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Data from a discrete choice experiment on improvements of rural landscape attributes are used to investigate the implications of discontinuous preferences on willingness to pay estimates. Using a multinomial error component logit model, we explore differences in scale and unexplained variance between respondents with discontinuous and continuous preferences and condition taste intensities on whether or not each attribute was considered by the respondent during the evaluation of alternatives.Results suggest that significant improvements in model performance can be achieved when discontinuous preferences are accommodated in the econometric specification, and that the magnitude and robustness of the willingness to pay estimates are sensitive to discontinuous preferences. Keywords

There is a growing literature on the design and use of stated choice experiments. Analysts have developed sophisticated ways of analysing such data, using a form of discrete choice model to identify the marginal (dis)utility associated with observed attributes linked to an alternative, as well as accounting for preference and scale heterogeneity. There is also a growing literature studying the attribute processing rules that respondents use as a way of simplifying the task of choosing. Using the latent class framework, we define classes based on rules that recognise the non-attendance to one or more attributes. These processing rules are postulated to be used in real markets as a form of cognitive rationalisation. The empirical study involves a choice amongst rural environmental landscape improvements in the Republic of Ireland. We estimate models and calculate a marginal willingness to pay (WTP) for four landscape improvements, and contrast it with the results from a model specification in which all attributes are assumed to be attended to with parameter preservation. We find that the marginal WTP is, on average, significantly higher when full attribute preservation specification is adopted, raising questions about the appropriateness of current practice that assume a fully compensatory attribute choice rule

ABSTRACT. A multi-attribute, stated-preference approach is used to value low and high impact actions on four major landscape components addressed by the Rural Environment Protection Scheme in Ireland. Several methodological issues are addressed: the use of prior beliefs on the relative magnitudes of parameters, standardized description of different levels of landscape improvements via image manipulation software, adoption of efficiency-increasing sequential experimental design, and sensitivity of benefit estimates to inclusion of responses from ''irrational'' respondents. Results suggest that Bayesian design updating delivers significant efficiency gains without loss in respondent efficiency, and estimates are upward-biased when irrational respondents are included. (JEL Q24, Q51)

This paper reports the findings from a discrete-choice experiment designed to estimate the economic benefits associated with rural landscape improvements in Ireland. Using a mixed logit model, the panel nature of the dataset is exploited to retrieve willingness-to-pay values for every individual in the sample. This departs from customary approaches in which the willingness-to-pay estimates are normally expressed as measures of central tendency of an a priori distribution. Random-effects models for panel data are subsequently used to identify the determinants of the individual-specific willingness-to-pay estimates. In comparison with the standard methods used to incorporate individual-specific variables into the analysis of discrete-choice experiments, the analytical approach outlined in this paper is shown to add considerable explanatory power to the welfare estimates.