Browsing by Author "Shapiro, Matthew"

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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.