Browsing by Subject "Uber"

Now showing 1 - 3 of 3
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    Essays on the Market Impacts of Regulatory Regimes
    (2018-05) Shapiro, Matthew
    This dissertation contains three essays, which focus on markets featuring heavy government intervention. The first two study the effects of Uber’s entry into the taxi industry of New York City. The final essay, coauthored with Boyoung Seo, studies intervention in the growing market for electric vehicles in California. In the first chapter I quantify the magnitude and distribution of the welfare offered by Uber’s cab-to-customer matching technology. I combine publicly available transportation data with data scraped from Uber and traffic cameras in New York City to estimate a model of demand for transportation services and imbed it in a spatial equilibrium framework in which Uber and taxis compete. Uber’s matching advantage depends on the density of the market. In consumer welfare terms, the introduction of Uber added only $0.10 per ride in the densest parts of New York but over $1.00 in the least dense. These results imply Uber’s appeal in its densest market has depended on advantages independent from its matching technology, including its lower regulatory burden. In the second chapter I document the potential of digitization to reduce statistical discrimination. First, I find that the search behavior of hail taxis, even controlling for profitability, highlights statistical discrimination against certain consumers. Second, Uber has mitigated the negative externalities in the cab markets among these consumers. A reasonable hypothesis is that Uber’s matching technology permits contracts without the cost of undirected searching in previously avoided areas of the city. In the final chapter, my coauthor and I assess the efficacy of vehicle subsidy programs and investment in a charging station network on demand for electric vehicles. In contrast to previous literature, we consider heterogeneity in tastes for electric vehicles and price elasticities across demographics, as well as the heterogenous marginal benefits of charging stations, and demonstrate the importance of both dimensions in correctly identifying the impact of subsidies and charging stations on demand. We use zip code-level data on vehicle purchases in California to estimate a random coefficient discrete choice model of automobile demand capable of proposing more efficient incentive structures.
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    The impact of ride hailing on parking (and vice versa)
    (Journal of Transport and Land Use, 2019) Henao, Alejandro; Marshall, Wesley E.
    Investigating emerging transportation services is critical to forecasting mode choice and providing appropriate infrastructure. One such infrastructure is parking, as parking demand may shift with the availability of ride-hailing services. This study uses ethnographic methods—complemented with passenger surveys collected when driving for Uber and Lyft in the Denver, Colorado, region—to gather quantitative and qualitative data on ride-hailing and analyze the impacts of ride-hailing on parking, including changes in parking demand and parking as a reason to deter driving. The study also examines relationships between parking time and cost. This includes building a classification tree-based model to predict the replaced driving trips as a function of car ownership, destination land type, parking stress, and demographics. The results suggest that: i) ride-hailing is replacing driving trips and could reduce parking demand, particularly at land uses such as airports, event venues, restaurants, and bars; ii) parking stress is a key reason respondents chose not to drive; and iii) respondents are generally willing to pay more for reduced parking time and distance. Conversely, parking supply, time, and cost can all influence travel behavior and ride-hailing use. This study provides insight into potential benefits and disadvantages of ride-hailing as related to parking.
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    Measuring Psychological Effects of Peer-to-Peer Reputation Systems Involving In-Person Exchanges
    (2019-07) Yousef, Mark
    Reputation systems such as those used by peer-to-peer services have proven significant in helping companies better understand and manage their users. Seemingly the new credit scores for the digital economy, these personal rating systems have unexplored consequences on human psyche. Using a case study of Uber passengers and drivers, this study examines stress and control levels associated with personal rating scores. We found that while drivers indicated more difficult experiences in response to the control of their scores, passengers had issues with distress in relation to factors commonly associated with bias, such as age and ethnicity. Both groups exhibited lower perceptions of distress the more times they had used Uber. Overall, the use of peer-to-peer reputation systems can be improved to provide users a higher level of control and lower distress in response to ratings.