Search with multiple attributes: Theory and empirics

Abstract

Multiple attribute search is a central feature of economic life: we consider much more than price when purchasing a home, and more than wage when choosing a job. Nevertheless, while single attribute search problems have been studied extensively, little is known about optimal search in multiple attribute environments. I introduce a partial characterization of optimal sequential search in a problem with multiple searchable attributes and alternatives, no order restrictions on search, and full recall. Upon applying the partial rational benchmark to a rich dataset I find that subjects systematically deviate from optimal sequential search by: (1) searching too deeply within alternatives and (2) switching too adjacently between alternatives. I explore how these deviations affect payoffs, and discuss why they may be boundedly rational.