Can We Fight Overconfidence in Gig Workers? Evidence from a Randomized Intervention

Last registered on February 28, 2024


Trial Information

General Information

Can We Fight Overconfidence in Gig Workers? Evidence from a Randomized Intervention
Initial registration date
February 19, 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
February 28, 2024, 4:36 PM EST

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


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Primary Investigator

Nova School of Business and Economics

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Pay and work hours fluctuate from month to month in many jobs. A particular case -- growing in popularity over time -- is gig jobs on online platforms. Variation in job outcomes is not a problem as long as workers can, on average, understand and predict them accurately. As a result of limited attention or cognitive capacity, or a desire for self-deception, workers may persistently hold inaccurate beliefs that lead to inefficient labor supply decisions. I will conduct an experimental randomized information treatment to try to make these errors less of a concern. I will measure effects over time on both beliefs and labor market decisions.
External Link(s)

Registration Citation

Pires, Pedro. 2024. "Can We Fight Overconfidence in Gig Workers? Evidence from a Randomized Intervention." AEA RCT Registry. February 28.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Job outcomes: hours worked, hourly and weekly pay, expenses
Other labor market variables: job search and information on other jobs
Beliefs of job outcomes for oneself and for other workers: hours worked, hourly and weekly pay, expenses
Misperception of job outcomes: difference between beliefs and actual outcomes
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
See pre-analysis plan for more details.
Experimental Design Details
Not available
Randomization Method
Simple randomization.
Randomization Unit
Randomization at the worker level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Between 900 and 1100 workers.
Sample size (or number of clusters) by treatment arms
Between 450 and 550 for each of the treatment and control groups.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Committee for Protection of Human Subjects (CPHS), UC Berkeley
IRB Approval Date
IRB Approval Number