Date of Award

8-2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Economics

Committee Member

Chungsang Tom Lam, Committee Chair

Committee Member

Matthew S. Lewis

Committee Member

F. Andrew Hanssen

Committee Member

James Brannan

Committee Member

Scott L. Baier

Abstract

This dissertation consists of two chapters: In the first chapter, we build a theoretical framework to study the dynamic entry interactions between two platforms with homogeneous products into city-based markets. This research is applicable for studying the entry strategies between, for example, Uber and Lyft; Groupon and Living Social, and other business models with the attributes of switching cost, network effect, and segregated markets. We address three questions in this paper: 1) What determines the expansion path of city-based platforms?; 2) What factors are affecting the market concentration structures; and 3) Under what conditions can a second mover become the market leader (with more than 50% of the market share)? We find that a significant degree of the network effect and large switching cost will build a natural barrier for the late entrant; Transaction-efficient markets with larger transaction volume are less likely to be concentrated than transaction-inefficient markets. We take consideration of entry cost and initial fund in our dynamic settings, and find that the uncertainty in market return will make the platforms' expansion path and the final outcome less predictable. However, on average, the capability of capturing the largest market first is crucial for both players; if a platform loses the opportunity of being the first to capture the largest market, it may have to raise a considerable amount of money to overcome its disadvantages in the following competitions.

In the second chapter, we empirically investigate the effect of the dynamic pricing system on ride-sharing platform drivers' labor supply. Rather than working-hour and wage-rate relation explored by previous and current literature, we examine the instantaneous response of drivers to price surges. Using data from New York City, we estimate the structural model through a constrained non-parametric instrumental variable (NPIV) approach. We find that the emergence of a price surge is a strong incentive for drivers, and the dynamic pricing scheme of ride-sharing platforms effectively solves the geographical disparity problem of uncoordinated taxi systems. Consequently, the overall accessibility and quantity of pickup service in the entire city will increase. In the absence of dynamic pricing, we show in a counterfactual analysis that platform drivers will be clumped in the Manhattan area and airports, a dilemma shared by the taxi drivers. The counterfactual context implies that 27 % of the total supply will be lost, including a significantly large 59% reduction in the non-Manhattan area.

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