Incorrectly accounting for taste heterogeneity in choice experiments: Does it really matter for welfare measurement?

A range of empirical approaches to representing preference heterogeneity have emerged in choice modelling. Researchers have been able to explore the differences which selection of a particular approach makes to welfare measures in a particular dataset, and indeed have been able to implement a number of tests for which approach best fits a particular set of data. However, the question as to the degree of error in welfare estimation from an inappropriate choice of empirical approach has not been addressed. In this paper, we use Monte Carlo analysis to address this question. Given the high popularity of both the random parameter logit (RPL) and latent class models among choice modellers, we examine the errors in welfare estimates from using the incorrect model to account for taste heterogeneity. Our main finding is that using an RPL specification with log-normally distributed preferences seems the best bet.