You Can Lead a Horse to Water: Spatial Learning and Path Dependence in Consumer Search

About this event

We develop and estimate a model of consumer search with spatial learning and path dependence. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space. The estimated model rationalizes patterns in data on online consumer search paths: search tends to converge to the chosen product in attribute space, and consumers take larger steps away from rarely purchased products. Eliminating spatial learning reduces consumer welfare by 12%: cross-product inferences allow consumers to locate better products in a shorter time. Spatial learning has important implications for the power of search intermediaries. We use simulations to show that consumer welfare can be significantly reduced by unrepresentative product recommendations. We characterize consumer-optimal product recommendations as those that are most informative about other products.