Latent class logits and discrete choice experiments: Implications for welfare measures

Current research practices include estimation of latent class logits on data collected with discrete choice experiments. This practice relies on a mismatch in the characterization of heterogeneity in preferences: while discrete choice experiments usually assume homogeneity, latent class logits seek for discrete heterogeneity. This paper uses Monte Carlo simulations to study whether this mismatch impacts the reliability of welfare estimates. The experiment design in this paper varies i) the amount of discrete heterogeneity, and ii) the amount of information available through either number of pseudo-respondents or number of choice sets. Resulting estimates are unbiased with relatively large dispersion in every simulated scenario. Due to the large dispersion, the null hypothesis that a welfare measure is zero cannot be rejected. This false conclusion is reached even under scenarios in which the amount of simulated available information is larger than the amount of information usually available in empirical applications. Since simulated scenarios closely resemble features of empirical applications, findings from this paper imply that an analyst planning the estimation of a latent class logit on discrete choice data will need either (i) to collect more information than usually gathered in empirical applications; or (ii) to gather information about the source and/or magnitude of discrete heterogeneity, and include this information in the sample size calculation; or (iii) to design discrete choice experiments seeking efficient WTP estimates; or (iv) a strategy combining the previous three options.