Distributional Approach to Risk Preferences

Last registered on February 04, 2026
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Pre-Trial

Trial Information

General Information

Title
Distributional Approach to Risk Preferences
RCT ID
AEARCTR-0017728
Initial registration date
January 19, 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
February 04, 2026, 9:57 AM EST

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

Locations

Country
Israel
Region
Country
United States of America
Region

Primary Investigator

Name
Nir Chemaya
Affiliation
BGU
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Brain Jabarian
PI Affiliation
Chicago Booth
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2026-01-26
End date
2026-01-27
Keywords
Behavior, Finance & Microfinance
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
In this experiment, we asked subjects to make decisions in 20 risk-elicitation tasks, in which they chose between lotteries following the experimental design of Holzmeister and Stefan (2021). The experiment was incentives and subjects were paid based on their decisions. The experimental design is structured as follows: the 20 decisions are organized into five blocks. In each block, subjects make decisions on four different risk-elicitation tasks. The same four tasks are repeated across all five blocks. As a result, each subject makes 20 decisions in total, corresponding to four distinct tasks, with each task repeated five times (once in each block). At the end of the experiment, one of the 20 tasks is randomly selected for payment.

Our research question is whether subjects’ preferences become more consistent over time. We examine this using our Distributional Approach to estimate risk preferences and to evaluate the predictive power of our method.
External Link(s)
Distributional Approach to estimate risk preferences

Registration Citation

Citation
Chemaya, Nir and Brain Jabarian. 2026. "Distributional Approach to Risk Preferences." AEA RCT Registry. February 04. https://doi.org/10.1257/rct.17728-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2026-01-26
Intervention End Date
2026-01-27

Primary Outcomes

Primary Outcomes (end points)
Our research question is whether subjects’ preferences become more consistent over time. We examine this using our Distributional Approach to estimate risk preferences and to evaluate the predictive power of our method.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In this experiment, we asked subjects to make decisions in 20 risk-elicitation tasks, in which they chose between lotteries following the experimental design of Holzmeister and Stefan (2021). The experiment was incentive-compatible, and subjects were paid based on their decisions. The experimental design is structured as follows: the 20 decisions are organized into five blocks. In each block, subjects make decisions on four different risk-elicitation tasks. The same four tasks are repeated across all five blocks. As a result, each subject makes 20 decisions in total, corresponding to four distinct tasks, with each task repeated five times (once in each block). At the end of the experiment, one of the 20 tasks is randomly selected for payment.
Experimental Design Details
Randomization Method
In our experiment, there is no treatment versus control group, since all subjects face the same risk elicitation tasks and the same experimental design. We use a randomization process in which each subject is assigned a different order of the risk elicitation tasks to control for order effects and potential history effects on their decisions. In addition, because payments are based on the outcomes of the lottery decisions participants make, the computer uses the corresponding lottery probability distributions to generate outcomes randomly and determine their payoffs.

However, in our analysis we use the results of Holzmeister and Stefan (2021) as a benchmark. In their study, subjects completed each of the four risk-elicitation tasks only once (one decision per task). We use these results as a control to show that subjects in our experiment behave similarly in the first round—when they make decisions in each of the four tasks—relative to later rounds, in which they gain experience and potentially learn.

Since we employ the same risk-elicitation tasks as Holzmeister and Stefan (2021), with comparable stakes (payoffs), and both experiments are conducted with similar student populations, this comparison is well suited for validation. If we nevertheless observe differences in the outcomes of the first four trials between our experiment and Holzmeister and Stefan (2021), we will run an additional control experiment in our laboratory that exactly replicates their design, in order to control for potential differences in lab environments or subject pools.
Randomization Unit
NA
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N=99
Sample size: planned number of observations
N=99
Sample size (or number of clusters) by treatment arms
NA
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Distributional Approach to Risk Preferences
IRB Approval Date
2025-07-17
IRB Approval Number
IRB25-0878
Analysis Plan

Post-Trial

Post Trial Information

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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