Advances in stated preference methods : discrete and continuous mixing distributions in logit models for representing variance and taste heterogeneity

• Main Author: Boeri, Marco
• Published: Queen's University Belfast 2011
• Subjects: 330.15195
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.554305

This thesis is a collection of four papers; one centred on a policy application of Contingent Valuation (CV) and three essays focused on econometric advances in Discrete Choice Experiments (DCE). The former paper represents the first attempt to appraise odour externalities in an urban context employing DBCV. The survey was administered to a sample of 1000 households in Le Havre (France). Results reveal a positive WTP for eliminating odour externalities which vary across areas, respondent characteristics and perceptions. The potential of latent class analysis in DCE is explored in the latter three papers. The PhD has been mainly focused on developing new tools for accommodating heterogeneity in tastes, variances and heuristics employing latent class (LC) analysis. As a result I formalised a type of LC models that can accommodate for heteroscedasticity and/or heterogeneity (depending on assumptions and parameterisation) within class as well as different heuristics across classes. More specifically the second paper introduces class heterogeneity within a LC model, which is obtained by specifying a discrete mixture of sets of continuous distributions. The model is applied to both simulated and real data in order to demonstrate its flexibility and the advantages for policy appraisal. The third paper introduces and formalises the idea of a heteroscedastic LC model using data from two recreational site choice studies (one elicited through stated preference methods and one through revealed preference methods) to compare various model specifications. In the fourth and final paper, the well known problem of preference and variance instability due to learning and fatigue in DCE is tackled by applying a scale-adjusted latent class model to uncover both types of instability simultaneously and probabilistically across the sample. Findings highlight the advantages in terms of model fit, interpretation and policy implications, that can be achieved when both types of instability are addressed concurrently.