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Abstract:Despite the ever-increasing demand, controversies have been surrounding the ride-hailing industry since the day of its rise. Tighter government regulation or even banning is called for around the world. In this paper, the author addresses the issue by designing a quasi-experiment and estimates how much Uber benefits consumers in a creative way. Using three datasets created before and after Uber service availability, and dividing San Francisco, the studied area, into grids of 4 kmĀ² each, the author is able to investigate consumer commuting behavior at an individual level and finds out Uber brings out at least $0.76 gains per commuter per trip and generates an annual consumer surplus of $100 million in San Francisco. The three datasets include the National Household Travel Survey (NHTS) Data from 2008 to 2009 when Uber service was not yet available, the origin-destination level Uber itinerary data and Google map data of 2017. The author first uses NHTS data to identify consumer preference in 2008 under a discrete choice framework. The author then constructs counterfactual scenarios in which Uber becomes an option with Uber and Google data, and finds out the consumer surplus changes Uber brought.
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