Risk taking on behalf of others - the importance of uncertainty revelation

Last registered on July 02, 2019

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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

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

July 02, 2019, 4:55 PM EDT

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

Locations

Country

Norway

Region

Norway

Primary Investigator

Name

Erik Ø. Sørensen

Affiliation

Norwegian School of Economics

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Other Primary Investigator(s)

PI Name

Xiaogeng Xu

PI Affiliation

NHH Norwegian School of Economics

Contact Investigator

PI Name

Bertil Tungodden

PI Affiliation

NHH Norwegian School of Economics

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PI Name

Alexander W. Cappelen

PI Affiliation

NHH Norwegian School of Economics

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Additional Trial Information

Status

In development

Start date

2019-07-03

End date

2019-10-01

Keywords

Other, Welfare

Additional Keywords

risk, uncertainty, paternalism

JEL code(s)

D81

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

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Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

We vary the date which decision makers are told the uncertainty in a risky decision is will be revelead: Immediate revelation, a short delay (1 week), a long delay (3 months), or never.

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

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?

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.

Supporting Documents and Materials

IRB