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The ride-hailing industry is a critical pillar of modern transportation infrastructure and generates massive amounts of revenue each year. Due to spatial mismatch and search friction, the conventional taxi business model, which is based on street hailing, leads to substantial matching inefficiency. With the advent of geolocation-based mobile apps, ride-hailing firms can now effectively bridge demand and supply via digitalized matching technology and benefit from more flexibility in setting their pricing menus. In this paper, we analyze an exogenous event that the largest taxi operator in Singapore added an origin-destination-based flat fare option to its existing metered fare option. We empirically examine the effect of flat fare pricing vis-à-vis metered pricing on the outcome of this two-sided marketplace. Specifically, we model taxi drivers’ location choices as a dynamic spatial oligopoly game in which vacant drivers decide where to search for passengers, given the search behaviors of their competitors, in the presence of trip uncertainties. We leverage the large number of agents in the taxi industry and solve for the Oblivious Equilibrium (Weintraub, Benkard, and Van Roy 2008), in which each taxi driver’s policy function is based on their beliefs about the transition of average industry states. We then plug supply estimates into the demand system and recover demand parameters with a parametric aggregate-level matching function that accounts for matching inefficiency on street hail trips. We find that drivers are risk-averse on flat fare trips, especially during peak hours when trip uncertainty is higher, and riders’ risk aversion on metered trips also confers a risk premium on the flat fare pricing option. Finally, we run two counterfactual experiments to quantify the economic value of risk aversion for both riders and drivers, and evaluate the benefit of a booking system that enables flat fares. Our findings have important managerial implications for the rapidly expanding ride-hailing industry.