Files
Abstract
Data from a discrete choice experiment is used to investigate the implications of
failing to account for attribute processing strategies (APSs). The research was designed to elicit the economic benefits associated with landscape restoration activities that were intended to remediate environmental damage caused by illegal dumping activities. In this paper we accommodate APSs using an equality constrained latent class model. By retrieving the conditional class membership
probabilities we recover estimates of the weights that each respondent assigned
to each attribute, which we subsequently use ensure unnecessary weight is not allocated to attributes not attended to by respondents. Results from the analysis
provide strong evidence that significant gains in models fit as well as more defensible and reliable willingness to pay estimates can be achieved using when the
APSs are accounted for.