A framework for analyzing rank ordered data with application to automobile demand

Abstract

In this paper we develop a general random utility framework for analyzing data on individuals' rank orderings. Specifically, we show that in the case with 3 alternatives one can express the probability of a particular rank ordering as a simple function of first choice probabilities. This framework is applied to specify and estimate models of household demand for conventional gasoline cars and alternative fuel vehicles in Shanghai based on rank ordered data obtained from a stated preference survey. Subsequently, the framework is extended to allow for random effects in the utility specification to allow for intrapersonal correlation in tastes across stated preference questions. The preferred model is then used to calculate demand probabilities and elasticities and the distribution of willingness-to-pay for alternative fuel vehicles. Keywords: Random utility models; GEV rank-ordered models; Car demand; Alternative fuel vehicles