Discrimination against the mentally ill

Last registered on May 04, 2022


Trial Information

General Information

Discrimination against the mentally ill
Initial registration date
February 10, 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
February 11, 2021, 11:59 AM EST

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

Last updated
May 04, 2022, 11:07 AM EDT

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


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Primary Investigator

Massachusetts Institute of Technology

Other Primary Investigator(s)

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
People with depression or anxiety, the most common mental illnesses, often deal with prejudice and unfavorable treatment at work. This could have economic costs if they are discriminated against relative to other equally-productive workers, or treated differently by coworkers in ways that harm productivity. How much does this happen, as opposed to discrimination just reflecting these disorders' direct effects on productivity? This project uses an online experiment to investigate discriminatory behavior towards depressed or anxious coworkers in a collaborative problem-solving task. I estimate discrimination in the preference for working with a person and in in-task behaviors, investigate the mechanisms behind such discrimination and its effects on earnings, and finally consider how this relates to the willingness of participants to reveal information about their mental illness.
External Link(s)

Registration Citation

Ridley, Matthew. 2022. "Discrimination against the mentally ill." AEA RCT Registry. May 04. https://doi.org/10.1257/rct.7100
Experimental Details


There are two roles in the experimental task: tourist and guide. The main intervention is whether I reveal a tourist's depression or anxiety symptoms to the guide, when the tourist in fact has these symptoms). Additional, I randomize whether I reveal that a tourist lacks these symptoms, when they do not in fact have them.

A secondary intervention is whether, when choosing roles in the task, participants are told (truthfully) that information on these same symptoms might be revealed to their coworker if they choose one of the roles.

I also randomize what other demographic information alongside mental health is revealed. Guides and tourists are also matched on a first-come first-serve basis as they enter the study, so this is quasi-random in that sense.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
- Willingness to pay to work with a given coworker, rather than receive a random new one, both before and after actually working with this person
- Earnings per hour in the task
- Willingness to pay to reveal/hide symptoms of mental illness
Primary Outcomes (explanation)
Willingness to pay is measured using a Becker-deGroot-Marschak procedure in which the guide is asked the minimum bonus payment that would persuade them to get a new tourist rather than work with this one, or vice versa, depending which option they intrinsically prefer.

Secondary Outcomes

Secondary Outcomes (end points)
- Effort in the task
- Politeness/kindness to the other person
- Beliefs about the other tourists' ability in the task
- Altruism toward the tourist
- Enjoyment of the task
Secondary Outcomes (explanation)
I measure effort in the task using measures such as the number, length and frequency of messages that the guide sends to the tourist. To measure politeness I use the occurrence of key words like please and thank you as well as sentiment analysis.

Beliefs on the likelihood that other participants succeeded or failed are elicited using a log-scoring rule.

Altruism is measured through a brief simple dictator game at the end of the experiment in which the guide can send some of their earnings to their tourist as a 'thank you'. Enjoyment of the task is measured through asking the guide directly to rate this at the end of the task.

Experimental Design

Experimental Design
My experiment revolves around an online, collaborative navigation task. Players on Amazon’s Mechanical Turk (MTurk) complete the task of guiding a tourist to a destination. They do four rounds of the navigation task divided into two batches of two rounds. In every round, guides see information about the tourist, with the key experimental treatment being whether this includes mental health. I measure this information in a baseline survey.

I elicit guides' willingness to pay to work with specific tourists before and after working with them and investigate how signals of mental health affect this demand. I also estimate how showing tourist mental health information to the guide, conditional on the tourist's actual mental health status, affects the guide's behavior in the task and ultimate earnings in the task.

I also elicit willingness to pay to hide or reveal signals of mental health to potential guides, alongside willingness to pay to reveal other information. This is done in between the first and second batch of two rounds.
Experimental Design Details
Not available
Randomization Method
All randomization is done by computer using a random number generator.
Randomization Unit
Randomization is at the tourist-batch level. That is, in each batch of two rounds I randomize what information is included in the profile of the tourist that will be shown to all potential guides. Thus, randomization is within guide as guides may see multiple tourists during a batch. Treatment is clustered insofar as a pair of guide and tourist may play multiple rounds within a batch, and across these rounds the treatment assignment will not vary.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
~880 pairings of participants. There will be 800 participants, who are twice (once at the start of each batch) put into 400 pairs of two, making 800 initial pair-ups. In some of these pair-ups the WTP elicitation mechanism will be implemented and this may lead to the guide and tourist rematching, likely in about 10% of cases (this depends on guides' answers in the mechanism). This implies I will observe about 880 different pairings of participants.
Sample size: planned number of observations
1600 rounds of the task (each person plays two rounds per batch).
Sample size (or number of clusters) by treatment arms
I will recruit participants and assign them to roles such that about 512 (64%) of the pairs have tourists with depression or anxiety symptoms. In 50% of the pairs I will reveal this information to the guide. This gives:
- ~256 pairs in which the tourist has depression or anxiety symptoms and this is revealed (main treatment)
- ~256 pairs in which the tourist has depression or anxiety symptoms and this is not revealed (main control)
- ~144 pairs in which the tourist does not have depression or anxiety symptoms and this is revealed
- ~144 pairs in which the tourist does not have depression or anxiety symptoms and this is not revealed
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials


Document Name
Second Experiment
Document Type
Document Description
This document pre-registers a second, follow-up experiment designed to investigate potential mechanisms behind the results from the main trial.
Second Experiment

MD5: 95b27253c498404979d6f517a7a03caf

SHA1: 7884e56909a89422a4df02bb1f9fae29f0efb256

Uploaded At: October 04, 2021


Institutional Review Boards (IRBs)

IRB Name
MIT Committee on the Use of Humans as Experimental Subjects (COUHES)
IRB Approval Date
IRB Approval Number