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Disruptive Change in the Taxi Business: The Case of Uber

May 1, 2016
By Judd Cramer and Alan B. Krueger

This paper, written by Princeton University economists Judd Cramer and Alan B. Krueger, uses data from private and public datasets to compare and measure the efficiency of taxicabs and peer-to-peer economy for-hire transportation services.

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This paper, written by Princeton University economists Judd Cramer and Alan B. Krueger, uses data from private and public datasets to compare and measure the efficiency of taxicabs and peer-to-peer economy for-hire transportation services, also known as “ridesharing” services.

Ridesharing is more efficient than taxicabs for drivers, Cramer and Krueger write.

“Regardless of the measure used, the results show a clear pattern: UberX drivers have a substantially higher capacity utilization rate than do taxi drivers in every city except New York, where the utilization rates are very similar,” Cramer and Krueger write. “In Boston, the time-based capacity utilization rate is 44 percent higher for UberX drivers than for taxi drivers, and in San Francisco it is 41 percent higher.”

There are a few reasons why taxicab for-hire transportation has lower network utilization rates, Cramer and Krueger write.

“There are several possible reasons why UberX drivers may achieve significantly higher capacity utilization rates than taxi drivers,” Cramer and Krueger write. “First, Uber utilizes a more efficient driver-passenger matching technology based on mobile Internet technology and smart phones than do taxis, which typically rely on a two-way radio dispatch system developed in the 1940s or sight-based street hailing. Second, in most cities Uber currently has more driver partners on the road than the largest taxi cab company.

“Apart from the technology, there are network efficiencies from scale, as pure chance would likely result in an Uber driver being closer to a potential customer than a taxi driver from any particular company given the larger scale of Uber,” Cramer and Krueger write. “Third, inefficient taxi licensing regulations can prevent taxi drivers who drop off a customer in a jurisdiction outside of the one that granted their license from picking up another customer in that location. Fourth, Uber’s flexible labor supply model and surge pricing probably more closely matches supply with demand during peak demand hours and other hours of the day.”