Heavily regulated industries such as transportation, healthcare and finance, are becoming targets for market disruption driven by entrants with higher technology and different cost structures. Uber was the first non-regulated supplier to have been allowed to compete in the New York City taxicab market, a historically heavy regulated industry. The smart-phone ride-hailing company joined the market with 3 unique characteristics: free entry, a dynamic pricing scheme, and a more efficient hailing mechanism enabled by technological advances. The success of this company grew rapidly, and in part, at the expense of New York City’s yellow-cab drivers.

This paper presents a structural model of partial equilibrium that seeks to explain the effects of the entry of Uber on market conditions. The model also captures the dynamic learning process of drivers about the market conditions and subsequent changes on labor supply decisions. The parameters in the consumer and producer’s problems, as well as those in the matching functions, are estimated via Maximum Likelihood Simulation and Estimation using a variety of data sets, such as: GPS and financial data of 19 million Uber rides and 500 million yellow-cab rides in 2015, 2010 Census data, American Community Survey data, and weather forecast data.

The results of this paper will provide novel insights about the effects of technology-enabled disruption on the conditions of formerly regulated markets. We will be able to identify the parameters that allowed for the success of Uber. Finally, this paper seeks to predict future labor conditions in this market and discuss policy implications via counterfactual analysis.