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Oren Kislev

dblp:221/2836 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 87% Smart cities and intelligent transportation · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational finance and economics › pricing
dynamic pricing
0.312018
Surge Pricing Moves Uber's Driver-Partners · EC 2018
Computational finance and economics
market design
0.312018
Surge Pricing Moves Uber's Driver-Partners · EC 2018
Smart cities and intelligent transportation
driver behavior analysis
0.112018
Surge Pricing Moves Uber's Driver-Partners · EC 2018

Methods — techniques the papers use, named apart from their topics

natural experiment · 0.3difference-in-differences · 0.3
YearPublicationVenuePosition
2018 Surge Pricing Moves Uber's Driver-Partners
abstract
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. We demonstrate that the ability to see the surge heatmap has a statistically significant impact on both outcomes. Ability to see the surge heatmap explains 10%-60% of Uber drivers' self-positioning decisions, attracts drivers toward areas with higher surge prices, and increases drivers' revenue on surged trips by up to 70%. This suggests that dynamic pricing helps drivers move to where riders' demand is largest, and that the resulting reduction in spatial search friction and spatial mismatch improves waiting times and welfare for both riders and drivers.
Alice Lu, Peter I. Frazier, Oren Kislev
EC3