Combining Revealed Preference Data with Stated Preference Data: A Latent Class Approach

A substantial literature exists combining data from revealed preference (RP) and stated preference (SP) sources, aimed either at testing for the convergent validity of the two approaches used in nonmarket valuation or as a means of drawing on their relative strengths to improve the ultimate estimates of value. In doing so, it is assumed that convergence of the two elicitation approaches is an “all or nothing” proposition; i.e., the RP and SP data are either consistent with each other or they are not. The purpose of this paper is to propose an alternative framework that allows for possible divergence among individuals in terms the consistency between their RP and SP responses. In particular, we suggest the use of a latent class approach to segment the population into two groups. The first group has RP and SP responses that are internally consistent, while the remaining group exhibits some form of inconsistent preferences. An EM algorithm is employed in an empirical application that draws on the Alberta and Saskatchewan moose hunting data sets used in earlier combined RP and SP exercises. The empirical results suggest that somewhere between one-third and one-half the sample exhibits consistent preferences. We also examine differences in welfare estimates drawn from the two classes.