Sunk-cost Effect and Biased Beliefs

Last registered on February 06, 2024
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Pre-Trial

Trial Information

General Information

Title
Sunk-cost Effect and Biased Beliefs
RCT ID
AEARCTR-0012836
Initial registration date
February 05, 2024

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
February 06, 2024, 5:29 PM EST

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

Locations

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Primary Investigator

Name
ZHONGHENG QIAO
Affiliation
Purdue University
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Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2024-02-09
End date
2024-12-13
Keywords
Behavior, Lab
Additional Keywords
JEL code(s)
D81, D83, D91
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Although the sunk cost effect is widely discussed in different research settings, the behavioral mechanism underlying this phenomenon remains elusive. In this project, I plan to conduct a lab experiment to explore subjects’ beliefs and decisions in a two-stage investment task. I aim to investigate whether subjects hold biased beliefs about the project when sunk costs are present. Furthermore, I will explore the behavioral mechanisms that potentially connect subjects' beliefs and the sunk-cost effect.
External Link(s)

Registration Citation

Citation
QIAO, ZHONGHENG. 2024. "Sunk-cost Effect and Biased Beliefs." AEA RCT Registry. February 06. https://doi.org/10.1257/rct.12836-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The investment tasks will be varied across treatments in a within-subject design. This variation includes a baseline investment task in which an ex-ante optimal choice might be ex-post suboptimal, a treatment where the ex-ante optimal choice is always ex-post optimal, and a treatment where computers make some decisions for the subjects. In addition, the information setting will be varied across treatments in a between-subject design.

Intervention Start Date
2024-02-09
Intervention End Date
2024-12-13

Primary Outcomes

Primary Outcomes (end points)
Investment rates
First-order beliefs
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Learning behavior across rounds
Risk attitudes
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This is a laboratory experiment, conducted in a standard university experimental economics lab, recruiting students broadly across the university. Subjects will be randomly assigned to a treatment, which is fixed throughout their experimental session.

The experiment comprises three blocks of 10-round repeated individual investment tasks, a lottery selection task, and a demographic survey. The three blocks of individual investment tasks consist of one block for each of the within-subject treatments. The order of the three blocks is randomized at the individual level.
Experimental Design Details
Not available
Randomization Method
Subjects will be recruited by email using ORSEE. They choose between a list of available sessions, and the session is randomized to a treatment before it is initialized. Within the session, individuals are randomized into different orders of tasks.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
200 subjects, each subject is a cluster.
Sample size: planned number of observations
200 subjects.
Sample size (or number of clusters) by treatment arms
I plan to collect data from about 150 subjects (6 sessions) in the standard information setting and about 50 subjects (2 sessions) in the rich information setting.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
The Purdue University Human Research Protection Program
IRB Approval Date
2024-01-08
IRB Approval Number
IRB-2023-1515
Analysis Plan