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. In this paper 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. When the partial rational benchmark is applied to a rich dataset subjects are found to 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 what might be driving them.