Data and Code for: Uber vs. Taxi: A Driver's Eye View

Principal Investigator(s): View help for Principal Investigator(s) Joshua Angrist, MIT; Sydnee Caldwell, Microsoft Research; Jonathan Hall, Uber Technologies, Inc.

Version: View help for Version V1

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Figure7_FW.do	text/x-stata-syntax	5.3 KB	07/15/2020 08:34:PM
Figure7_Taxi.do	text/x-stata-syntax	4.9 KB	04/28/2020 09:02:PM
Figure8.do	text/x-stata-syntax	3.3 KB	04/29/2020 07:51:AM
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Formatted-Tables.xlsx	application/vnd.openxmlformats-officedocument.spreadsheetml.sheet	109.2 KB	04/30/2020 05:45:AM
README.pdf	application/pdf	83.1 KB	07/15/2020 09:12:PM
Table1.do	text/x-stata-syntax	2.5 KB	04/29/2020 09:27:AM
Table3.do	text/x-stata-syntax	2 KB	04/28/2020 06:31:PM
Table5_FW.do	text/x-stata-syntax	4.8 KB	04/28/2020 06:41:PM

Project Citation:

Angrist, Joshua, Caldwell, Sydnee, and Hall, Jonathan. Data and Code for: Uber vs. Taxi: A Driver’s Eye View. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-06-15. https://doi.org/10.3886/E118881V1

Project Description

Summary:

Rideshare drivers pay a proportion of their fares to a ride-hailing platform operator, a commission-based compensation model used by many 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, keeping every fare dollar net of lease costs and other expenses. We assess these compensation models using an experiment that offered random samples of Boston Uber drivers opportunities to lease a virtual taxi medallion that eliminates the Uber fee. Some drivers were offered a negative fee. Drivers’ labor supply response to our offers reveals a large intertemporal substitution elasticity, on the order of 1.2, and higher for those who accept lease contracts. At the same time, our virtual lease program was under- subscribed: many drivers who would have benefitted from buying an inexpensive lease chose to sit out. We use these results to compute the average compensation required to make drivers indifferent between rideshare and taxi-style compensation contracts. The results suggest that rideshare drivers gain considerably from the opportunity to drive without leasing.

Scope of Project

Subject Terms: labor markets; hours

JEL Classification:
      J20 Demand and Supply of Labor: General
      J22 Time Allocation and Labor Supply
      J33 Compensation Packages; Payment Methods

Geographic Coverage: Boston, MA

Time Period(s): 8/2016 – 10/2016 (Fall 2016)

Universe: Uber drivers in Boston who were active on the platform (more than 4 completed trips) in the month leading up to August 16, 2016 and whose weekly hours averaged between 5 and 25 hours/week (excluding 0's) were eligible for the experiment. We randomly selected 1600 of these drivers for inclusion in the experiment. Only data for these drivers was used in the paper.

Data Type(s): administrative records data; experimental data

Collection Notes: All the results in the paper use proprietary and confidential microdata from Uber Technologies, Inc. Researchers interested in using these files for replication or a related academic project should execute a data use agreement with Uber, and arrange for secure access to the relevant files. The authors have retained copies of the underlying data, which can be made available pursuant to the data use agreement between MIT and Uber. Contact Uber’s Chief Economist to initiate project review.

Methodology

Sampling:

Boston Uber drivers were eligible for inclusion in the experiment if they took at least four trips and drove an average of 5-25 hours per week in the four weeks prior to selection (the last 3 weeks of July and the first week of August 2016). A total of 1600 eligible drivers were selected for inclusion in the experiment. Drivers were selected within strata defined by hours bandwidth (high/low) and 3 groups based on car age and commission (car 2003 or older, newer car & 20% fee, newer car & 25% fee).

Data Source: Uber Technologies, Inc.

Unit(s) of Observation: Driver-week

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Published Versions

V1 [2021-06-15]

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