and more common in the literature, does not explicitly assume any spatial relationship between individuals. In contrast, the geographically weighted approach differs in this aspect and can provide additional insight on spatial patterns of individuals’ preferences. Our study shows that although the geographically weighted discrete choice models have some advantages, it is not without drawbacks, such as the difficulty and subjectivity in choosing an appropriate bandwidth. We find a number of notable differences in WTP estimates and their spatial distributions. At the current level of development of the two techniques, we find mixed evidence on which approach gives the better results.

Suggested Citation

  • Wiktor Budziński & Danny Campbell & Mikołaj Czajkowski & Urška Demšar & Nick Hanley, 2016. "Using geographically weighted choice models to account for spatial heterogeneity of preferences," Working Papers 2016-17, Faculty of Economic Sciences, University of Warsaw.
  • Handle: RePEc:war:wpaper:2016-17
    as

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    File URL: http://www.wne.uw.edu.pl/index.php/download_file/2764/
    File Function: First version, 2016
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    discrete choice experiment; contingent valuation; willingness to pay; spatial heterogeneity of preferences; forest management; passive protection; litter; tourist infrastructure; mixed logit; geographically weighted model; weighted maximum likelihood; local maximum likelihood;

    JEL classification:

    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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