Referring into opportunities II: Field experiment on proactive help

Last registered on August 06, 2024

Pre-Trial

Trial Information

General Information

Title
Referring into opportunities II: Field experiment on proactive help
RCT ID
AEARCTR-0014134
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

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Affiliation
Luxembourg Institute of Socio-Economic Research

Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2024-08-05
End date
2024-09-30
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. The opportunity allows nominated candidates to participate in 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 up to three 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.

This study complements a previous study on proactive help conducted in 2022, where faculty members where invited to nominate students to a combined opportunity, access to an international training program plus participating in the lottery for a valuable prize. Data from the previous experiment allows me to identify proactive helpers, those who chose to make a referral in the first study. This new experiment allows me to also evaluate how consistent in helping decisions across time and opportunities.

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 II: Field experiment on proactive help." AEA RCT Registry. August 06. https://doi.org/10.1257/rct.14134-1.0
Experimental Details

Interventions

Intervention(s)
In the invitation to providing up to three referrals, 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. It is also measured by the aggregate outcome of the number of recommendations made (between 0 and 3).
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.
I also measure selection as the distribution of characteristics of recommended students within the set of nominations made by a faculty member.
Help acceptance is measured as the likelihood that a nominated student accepts the offered opportunity and registers for the lottery of the valuable prize.
Quality of selection is measured by the academic merit of the nominated student (GPA).

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 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? and on the amount of help provided?
(2) What is the impact of being a proactive helper in the likelihood of helping again?
(3) What is the impact of the awareness message on the selection biases of who is nominated for the opportunity?
(4) What is the impact of the awareness message on the likelihood of nominating a candidate that accepts the help offered?
(5) 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. I also balance the assignment to treatment or control in the first experiment (conducted in 2022), as well as on the choice of having helped in that initial experiment.
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)
IRB

Institutional Review Boards (IRBs)

IRB Name
Universidad Autonoma de Bucaramanga
IRB Approval Date
2024-06-30
IRB Approval Number
N/A