Dockless, shared e-bikes and e-scooters are a rapidly-growing segment of the local transportation market despite being taxed at higher rates per mile of travel than many conventional transportation modes, including motor vehicles. This paper asks what the optimal tax rate for shared bikes and e-scooters would be when accounting for environmental externalities, internalities caused by biased beliefs about accident risk, and redistribution. We derive a sufficient-statistics model that describes how optimal tax rates depend on both extensive-margin (number of trips) and intensive-margin (duration of trips) demand responses to price changes, as well as the income of bike and e-scooter users, internalities, and net externalities relative to other travel modes. We estimate the parameters of this model using trip-level data from a large bike and e-scooter company, and find that in many U.S. cities, use of its vehicles is concentrated among low-income individuals. A nationwide dataset of emergency department admissions shows that e-scooter use generates substantial social costs in terms of healthcare expenditures and lost quality of life due to injury. The optimal tax weighs these costs against environmental benefits and redistributive concerns.
