Distributed service with proximal capacity and pricing on a two-sided sharing economy platform

Abstract

In this article, we characterize the relationship between spatial pricing and capacity based on distributed service design (DSD) decisions in a two-sided sharing economy platform. We leverage theoretical tenets on two-sided markets and on spatial pricing and capacity management in the sharing economy to inform a set of empirical and simulation models. Empirically, we use data on 156,520 observations of dynamic pricing and capacity distribution within Uber's San Francisco region. Estimation of a spatial econometric model reveals that the number of active drivers in neighboring zones negatively impacts the price in focal zones. Simultaneously, we find that spatial proximity is a significant factor in determining the distribution of prices when service demand levels are sufficiently high. We leverage this simultaneity finding to advance the literature on the sharing economy by incorporating operational considerations such as distributed capacity into service design. We link these econometric results with profit and welfare using a simulation that tests a variety of DSD pricing strategies under varying elasticity and revenue-sharing conditions. Our findings offer guidance to firms managing two-sided sharing economy platforms on tracking demand- and supply side price elasticity levels as well as revenue sharing spread when seeking to maximize profit, welfare, or both.