Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ridesharing

This paper uses both empirical and theoretical methods to answer salient questions about possible geographical distortion of supply from demand: (i) how to empirically infer whether some regions are "under-supplied" relative to others; (ii) howto identify mechanisms that lead to unequal access to supply across regions; and (iii) how to design policies that alleviate geographical supply inequities. If supply in a spatial market (such as rideshare) is geographically distorted from demand, it will lead to disproportionately low demand-fulfillment rates in some regions relative to other regions. However, empirically identifying such spatial distortions is challenging since unfulfilled demand is unobserved. To deal with this issue, we devise an approach, called relative outflows analysis, which has a simple implementation and minimal data requirements. Our method takes advantage of the overlooked fact that individuals do not migrate as often as they take rides, hence for every trip there is a "trip back." Suppose, for example, that Lyft's "relative outflow" in Staten Island (i.e., the number Lyft rides exiting Staten Island divided by those entering it) is consistently around 0.6. Then we conclude that Lyft's supply is distorted away from Staten Island; because the same population that chooses Lyft over other options to enter the borough, is likely to choose other options over Lyft to exit. This conclusion becomes stronger if Uber's relative outflow in Staten Island is consistently close to 1.