A Selection Bias Approach to Destination Choice

Abstract

Research on consumer search behavior commonly envisages destination choice as a two‐step process: (1) delineate the search set, and (2) evaluate choices therein. However, much of the empirical work in destination choice—including logit and nested logit formulations—models only the latter, and not the set delineation itself. In the presence of correlation between error terms in set delineation and choice selection, statistical estimators are biased, a problem that Heckman and others have called selection bias. In this paper, an alternative two‐stage method is proposed to estimate the parameters of models of set delineation and choice selection. Monte Carlo simulation is used to explore the properties of these two‐stage estimators, and to show the magnitude of bias inherent in traditional methods of estimation.