Anchoring and Subjective Belief Distributions

Last registered on July 04, 2024
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Pre-Trial

Trial Information

General Information

Title
Anchoring and Subjective Belief Distributions
RCT ID
AEARCTR-0011396
Initial registration date
May 10, 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
May 17, 2023, 2:15 PM EDT

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

Last updated
July 04, 2024, 4:21 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
United States of America
Region

Primary Investigator

Name
Margaret Samahita
Affiliation
University College Dublin
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Other Primary Investigator(s)

PI Name
Håkan Holm
PI Affiliation
Lund University
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PI Name
Erik Wengström
PI Affiliation
Lund University
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PI Name
Roel van Veldhuizen
PI Affiliation
Lund University
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Additional Trial Information

Status
On going
Start date
2023-05-11
End date
2024-07-31
Keywords
Behavior
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The purpose of this project is to study how anchors affect estimations of subjective belief distributions (SBDs). Anchoring is a well-known judgment bias in decisions. Although the impact of anchors has been studied extensively on estimations of single-number summary statistics, its impact on higher moments of SBDs is to a large extent unexplored. This makes it valuable to study since SBDs play an important role in economic theory.

A second study using a number input interface is conducted for robustness check.
External Link(s)

Registration Citation

Citation
Holm, Håkan et al. 2024. "Anchoring and Subjective Belief Distributions." AEA RCT Registry. July 04. https://doi.org/10.1257/rct.11396-2.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Low anchor: The subjects are asked if they believe that the a variable is lower or higher than the value of a low anchor
High anchor: The subjects are asked if they believe that the a variable is lower or higher than the value of a high anchor

Mean elicitation The subjects are asked to guess the value of a variable.
Subjective belief distribution (SBD) elicitation: The subjects are asked to input the distribution of a variable using the click-and-drag method (Crosetto and de Haan, 2023)

In a robustness check, we use the number input interface (Crosetto and de Haan, 2023) to elicit distribution.
Intervention Start Date
2023-05-11
Intervention End Date
2024-07-31

Primary Outcomes

Primary Outcomes (end points)
-mean of variable
-spread of variable, measured using its variance and coefficient of variation
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
-skew of variable
-match of variable to true value
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We start by giving all subjects information about central concepts in the survey such as the average and the frequency distribution of a set of values. We also let subjects answer a few control questions that are designed to check that the subjects understand the concepts.

The next step is to inform subjects that we have collected information about a given price distribution. It will be a distribution of historical prices for a one-night hotel room in the city of Rome. This distribution was selected since we want the subject to have some idea about the price distribution but not too much information about it.

Subjects are then randomly allocated to one of the following five treatments, where subjects’ beliefs are elicited with monetary incentives.

1. Control elicitation of Distribution (CD). We elicit the SBDs of the hotel room prices using the procedure suggested by Crosetto and de Haan (2023). There is no anchoring in this treatment.

2. Low anchor elicitation of Mean (LM). The subjects are first asked if they believe that the average price is lower or higher than the value of a low anchor. After that subjects are asked to guess the average price of the hotel room.

3. Low anchor elicitation of Distribution (LD). The subjects are first asked if they believe that the average price is lower or higher than the value of a low anchor. After that, we elicit the SBDs of the hotel room prices by the same technique as in CD.

4. High anchor elicitation of Mean (HM). The subjects are first asked if they believe that the average price is lower or higher than the value of a high anchor. After that subjects are asked to guess the average price of the hotel room.

5. High anchor elicitation of Distribution (HD). The subjects are first asked if they believe that the average price is lower or higher than the value of a high anchor. After that, we elicit the SBDs of the hotel room prices by the same technique as in CD.

We then run a 2nd elicitation so that subjects who received the LM and HM will receive CD and subjects who received CD, LD, and HD will be asked to guess the average hotel prices. (Hence, they receive the same treatment as in LM and HM but without any anchor, which will be denoted as M.) In connection with the 2nd elicitation, we will ask how certain the subjects are about their estimations following the elicitation of cognitive uncertainty (CU) by Enke and Graeber (2022). The 2nd round elicitations are of secondary importance and will only be used in the exploratory analysis.

After the treatment, all subjects answer questions about demographics, cognitive reflection, investment behavior, and financial literacy.

In a robustness check, we use the number input interface (Crosetto and de Haan, 2023) to elicit distribution.
Experimental Design Details
Not available
Randomization Method
Qualtrics randomizer will be used to allocate subjects into one of the five treatments
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
750 individuals
Sample size: planned number of observations
750 individuals
Sample size (or number of clusters) by treatment arms
150 individuals per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University College Dublin
IRB Approval Date
2023-03-09
IRB Approval Number
HS-LR-23-37-Samahita
Analysis Plan

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