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What Attracts and Deters Women from Economics?

Last registered on November 15, 2023

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Pre-Trial

Trial Information

General Information

Title

What Attracts and Deters Women from Economics?

RCT ID

AEARCTR-0012381

Initial registration date

November 01, 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

November 15, 2023, 12:53 PM 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

Katelyn Cranney

Affiliation

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Other Primary Investigator(s)

PI Name

PI Affiliation

Tufts University

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PI Name

PI Affiliation

Brigham Young University

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PI Name

PI Affiliation

Syracuse University

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Additional Trial Information

Status

In development

Start date

2023-10-27

End date

2025-10-27

Keywords

Education, Gender

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Economics and other STEM fields like Mathematics are dominated by men, but research finds that simple low cost informational interventions can impact interest in economics, and that higher-cost mentoring interventions–especially from women–have the potential to attract more women to these fields. By administering information and providing mentor opportunities to women at the early stages of their education, we can increase the number of women in these fields and lessen the gender gap. We propose a multi-step, multi-site, theory informed randomized controlled trial (RCT) designed to increase the presence and success of women throughout the economics discipline while also measuring interest in Mathematics. First, we will administer an incentive-compatible pre-survey to high school and college students investigating what attributes attract women to Economics. This will inform the design of our RCT. Second, we will run a theoretically-informed RCT on college and high school students which randomizes informational interventions that highlight one of the pre-survey informed attributes that attract women to economics (e.g. mentoring). Our theoretical model predicts that by sending both high and low signals about a specific attribute, we can disentangle the effect of beliefs about this attribute versus other deterrents in pushing women away from Economics and Mathematics. Through our collaboration with the respective partners, we will measure self-reported interest in Economics and Mathematics for all of our sample, subsequent course enrollments/grades for those in our college sample as well as interest/completion of a college peer mentoring program. We will concentrate our analysis on measuring interest in courses, majors and minors in both Economics and Mathematics. Third, for those in our college sample who are interested in peer-mentoring, we randomize whether they receive mentoring from a male or female mentor in an important extension of Canaan and Mouganie (2021).

External Link(s)

Registration Citation

Citation

Buzard, Kristy et al. 2023. "What Attracts and Deters Women from Economics?." AEA RCT Registry. November 15. https://doi.org/10.1257/rct.12381-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We will collect data from high school, college, and general population surveys about what attributes attract and deters students from studying Economics which we expect also deter them from other STEM fields like Mathematics. We will use the results of this survey to design an information intervention. We will test whether this information intervention increases likelihoods that students are interested in economics and mathematics.

Intervention Start Date

2023-10-27

Intervention End Date

2025-10-27

Primary Outcomes

Primary Outcomes (end points)

We also plan to elicit within the same survey respondents’ interest in Economics, their beliefs about the attributes we identified in our pre-survey, and whether they would be interested in enrolling in an Economics mentoring program. These are our short-term primary outcomes.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Our secondary long-term outcomes are whether students enroll in an Economics/Mathematics course and whether they express interest in an Economics mentoring program. We will administer periodic surveys to our high school and college sample to measure self-reported interest in Economics/Mathematics and beliefs about the attributes identified in the pre-survey. For our college sample we will also measure course enrollments, grades, major and minor selection over the subsequent years after our informational intervention and mentoring intervention with a particular interest in identifying interest in Economics and Mathematics.

Secondary Outcomes (explanation)

Experimental Design

Experimental Design

First, we will administer a pre-survey to high school and college students investigating what attributes attract women to Economics. This will inform the design of our RCT.

Second, we will run a theoretically-informed RCT on college and high school students which randomizes informational interventions that highlight one of the pre-survey informed attributes that attract women to economics. We will measure self-reported interest in Economics and Mathematics for all of our sample, subsequent course enrollments/grades for those in our college sample as well as interest/completion of a peer mentoring program.

Finally, for those in our college sample who are interested in peer-mentoring, we randomize whether they receive mentoring from a male or female mentor. Again, we will observe self-reported interest in Economics/Mathematics and subsequent course enrollment/grades.

Experimental Design Details

Not available

Randomization Method

We will randomize using an online statistical software tool.

Randomization Unit

We will randomize at the student level, possibly group level.

Was the treatment clustered?

No

Experiment Characteristics

Sample size: planned number of clusters

We plan to cluster at the subject level, and we hope to have approximately 10,000 subjects in our study.

Sample size: planned number of observations

In total, we plan to have approximately 10,000 subjects in our study, with the majority of those being first year students at Tufts, Syracuse, and Brigham Young University

Sample size (or number of clusters) by treatment arms

For the informational treatment, we will randomize one-third of the sample at each site to each message (baseline, economics low signal, economics high signal).

For those who express interest in a peer-mentoring program, we plan to randomize whether they are paired with a female or a male senior peer mentor. We will stagger the timing of mentoring so that we can measure the effects of both any mentoring and female mentoring.
Here we will likely have fewer female mentors than male mentors available so the treatments may not be balanced, but we hope to randomize half of our sample to a male mentor and half of our sample to a female mentor.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Concentrating on binary outcomes (e.g. enrolled in an Economics/Mathematics course, expressed interest in an Economics mentoring program) we estimate we will be able to detect changes in enrollment between our three informational interventions across all schools of at least 3 percentage points, and within schools, with Tufts being the smallest sample, of at least 10 percentage points using the summary statistics from Bayer et al. (2019) to inform our estimates. In our high school sample, we will only be able to detect differences in binary outcomes of at least 10 percentage points. For our peer mentoring program, we are unsure of the actual interest in the program. This is, in fact, one of our outcome variables for the informational intervention RCT. Assuming that 100 students at each university enroll, we will be powered to detect changes in binary outcomes in our total sample of 300 of at least 27 percentage points assuming a baseline rate of 30%.

Supporting Documents and Materials

Institutional Review Boards (IRBs)

IRB Name

IRB Approval Date

IRB Approval Number