Modeling Unobserved Heterogeneity in Contingent Valuation of Health Risks

Abstract

Human preferences for alternative levels of health risks can be heterogeneous. In this paper we consider a flexible distribution approach to model health values elicited with the dichotomous choice contingent valuation method. Rigid parametric structures cannot model sample heterogeneity while imposing strong assumptions on the error distribution. We consider a mixture of normal distributions which can approximate arbitrary well any empirical distribution as the number of mixtures increases. The model is applied to data on willingness to pay for reducing the individual risk of an episode of respiratory illness. The mixture distribution model is compared with the rigid probit model using a Bayes factor test. The results show that the mixture modeling approach improves performance while allowing for the consideration of alternative groups of individuals with different preferences for health risks.