Referring into opportunities: Field experiments on proactive and reactive help

Last registered on August 06, 2024
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Pre-Trial

Trial Information

General Information

Title
Referring into opportunities: Field experiments on proactive and reactive help
RCT ID
AEARCTR-0014135
Initial registration date
August 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
August 06, 2024, 4:06 PM EDT

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
Manuel Munoz
Affiliation
Luxembourg Institute of Socio-Economic Research
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2024-08-05
End date
2024-09-30
Keywords
Behavior, Education
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
In this study, I investigate the role of social networks and biases in access to opportunities. I focus on settings where such opportunities require third-party referrals, such as nominations and recommendations. For this, I conduct a natural field experiment in which university students are candidates for the offered opportunities and faculty members are referrers. Opportunities include access to an international training program as well as to the lottery of a valuable prize. I explore how the ties between candidates and referrers (social networks) influence the provision and receipt of referrals, potentially perpetuating biases that disadvantage certain groups in proactive help settings.

In the experiment, I provide faculty members with the opportunity to recommend students for the beneficial opportunities. By analyzing social network data, I can identify not only who receives recommendations but also who is excluded. I divide faculty members into two groups: the control group receives a message emphasizing academic merit, while the experimental group also receives a reminder not to discriminate based on sex, social class, or other demographics. This design allows me to uncover inherent biases in the recommendation process and assess the effectiveness of the awareness message in reducing these biases.

The findings from this experiment will provide insights into the presence and extent of biases in referral processes and the effectiveness of awareness interventions in mitigating these biases. I aim to inform strategies for enhancing access to opportunities for underrepresented groups by increasing awareness of potential biases.
External Link(s)

Registration Citation

Citation
Munoz, Manuel. 2024. "Referring into opportunities: Field experiments on proactive and reactive help." AEA RCT Registry. August 06. https://doi.org/10.1257/rct.14135-1.0
Sponsors & Partners

Partner

Name
Universidad Autónoma de Bucaramanga (UNAB)
Type
private_company
URL
https://unab.edu.co/
Experimental Details

Interventions

Intervention(s)
In the invitation to providing a referral, faculty members are requested to nominate students with high academic merit. The intervention uses an additional awareness message in the invitation, where potential biases are made salient and faculty members are asked not to exclude anyone because of their demographics but to only focus on their merit.
Intervention Start Date
2024-08-05
Intervention End Date
2024-09-30

Primary Outcomes

Primary Outcomes (end points)
Help-giving, Selection (biases), Help acceptance, Quality of selection
Primary Outcomes (explanation)
Help-giving is measured as the binary choice of recommending a student for the opportunity or not.
Selection (biases) is measured as the binary choices of nominating a student among all potential candidates a faculty can nominate. The choice set is the entire set of connections between a faculty member and all the students she has taught a class to. The selection is the specific student that is chosen, in contrast to the set of students that are not.
Help acceptance is measured as the likelihood that a nominated student accepts the offered opportunity and registers to the program
Quality of selection is measured by the academic merit of the nominated student (GPA), which is an ex-ante measure. It is also measured as likelihood that the nominated student completes the training program.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
I will conduct a randomized control trial (RCT) with about 700 faculty members and two groups, randomized at the level of the individual participant.
350 faculty members in the first treatment arm will receive an invitation to nominate a student to participate in an international training program as well as in the lottery for a valuable prize. 350 faculty members in the second treatment arm will receive the same invitation, which also includes an awareness message that requests them not to exclude anyone because of their demographics and to focus only on their academic merit.

The experimental design allows me to address the following research questions:
(1) What is the impact of the awareness message on the likelihood of giving proactive help?
(2) What is the impact of the awareness message on the selection biases of who is nominated for the opportunity?
(3) What is the impact of the awareness message on the likelihood of nominating a candidate that accepts the help offered?
(4) What is the impact of the awareness message on the quality of the nominated candidate?
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Individual level. I use block randomization to balance individual characteristics of the faculty members such as gender and tenure.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
700 faculty members
Sample size: planned number of observations
700 faculty members
Sample size (or number of clusters) by treatment arms
350 faculty members in the control
350 faculty members in the treatment (awareness message)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

Documents

Document Name
Prior Pre-registration
Document Type
other
Document Description
The original data collection took place in 2022, and was pre-registered in AsPredicted with reference number #99285, using the title "Biases in recommendations to help students". At this time, I only had access to the data of the natural field experiment, who recommends whom.

The partnership with UNAB will allow me to access co-enrollment measures. This includes which courses students take, when, and who teaches those courses. This will allow me to build a rich dataset on network measures, defined by the connections between a faculty member and a student through the classes they shared together. Such network data complements the experimental data and allows me to go beyond simpler analyses such as OLS, which focus only on the choices made, and to use more elaborate measures to estimate biases and selection, such as Random Utility Choice models, which take into account not only the individuals chosen but also the entire set of students that could have been referred but were not, for each faculty member.
File
Prior Pre-registration

MD5: 53a7f60273ae3d84694cb3d969081af6

SHA1: c01f0594542b845af37c546ef1bec0ef7597f3fa

Uploaded At: August 05, 2024

IRB

Institutional Review Boards (IRBs)

IRB Name
Universidad Autonoma de Bucaramanga
IRB Approval Date
2022-06-29
IRB Approval Number
N/A
Analysis Plan