Disruptive Change in the Taxi Business: The Case of Uber
Open accessAbstract
In most cities, the taxi industry is highly regulated and utilizes technology developed in the 1940s.Ride sharing services such as Uber and Lyft, which use modern internet-based mobile technology to connect passengers and drivers, have begun to compete with traditional taxis.This paper examines the efficiency of ride sharing services vis-à-vis taxis by comparing the capacity utilization rate of UberX drivers with that of traditional taxi drivers in five cities.The capacity utilization rate is measured by the fraction of time a driver has a fare-paying passenger in the car while he or she is working, and by the share of total miles that drivers log in which a passenger is in their car.The main conclusion is that, in most cities with data available, UberX drivers spend a significantly higher fraction of their time, and drive a substantially higher share of miles, with a passenger in their car than do taxi drivers.Four factors likely contribute to the higher capacity utilization rate of UberX drivers: 1) Uber's more efficient driver-passenger matching technology; 2) the larger scale of Uber than taxi companies; 3) inefficient taxi regulations; and 4) Uber's flexible labor supply model and surge pricing more closely match supply with demand throughout the day.
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The growth of online gig platforms creates challenges for traditional incumbent industries because these platforms have better matching algorithms, flexible schedules, and lower entry barriers.
“The rise of these gig platforms has brought challenges to traditional, incumbent industries 1 due to their superior matching algorithms (Cramer and Krueger 2016), flexible work schedules (Hall and Krueger 2018), and lower entry barriers (Schwellnus et al. 2019).”
From Skill-Biased Technical Change, Again? Online Gig Platforms and Local Employment (2025) · cites this paper as Cramer and Krueger 2016
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- Authors
- Cramer, J., & Krueger, A.
- Affiliations
- National Bureau of Economic Research (US)
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2016-03
- OpenAlex
- W3123953956
- Cited by
- 158
- References
- 7
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 12 · 2022: 15 · 2021: 62 · 2020: 14 · 2019: 19 · 2018: 12 · 2017: 9 · 2016: 4 · 2015: 1
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Social Sciences › Business, Management and Accounting › Marketing › Sharing Economy and Platforms
- Keywords
- Uber, ride-sharing, taxi industry, capacity utilization, driver productivity, taxi regulation, surge pricing, disruptive innovation, ride-hailing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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