Make Every Second Count: Time Allocation in Online Shopping

Joint with Yufeng Huang and Ilya Morozov.

Abstract

We study how the opportunity cost of time affects the online shopping behavior of consumers. To this end, we build a novel and comprehensive e-commerce dataset describing how consumers allocate their shopping time across retailers, categories, and products. Using these data, we first show that retired consumers spend more time shopping online, suggesting they are less time-constrained and thus have lower search costs. We also show that consumers spend more time shopping online when cold weather makes outdoor activities unappealing, thus reducing consumers’ opportunity cost of time. In both cases, consumers deepen their search by looking at more products and broaden it by visiting more product categories, but they do not extend their search beyond one retailer per category. We then estimate a parsimonious model of time allocation and search and show that consumers face a high opportunity cost of time, which translates into high per-product search costs. This high opportunity cost of time might explain why the previous literature estimates search costs to be substantial even in seemingly frictionless online markets.