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

Wiktor Budzinski, Danny Campbell, Mikolaj Czajkowski, Urska Demsar, Nick Hanley

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12 Citations (Scopus)
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In this paper, we investigate the use of geographically weighted choice models for modelling spatially clustered preferences. We argue that this is a useful way of generating highly-detailed spatial maps of willingness to pay for environmental conservation, given the costs of collecting data. The data used in this study come from a discrete choice experiment survey of public preferences for the implementation of a new national forest management and protection programme in Poland. We combine these with high-resolution spatial 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 characteristics of the forests close to where respondents live. These 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 location-specific estimates of WTP. We find that there are indeed strong spatial patterns to the benefits of changes to the management to national forests. People living in areas with more species-rich forests and those living nearer bigger areas of mixed forests have significantly different WTP values than those living in other locations. This kind of information potentially enables a better distributional analysis of the gains and losses from changes to natural resource management, and better targeting of investments in forest quality.
Original languageEnglish
JournalJournal of Agricultural Economics
VolumeEarly View
Early online date29 Dec 2017
Publication statusE-pub ahead of print - 29 Dec 2017


  • 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


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