Multi-homing gives platforms access to a larger pool of supply; however, it also changes the nature of competition between platforms in a market. It is not clear whether this works to the advantage of platforms or not. In light of this, we ask a fundamental question: When is it better for two-sided platforms to pool their workers? We answer this question through a game-theoretic study. We identify the key trade-offs associated with pooling decision and highlight the key role of scale.

Gig workers prevalently "multihome'' by dynamically allocating their services in real-time between multiple platforms. As a growing number of platforms access the same pool of workers to complete their gigs, the question of how workers choose between competing platforms has grown in salience. In this work, we study gig workers' multihoming decisions by using machine learning methods to estimate a structural model from ride-hailing proprietary data combined with publicly reported trips data.

Using a large panel dataset with repeated choices of riders for both Uber and Lyft, we estimate a structural “consider-then-choose” model to better understand the trade-offs faced by riders. We find that riders' choices are not fully explained by operational factors such as price and waiting time, indicating that riders view the platforms as differentiated services and not as commodities. We also find that the multi-homing behavior of riders is only observed for a small fraction of the rides.