Gender Bias in Career Advice

Last registered on December 21, 2023
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Pre-Trial

Trial Information

General Information

Title
Gender Bias in Career Advice
RCT ID
AEARCTR-0012717
Initial registration date
December 18, 2023

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
December 21, 2023, 7:57 AM EST

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
Cansın Arslan
Affiliation
University of Exeter
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Other Primary Investigator(s)

PI Name
Oliver Hauser
PI Affiliation
University of Exeter
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PI Name
Sarah Smith
PI Affiliation
University of Bristol
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Additional Trial Information

Status
In development
Start date
2023-12-18
End date
2043-12-17
Keywords
Education, Gender
Additional Keywords
JEL code(s)
A21, I21, J16
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Women are underrepresented in many technical and scientific subjects at university including Science, Technology, Engineering, and Mathematics (STEM) and Economics. In the United Kingdom, women account for only 32% of Economics undergraduate students. Preferences for fields of study are largely formed before university and, among many others, stereotypes formed or reinforced before enrolment are important. One factor that likely influences subject choices is recommendations that students receive from career advisers about university subjects. However, we know little about to what extent career advice is potentially (gender) biased. If such biases exist, one way to reduce them is through training and informing career advisers about those pitfalls. This project aims to fill this gap in knowledge by shedding light on the university subject recommendations made by career advisers to secondary school students leading up to their decision of what subject to study at university.

This project seeks to understand to what extent career advisers are potentially (gender) biased in their recommendations, such that they might be more likely to recommend gender-stereotypical subjects to students. We intend to have a better understanding of the subject recommendations that career advisers make to secondary school students and explore if information provision about gender bias in Economics can reduce gender-stereotypical recommendations. To this end, we will first collect recommendations from career advisers and ask them to complete an Implicit Association Test (IAT) focusing on bias related to subjects through an online survey. Next, we will run an online workshop for career advisers and inform them about (gender bias in) Economics and reveal their IAT results. We aim to evaluate the effects of this workshop on recommendations in a follow-up survey.
External Link(s)

Registration Citation

Citation
Arslan, Cansın, Oliver Hauser and Sarah Smith. 2023. "Gender Bias in Career Advice." AEA RCT Registry. December 21. https://doi.org/10.1257/rct.12717-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The intervention is the variation in content of a workshop aimed at improving career advice delivered through Zoom. All workshop participants (careers advisers) complete a baseline survey as part of the registration form for the workshop. Registered participants are then randomly assigned to either a treatment or a control group.

Careers advisers in the treatment group complete an Implicit-Association Test (IAT) on gender-science during the baseline survey and are subsequently invited to a workshop, in which they are informed about Economics as a subject in general and then about gender discrepancies in Economics and the potential role of bias (of career advisers, among others) that could facilitate such segregation by subject areas at university level. Careers advisers will then receive personalized feedback (e.g. potential implicit bias) from the Gender-Science IAT.

In contrast, careers advisers in the control group complete an alternative IAT on US presidents during the baseline survey and are subsequently invited to a workshop, in which they do not receive information about gender bias but instead they are informed about stereotypes about subjects and Economics as a subject in general (similar information to the treatment group). Control group participants will be informed about the results from the Presidents IAT.

The length of the two workshops across treatments will be roughly equal, so that the only difference between the two conditions is the type of IAT (gender-science versus presidents) and the content of the workshop (a focus on gender differences and the role of gender bias in the treatment versus the role of stereotypes about university subjects in the control).

As part of the follow-up survey, the treatment will then be evaluated in how it affects careers advisers’ university subject recommendations based on (real) student profiles that are shown to participants are the end of the workshop. Student profiles include students’ A-level subjects, grades, and sector and work preferences and career interests.
Intervention Start Date
2024-02-01
Intervention End Date
2026-02-01

Primary Outcomes

Primary Outcomes (end points)
Overall gender-stereotypicality score of subjects recommended
If Economics was recommended to the student (binary)
Order of Economics (reverse order weighted)

Primary Outcomes (explanation)
Careers advisers will be asked to make three (ordered) subject recommendations for a total of 20 students (10 female and 10 male students) based on students’ A-level subjects, grades, and sector and work interests.
We will analyze overall gender-stereotypicality of the three recommendations at the student level. In particular, we will analyze either maleness score (if the student is male) or femaleness score (if the student is female) of the recommendations. We will measure overall maleness/femaleness of all subjects recommended to a student by multiplying maleness/femaleness score of each recommended subject by its relative weight (reverse ordering so that the first recommendation has more weight than the third recommendation). Femaleness (maleness) of the subjects will be based on current female (male) representation of subjects in the UK.
Additionally, we will analyze whether Economics was recommended (binary) and, if there’s sufficient variation, how early Economics was recommended (using reverse ordering weights).
We are particularly interested if gender-stereotypicality of recommendations differ by treatment and by gender of the student profile.

Secondary Outcomes

Secondary Outcomes (end points)
For longer-term outcomes, the goal is to connect the data to students’ actual subject choices at university in the following year, based on whether their career advisor received the treatment or control workshop. However, it is currently unclear if, to what extent or when we would be able to receive individual or school-level data on students’ actual decision and it does therefore not form part of our main analyses plan.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will randomly assign male and female fake names to each student profile to which careers advisers will make recommendations. Careers advisers will be presented with the names and avatars (only varying the gender) of the students along with A-level subjects, grades, and sector and work interests. It will be common knowledge that the profile names were changed to protect anonymity of students. We aim to assign 10 female and 10 male students (20 students in total) to each adviser.

We will collect baseline recommendations for 10 students and also ask advisers if they are willing to revise their decisions in the follow-up survey. We will also ask them to make recommendations for 10 new students. In total we will analyze recommendations made to 20 students per adviser. In the initial trial, we randomly assign 120 careers advisers into a treatment and a control group (60 each). The intervention follows the setup outlined under Interventions above. The outcomes will be as described in the Primary and Secondary Outcomes above.
Experimental Design Details
Not available
Randomization Method
Randomization of treatment assignments is done by a computer.
Randomization Unit
Careers advisers
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
120 careers advisers
Sample size: planned number of observations
We are expecting to have 120 careers advisers and 10 student profiles per adviser
Sample size (or number of clusters) by treatment arms
60 advisers in each of the two treatment arms and 10 student profiles per adviser
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Exeter Research Ethics and Governance
IRB Approval Date
2023-11-16
IRB Approval Number
N/A
IRB Name
University of Bristol Research Ethics Committee
IRB Approval Date
2023-11-10
IRB Approval Number
N/A
Analysis Plan