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Economics of Taxis and Uber

Paper Session

Sunday, Jan. 7, 2018 1:00 PM - 3:00 PM

Marriott Philadelphia Downtown, Meeting Room 403
Hosted By: American Economic Association
  • Chair: Henry Farber, Princeton University

Has Uber Made It Easier to Get a Ride in the Rain?

Abel Brodeur
,
University of Ottawa
Kerry Nield
,
Bank of Canada

Abstract

Using all Uber rides in NYC, we show that the number of Uber rides per hour is about 25 percent higher when it is raining while the number of taxi rides per hour increases by only 4 percent in rainy hours. We then show that the number of taxi rides per day is unrelated to rain or hours of rain. Conversely, the number of Uber rides per day increases by 7.7 percent for a day with rain. An additional hour of rain increases the number of Uber rides per day by 2.5 percent. This provides evidence that the standard neoclassical inter-temporal model of labor supply (rather than reference-dependent preferences) as implemented by Uber’s dynamic pricing algorithm is effective in increasing the supply of Uber rides during rain storms. Taxi supply is not as responsive to rain-related earning opportunities. We also demonstrate that the number of taxi rides per hour decreased by approximately 8 percent after Uber entered the NYC market. Last, our estimates suggest that the total number of rides per hour increased by 9 percent since Uber entered the market and that it is relatively easier to get a ride in rainy than in non-rainy hours in post-Uber years.

New York City Taxis in an Uber World

Kristin Mammen
,
City University of New York-Staten Island
Hyoung Suk Shim
,
City University of New York-Staten Island

Abstract

We empirically examine the effect of Uber presence on (medallion) taxi trip demand in NYC. We estimate Uber elasticities of demand for the Yellow cab and Green cab using NYC medallion taxi trip records and Uber pick up records from April to September 2014, and January to June 2015. The elasticities are estimated along with instrumental variable estimations of a taxi trip demand model. We find an empirical evidence that, in overall, Uber is a complement, rather than a substitute, for both Yellow cab and Green cab passengers. For Yellow cab. the positive and significant elasticity is estimated only in Manhattan below 110th street, where 91% of daily Yellow cab trip and 72% of daily Uber trip were occurred. In addition, we find different elasticity estimate in different time and day at different area. Negative Uber elasticity of Yellow cab estimate in Manhattan below 110th street during the morning rush hour implies that; Uber turn out to be a substitute during the morning rush hour in the central business district of Manhattan.

Pay for Performance or Performance for Pay? The Case of Food Delivery Drivers

Erik P. Duhaime
,
Massachusetts Institute of Technology

Abstract

While many managers assume that pay-for-performance incentive schemes align employees’ incentives with their own, they may also discourage employees’ socially based motivations to perform at high levels. I test this hypothesis by conducting a multi-year field experiment to assess how tipping affects food delivery drivers’ performance. Standard economic theory suggests that only the promise of future rewards motivates workers, and therefore tipping drivers at the time of ordering should decrease their motivation to deliver food quickly. On the other hand, tipping at the time of ordering may increase drivers’ motivation through activating feelings of goodwill and reciprocity. I find that tipping at the time of ordering leads to significantly slower delivery times than withholding the tip until the time of delivery. However, I also find evidence for the reciprocity hypothesis insofar as larger up-front tips lead to significantly faster delivery times than smaller up-front tips.

The Supply Side of Discrimination: Evidence From the Labor Supply of Boston Taxi Drivers

Osborne Jackson
,
Federal Reserve Bank of Boston

Abstract

This paper investigates supply-side discrimination in the labor market for Boston taxi drivers. Using data on millions of trips from 2010-2015, I explore whether the labor supply behavior of taxi drivers differs by the gender, racial/ethnic, or age composition of Boston neighborhoods. I find that disparities in shift hours due to neighborhood demographics exist even when differences in local earnings opportunities are taken into account. I observe heterogeneity in the amount that drivers discriminate and find this discrimination is primarily statistical rather than taste-based. As drivers gain experience and learn to better anticipate wage variation in different areas, discrimination decreases.
JEL Classifications
  • R4 - Transportation Economics