Privatizing gains and socializing risks: Does responsibility affect risky decision making for others?

Last registered on June 29, 2021

Pre-Trial

Trial Information

General Information

Title
Privatizing gains and socializing risks: Does responsibility affect risky decision making for others?
RCT ID
AEARCTR-0007832
Initial registration date
June 28, 2021

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
June 29, 2021, 2:24 PM EDT

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

Locations

Primary Investigator

Affiliation
Radboud University

Other Primary Investigator(s)

PI Affiliation
Portsmouth University

Additional Trial Information

Status
In development
Start date
2021-06-30
End date
2021-12-05
Secondary IDs
Abstract
We consider the group size effect in principal-agent settings, i.e. whether the number of principals determines the agents action.

The aim of this study is to extend our previous work and systematically increase group sizes to generate and fully test a model of the impact of group sizes on decisions taken. We need to include responsibility effects in economic decision models to predict choices of key decision makers such as politicians or governmental regulators. Other-regarding preference models only internalise the effects for others as a trade-off between payments for the decision maker and the affected parties. To fully understand the effects of the number of people affected by a decision, we need to include a responsibility parameter in these decision models. The impact might be linear, as current results suggest, but it could also be non-linear where, e.g., a very large group could be viewed as one entity with increased social distance and lower impact of responsibility on the decision. This study will extend existing social preference models and test the model predictions in a large-scale online experiment.
External Link(s)

Registration Citation

Citation
Füllbrunn, Sascha and Wolfgang Luhan. 2021. "Privatizing gains and socializing risks: Does responsibility affect risky decision making for others?." AEA RCT Registry. June 29. https://doi.org/10.1257/rct.7832-1.0
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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-07-05
Intervention End Date
2021-12-05

Primary Outcomes

Primary Outcomes (end points)
The key outcome variable is the amount invested, i.e. either 0, 1000, 2000, ... 10,000. We test whether this one varies with the group size (within-subjects) and compare it across treatments.
Primary Outcomes (explanation)
We will estimate the functional relationship between group size and the amount invested. Economic theory would have no prediction in treatment FLAT and full investment in treatment CONVEX. However, due to a responsibility effect, we expect a change of investment levels with an increase in group size in CONVEX and less so in FLAT. We further test for the order effect but have no a-priory prediction.

Secondary Outcomes

Secondary Outcomes (end points)
In addition, we elicit subjects' characteristics that have been shown to influence risk-taking and elicit risk preferences using the Dohmen et al (2011) risk elicitation task,
Secondary Outcomes (explanation)
We will expand the estimated functional relationship between group size and the amount invested by including subjects' characteristics to control for these when estimating the effect of the group size and to establish the impact of these effects on their own and their effect on the functional shape of the impact of group size.

Experimental Design

Experimental Design
Hidden
Experimental Design Details
We answer the research question – Does the number of principals affect the agent’s decisions for the principals? – by considering a principle-agent relationship in which we ask subjects to take investment decisions for a varying number of principals. We consider a within-subject design where subjects decide varying group sizes (1,5,10,25,50,100). We consider a between-subject design for 1) the order of group sizes, i.e. either starting 1, 5,… (LOW) or 100, 50,… (HIGH) and 2) the incentive structure, i.e. either FLAT or CONVEX. Hence, we have the treatments LF, HF, LC, and HC. In FLAT, the agents get a flat payment. In CONVEX, the agents earn the flat payment or a proportional bonus when the investment was successful. Hence, we consider a classical 2x2 factorial design.

We consider a situation, in which an agent (A) invests the monetary endowments of n principals (P) in a high-risk project with negative expected returns. We amend the investment game introduced by Gneezy and Potters (1997): The N principals are endowed with identical amounts E=10,000 ECU. The agent invests the same amount X (between 0 and E) for each of the four principals. With a probability of 1/6 the project is successful and returns 4.5 times the investment. With a probability of 5/6, however, the investment is lost. The principals keep all money that is not invested. Note that any amount invested has a negative expected return and therefore a risk-averse investor would not invest. The agents do not participate in the gains or losses of the investment.
Randomization Method
Randomization will be implemented as an algorithm.
Randomization Unit
We invite people from Prolific and randomly apply treatments in equal size until we get the number of observations.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters.
Sample size: planned number of observations
204 observations for each treatment, four treatments in total
Sample size (or number of clusters) by treatment arms
204 observations for each treatment, four treatments in total
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Comparing two treatments with alpha=0.05 and power of 0.80 the detectable effect size is 0.2780 (G*Power 3.1.9.7 - Sensitivity for t-tests)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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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