Essays on Patching Markets

Abstract

A patching market is a market that involves either piracy or matching. In this dissertation, I study both types of patching markets. In Chapter 1, I assess losses from piracy by merging novel title-level data on pirated and legal viewing for 660 titles on two popular streaming platforms (Netflix and Disney+) from January 2021 to June 2022. I estimate that during this period, pirates lowered platforms' revenues by 0.3%, equal to about $70 million. In Chapter 2, I study a model of centralized school choice in which students engage in costly search over schools before submitting preference reports to a clearinghouse. I show that common preferences generate search externalities, and the intensity of which vary by search cost and search protocol. In Chapter 3, I study a model of dynamic matching with transfers where I provide conditions under which transfers increase or decrease efficiency relative to a setting without transfers. I also provide asymptotics on equilibrium welfare as waiting costs vanish.