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Simple Elicitation of Risk Attitudes, SERA
Last registered on August 04, 2020

Pre-Trial

Trial Information
General Information
Title
Simple Elicitation of Risk Attitudes, SERA
RCT ID
AEARCTR-0005763
Initial registration date
May 01, 2020
Last updated
August 04, 2020 6:30 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
University of Zurich
Other Primary Investigator(s)
Additional Trial Information
Status
Completed
Start date
2020-05-10
End date
2020-05-31
Secondary IDs
SERA
Abstract
Attitudes toward risk are one of the primitive concepts in economics. Moreover, measuring risk preferences is critical for economic analysis and policy prescriptions alike. Therefore, it is only natural that economists have developed a variety of experimental methodologies. However, “risk elicitation is a risky business” (Friedman et al., 2014) with some of the current established methods being hard to understand for participants, difficulty to implement, or not able to capture attitudes well enough. In this study we propose an improved, simple but effective method to elicit on-line individuals’ attitudes toward risk, SERA. This new method sits between a fixed design (i.e., Multiple Price List, Holt & Laury, 2002) and a dynamically optimal design (i.e., BOARD, Wang, Filiba, & Camerer 2019) using an adaptive three questions procedure. Hence, it strikes a compromise among quality of the data, simplicity of implementation (for experimenters), and easiness to understand (for participants).
In an empirical validation we perform a within-subjects comparison of SERA with the most commonly used risk elicitation procedures (the MPL and the investment task) using as a metric of judgement the proportion of out-of-sample correctly predicted choices.
External Link(s)
Registration Citation
Citation
Garagnani, Michele. 2020. "Simple Elicitation of Risk Attitudes, SERA." AEA RCT Registry. August 04. https://doi.org/10.1257/rct.5763-2.0.
Experimental Details
Interventions
Intervention(s)
SERA, a new method to elicit attitudes toward risk, is compared to established alterantive methods.
Intervention Start Date
2020-05-10
Intervention End Date
2020-05-31
Primary Outcomes
Primary Outcomes (end points)
Proportion of correctly predicted choices out-of-sample across 3 different elicitation methods (SERA, MPL, investment task).
Primary Outcomes (explanation)
Proportion of correctly predicted choices (0,1) over 32 binary lottery choices.
Secondary Outcomes
Secondary Outcomes (end points)
Gender differences in risk attitudes.
Correlation between the estimated risk attitudes across the 3 eliciation methods.
Robustness of SERA to a within-subject repetition with re-scaled values.
Secondary Outcomes (explanation)
Gender differences in risk attitudes as evaluated across the 3 elicitation methods will be studied in an explorative way.
Experimental Design
Experimental Design
Within-subjects comparison of different risk-elicitation methods (SERA, MPL, investment task, qualitative risk assessment).
Experimental Design Details
All subjects will be presented with the same list of tasks and in the same order. Specifically, SERA, MPL, investment task, 32 binary-choice lotteries, 4 binary-choice lotteries involving a dominance relation, SERA with re-scaled values, gender question, qualitative risk assessment, attention checks.
Randomization Method
The order of the 36 lotteries is randomized and fixed to be the same for each participant.
One out of all decision situations will be randomily used to determine the earnings of subjects.
Randomization Unit
Individuals are the independent level of analysis in this study.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
N=400.
Sample size: planned number of observations
N=400.
Sample size (or number of clusters) by treatment arms
N=400 is the desired sample size. Subjects will be recrited until the desired size is reached. However, because the experiment is performed on-line, more subjects will be invited to participate in the experiment. Subjects exceeding the desired sample will be included in the analysed sample.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
N=400 based on a power analysis for detecting a small effect size according to a test of proportions with multiple testing correction (SERA vs. MPL; SERA vs. Investment task) and in order to obtain a power of at least 0.8.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Post-Trial
Post Trial Information
Study Withdrawal

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Intervention
Is the intervention completed?
Yes
Intervention Completion Date
May 15, 2020, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
May 15, 2020, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
403
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
403
Final Sample Size (or Number of Clusters) by Treatment Arms
403
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
No
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS