Morality Meets Risk: Predicting Others’ Excuse-Driven Behaviors

Last registered on August 10, 2023

Pre-Trial

Trial Information

General Information

Title
Morality Meets Risk: Predicting Others’ Excuse-Driven Behaviors
RCT ID
AEARCTR-0011896
Initial registration date
August 07, 2023

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
August 10, 2023, 1:36 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
Renmin University of China

Other Primary Investigator(s)

PI Affiliation
Aix-Marseille School of Economics

Additional Trial Information

Status
In development
Start date
2023-08-07
End date
2023-10-31
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
While individuals often engage in morally right actions, they prioritize their self-interest over moral considerations. Previous research has shown that people often manipulate information processing to rationalize their egoistic behavior, using situational uncertainties as excuses to justify their choices. However, a crucial issue is whether individuals can recognize the excuse-based actions of others. The research investigates individuals’ ability to predict others’ behavior in risky decision-making. In our laboratory experiment, participants make a decision task and a prediction task. The decision task will assess participants’ excuse-driven risk preferences, while the prediction task will delve into their predictions regarding others’ behaviors under the same risk scenario. Our hypothesis suggests that individuals possess the capacity to anticipate the excuse-driven behavior of others, but they may underestimate the intensity of self-interested actions exhibited by their peers.
External Link(s)

Registration Citation

Citation
DONG, Wanxin and Jiakun Zheng. 2023. "Morality Meets Risk: Predicting Others’ Excuse-Driven Behaviors." AEA RCT Registry. August 10. https://doi.org/10.1257/rct.11896-1.0
Experimental Details

Interventions

Intervention(s)
The experiment consists of two parts and adopts a within-subject design. The first part is a decision task for subjects to elicit their excuse-driven risk preferences. The second part is a prediction task to investigate participants’ predictions of others’ excuse-driven behavior under the same risk scenario. Two parts are presented in random order at the individual level.
In each part, there are four blocks of decision tasks including 12 price lists. Price lists in each block only differ in 3 different probabilities, i.e. {90%, 50%, 10%}, and four blocks are presented in a random order for each participant. The price lists include similar decisions as elaborated in Dong and Zheng (2023).
Intervention (Hidden)
Intervention Start Date
2023-08-07
Intervention End Date
2023-09-30

Primary Outcomes

Primary Outcomes (end points)
- excuse-driven risk preferences: participants' valuations of different lotteries
- predictions of others’ excuse-driven behavior
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Building upon the work of Dong and Zheng (2023), we conduct a laboratory experiment consisting of two essential parts: a decision task and a prediction task. The decision task will assess participants’ excuse-driven risk preferences, while the prediction task will delve into their predictions of others’ excuse-driven behavior under the same risk scenario. In the decision task, participants complete a series of price lists involving risk. The price lists include similar decisions as elaborated in Exley (2016). Participants’ choices reveal their certainty equivalences of different lotteries such that we can estimate their excuse-driven risk preferences. In the prediction task, participants face the same decision tasks as in the decision task. Instead of making decisions for themselves, participants are asked to predict the responses to these tasks by other participants in their session. Two parts are presented in random order at the individual level.
Experimental Design Details
In each part, participants encounter twelve price lists. Each price list consists of 21 binary decisions. Participants make binary decisions between Option A and Option B for participants themselves or for the other participants in the same session. Option A columns always involve a consistent self-lottery or others-lottery given to themselves or other participants. The lottery yields 10 yuan with probability P and 0 with probability 1 − P, P ∈ {0.90, 0.50, 0.10}. Option B columns always involve a self-certain amount or an others-certain amount given to the corresponding participant. The amount increases evenly from 0 to 10 yuan along the 20 rows of the price list. Participants always start by selecting Option A and then switch to Option B at a switch point where Participants are indifferent between the lottery and a certain amount. As such, we can estimate the certainty equivalents of self-lotteries or others-lotteries in terms of self-valuations or others-valuations. The estimation process closely follows the derivations of Exley (2016) and Dong and Zheng (2023).
Randomization Method
randomization done by a computer.
Randomization Unit
individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
100 subjects in Beijing (50 males and 50 females), and 200 full-time students from Renmin University of China.
Sample size: planned number of observations
100 subjects in Beijing (50 males and 50 females), and 200 full-time students from Renmin University of China.
Sample size (or number of clusters) by treatment arms
300 subjects in the decision task and 300 subjects in the prediction task.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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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?
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