Correlation sensitivity: Understanding the mechanisms

Last registered on October 18, 2024

View Trial History

Pre-Trial

Trial Information

General Information

Title

Correlation sensitivity: Understanding the mechanisms

RCT ID

AEARCTR-0014534

Initial registration date

October 08, 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

October 18, 2024, 4:38 PM EDT

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

Locations

Country

Austria

Region

Wien

Primary Investigator

Name

Moritz Loewenfeld

Affiliation

Toulouse School of Economics

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Jiakun Zheng

PI Affiliation

Additional Trial Information

Status

Completed

Start date

2024-10-08

End date

2024-10-17

Keywords

Behavior

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

A recent study by Loewenfeld and Zheng (2023) documented that individuals are correlation-sensitive and exhibit behavior patterns compatible with decreasing sensitivity to payoff differences (DSPD). These behavioral regularities can be explained by two competing theories: correlation-sensitive preferences, as characterized by Lanzani (2022), with concavity, or probability dominance, as advocated by Diecidue et al. (2020). In this study, we conduct a controlled lab experiment with students to examine the mechanisms underlying correlation sensitivity in decision making.

External Link(s)

Registration Citation

Citation

Loewenfeld, Moritz and Jiakun Zheng. 2024. "Correlation sensitivity: Understanding the mechanisms ." AEA RCT Registry. October 18. https://doi.org/10.1257/rct.14534-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Participants are given the task of making a series of binary decisions, where they must choose between Lotteries A and B. The outcomes of each option are uncertain and determined by computer-generated random draws. The experiment utilizes a within-group designs. Within-group comparisons involve participants selecting between the same Lotteries A and B, but with varying correlations between them, allowing for a distinction between two competing theories. Additionally, one lottery is adjusted to be first-order stochastically dominant by adding a fixed premium to all its outcomes, enabling the assessment of preference strength. Following the main experiment, participants are asked if they wish to learn the specific correlation between Lotteries A and B while keeping it unknown, and then proceed to make binary decisions again. Here, we adopt a within-subject design where subjects experimental three experimental conditions: revealing correlation structure is free, costs them 2% of the lotteries' expected payoffs, or helps them win that amount. Subsequently, participants are required to complete an anonymous questionnaire, providing basic individual information such as age and gender.

Intervention (Hidden)

Intervention Start Date

2024-10-08

Intervention End Date

2024-10-17

Primary Outcomes

Primary Outcomes (end points)

Choice_{DSPD}: subjects make binary choices as predicted by correlation-sensitive preferences with concavity.
Choice_{PD}: subjects make binary choices as predicted by probability dominance.
Choice_{Reveal}: Subjects want to reveal correlation structure when it is hidden.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Participants are given the task of making a series of binary decisions, where they must choose between Lotteries A and B. The outcomes of each option are uncertain and determined by computer-generated random draws. The experiment utilizes a within-group designs. Within-group comparisons involve participants selecting between the same Lotteries A and B, but with varying correlations between them, allowing for a distinction between two competing theories. Additionally, one lottery is adjusted to be first-order stochastically dominant by adding a fixed premium to all its outcomes, enabling the assessment of preference strength. Following the main experiment, participants are asked if they wish to learn the specific correlation between Lotteries A and B while keeping it unknown, and then proceed to make binary decisions again. Here, we adopt a within-subject design where subjects experimental three experimental conditions: revealing correlation structure is free, costs them 2% of the lotteries' expected payoffs, or help them win that amount. Subsequently, participants are required to complete an anonymous questionnaire, providing basic individual information such as age and gender.

Experimental Design Details

Randomization Method

computer

Randomization Unit

individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

We conduct 2 classroom experiments. The number of participants will be determined by attendance. We expect around 40 and 50 participants respectively.

Sample size: planned number of observations

Same as above.

Sample size (or number of clusters) by treatment arms

Same as above, as this an experiment in individual decision making without between subject treatment.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB