Separating generalizable from source-specific preference heterogeneity in the fusion of revealed and stated preferences

Preference heterogeneity is one of the central behavioral concepts in applied econometrics. Its centrality is particularly evident in the choice modeling literature, notably in its widespread application to environmental and health economics, marketing, and transport. Despite conceptual and empirical advances in modeling preference heterogeneity, the generalizability of preference heterogeneity to different decision contexts and different data generation processes remains an open question. The basic premise of this paper is that latent sources of preference heterogeneity can be decomposed into components general to decision contexts and others specific to them. We study the structure of preference heterogeneity in different data generation processes with the goal of reliably identifying common (presumably generalizable) and specific (presumably not generalizable) sources of preference heterogeneity. The contribution of the paper is both conceptual and methodological, leading to the testing of five rival model specifications which together elucidate the heterogeneity structure present in two preference data sources of the same choice behavior. In the empirical application, we find that the multitrait-multimethod model of preference heterogeneity has the best fit and most sensible interpretations, indicating that while each data source contributes uniquely to certain heterogeneity components, both data sources contribute also to common (generalizable) preference heterogeneity. Recognition of the separability of the common versus source-specific preference heterogeneity will lead to more reliable and accurate demand model forecasts and assessments of welfare impacts.