We study a central economic problem for peer-to-peer online marketplaces: how to create successful matches when demand and supply are highly variable. To do this, we develop a parsimonious model of a frictional matching market for services, which lets us derive the elasticity of labor demand and supply, the split of surplus between buyers and sellers, and the efficiency with which requests and offers for services are successfully matched. We estimate the model using data from TaskRabbit, a rapidly expanding platform for domestic tasks, and report three main findings. First, supply is highly elastic: in periods when demand doubles, sellers work almost twice as hard, prices hardly increase and the probability of requested tasks being matched only slightly falls. Second, we estimate average gains from each trade to be $37. Because of the matching frictions and search costs needed to find potential matches, the ex-ante gains are more modest, but are maximized by the elastic labor supply: if the number of hours worked were held constant, there would be 15 percent fewer matches in equilibrium. Third, we find that platform success varies greatly across cities. The cities which grow fast in the number of users are also those where the market fundamentals promote efficient matching of buyers and sellers. This heterogeneity in matching efficiency is not attributable to scale economies, but is instead related to two measures of market thickness: geographic density (buyers and sellers living close together), and level of task standardization (buyers requesting homogeneous tasks).
(joint with Zoë Cullen)