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Risk taking on behalf of others - the importance of uncertainty revelation
Last registered on July 02, 2019

Pre-Trial

Trial Information
General Information
Title
Risk taking on behalf of others - the importance of uncertainty revelation
RCT ID
AEARCTR-0004403
Initial registration date
July 01, 2019
Last updated
July 02, 2019 4:55 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
Norwegian School of Economics
Other Primary Investigator(s)
PI Affiliation
NHH Norwegian School of Economics
PI Affiliation
NHH Norwegian School of Economics
PI Affiliation
NHH Norwegian School of Economics
Additional Trial Information
Status
In development
Start date
2019-07-03
End date
2019-10-01
Secondary IDs
RCN 262675
Abstract
Much decision making is decision making on behalf of others, and it is not and there are competing theories of how such decisions should be made. When making decisions on behalf of others, it might also be that the decision maker will never learn the outcomes of the decision only with delay, or possibly not at all. We explore how decisions on behalf of others is made in a setting where the revelation of uncertainty is either immediate, delayed, or it will never happen. Data from a large scale online experiment is analyzed within hierarchical Bayesian model of rank-dependent utility.
External Link(s)
Registration Citation
Citation
Cappelen, Alexander W. et al. 2019. "Risk taking on behalf of others - the importance of uncertainty revelation." AEA RCT Registry. July 02. https://doi.org/10.1257/rct.4403-1.0.
Former Citation
Cappelen, Alexander W. et al. 2019. "Risk taking on behalf of others - the importance of uncertainty revelation." AEA RCT Registry. July 02. https://www.socialscienceregistry.org/trials/4403/history/49164.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-07-03
Intervention End Date
2019-07-10
Primary Outcomes
Primary Outcomes (end points)
The choice of a risky alternative vs a safe alternative. Each individual draws 4 out of a list of 10 risky alternatives, and for each risky alternative chooses between this and 7 safe alternatives, for 28 binary decisions from each individual.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
1) Subjective measures of own risk tolerance (1-7 scale),
2) beliefs about average risk tolerance in the study (1-7 scale),
3) own tendency to support social initiatives (1-7 scale).
4) Self reported feeling during experiment (subjective expression)
5) Basic demographics
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Participants are recruited from the general population, via a commercial survey agency. On research team platform, participants choose between risky prospects and safe outcomes for random draws of "recipients" (that are recruited via an online labor market). Four treatments differ in what decision makers are told about how they will be informed about outcomes when risky alternatives are chosen (different points in time). Post decision making, a few survey questions are asked of the participants. The recipients are recruited and paid after the the final revelation of uncertainty to the decision makers (and decision makers are informed about this).
Experimental Design Details
Randomization Method
Individual level randomization (by computer, dynamically during survey) into one out of four treatments. Post data collection, a random subset of decisions are drawn (by computer) to determine earnings of recipients (In total, 1/5 recipients per decision maker).
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
2000 decision makers
Sample size: planned number of observations
2000 decision makers,
Sample size (or number of clusters) by treatment arms
500 decision makers in each of the 4 treatments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We plan to estimate a choice model in which the minimum detectable effect size is not clearly defined, but for each (risky alternative / safe alternative) we have a minimum detectable effect size of 12.8 percentage points (baseline p=0,2, alpha=0.05, power=0.8) between treatments.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
NHH Norwegian School of Economics IRB
IRB Approval Date
2019-03-04
IRB Approval Number
NHH-IRB 06/19
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers