The Perceived Effect of Income on Happiness: An Experiment

Last registered on April 06, 2026
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Pre-Trial

Trial Information

General Information

Title
The Perceived Effect of Income on Happiness: An Experiment
RCT ID
AEARCTR-0018251
Initial registration date
April 03, 2026

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
April 06, 2026, 9:34 AM EDT

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

Locations

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Primary Investigator

Name
Ricardo Perez-Truglia
Affiliation
UC Berkeley
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Other Primary Investigator(s)

PI Name
Rafael Macedo Rubiao
PI Affiliation
UCLA
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Additional Trial Information

Status
In development
Start date
2026-04-30
End date
2028-04-30
Keywords
Behavior, Welfare
Additional Keywords
Happiness, income, information
JEL code(s)
I31, z13
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines how individuals perceive the effect of income on happiness. We conduct an information-provision experiment that presents scientific evidence on the relationship between income and happiness. We measure the treatment’s effects on both beliefs and behavior, including hypothetical choices and real-world decisions.
External Link(s)

Registration Citation

Citation
Macedo Rubiao, Rafael and Ricardo Perez-Truglia. 2026. "The Perceived Effect of Income on Happiness: An Experiment ." AEA RCT Registry. April 06. https://doi.org/10.1257/rct.18251-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We designed an information-provision experiment that provides respondents with scientific evidence on the effect of income on happiness.
Intervention Start Date
2026-04-30
Intervention End Date
2026-05-30

Primary Outcomes

Primary Outcomes (end points)
The mean outcomes are participants’ beliefs about how income affects life satisfaction, two real-world decisions, and a series of hypothetical choices.
Primary Outcomes (explanation)
All outcomes are measured in the baseline and follow-up surveys. For more details, see the attached PDFs containing the full survey instruments for both surveys. In summary, we measure individuals’ perceptions of the effect of a 20% increase in income on life satisfaction, both for the average person and for themselves. Through a real-time interview with an AI chatbot, we elicit a real-world decision that involves a trade-off between income and another consideration, such as working extra hours. We measure the intention to make that decision at the end of the baseline survey and whether the individual actually made the decision one month later in the follow-up survey. Because participants work on the survey platform, a decision common to all of them is how many surveys to complete over the following 30 days; this is the second real-world outcome we study. We also examine a series of hypothetical decisions, such as choosing between pairs of job offers (e.g., where one offers higher income but a longer commute). For most outcomes, we elicit both what the individual would choose and what they believe would make them happier.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We designed an information-provision experiment that provides respondents with scientific evidence on the effect of income on happiness. We measure the effects on beliefs about how income affects life satisfaction, two real-world decisions, and a series of hypothetical choices.

As in other information-provision experiments, a key feature of the research design is heterogeneity in prior beliefs. Relative to the scientific evidence presented to them, some individuals may underestimate or overestimate the effect of income on happiness; among those who overestimate it, some may do so only slightly, while others may do so by much more. We leverage this variation in prior beliefs to analyze the effects of the information.
Experimental Design Details
Not available
Randomization Method
Randomization done by Qualtrics platform
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
2,500 individuals
Sample size: planned number of observations
2,500 individuals
Sample size (or number of clusters) by treatment arms
1,250 individuals control, 1,250 individuals treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
UCLA's North General Institutional Review Board
IRB Approval Date
2026-01-27
IRB Approval Number
IRB-25-1865
Analysis Plan