Biased estimates in discrete choice models: the appropriate inclusion of psychometric data into the valuation of recycled wastewater

The introduction of measurement bias in parameter estimates into non-linear discrete choice models, as a result of using factor analysis, was identified by Train et al. (1987). They found that the inclusion of factor scores, used to represent relationships amongst like variables, into a subsequent discrete choice models introduced measurement bias as the measurement error associated with each factor score is excluded. This is an issue for non-market valuation given the increase in popularity of including psychometric data, such as primitive beliefs, attitudes and motivations, in willingness to pay estimates. This study explores the relationship between willingness to pay and primitive beliefs through a case study eliciting Perth community values for drinking recycled wastewater. The standard discrete decision model, with sequential inclusion of factor scores, is compared to an equivalent discrete decision model, which corrects for the measurement bias by simultaneously estimating the underlying latent variables using a measurement model. Previous research has focused on the issue of biased parameters. Here we also consider the implications for willingness to pay estimates.