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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 preference heterogeneity. Our main finding is that using an RPL specification with log-normally distributed preferences seems the best bet.

Despite the vital role of utility functional form in welfare measurement, the implications of working with incorrect utility specifications have not been examined in the choice experiments (CE) literature. This paper addresses the importance of the specification of both non-monetary attributes and the marginal utility of income. Monte Carlo experiments have been conducted wherein different attribute specifications and assumptions for the Cost parameter –that is, different functional forms of utility– have been assumed to generate simulated choices on which Multi-Nomial Logit and Mixed Logit models have been estimated under correct and incorrect assumptions about the true, underlying utility function. The inferred values have been compared with the true ones directly calculated from the true utility specifications. Results show that working with simple experimental designs and continuous-linear specifications makes attribute specification irrelevant for measuring attribute marginal values regardless of the true effects the attribute has on utility.