Essays in Platform and Network Economics

Abstract

This dissertation studies competition, market structure and pricing mechanics in marketsthat feature network economics and platform economics. The first chapter studies the network structure of small cell deployment within a city, by estimating and comparing the network effect of a carrier’s existing small cells and the competition effect of rivals’ small cell network. Using a fixed-effects model as well as an IV model where cost instruments are used for a firm’s small cell deployment decision, the paper finds strong network effect outweighing the competition effect in carriers’ entry decisions, with network effect contributing to 70-90% of the variation in small cells. While small in magnitude, competition effect shapes the dominance of a carrier in a very local region, for example in the downtown area. Policy simulations based on the model estimates suggest a government subsidy targeted at the neighboring area of current network centers to be most effective in accelerating small cell deployment. Subsidy targeted at high cost low density areas do not lead to enough private investment to further deploy. In the second chapter, we document how online lenders exploit a flawed, new pricing mechanism in a peer-to-peer lending platform: Prosper.com. Switching from auctions to a posted-price mechanism in December 2010, Prosper assigned loan listings with different estimated loss rates into seven distinctive rating grades and adopted a single price for all listings with the same rating grade. We show that lenders adjusted their investment portfolios towards listings at the low end of the risk spectrum of each Prosper rating grade. We find heterogeneity in the speed of adjustment by lender experience, investment size, and diversification strategies. It took about 16 – 17 months for an average lender to take full advantage of the “cherry-picking” opportunity under the single-price regime, which is roughly when Prosper switched to a more flexible pricing mechanism. The third chapter is based on the observation that previous literature of consumer online rating has focused on information aggregation and the effect of ratings on own demand. The chapter seeks to find evidence on the demand effect of online ratings across local businesses. There are two competing mechanisms through which ratings of neighboring businesses could affect the performance of one local business. On one hand, the spillover effect predicts that a highly rated business would not only increase its own performance, but also the performance of surrounding businesses. On the other hand, the effect could be in the other direction: higher ratings drive more consumers to one business while lowering sales of neighboring businesses. This project is aimed at identifying these effects by exploiting the variation in neighborhood structure, geographic proximity of local businesses. The results show evidence on both competition effects and spillover effects of neighborhood business online reputation. Both these effects would decrease as I consider a neighborhood that is more distantly located from the center restaurant.