Although the contingent valuation literature emphasises the importance of controlling for respondents’ consequentiality perceptions, this literature has rarely accounted for the difference between payment and policy consequentiality. We examine the influence of the randomly assigned tax amount on consequentiality self-reports and their potential endogeneity using data from a single dichotomous choice survey about reducing marine plastic pollution in Norway. Results show that consequentiality perceptions are a function of the tax amount, with payment consequentiality decreasing and policy consequentiality increasing with higher tax amounts. We discuss the challenge of finding valid instruments to address potential endogeneity of consequentiality perceptions.
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.
The paper investigates the spatial heterogeneity of public's preferences for the implementation of a new country-wide forest management and protection program in Poland. Spatial econometric methods and high resolution geographical information system data related to forest characteristics are used to explain the variation in individual-specific willingness to pay (WTP) values, derived from a discrete choice experiment study. We find that respondents' WTP is higher the closer they live to their nearest forest, and the scarcer forests are in the area where they live. Interestingly, the higher the ecological value of forests in respondents' area, the more people prefer extending areas of national forest protection. We also investigate spatial patterns in individual-specific WTP scores and in latent class membership probabilities, finding that preferences are indeed spatially clustered. We argue that this clustering should be taken into account in forest management and policy-making.