Trading-off Equity and Efficiency: The role of Information II

Last registered on November 13, 2019
View Trial History

Pre-Trial

Trial Information

General Information

Title
Trading-off Equity and Efficiency: The role of Information II
RCT ID
AEARCTR-0005018
Initial registration date
November 11, 2019

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
November 13, 2019, 11:10 AM EST

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

Locations

Country
United States of America
Region

Primary Investigator

Name
Stefan Meißner
Affiliation
Norwegian School of Economics
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2019-11-07
End date
2019-11-15
Keywords
Other
Additional Keywords
Inequality Acceptance, Information Mechanisms
JEL code(s)
D63, D64, D82
Secondary IDs
Abstract
The experiment poses the question: Are people willing to provide additional information in situa-
tions where some will make better and some will make worse decisions based on the information?
The results add to the literature on inequality acceptance.
External Link(s)

Registration Citation

Citation
Meißner, Stefan. 2019. "Trading-off Equity and Efficiency: The role of Information II." AEA RCT Registry. November 13. https://doi.org/10.1257/rct.5018-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2019-11-07
Intervention End Date
2019-11-15

Primary Outcomes

Primary Outcomes (end points)
The primary outcome variable is a binary choice variable for a decision that will lead to inequality between two workers that were recruited on Amazon mTurk.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment is a online-survey where participants make decisions over outcomes that matter for others. For that purpose, a representative sample of the US adult population was recruited. They answered basic demographic questions and then made a decision with real-outcomes for mTurk-workers.
Experimental Design Details
Randomization Method
Randomization is done by the algorithm used by the Norstat survey engine.
Randomization Unit
Individual
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
1000 Individuals
Sample size: planned number of observations
1000
Sample size (or number of clusters) by treatment arms
500 participants per experimental condition
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
0.1 SD with 80% power
Supporting Documents and Materials

Documents

Document Name
PAP
Document Type
other
Document Description
This document contains the full Pre-Analysis-Plan. It gives information about methods, instruments and tests as well as further information on the sample.
File
PAP

MD5: db16a4c44d503affce4bcf65feaf0a7c

SHA1: 0cb4651e1e09097b42ba0923aa00d369829bb0fd

Uploaded At: November 11, 2019

IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan - November 2019 Incl Survey instruments

MD5: db16a4c44d503affce4bcf65feaf0a7c

SHA1: 0cb4651e1e09097b42ba0923aa00d369829bb0fd

Uploaded At: November 11, 2019

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