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Abstract
The structure of network externalities influences platform competition and can determine whether a two-sided market is winner-takes-all or highly contestable. Yet, because of analytical challenges, relatively little is known about these structures in different markets. This thesis investigates how the structure of externalities impacts the performance of platforms and competitive outcomes. In the first chapter, we propose a model of platform competition in ride-hailing, identifying the structure of externalities that arise from congestion. After finding representations of congestion, which serve as micro-foundations for platform competition, we provide general conditions for the existence and uniqueness of equilibrium. Our results demonstrate that ride-hailing platforms benefit from economies of scale, becoming more efficient as they attract more riders and drivers. This could lead to winner-takes-all scenarios in markets with low demand or attractive outside options. However, service variability arising from differing distances between riders and drivers introduces differentiation, enabling multiple platforms to coexist in suitable markets. In such cases, entrants can leverage service variability to enter, potentially driving profits to zero and making the market highly contestable. The results challenge conventional wisdom, suggesting that having a large network may no longer guarantee survival and that platforms need credible commitment mechanisms to retain users. We discuss the reasons behind these observations, shedding light on potential strategies that platform managers can follow while avoiding the ones that can inadvertently harm competition. In the second chapter, we extend the model to investigate the platforms' decisions of using loyalty programs. The platforms can either attract multihoming drivers from a shared pool or offer loyalty programs that incentivize exclusive participation through bonuses and service commitments. We analyze equilibrium outcomes under duopoly, oligopoly, and entry scenarios and identify conditions under which platforms prefer loyalty programs over a pool of drivers. Our findings highlight that loyalty programs can benefit both platforms, riders, and drivers. When there is no entry, they help reduce costs and full prices and benefit riders. When there is entry, they help platforms deter entrants. In both cases, drivers are better-off due to heterogeneity in preferences.