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Discrimination on online markets: Evidence from a field experiment

Last registered on October 06, 2025
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Pre-Trial

Trial Information

General Information

Title
Discrimination on online markets: Evidence from a field experiment
RCT ID
AEARCTR-0007019
Initial registration date
March 17, 2021

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
March 18, 2021, 6:15 AM EDT

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

Last updated
October 06, 2025, 4:18 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
France
Region

Primary Investigator

Name
Morgane Laouenan
Affiliation
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Other Primary Investigator(s)

PI Name
morgane laouenan
PI Affiliation
CNRS
Contact Investigator
PI Name
Pierre Deschamps
PI Affiliation
SOFI (Stockholm University)
Contact Investigator
PI Name
Guillaume Chapelle
PI Affiliation
Cergy Paris Université
Contact Investigator
PI Name
Xavier Lambin
PI Affiliation
Grenoble School of Management
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2020-11-30
End date
2021-12-31
Keywords
Gender, Labor
Additional Keywords
Discrimination
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We study the extent and type of ethnic and gender discrimination that exists on two of the most widely used "P2P" (person to person) online marketplaces in France: an online classified advertisements website and a ride sharing platform. The first phase involves data scraping in which we collect large amounts of data on real ads and rides that are publicly available through the platforms’ APIs. This will allow us to better understand the characteristics of the two markets and provide correlational evidence on the existence of discrimination and its magnitude in different submarkets. As is typical in the literature using French data, we will categorize the first names (in the case of classified ads) to proxy for the gender and/or minority status (supposed non-French origin) of the buyers and sellers, as it is illegal to categorize ethnic status in France. Using insights from this observational data, we will then implement a randomized controlled trial (RCT) in which we will create sets of fictitious profiles (both buyers and sellers of fictitious goods and both fictitious drivers and passengers) that are closely matched on all observable characteristics but differ on the supposed minority status or gender. This will allow us to provide causal evidence on whether discrimination exists in these online marketplaces. Furthermore, by experimentally manipulating the fictitious buyer and seller profiles on other dimensions and exploiting existing market heterogeneity, we aim to better understand the ways in which online markets can be designed in order to minimize the negative manifestations of discrimination.
External Link(s)

Registration Citation

Citation
Chapelle, Guillaume et al. 2025. "Discrimination on online markets: Evidence from a field experiment." AEA RCT Registry. October 06. https://doi.org/10.1257/rct.7019-2.1
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2021-03-31
Intervention End Date
2021-09-30

Primary Outcomes

Primary Outcomes (end points)
Supply side:
-number of inquiries by type (positive, negative, neither) fictitious and real goods
-any inquiry
-time to first inquiry, delay time in correspondence

Demand side:
-number of responses by type (positive, negative, neither) for inquiries to goods/rides
-any response
-time to first response, delay time in correspondence
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
For both demand and supply:
-Offered or bargained purchase amount(s): mean, best, worst, difference with listed price
-Context of potential trade and response characteristics: buyer/seller included name, direct contact information (telephone, address, email); type of communication channel: phone, email, online message system, text messages, face to face; pick-up and drop off locations and times for rides and exchanges.
-Scams. Following Doleac and Stein (2013) we will use text analysis to tag suspicious inquiries/responses that appear to be scams.
-Reactions to modality of transaction (secure payment): hesitant, positive, negative
- Request for further information + Direct buy
-Language used: number of words in response, politeness and insults or pejorative language
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We create sets of advertisements for common goods and rides using fictitious profiles (both buyers and sellers of goods for the online classified ads and both passengers and drivers for ride sharing) that are randomly assigned minority and gender status through first names and computer-generated profile pictures. On the supply side, these adverts are then posted in the online marketplaces. On the demand side, these fictitious profiles are used to inquire about real goods in the online marketplaces.
Experimental Design Details
Online profiles will be randomly assigned a supposed ethnic origin (French or North-/sub–Saharan African origin), a supposed gender (male, female) or a neutral pseudonym in which the ethnicity, gender or both cannot be deduced by the counter party.

In addition, we will experimentally vary the following dimensions of heterogeneity for goods sold (supply side sellers):
-Availability of secure payment
-Prices
-Completeness of information
-Politeness/cordiality/spelling errors/contact information
-Gender

On the demand side (buyers)
-Prices (offered)
-Completeness of information
-Politeness/cordiality/spelling errors/contact information
-Gender

We will also exploit real market level heterogeneity in:
-Categories of goods
-Competition
-Underlying bias (local vote shares for extreme right political party)
-Underlying minority share in market and in geographic area
-Existing ethnic/gender composition of car (for ride sharing platform)
-Location: local crime rates, employment levels, segregation
Randomization Method
Computer
Randomization Unit
Individual advertisements and fictitious buyer profiles
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
3000 advertisements and 3000 fictitious buyer profiles
Sample size: planned number of observations
3000 advertisements and 3000 fictitious buyer profiles
Sample size (or number of clusters) by treatment arms
Advertisements and buyer profiles will be randomized majority, minority or anonymous status with 1/3 probability
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
INSEAD INSTITUTIONAL REVIEW BOARD
IRB Approval Date
2020-11-30
IRB Approval Number
202091
IRB Name
INSEAD INSTITUTIONAL REVIEW BOARD
IRB Approval Date
2024-11-04
IRB Approval Number
2024-67
Analysis Plan

Analysis Plan Documents

PAP-Survey_experiment_for_BlaBlaCar

MD5: 536f32ccc53101b73d60d1d95504ba56

SHA1: f38afd47dfdf4bc8e6043a575767f63e366249d5

Uploaded At: March 19, 2025

PAP-March_17-2021

MD5: 23deb7e93ae10ba653af4cb7a50ab90a

SHA1: fd519ffe7baefb44aa2f3c5f96fd78b38a7b96b0

Uploaded At: March 17, 2021

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials