Flexible mixture distribution modeling of dichotomous choice contingent valuation with heterogenity

This paper considers the performance of a model of mixture normal distributions for dichotomous choice contingent valuation data, which allows the researcher to consider unobserved heterogeneity across the sample. The model is flexible and approaches a semi-parametric model, since the normality assumption can be removed by augmenting the number of mixture distributions. Bayesian inference allows for simple estimation of the model and is particularly appropriate for conducting inference with finite data sets. The proposed model is compared with other semi-parametric and parametric approaches using Monte Carlo simulation, under alternative assumptions regarding heteroskedasticity and heterogeneity in sample observations. It is found that the mixture normal model reduces bias and improves performance with respect to an alternative semi-parametric model, particularly when the sample is characterized by heterogeneous preferences. The application of the model to empirical data on the recreational value of natural areas in the Canary Islands confirmed the expected results.