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Results for Economics

Dynamic Pricing and Matching in Ride-Hailing Platforms

N. Korolko, D. Woodard, C. Yan, H. Zhu
Ride-hailing platforms such as Uber, Lyft and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research areas in the fields of economics, operations research, computer science, and transportation engineering. […] [PDF]

Labor Market Equilibration: Evidence from Uber

J. Hall, J. Horton, D. Knoepfle
Using a city-week panel of US ride-sharing markets created by Uber, we estimate the effects of sudden fare changes on market outcomes, focusing on the supply-side. […] [PDF]

Uber Happy? Work and Well-being in the “Gig Economy”

T. Berger, C. B. Frey, G. Levin, S. R. Danda
We explore the rise of the so-called “gig economy” through the lens of Uber and its drivers in the United Kingdom. Using administrative data from Uber and a new representative survey of London drivers, we explore their backgrounds, earnings, and well being. […] [PDF]
The 68th Panel Meeting of Economic Policy, 2019

Surge Pricing Moves Uber’s Driver-Partners

A. Lu, P. I. Frazier, O. Kislev
We study the impact of dynamic pricing (so-called “surge pricing”) on relocation decisions by Uber’s driver-partners and the corresponding revenue they collected. Using a natural experiment arising from an outage in the system that produces the surge pricing heatmap for a portion of Uber’s driver-partners over 10 major cities, and a difference-in-differences approach, we study the short-run effect that visibility of the surge heatmap has on 1) drivers’ decisions to relocate to areas with higher or lower prices and 2) drivers’ revenue. […] [PDF]
ACM Conference on Economics and Computation (ACM EC), 2018

Driver Surge Pricing

H. Nazerzadeh, N. Garg
Uber and Lyft ride-hailing marketplaces use dynamic pricing, often called surge, to balance the supply of available drivers with the demand for rides. We study pricing mechanisms for such marketplaces from the perspective of drivers, presenting the theoretical foundation that has informed the design of Uber’s new additive driver surge mechanism. We present a dynamic stochastic model to capture the impact of surge pricing on driver earnings and their strategies to maximize such earnings. […] [PDF]

The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers

C. Cook, R. Diamond, J. Hall, J. A. List, P. Oyer
The growth of the “gig” economy generates worker flexibility that, some have speculated, will favor women. We explore this by examining labor supply choices and earnings among more than a million rideshare drivers on Uber in the U.S. […] [PDF]

Uber vs Taxi: A Driver’s Eye View

J. Angrist, S. Caldwell, J. Hall
Ride-hailing drivers pay a proportion of their fares to the ride-hailing platform operator, a commission-based compensation model used by many internet-mediated service providers. To Uber drivers, this commission is known as the Uber fee. By contrast, traditional taxi drivers in most US cities make a fixed payment independent of their earnings, usually a weekly or daily medallion lease, but keep every fare dollar net of expenses. […] [PDF]

Surge Pricing Solves the Wild Goose Chase

J. C. Castillo, D. Knoepfle, E. G. Weyl
Ride-hailing apps usually match more efficiently than taxis, but they can enter a failure mode anticipated by Arnott (1996) that we call wild goose chases. High demand depletes the platform of idle drivers, so cars must be sent to pick up distant customers. Time wasted on pick-ups decreases drivers’ earnings, leading to exit and exacerbating the problem. […] [PDF]
ACM Conference on Economics and Computation (ACM EC), 2018

An Analysis of the Labor Market for Uber’s Driver-Partners in the United States

J. Hall, A. Krueger
Uber, the ride-sharing company launched in 2010, has grown at an exponential rate. This paper provides the first comprehensive analysis of the labor market for Uber’s driver-partners, based on both survey and administrative data. […] [PDF]

Using Big Data to Estimate Consumer Surplus: The Case of Uber

P. Cohen, R. Hahn, J. Hall, S. Levitt, R. Metcalfe
Estimating consumer surplus is challenging because it requires identification of the entire demand curve. We rely on Uber’s “surge” pricing algorithm and the richness of its individual level data to first estimate demand elasticities at several points along the demand curve. We then use these elasticity estimates to estimate consumer surplus. […] [PDF]

The Effects of Uber’s Surge Pricing: A Case Study

J. Hall, C. Kendrick, C. Nosko
A sold-out concert in Madison Square Garden provides an illustration of the power of surge to equilibrate supply of and demand for rides with Uber. Surge pricing draws more drivers into the area after the concert ends, and causes riders to sort into requesting a ride (or closing the app without requesting a ride) according to their willingness to pay relative to taking an alternative form of transportation. […] [PDF]

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