Using information to curb racial discrimination: Evidence from Ride Sharing

Last registered on October 28, 2024
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Pre-Trial

Trial Information

General Information

Title
Using information to curb racial discrimination: Evidence from Ride Sharing
RCT ID
AEARCTR-0014658
Initial registration date
October 26, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
October 28, 2024, 1:36 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country
United States of America
Region
Boston, Los Angeles, Seattle

Primary Investigator

Name
Bora Ozaltun
Affiliation
UC Berkeley
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Other Primary Investigator(s)

PI Name
Christopher Knittel
PI Affiliation
MIT
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PI Name
Don MacKenzie
PI Affiliation
University of Washington
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PI Name
Michiko Namazu
PI Affiliation
Uber Technologies Inc.
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PI Name
Dan Svirsky
PI Affiliation
Uber Technologies Inc.
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PI Name
Stephen Zoepf
PI Affiliation
U.S. Department of Transportation
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Additional Trial Information

Status
Completed
Start date
2018-09-01
End date
2018-11-30
Keywords
Behavior, Firms & Productivity, Labor
Additional Keywords
Discrimination
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We test whether more information about customers decreases racial bias. Our setting is the market for shared mobility services. Prior work by Ge et al. (2020) found that Uber drivers are two times more likely to cancel a ride if the passenger's name is one used predominantly by African Americans. In a randomized control trial, we test whether two alterations to the Uber platform app reduce racial discrimination. Within the standard Uber app, drivers see only the passenger's rating before accepting a ride. Once they accept the ride, they see the name of the passenger. In the first intervention, we increased the size of the font of the rating to draw attention to the quality of the passenger. In the second intervention, the passenger's name appears from the beginning. Using the control group observations, we confirmed that the more likely African Americans were to use a name, the more likely a driver cancels the ride. However, increasing the font size of the passenger's rating eliminates this racial bias. In contrast, we do not find much evidence that showing the name on the initial screen reduces or increases cancellation rates.
External Link(s)

Registration Citation

Citation
Knittel, Christopher et al. 2024. "Using information to curb racial discrimination: Evidence from Ride Sharing." AEA RCT Registry. October 28. https://doi.org/10.1257/rct.14658-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Our experiment ran in Boston, Los Angeles, and Seattle from September 2018 to November 2018. Before describing the treatments, it is important to describe the standard screen Uber drivers see when offered a fare. Uber drivers see various information about the potential trip. First, they see a map of the passenger's location and the fastest route to the passenger. The text in the middle reports the type of trip it is (UberX in this case), the rating of the passenger (4.92), the time and distance to the passenger (3 minutes and 1.2 miles, respectively), and the surge price inflater of the trip (1.6x). Notably, the passenger's name is not listed. For this reason, a cancellation is required by the driver to act on their bias.

We worked with Uber to implement two treatments. The first treatment increased the font size and color of the passenger's rating. Everything else about the initial screen remained unchanged. The second treatment added the passenger's name to the initial screen (right panel), again keeping everything else the same. Randomization took place at the driver level. In each city, 3600 drivers were included in the study, with 1200 in the control group and 1200 in each treatment group. Unlike in Ge et al. (2020), we did not randomize passenger names.

Intervention (Hidden)
Intervention Start Date
2018-09-01
Intervention End Date
2018-11-30

Primary Outcomes

Primary Outcomes (end points)
Our primary outcomes are all related to trips. We observe each trip, passenger name, driver id, drive accepted, drive cancelled, as well as a set of covariates.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our experimental design is a Randomized Control Trial (RCT).
Experimental Design Details
Randomization Method
Randomization of drivers ex-ante by a computer at the office.
Randomization Unit
The randomization unit is at the driver level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
We run the study in 3 cities.
Sample size: planned number of observations
In each city, we have 3600 drivers. A total of 10,800 drivers.
Sample size (or number of clusters) by treatment arms
We observe trip level data. We have 3 cities, 3600 drivers, and 100-1000 trips per driver.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Commonwealth of Massachusetts Department of Public Health
IRB Approval Date
2019-06-11
IRB Approval Number
1425401
IRB Name
Study has received IRB approval. Details not available.
IRB Approval Date
Details not available
IRB Approval Number
Details not available
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
November 30, 2018, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
November 30, 2018, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
3 cities.
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
No
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials