Using Geographically Weighted Choice Models to Account for the Spatial Heterogeneity of Preferences

In this paper, we investigate the prospects of using geographically weighted choice models for modelling of 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 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 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 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 location-specific estimates of WTP. We find that there are indeed strong spatial patterns to the benefits of changes in management to national forests. People living in areas with more species-rich forests and those living nearer to higher areas of mixed forests have significantly different WTP values than those living in other locations. This kind of information enables a better distributional analysis of the gains and losses from changes to natural resource management, and better targeting of investments in forest quality