Perceived correlations in risk attitudes

Last registered on June 26, 2022

Pre-Trial

Trial Information

General Information

Title
Perceived correlations in risk attitudes
RCT ID
AEARCTR-0009404
Initial registration date
June 22, 2022

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 26, 2022, 5:23 AM EDT

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

Locations

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Primary Investigator

Affiliation
National University of Singapore

Other Primary Investigator(s)

PI Affiliation
National University of Singapore
PI Affiliation
National University of Singapore

Additional Trial Information

Status
In development
Start date
2022-06-27
End date
2022-08-26
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We investigate the perceived links between risk preferences within and across domains defined over probability (low/moderate) and outcome (prospect/hazard). In an experimental setting, we first elicit subjects' risk taking behaviors in different domains, and then measure their beliefs about links of behaviors within the same domain and across different domains. We test whether subjects overestimate within-domain links (i.e., underestimate noise), and document how they perceive the cross-domain links. We further examine whether subjects give sufficient decision weights to the corresponding within-domain links when they evaluate a cross-domain link.
External Link(s)

Registration Citation

Citation
Fu, Jingcheng, Wencong Li and Songfa Zhong. 2022. "Perceived correlations in risk attitudes." AEA RCT Registry. June 26. https://doi.org/10.1257/rct.9404
Experimental Details

Interventions

Intervention(s)
The experiment consists of two parts. The first part measures risk preferences in four domains using the standard choice list whereby subjects make decisions between the lottery and a list of sure amounts. In the second part, which is the main focus of the experiment, subjects provide incentivized judgments of the links between lotteries within the same domain and across different domains. The links are presented in the form of how well the choices for lottery X predict those for lottery Y.
Intervention Start Date
2022-06-27
Intervention End Date
2022-08-26

Primary Outcomes

Primary Outcomes (end points)
The perceived within-domain PCs (two guesses for each of the four domains per subject);
The perceived cross-domain PCs (two guesses for each of the six pairs of domains per subject).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Risk preferences elicited using choice lists; questionnaire responses (confidence, risk preferences, demographics and academic background).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment consists of two parts. In Part 1, we elicit the certainty equivalents (CEs) for binary lotteries in four domains based on probability and outcome: moderate prospect, long-shot prospect, moderate hazard, and long-shot hazard. The CEs are elicited using standard multiple price lists. Losses are deducted from the participation fee that subjects receive at the beginning of the experiment to ensure that the overall payment from the experiment is never negative. The Part 1 decisions are converted into ratings for the lotteries. For each lottery, we count the number of times a subject chooses the lottery over sure amounts as their rating. A subject with a higher rating is more willing to take risk for this lottery, compared to one with a lower rating.

In Part 2, subjects are asked to express their perceptions of the links between ratings of different lotteries using the percent concordant (PC). This number answers the question that, suppose a pair of subjects are taken at random, what is the probability that the one who has a higher rating for one lottery also has a higher rating for another lottery. With four domains of lotteries, there are four within-domain links and six cross-domain links. We ask subjects to predict each link twice. One of the 20 questions is randomly selected to determine whether subjects receive S$15 using a Binarized Scoring Rule.
Experimental Design Details
Not available
Randomization Method
Randomization of question format and order is implemented by Qualtrics.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
300 subjects.
Sample size: planned number of observations
2,400 (300 subjects × 4 domains × 2 duplicates) guesses of within-domain links; 3,600 (300 subjects × 6 domain pairs × 2 duplicates) guesses of cross-domain links.
Sample size (or number of clusters) by treatment arms
300 subjects (one treatment).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We are able to detect a significant difference with significance level of 0.05 and power of 0.8 when the guesses deviates from the true value by more than 0.16 SD using one-sample t-test.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Department of Economics Departmental Ethics Review Committee, National University of Singapore
IRB Approval Date
2022-06-21
IRB Approval Number
N/A
Analysis Plan

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