Vineet Kumar Publications

This paper develops a strategy with simple implementation and limited data requirements to identify spatial distortion of supply from demand -or, equivalently, unequal access to supply among regions- in transportation markets. We apply our method to ride-level, multi-platform data from New York City (NYC) and show that for smaller rideshare platforms, supply tends to be disproportionately concentrated in more densely populated areas. We also develop a theoretical model to argue that a smaller platform size, all else being equal, distorts the supply of drivers toward more densely populated areas due to network effects. Motivated by this, we estimate a minimum required platform size to avoid geographical supply distortions, which informs the current policy debate in NYC around whether ridesharing platforms should be downsized. We nd the minimum required size to be approximately 3.5M rides/month for NYC, implying that downsizing Lyft or Via-but not Uber{can increase geographical inequity.

A critical element of word of mouth (WOM) or buzz marketing is to identify seeds, often central actors with high degree in the social network. Seed identification typically requires data on the full network structure, which is often unavailable. We therefore examine the impact of WOM seeding strategies motivated by the friendship paradox to obtain more central nodes without knowing network structure. But higher-degree nodes may communicate less with neighbors; therefore whether friendship paradox motivated seeding strategies increase or reduce WOM and adoption remains an empirical question. We develop and estimate a model of WOM and adoption using data on microfinance adoption across 43 villages in India for which we have data on social networks. Counterfactuals show that the proposed seeding strategies are about 15-20% more effective than random seeding in increasing adoption. Remarkably, they are also about 5-11% more effective than opinion leader seeding, and are relative more effective when we have fewer seeds.

A critical element of word of mouth (WOM) or buzz marketing is to identify seeds, often central actors with high degree in the social network. Seed identification typically requires data on the full network structure, which is often unavailable. We therefore examine the impact of WOM seeding strategies motivated by the friendship paradox to obtain more central nodes without knowing network structure on adoption. Higher-degree nodes may be less effective as seeds if these nodes communicate less with neighbors or are less persuasive when they communicate; therefore whether friendship paradox motivated seeding strategies increase or reduce WOM and adoption remains an empirical question. We develop and estimate a model of WOM and adoption using data on microfinance adoption across 43 villages in India for which we have data on social networks. Counterfactuals show that the proposed seeding strategies are about 15-24% more effective in increasing adoption relative to random seeding. These strategies are also about 5-13% more effective than the firm’s leader seeding strategy, and are relatively more effective when we have fewer seeds.