Fairness Preferences and University Admission

Last registered on January 11, 2021
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Pre-Trial

Trial Information

General Information

Title
Fairness Preferences and University Admission
RCT ID
AEARCTR-0006994
Initial registration date
January 11, 2021

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
January 11, 2021, 6:54 AM EST

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

Locations

Country
Germany
Region

Primary Investigator

Name
Robert Stüber
Affiliation
New York University Abu Dhabi
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Other Primary Investigator(s)

PI Name
Dorothea Kübler
PI Affiliation
WZB Berlin Social Science Center
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Additional Trial Information

Status
In development
Start date
2021-01-13
End date
2023-06-01
Keywords
Behavior, Education, Labor
Additional Keywords
Formation of Preferences, Fairness Preferences, Motivated Beliefs, Lab in the Field Experiment
JEL code(s)
C91, C93, D01, D63, D83, J20
Secondary IDs
Abstract
In this project, we investigate fairness preferences in the field. In particular, we analyze how fairness preferences are formed and whether they are influenced by real-life events. We analyze the admission process for medical studies, one of the most prestigious study fields in Germany. We also analyze whether making one's own outcome of the admission process salient leads to a divergence of preferences and beliefs, and whether providing balanced information about the process is a remedy against this potential divergence.
External Link(s)

Registration Citation

Citation
Kübler, Dorothea and Robert Stüber. 2021. "Fairness Preferences and University Admission." AEA RCT Registry. January 11. https://doi.org/10.1257/rct.6994-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We consider three experimental treatment conditions with which we investigate whether admitted and non-admitted applicants for a study place in the medical fields differ in their beliefs and preferences, whether making one's own outcome of the admission process salient leads to a divergence of preferences and beliefs, and whether providing balanced information about the process is a remedy against this potential divergence.
Intervention Start Date
2021-01-13
Intervention End Date
2022-12-31

Primary Outcomes

Primary Outcomes (end points)
- Belief about dropout rate of students for medicine and related fields
- Belief about the satisfaction of the general public with physicians
- Willingness to support a petition against current admission process: self-reported willingness to signing petition, clicking on link that leads to petition, actual signing of petition
- Donation decision: share donated to a state account and a non-governmental organization
Primary Outcomes (explanation)
We construct our main outcomes by deriving for each outcome the difference between admitted and non-admitted applicants.

Secondary Outcomes

Secondary Outcomes (end points)
- Perception that the current admission process is fair (survey question)
- Perception that the current admission process selects suitable candidates (survey question)
- Self-reported trust in authorities, universities, and the federal government (survey questions)
Secondary Outcomes (explanation)
As a summary measure for a participant's reported trust in institutions we construct a linear index using the reported trust in authorities, universities, and the federal government.

Experimental Design

Experimental Design
We conduct lab in the field (online) experiments with individuals who apply for a study place in medicine and related fields. We use experimental designs to elicit individual beliefs and preferences about the admission process and fairness more generally.


Experimental Design Details
1. Efficiency of the admission process:

To measure whether the admission decision affects participants' beliefs about the efficiency of the admission process, we ask the participants to state their belief about the share of students who have dropped out from studying medicine in the last years. We incentivize the belief elicitation using a linear payoff function, i.e., participants receive 2€ if they state the actual dropout rate or if their estimate is within 5 percentage points of the actual dropout rate, they receive 1.5€ if their estimate is within 6 and 10 percentage points of the actual dropout rate, and so on.

Participants also state their belief about how satisfied the general public is with physicians. Specifically, we ask participants to estimate the average “satisfaction grade” participants in a separate survey gave the physician for their last treatment. We again incentivize the belief elicitation using a linear payoff function, i.e., participants receive 2€ if they state the actual GPA or if their estimate is within 0.25 grade points of the actual grade, they receive 1.5€ if their estimate is within 0.26 and 0.5 percentage points of the actual grade, and so on.

2. Approval of the current admission process:

We elicit participants approval of the current admission process by giving them the opportunity to sign a petition that demands changing the current process. Our primary measure is participants' self-reported willingness to sign the petition. We also measure the share of participants clicking on the petition link as well as the share actually signing the petition.

3. Approval of governmental institutions:

To investigate whether the admission decision also affects participants' general attitude towards governmental institutions, participants are asked to make a donation decision between a state account (serving the purpose of reducing public debt) and a non-governmental organization (that has the goal of fighting political corruption). Participants can divide a 100€ donation between the state account and the non-governmental organization. We randomly select five of these participants whose donation decision is implemented.
Randomization Method
The randomization is done by a computer.
Randomization Unit
The randomization unit is the individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
About 1200 clusters.
Sample size: planned number of observations
About 1200 individuals
Sample size (or number of clusters) by treatment arms
About 400 individuals in each 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
WZB Research Ethics Committee
IRB Approval Date
2020-11-26
IRB Approval Number
2019/5/84
Analysis Plan

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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